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Marketing data is the lifeblood of most modern businesses. It offers insights into who your customers are and what they want. It empowers you to design and deploy effective and efficient campaigns. It informs your messaging. In no uncertain terms, it powers your ability to bring in revenue. 

It should be no surprise, then, that most modern businesses are generating and collecting massive amounts of data. 

While that’s a good thing, knowing how to manage this data can be a challenge. And when data isn’t managed properly, it can cause that data to become less useful. That’s because the more data you store, the harder it is to see what data you have and to extract value from it. 

Poor data management is such a problem that a recent study shows that marketers waste 21 cents of every media dollar because of poor-quality data.

In short, proper marketing data management processes aren’t a nice-to-have: They’re a necessity. 

Below, we look at what marketing data management is and how it works. We also walk through some challenges and best practices associated with data management and answer other commonly asked questions.

What is marketing data management?

Marketing data management refers to the processes a business deploys to gather, organize, store, and secure its marketing data so that it can easily analyze the information and extract meaningful insights from it. 

In other words, proper marketing data management gives organizations control over their data, which often lives in various sources and platforms across many departments. These siloes make it difficult to gain complete visibility of all of an organization’s data. 

Proper data management unifies organizational data for better data integrity, accuracy, availability, accessibility, and security. Combining data from several sources creates a single source of truth that allows your company to make more complete customer profiles and add value to the products and services you provide.

Marketing data vs. customer data

Customer data refers to any information that a business collects directly about an individual customer or user. This includes zero-party data that a customer shares directly with your business, such as their name, contact information, location, date of birth, and interests. 

It also includes first-party data that you collect from a user as they interact with your website or business, such as keystrokes, mouse clicks, page and product views, and more. Businesses use customer data to improve the customer experience and provide more tailored and personalized marketing.

Marketing data, on the other hand, is a broader bucket of information that can be used to improve your marketing strategies. This includes customer data (above), as well as market research, competitor intelligence and marketing metrics. 

Marketing data helps you understand who customers are and evaluate the effectiveness of your current marketing strategies to create more effective campaigns and optimize your processes.

Marketing data management challenges

The pace of today’s businesses and the high volumes of data they generate make data management an ever-increasing challenge. Let’s look at some of the top challenges organizations face with data management.

Complying with changing data requirements

If your business collects and stores data about your customers, it’s essential that you comply with the relevant data privacy and protection laws and regulations that apply to you. Failure to do so can result in significant fines and penalties, legal battles, regulatory action, and damaged customer trust.

What’s worse, these laws are complex, ever-changing, and lack any real standardization. Examples include the GDPR in Europe, and both the CCPA and CPRA in California and the US.

Difficulty accessing and activating data

In many organizations, data is siloed — living in different systems such as your CRM, website management system, POS, accounting system, behavioral tracking tools, and more. Often, these systems are only accessible to different members of your team. 

That means it’s harder to know what data you have, where it’s stored, and how you can use it. It also increases the time it takes for users to get the information they need and makes it more difficult to benefit from real-time insights into your target audience.

Marketing data management best practices

Implementing data management best practices can reduce the risk of dirty data and ensure that your data remains at its highest quality. Consider the following data management best practices.

1. Integrate your data

As mentioned, one of the major challenges of marketing data management is siloed data. Customers interact with your business across several touch points. Data can be used more effectively if it is aggregated from across all sources so that it can be analyzed together. This allows you to see the complete customer journey to better identify patterns and contexts. 

2. Prioritize data security

When collecting customer data, it’s important to adhere to the laws and regulations related to data security and customer privacy. Make sure everyone involved understands these requirements to ensure your data management system remains compliant.

3. Focus on quality data

Be conscious of the data you are collecting. Good data in means good data out. Outdated or inaccurate data can affect the accuracy of your marketing analytics and decision-making. Check data for inconsistencies, incorrect formatting, spelling errors, and duplicate data, and train employees to input data correctly to ensure your data's continued integrity and quality.

4. Analyze and apply your data

To make the most of your data, you need to analyze it. Integrating and organizing your data makes it easier to analyze and activate. You can leverage the insights gained from analysis to adjust and optimize marketing strategies or create new ones based on data patterns and trends. Today, it's often possible to leverage artificial intelligence (AI) and machine learning (ML) to automatically clean these insights.

5. Effectively communicate data across teams

Effective marketing requires significant collaboration between teams across the organization. Marketing teams need to communicate findings with product development and sales departments as well as management. 

Summarizing data into actionable reports for each team makes cross-company collaboration easier and helps them to see how your marketing campaigns are impacting their individual goals.

How a CDP and CDW can help with marketing data management

Manual marketing data management can be a complicated and heavy lift, especially if you’re starting from the ground floor. The good news is that it doesn’t need to be a manual process. The right tools, such as a cloud data warehouse (CDW) and customer data platform (CDP), make all the difference.

A cloud data platform like Snowflake is essentially a system that consolidates data from multiple channels. It pulls data from each individual system that contains it, and then cleans, organizes, and structures this data. 

Meanwhile, a customer data platform like the Simon CDP accesses all of the raw data contained in your CDW and converts it into a usable format — in the form of customer profiles, audiences, segments, personalized campaigns, and more.

In short, these two systems work together to help you better manage your marketing data so that you can put it to use. They also make it easier to comply with each of the best practices discussed above.

Ready to get a better handle on your business’s marketing data management processes? Take a look at our CDP Buyer’s Guide to learn more about what to look for as you evaluate your options.

Interested in learning more about how Simon Data and Snowflake can help you truly unlock the power of your marketing and customer data? Request a demo today.

Recently, Business Insider reported that 71% of shoppers expect personalized marketing from brands, and 76% are frustrated if this expectation isn’t met.

Speaking of expectations, I’m not at all surprised to hear this; in fact, it’s something I’ve seen occurring over the past few years throughout my time at Snowflake. 

Today, consumers have countless options, and information about products and brand experiences is consistently in front of them through social media. The reality is that meeting consumers where they are at with personalized content matters. It matters more now than ever before. 

I’ve spent a large part of my career working with retailers, brands, and the broader advertising ecosystem and observed firsthand how companies have increasingly invested in marketing and personalization efforts over the years. Often, it’s a topmost priority and core to the success of the business.

“The reality is that meeting consumers where they are at with personalized content matters. It matters more now than ever before.”

The retail industry has evolved over the years, fueled by technological advancements (genAI, I’m not just talking about you!) and shifting consumer behaviors. COVID-19, in particular, was a period that foundationally changed how and what consumers purchased as they navigated the macroeconomic uncertainty. 

As the landscape continues to evolve, there are four big influential trends to consider that will impact that approach:

• Discretionary spending due to inflation: An increasing number of shoppers are looking towards discounts or shopping opportunities with lower-cost providers unless it’s perceived as a high-value product. 
• Concern over customer data privacy when GDPR/CCPA accelerated the focus on consumer privacy and will continue to be at the core of consumer engagement.
• The focus is on AI-driven individualization to deliver the right message, product, or experience at the individual level rather than broader segments.
• Providing an omnichannel experience that meets the consumers where they are, whether in-store, on social media, in a mobile app, on connected devices, or on a digital website.

Understanding the individual consumer’s needs and preferences has become imperative for retailers to thrive in a competitive market. AI will further accelerate how retailers can drive these experiences through AI-driven analytics, predictive algorithms, dynamic content optimization, hyper-personalized marketing campaigns, and innovative search technologies. 

That said, running relevant, personalized campaigns at scale is a difficult task without the right data strategy and foundation. Data and data access are the lifeblood of modern marketing. 

How customer marketing has evolved

In the early days, data management was characterized by siloed systems. Customer data resided in separate repositories, such as POS systems, CRM platforms, email marketing tools, and warehouses.

Each department within the organization maintained its own dataset, leading to duplicated efforts, inconsistent data quality, and limited visibility into the customer journey. This posed several limitations (below) that often resulted in generic marketing campaigns. 

• Retailers struggled to gain a comprehensive view of their customers and had incomplete/inaccurate customer profiles
• Inability to orchestrate campaigns seamlessly cross-channel 
• Duplicate customer records and conflicting data entries became common issues. Without a centralized mechanism for data governance, retailers faced challenges in maintaining data integrity and ensuring consistency across systems
• Privacy challenges, like duplicate copies of data and inefficient methods of data sharing (the physical movement of data), led to increased concerns about consumer privacy and how data was being used
• Rigid data collection and modeling requirements that required orchestration one-off across separated repositories 

Recognizing the inherent limitations of siloed systems, ambitious retailers began to explore unified approaches to data management. This shift towards integration and consolidation marked a pivotal moment, allowing retailers to break down organizational silos and harness the full potential of their data assets.

The emergence of Customer Data Platforms (CDPs) 

CDPs emerged as catalysts for change in the industry as a result. CDPs serve as centralized repositories for customer data, aggregating information from disparate sources and providing marketers with a single source of truth for customer insights. 

By consolidating data silos, CDPs enabled retailers to create unified customer profiles that captured the entirety of the customer journey, from initial awareness to post-purchase engagement. 

Advanced analytics and machine learning algorithms empowered marketers to glean actionable insights from vast datasets, uncovering patterns and trends that drive customer behavior.

However, as companies matured in their journey, this created another challenge. 

While CDPs enabled marketers to bring together customer data and segment affinities, they created another data silo from where the rest of the business data lived — think supply chain, product catalog, finance, and sales data — isolated from the rest of the data that traditionally lived in a data warehouse. 

As retailers focused more on data-driven insights and actions, the need for data access across all parts of the business became a requirement.

In line with modern marketing technologies, a multi-cloud native database approach emerged as a solution to breaking down these silos in a more effective manner. 

Snowflake vs. traditional cloud data warehouses

Snowflake, a cloud data platform, was a pioneer in bringing the multi-cloud native architecture to reality. Snowflake revolutionized the way organizations store, manage, and analyze their data.

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Snowflake’s unique architecture enables high performance and concurrency for data-intensive workloads with privacy and governance at the forefront, and it varies from traditional data warehouses in several ways for marketing use cases. How, you may ask?

Traditional data warehouses struggle with the sheer volume and variety of data

For one, traditional data warehouses often struggle to handle the volume and variety of data generated by modern marketing channels, such as social media, mobile apps, and online advertising. Snowflake's cloud-native architecture enables automatic scaling of both compute and storage resources, allowing marketers to handle large volumes of data and complex analytical workloads with ease. ‍

Traditional data warehouses require manual intervention to manage and activate

Another challenge of traditional data warehouses is that they often require manual intervention to scale resources, leading to inefficiencies and downtime during peak periods (i.e., holidays like Black Friday and Cyber Monday). Snowflake's architecture enables automatic scaling of both compute and storage resources, allowing organizations to handle fluctuating workloads.

Traditional data warehouses are also often limited in terms of data types and storage formats, requiring data to be structured and preprocessed before loading into the warehouse. Snowflake supports a wide range of data types and storage formats, including structured, semi-structured, and unstructured data. 

This flexibility allows data teams to ingest and analyze diverse data sources, such as customer transactions, website interactions, social media posts, and more, without the need for complex transformations or preprocessing.‍

Snowflake supports real-time use cases at scale

Finally, traditional data warehouses don’t support real-time use cases at scale. Snowflake enables data teams to perform real-time analytics on data, allowing teams to monitor and analyze customer interactions as they happen. 

This real-time insight enables marketers to identify trends, detect anomalies, and take action in the moment, ultimately improving marketing campaign effectiveness.

Snowflake further introduced a paradigm shift with connected and native application frameworks, which enabled the concept of bringing the technology to the data vs. physically moving data around — a game changer for retail marketers and also the IT and engineering teams managing the infrastructure behind the scenes.

Snowflake + CDP: The dream team for personalized marketing

The integration of Snowflake and connected CDPs allows Snowflake to be the backbone of the data infrastructure, storing and processing vast amounts of data, and creating a single source of truth. 

The connected CDPs can then leverage this data to build enriched customer profiles, incorporating demographic information, purchase history, and browsing behavior. This unified view of the customer serves as the foundation for personalized marketing initiatives.

The connected CDP journey is characterized by the following key components:

Data integration and enrichment: By combining customer data, transactional data, online interactions, social media engagement, and other touchpoints with an Identity backbone, retailers gain a holistic view of each customer.

Segmentation and targeting: With Customer 360, marketers can segment their audience based on demographics, preferences, purchase history, and behavioral patterns. 

Personalized campaigns and experiences: Leveraging Snowflake's analytical capabilities and CDPs' real-time data processing, marketers can orchestrate personalized campaigns and experiences across channels. 

The GenAI/AI capabilities through Snowflake Cortex, Container Services, and Snowpark ML will further streamline the ability to analyze customer data, identify the key patterns & preferences, enabling businesses to tailor the customer experience at scale.

Predictive analytics will help forecast future customer behavior based on historical data to help businesses anticipate customer needs and proactively engage with them through targeted campaigns and offers.

Continuous optimization and iteration: Dynamic customer personalization is an iterative process that requires continuous optimization and refinement. By analyzing campaign performance, monitoring customer feedback, and leveraging A/B testing, marketers can fine-tune their strategies to maximize engagement and conversion rates over time.

Let’s talk about the Snowflake Data Cloud for Marketing and what that means in the larger personalization context. The goal is threefold:

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1. Easy-to-use, single platform to unify marketing and enterprise data Integrate customer data and marketing data — in any format — from internal and external sources, across channels, and in near real-time, and leverage native AI to automate and optimize your marketing workflows.

• Unify all marketing, customer, and enterprise data with no friction in a single platform leveraging Snowflake connectors
• Execute real-time marketing use cases using Snowpipe streaming and dynamic tables
• Democratize insights to marketers and automate workflows with native AI/ML using Snowflake Cortex and Snowpark

2. Privacy-first, trusted, and secure platform with built-in governance Harness a fully managed, highly governed, multi-cloud platform to minimize overhead, mask sensitive data, and collaborate with privacy on customer data — without exposing it — via Global Data Clean Rooms.

• Govern customer and marketing data using Snowflake Horizon
• Collaborate without compromising privacy using Snowflake Data Sharing and Data Clean Rooms
• Scale at any volume, across clouds with Snowflake’s Snowgrid

3. Robust martech ecosystem and catalog of best-in-breed applications Execute the full marketing lifecycle with a catalog of marketing data cloud applications, partner with leading publishers and agencies, and enrich customer profiles securely with thousands of data products from Snowflake Marketplace with no ETL.

• Enrich profiles and resolve identities with no ETL using Snowflake marketplace
• Marketing ecosystem, connected to the data using Snowflake’s applications
• The advertising stack is agnostic and interoperable using Snowflake’s Network effect with leading publishers and agencies

Embracing a cloud-native approach to deliver customer marketing experiences

A connected CDP plays a significant role in unlocking value within the Snowflake Marketing Data Cloud. Data teams can focus on the data foundation, which includes building customer360, while marketing teams can focus on planning and activation activities across channels. 

This is what we mean by modernizing CDPs via a cloud-native approach. That, then, allows marketing teams to run analysis, streamline workflows, deliver a personalized customer experience, and understand their marketing strategy effectiveness while data teams help make sense of relative channel performance (campaign intelligence).

CDPs can mean different things depending on where a business is in their journey. However, the maturity curve typically covers two phases: Customer 360 and planning and activation. 

The building blocks that summarize a CDP solution are:

1. Data capture: Built-in ingestion capabilities, including first- and third-party data collection
2. Identity resolution and enrichment: Also known as an identity graph, which stitches unknown user activity to known users
3. Semantic unification (single view): Integration of all customer data to create a single view of the state of customers
4. Segmentation and orchestration: Point-and-click interface for marketers to define audiences and orchestration
5. Data activation: Activate segments to channels and retrieve data from Customer 360 in real-time for channel or product personalization
6. Analytics: CDPs can produce analytics reports that provide insights into consumer behavior and the customer journey

Snowflake serves as the foundation for storing and processing vast amounts of data, including customer data. At the same time, the CDP can focus on utilizing customer-specific data for marketing and customer experience initiatives. 

This integration enables comprehensive data management and analytics capabilities across the organization while fueling consumer experiences through personalized engagement.

The marketing data cloud space continues to develop

As we continue to develop the capabilities, the partnerships will continue to deepen as will the technology itself. 

GenAI/AI is of interest to many as you can imagine – just the other day, I was talking to a customer about a GenAI-powered chatbot in the form of a shopper’s assistant that can engage with customers in natural language conversations and understand user preferences, behaviors, and histories. That is valuable information to include in the CDP funnel and journey especially to drive targeted outreach.

Suppose you think about the content creation process today. It requires several manual steps — from gathering the information to defining KPIs and content distribution channels to actually building the content and publishing the content. 

GenAI models can take inputs such as text and turn that into an image, generating content in a quick, automated fashion. More broadly, it can help advertisers target their ads towards specific content categories based on relevant interests and preferences, including themes, moods, and genres, and allow those to be inserted in a way that is scalable and in real-time. CDPs play a role in getting the right content to the right people.

Conclusion

Today’s retail landscape demands one thing loud and clear: hyper-personalized customer experiences. But fragmented systems and siloed customer data make delivering these marketing experiences feel impossible. 

Fortunately, as consumer demands have evolved, so have the concept and technology around the marketing data cloud — and it continues to evolve every day, especially when it comes to genAI and machine learning.

While Snowflake’s platform provides the single source of truth through data storage, security, compliance, and processing capabilities businesses need, connected CDPs like Simon Data help marketing teams access comprehensive customer 360s and activate that data to build the 1:1 personalized customer experience customers crave.

To learn more about how Snowflake and a connected CDP can help, check out our latest guide.

We are all inundated daily with emails, DMs, pings, texts, and more, with every one of them demanding our attention. Some of these messages we’ve asked for. Others are spam. The overwhelming nature of it all makes us as recipients collectively harder to reach. 

This is why email marketing personalization is more than just a nice-to-have for an email marketing program. It is quickly becoming table stakes.

Recipients now expect nearly all the offers and ads they receive online to be tailored to their interests and desires, regardless of channel. This is no different from the email messages they receive from brands.

Whether it is declining repeat purchases, withering conversions per message, or Gmail thinking your messages are spam, the best fix for email marketing challenges is most often one that makes sure as many recipients as possible are getting what they want. 

The most effective way for marketers to deliver what recipients want is through increased strategic personalization. This guide is here to empower you, the marketer, to create more personalized emails for deeper engagement, higher ROI, and better deliverability.

The power (and benefits) of personalized email marketing 

Any email deliverability professional will tell you that sending messages recipients want and find engaging is the key to getting email messages delivered to recipient inboxes. At its core, email personalization strategies help marketers move beyond blasting the same message to each recipient in their database. 

By using data to send the right message to the right recipient at the right time, marketers who deeply personalize messages greatly increase the likelihood that their messaging will resonate positively with the recipient. 

Just as importantly, senders focusing on personalized messages over batch and blast strategies send way fewer messages to recipients who are likely to report the message as spam or simply not engage. 

Improved open rates

One of the first benefits marketers see from increased investment in personalized email messages is higher open rates. While open rates are nowhere near a perfectly accurate measure of success, senders personalizing messages often increase open rates by up to 20%. 

These types of increases tell senders that higher percentages of mail are reaching inboxes and that those messages are resonating.

Higher click-through rates (CTR)

Along with increased open rates, click-through rates for deeply personalized messages can be up to 30-40% higher compared to batch and blast marketing. While also not a perfect measure of human engagement, higher click rates are yet another good sign that mailbox provider spam filters see your recipients being more and more positively engaged. 

By increasing click-through rates, marketers can drive more traffic to their websites or apps, allowing for even more accurate insight into future personalization opportunities. 

Increased conversion rates

Of course, the most meaningful benefit most senders see with investments into personalized messaging is more conversions per message. Personalized online experiences are increasingly the norm and messages that feel irrelevant do more than just fail to meet recipient expectations — they erode engagement and loyalty over time. 

The more senders rely on sheer volume increases to increase the raw number of conversions, the less efficient those messages become. This messaging inefficiency is the exact type of overarching negative signal that spam filters quickly pick up on. 

Put bluntly, sending too many messages to unengaged addresses greatly increases the likelihood of future spam filtering. 

Strategic personalization in terms of both targeted content and message timing increases the rate of conversions per message allowing marketers to optimize marketing efforts confidently without harming their brand’s ability to continue delivering email to the inbox.

What we mean by personalized email marketing

By this point, you might be wondering what is meant by strategic, meaningful, and deep personalization. 

The more a message is tailored to the recipient’s individual wants, needs, and behaviors, the more effective it is likely to be at both increasing conversions and creating the types of signals that inbox providers and spam filters like to see. 

Meaningful personalization requires data. However, improving personalization doesn’t have to be a data science exercise. Senders who include personalized product recommendations based on past purchases, special birthday offers, or even by providing early access to special offers to the best customers often see quick improvements in overall conversions per message across all email sending. 

Triggered messaging, such as abandoned cart messages or a lead nurturing series, are extremely effective at improving the recipient experience. As you refine your strategy and collect insights, optimizing and deepening personalization strategies becomes more data-driven and much less a matter of guesswork.

Mastering customer data for personalized email marketing

For many marketers, easy, actionable access to data remains the biggest challenge when it comes to personalized messaging. With so many marketing tools at a brand’s disposal, it is still too common that the data those tools produce is siloed and difficult to combine without strong SQL skills or regular, involved access from a data team. This is where a CDP can be an email marketing game-changer.

CDPs, like Simon Data, give marketers a centralized, complete view of each recipient, along with segmentation tools that allow for easy use of data points outside of standard contact fields and email engagement data. 

"The most effective and simplest way for marketers to respond to the challenges they face today is to use the data available to identify and deliver the messaging and offers that each recipient is most likely to want."

Personalization becomes much more effective as marketers utilize data points like purchase information and purchase dates, website visit data, typical email engagement times, etc., to deliver a wanted, engaging experience to their recipients. 

The difference between “Hello, {first_name}” and crafting customer journeys that account for many facets of recipient behavior and lifecycle often lies with how easy it is for the marketer to make the data connections. CDPs are all about data connections.

How to build your personalized email marketing strategy

The first step in improving and growing your personalization strategy should always be an audit of the data points you have at your disposal and what those data points tell you about your recipients. 

• Do you have any conversion data you can use such as the recipient’s purchase dates and history? These can be hugely beneficial data points for identifying which recipients might be most responsive to which offers. 
• Can you segment your audience based on purchase frequency and recency? If so, aligning recipient content and recipient frequency strategies becomes much simpler.
• Maybe you know which addresses have opened an email recently and which ones haven’t? You can still use that data point to inform what content you send and how often you send it to different cohorts of recipients. 
• Are there data points that you could collect moving forward to make this work easier in the future? 

All of these things are made much simpler and effective with a CDP, but many are still possible otherwise with a little creativity.

Identified data points tell you more about your recipient’s wants and behaviors. You can create a general outline of how you want to utilize those data points and what you expect them to do. 

I suggest highlighting three general areas of personalization strategy: 

1. Content selection
2. Lifecycle journey building
3. Audience segmentation

Personalization is almost always thought of in terms of content — and for good reason. We live in a world where algorithms know with shocking accuracy what offers to serve us and when. Recipients largely expect the same from email. 

You might not have Gmail’s algorithms at your disposal, but you can start thinking more like them. Here are some ways to do so.

Use customer purchase history to create personalized messaging

If you offer different products or services to customers, a great place to start is to consider what purchasing one product over another might tell you about a customer’s future needs and how you might provide the types of content that help them along their journey toward the next purchase.

You probably already have the data you need to begin doing this, but if not, common sense can go a long way.

Collect zero- and first-party data to make informed decisions

If, for example, you collected specific interest information from subscribers when they signed up for your email program, use that data to focus the offers you send based on what that recipient wanted when they signed up for your mail. 

Find ways to build into your email collection processes ways to get important feedback from subscribers. Asking the right questions when a subscriber provides their email address to your brand can be invaluable information for personalization decisions.

A/B test and experiment with personalized content

Test different types of content between different cohorts of recipients to learn how recipients at different stages of the customer lifecycle engage with different types of messaging and calls to action. Likely, calls to action that most resonate with new subscribers or customers are not as engaging to a customer who purchased a year ago and vice versa.

As you consider sending content and offers that are more tailored to different recipients or types of recipients, also consider different types of receiving journeys your recipients will experience based on their behaviors.

Tailor your email marketing campaigns to the customer journey

Thinking in terms of journeys might feel new to email marketers, but each recipient is on their own journey over time with how they relate to your brand or product. If you can identify patterns in how recipients change their engagement behaviors over time, you can start to optimize your content specifically for those cohorts of recipients. 

Remember: No one is more engaged with your brand than a new customer. Personalized post-purchase journeys can help customers get the most out of your product or service while encouraging greater brand loyalty.

You can also use conversion data with email sending data to understand how recipient engagement changes over time. For example, identifying customers who’ve purchased in the last 12 months are much less likely to make a repeat purchase when they’ve gone through more than 45 email messages without engaging.

This means you should consider changing both the content and frequency of messaging for customers that are nearing that engagement mark. This type of personalization strategy is a much more effective and less risky way to re-engage recipients than a batch and blast re-engagement campaign. 

Or, perhaps you see evidence in your data that a significant portion of seemingly unengaged recipients make a repeat purchase around 14 months from their last purchase date. This insight could lead you to fashion-targeted content designed for recipients as they approach that date to help increase the rate of re-engagement and repeat purchases.  

Segment your customers for maximum personalization

Finally, consider how you can segment your audience in personalized ways. Not every recipient needs every message across every communication channel a brand utilizes. 

For recipients who do not engage with email, sending them more emails is rarely the answer. Try SMS or push notifications with more targeted calls to action designed to re-engage them. 

You’ve probably heard of send time optimization as a strategy, but send time personalization is even more powerful. Use event timestamps to identify the cohorts of your messages that typically engage several hours after the message was delivered so that you can make sure to send messages at relevant times rather than your blast getting buried in the inbox.

Reward your most loyal customers with earlier access to offers, better deals, or even more thank yous. Being treated specially from time to time can build the type of brand engagement that lasts. Loyalty programs go a long way, especially when the content is deeply personalized.

The key to success with email personalization is listening to your recipients and continually optimizing based on the data you have. The more granularly you can analyze your data, the more accurately and granularly you can personalize all aspects of your messaging strategy. 

Utilizing a tool like a CDP simplifies this work in ways that enable marketers to make analysis and optimization decisions with far less assistance required from data teams, fewer third-party consultants, and without the need to learn a new skill or query language.

Conclusion

The current reality of email marketing is one where sending high volumes of the same message to all of your recipients pleases neither your customers nor inbox providers or spam filters. 

Customer expectations of personalized brand experiences are only growing in sophistication. Mass batch and blast sending increasingly create types of negative signals, such as unengaged recipients and spam complaints, that spam filters consider signs of unwanted mail and cause for increased filtering. 

The most effective and simplest way for marketers to respond to these challenges is to use the data available to identify and deliver the messaging and offers that each recipient is most likely to want.

If you don’t currently use a CDP, now is the time to explore how they can help you get the most out of email and your other messaging channels. Simon Data’s CDP gives marketers the power and tooling to deepen messaging personalization strategies and execute sophisticated, data-driven customer journeys without a PhD in data science or having to be a coding expert. 

While Simon Data can bring the power of a CDP to almost any email-sending platform your brand uses, we also offer Simon Mail, an email-sending engine purpose-built to help marketers get the most out of their data for personalization and email segmentation. To learn more, read our guide.

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The customer marketing landscape is undergoing a seismic shift. Fueled by the rise of artificial intelligence, stricter privacy regulations, and tightening budgets, brands are forced to rethink their traditional engagement strategies. Adding to the challenge is a public that has become overloaded and weary of constant solicitations. (According to Pew Research Center, over half of email users distrust email due to spam.) 

To overcome these hurdles, brands must cultivate genuine connections – a feat reliant on personalized dialogues, not generic blasts.

This guide explores how consumer brands can navigate this evolving frontier. We'll delve into innovative strategies that leverage AI, navigate privacy regulations, and build meaningful connections with customers in a hyper-competitive landscape. We’ll also consider the tools and platforms needed to empower these strategies. 

Establish an evolving dialogue

For consumer brands, the key lies in fostering a meaningful dialogue with customers that evolves as the relationship deepens. (That's what customer relationship management is all about, after all.) 

One of the best ways to keep your campaigns both relevant and effective is to segment your audience by lifestage and engagement level. Focusing on the life stage allows you to customize the programs to address the needs of the customer based on their tenure.

When you’re speaking with a “prospect” (a new user who has never purchased), the focus will be on educational elements, such as the benefits of your product, how it works, and what other customers say about it.  

Once they make their first purchase and become an “active customer,” you’ll focus on ensuring satisfaction, encouraging repeat purchasing, and acknowledging and rewarding milestones to engender loyalty.  

Done right, your most loyal customers will become “advocates” who refer other customers to your brand. And, while you’ll work to keep folks loyal for as long as possible, it’s crucial to also segment for “lapsed customers” by tracking declining purchases, website visits, and communications engagement (email click-through, open rates, etc.).

For these customers, you’ll want to develop re-engagement programs based on what you know about their preferences and past behavior. For example, for those customer at the highest risk of completely churning out you’ll want to deploy your richest offer.

Importantly, each of these lifestages should receive different communications with different content, cadences, and objectives.  

Managing all that can be a tall order unless you have a CDP that allows for the creation of segments based on multiple data points. The good news is that once those data points are established, it becomes relatively easy to maintain those segments. 

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Then, the segments themselves become a helpful tool to monitor the health of your customer base by tracking the growth of your most valuable and most engaged segments as a percent of your overall customer base.      

Mobilize data to truly understand your customers

A common internet meme highlights the pitfalls of using data without understanding your customer. It shows two people with identical demographic profiles: men born in 1948, raised in the UK, wealthy, twice-married, and residing in castles. 

However, when their photos identify them as King Charles and rocker  Ozzy Osbourne, you realize how uninformed you can be if you don’t push beyond surface data.

Understanding your customer requires leveraging all the data you can muster. This includes sales history, website and app activity, category preferences, and engagement level. 

But you likely have additional data that is custom to your business.  For example, if you are in the pet space, the type, breed, and age of your customer’s pets are all invaluable data points.  If you’re in the coffee business, understanding whether a customer prefers cold brew, pour over, or espresso is critical to creating a customized experience.  

Armed with these datapoints, you’ll be able to create clusters that will make your programs sing to each and every customer because you’ll be playing their song (“Whether it be “God Save the King” or “Crazy Train”).

This is where AI can help in a couple of ways. First of all, AI tools can analyze all of this data with greater efficiency and depth than previously possible without a large data team. This allows you to uncover hidden patterns and identify nuanced segments that traditional methods might miss. Secondly, once these clusters are established, generative AI can modify text for each segment, working off your original copy but tweaking for each audience variation.

“Understanding your customer requires leveraging all the data you can muster. This includes sales history, website and app activity, category preferences, and engagement level.”

A crucial aspect of this strategy is ensuring that all data is collected in a single Customer Data Warehouse (CDW) or Cloud Data Platform. This will ensure that your data is as complete as possible, and that it can be structured for easy downstream manipulation. With this CDW at your disposal, you’ll be empowered to both define the right segments and customize the content to create a true 1:1 dialogue.

Speak to your customers wherever they are

Multi-channel marketing is not just about using the many channels at your disposal. It is about orchestrating a 1:1 conversation that is consistent across all those channels.  

If your emails to a customer focus on purchasing winter clothing, your push notifications are asking them to refer a friend, and your Facebook ads are offering 15% off the new spring clothing line, your customer is going to be confused, overwhelmed, or both.  

What’s worse, you’ll have missed an opportunity to make a more impactful connection with that customer by delivering a consistent message across all the touchpoints they’re engaging with.

Multichannel marketing also ensures that no channel will be a single point of failure.  If someone misses your email, the SMS the next day will get their attention.  And if they don’t have time to respond to the text at that moment, the follow-up remarketing ads on Instagram or YouTube will serve as a third reminder.    

New research suggests the "seven-touchpoint rule" for customer purchases might be an underestimate.  So, leveraging multiple channels to accelerate the cycle makes good sense. 

This strategy can become particularly efficient when you stack your lower-cost channels (such as email and push) ahead of your more expensive remarketing channels, such as paid social or video ads. 

If you can manage all of these channels through a single CDP, and if that CDP is empowered with real-time data, you’ll be able to set up segmentation rules that suppress anyone who purchased from the email from the next day’s paid media ads. 

Personalize the customer experience

Personalized communications have been the goal of every good CRM solution for the past 20 years.  And yet, many brands fall back on the blast email approach, largely because it provides a quick hit by reaching out to your entire base with a single message (usually an aggressive offer.) 

However, that benefit is likely only a near-term win, and in the long run will burn out your customer base and undermine your communications program.

Instead, the best-of-breed brands, those that have a true 1:1 dialog with their customers, make certain that their campaigns are relevant to each and every user by deploying several personalization tactics from their marketing arsenal.

1. Product recommendations

Surfacing the right product when your customer is ready to purchase is a prime way to demonstrate that you understand their wants and needs.

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It makes their shopping experience feel intuitive and drives both near-term conversion and future engagement with your communications, ultimately leading to repeat purchasing. And with the growth of generative AI, the presentation can also be enhanced, highlighting not just the right product, but also why it is being recommended.  

2. Targeted offers

Your customers are not all alike, and your offers to them shouldn’t be either.  Customer A will respond best to a free gift-with-purchase, while Customer B is best engaged with a price discount (even if the offer value is the same in both cases).

Equally important is the fact that customers have different potential value to you, and so your offer should vary to maximize that value. Predictive AI can help you thread that needle by scoring each customer for likelihood to purchase and estimated LTV. 

This allows you to deploy the right offer to each customer, saving your richest offers for those customers with the highest potential value.

3. Customized content

Having the right data is only half the battle. The real unlock is to weave that data throughout your communications. Next-level marketers are baking dynamic content into each and every communication.  

A leading dog subscription service, for example, structures much of their communications around their customer’s dogs, creating headlines and copy blocks that dynamically insert the dog’s name, breed, and even playstyle and food preferences. 

While this would have required custom coding in every email a few years ago, some CDPs are leveraging AI so that marketers can define what they want in plain English within their copy blocks, with the custom data coding handled dynamically on the backend.

Automated journeys on steroids

With the right triggers set up throughout the customer experience, your CDP can serve as an “Always On” marketing solution, vigilantly watching for customer behavior and then firing up the right communications series based on that behavior.  

We already see the concept operate with common practices such as journeys for Abandoned Cart (triggered when a customer leaves the checkout flow before placing an order) or Abandoned Browse (sent when a customer spends longer than average time on a Product Detail Page, but doesn’t purchase.) Abandoned Cart journeys can be powerful revenue drivers, particularly when augmented by Simon Identity to include undocumented users.

However, smart marketers are looking for ways to expand the richness of triggered communications. For example, browsing three different espresso machines might trigger a drip campaign (if you’ll excuse the pun) on “How to Make the Perfect Cappuccino at Home,” weaving in guidance on how to choose the right machine along with coffee recommendations… and even a discount on your first purchase.  

This now transforms your brand from a storefront to a trusted advisor. And this concept can also turn your newsletter into a signal generator, using article click-throughs to trigger customer journeys as a follow-up.

Make authentic connections to build customer loyalty

So far we’ve talked about how to leverage personalization, multi-channel communications, and automated journeys to engage and delight your customers.  But none of that will matter if you don't focus on the long-term customer relationship. The foundation of that long-term relationship is trust.

As with any strong relationship, your customers’ trust hinges on transparency and authenticity. There is a heightened sensitivity to the collection and use of customer data because too many brands have abused the privilege.  

However, successful companies are fostering trust by being upfront about where and how they gather customer information. Crucially, the data is then used to enhance the customer experience. A good rule of thumb? Only request data if it has a clear benefit for your customers. And then hold yourself accountable for delivering on that benefit. 

By using data to create relevant opportunities and personalized connections that delight your customers, you can truly differentiate your brand.  

Respect your customer and compliance will follow

While customer data is the lifeblood of the modern marketing program, it brings with it challenges and responsibilities in the rapidly evolving area of privacy and compliance.  

Navigating regulations like CCPA and GDPR can feel overwhelming. The key to balancing data-rich personalization with compliance lies in three core principles: 

• Prioritize user consent
• Be transparent about data collection
• Ensure robust data security everywhere, within your company and with partners like your CDP

Privacy concerns are also impacting the targeting options available to you in ad networks. In a nutshell, ad networks have traditionally used third-party cookies (a tiny piece of tracking code left on a user’s browser) to collect data on that user’s internet activity, which, in turn, would enable marketers to target certain users in that ad network.

For example, a wine subscription service might look to target folks who have visited other wine and spirits sites. However, due to the recent increase in privacy concerns, third-party cookies are largely being phased out, rendering this strategy ineffective.

The good news is that this is where a robust CDP can come to the rescue. Properly collected “first party” user data (the data you collect and observe directly from your users) will allow you to create segments that you can feed directly into Facebook, Instagram, Google Adwords, YouTube, and other platforms for remarketing (advertising to those users)or the building of look-alike audiences (advertising to new users who are similar to your customers.)   

Test, learn, deploy, repeat

Given the many changes and opportunities in the evolving world of marketing, experimentation should be standard operating procedure. A best practice is to test all new concepts: personalized content versus generic, 1:1 recommendations versus site-wide best sellers, and automated campaigns versus no follow-up.  

Even if you feel strongly that a concept will work, you can structure the experiment as a backtest, where 80% of the audience gets the new treatment and only 20% receives the control. 

This increases the impact of the new treatment while still quantifying the results (whether positive or negative). That quantification is invaluable because it allows you to project impact if you expand the program, calculate ROI if additional investment is needed, and look for additional areas of improvement.  

For example, if a test campaign delivers higher revenue-per-email, it is still important to understand if a conversion rate improvement or an average-order-value increase drove that.  If the conversion rate increases but AOV drops, you can further optimize performance by focusing on the offer or the merchandising on the landing page to increase AOV.

Ensure the proper measures of success

While open rate, click-through, and click-to-open are traditional CRM measures, they should not be the measures of success for your program. One reason is that open rates have become inflated since Apple rolled out its Mail Privacy Protection controls to users in 2021 (which preloads the tracking pixels for any user who has privacy turned on, regardless of whether the email is opened or not.)  

More importantly, none of these metrics address the business impact that you are crafting these programs to drive. They have clear value as diagnostic tools to understand program engagement and creative effectiveness, but they are measuring just a step in the process, not the overall success of the program.

Instead, you’ll want to ensure that your CDP is well integrated with the sales data that drives your business and can tie it back to individual campaigns. 

This enables you to report on your programs’ true success measures — such as conversion rate, revenue, and average order value — at the campaign and program level. These true business metrics should also be leveraged as the success measures for A/B testing, ensuring that your impact drives business growth, not just improved CRM engagement. 

“While open rate, click-through, and click-to-open are traditional CRM measures, they should not be the measures of success for your program. None of these metrics truly address the business impact that you are crafting these programs to drive.”

But instrumentation shouldn’t stop there. Your CDP should also provide broader views such as customer LTR (Lifetime Revenue) and predicted LTR. This enables you to understand and maximize the health of your entire customer base.

To achieve all of this, data integration is key. It is important to choose a CDP partner that can efficiently integrate with the leading data platforms, including Snowflake, and that provides a level of trust and transparency in managing those datasets in their platform, given the sensitivities to customer privacy and financial confidentiality.

The promise of a new frontier in customer marketing

It’s not an overstatement to say that customer marketing is experiencing a period of change not seen in several years. The rise of AI, the impacts of privacy, and the tightening of budgets are all contributing to a landscape that will require new paths forward.  

But with change comes opportunity, and those marketers who prioritize a 1:1 dialogue with their customers, who work to build relevance, trust, and loyalty, and who leverage the scale and efficiency of a leading-edge customer data platform will find themselves on the winning side of the current marketing revolution.

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It’s one thing to collect insightful data but it’s another to transform that raw data into something actionable. We call it “big data” for a reason — there’s a lot of information at your disposal. Without the help of some method or algorithm to make sense of that information, it will remain useless.

An RFM analysis is one of the methods you can use to make big data actionable, and it’s indispensable for customer marketing. There’s good news, too: an RFM analysis can be simple. Even without a background in Python or SQL, you can conduct this analysis to segment your audience of customers and target them more effectively.

What is an RFM analysis, anyway?

RFM stands for recency, frequency, and monetary value. It’s a method of data analysis born from the prehistoric days of direct mail marketing. In their paper on predictive analytics, Jan Roelf and Tom Wansbeek devised a more effective way of targeting customers with direct mail by predicting which customers were most likely to make another purchase.

This probably sounds familiar to you, even if you don’t target customers with this exact method. Nowadays, you can round data up in handy martech software like CDPs to segment and study your audience. CDPs can do the heavy lifting for you and prepare your data for an RFM analysis, so you’ve likely been doing some type of this method without a formal name.

Let’s break the RFM analysis into its essential parts:

• Recency: When was the last time a customer interacted with your company? If a customer interacted with your brand recently, they’re more likely to have your product on their mind. “Interaction” includes website visits, purchases, app usage, and even social media engagement. When Roelf and Wansbeek devised the RFM analysis, they couldn’t have imagined the types of customer data we can gather to gauge how recently someone’s interacted with a brand. Most often, companies measure recency in days.
• Frequency: How often does a customer interact with your company? As you might imagine, customers who check your website every day or chain purchases are more likely to make another purchase.
• Monetary value: How much has a customer spent on your company’s products? It seems like common sense: big spenders have more money to freely spend, and that means more money to spend on your products.

You can calculate an RFM score this way: 

(Recency score x recency weight) + (Frequency score + frequency weight) + (Monetary value score + monetary value weight). 

Or you could have a CDP calculate it for you behind the scenes. If your RFM score is a larger number, you have a better result. Simple, right?

Who can use RFM analysis?

You used to need a background in statistics or data analysis to calculate RFM scores. Taking complex data sets and running Python or SQL scripts on them was your best chance to gather customer insights.

Thankfully, modern marketers can run an RFM analysis with many contemporary tools. We’ve mentioned CDPs as one helpful option because they gather all your customer data in one place, unify that data, and then surface insights from within it.

Benefits of RFM analysis

Going back to the roots of an RFM analysis, have you ever gotten a piece of promotional mail that felt so irrelevant to you that it went directly into the trash? Maybe you hadn’t bought from that brand for years, or you purchased once and didn’t expect to be bombarded with weekly mail. 

An RFM analysis mitigates the risk of annoying your customers — it also increases your chances of making more money. Here’s how.

Fast customer segmentation

Segmenting customers is essential for better marketing campaigns. You can put your customers in buckets and personalize your messaging for each group. These are some audience segments an RFM analysis can identify:

• Churn and unsubscribe risk customers: If customers’ RFM scores have dropped, particularly in the frequency or recency categories, this is your chance to re-engage them before they drop off for good.
• High-value accounts and customers: Calculating a monetary value for a customer means you can identify which ones pay you the most money. This is the segment of your audience you want to pay the most attention to and consider how to raise their purchase frequency. 
• Frequent shoppers: Customers with a high-frequency score are return shoppers, and, as such, they’re loyal to the brand even if they don’t pay top dollar. They’re an audience segment that can leave helpful reviews, and you can engage them with deals that expedite their purchases.
• Discount shoppers: You can target customers who purchase often for a low monetary value by offering promotional prices. This group may not be a priority one, but they’re an audience segment that could respond well to deals, especially if they’re personalized.
• Highest-ranking customers: Customers who have the best scores overall are your brand advocates. This is an audience segment you want to spread the word about your brand through reviews, social media callouts, surveys, and so on. They’re also a good audience to pitch new products and features to, or to ask for case studies. 

Easier remarketing

We identified some groups that are likely to purchase again in the segments above. But what do you say to them? 

Understanding RFM scores lets you tweak messaging for better retargeting. For instance, your most loyal customers in all aspects of RFM probably don’t need a discount to be incentivized to buy. On the other hand, customers at risk of churn might appreciate a discount on the upcoming renewal.

Personalized email campaigns

Just like using RFM for better snail mail, you can apply analysis to email for more personalized campaigns. With the knowledge you get from an RFM analysis, you can target people at risk of churn with reminder emails before their renewal, or you can ask loyal customers to leave your brand a review.

Less risk of spam

An RFM analysis reduces the risk of your marketing efforts going directly into the trash — whether by direct mail, email, or any other modern means. Segmenting your audience gives you the chance to get in front of your audience at the right time with the right message.

Better budget allocation

Marketing budgets never seem to have enough money. Less is always more. Use an RFM analysis to stretch that money further by only targeting audiences that are going to spend. After all, 80 percent of your sales will come from 20 percent of your customers.

A quick warning about RFM analysis

Never take one method to slice up your data and use it as your only course of action. An RFM analysis works best if you also study demographic data, product data, and so on. The more recent this data, the better, so prioritize software that gives you real-time updates.

In addition, RFM analysis is most powerful when paired with predictive analytics. The RFM analysis can tell you what customers historically do, and predictive analytics can tell you what to do about these trends. If you don’t have a plan of action after your RFM analysis, then the data is as good as useless.

Implementing RFM analysis for customer marketing

An RFM analysis doesn’t have to be complicated. First, you need the customer data to analyze. If you aren’t already aggregating your data in a Cloud Data Platform like Snowflake, this is an excellent option to pull disparate sources and reports into one place, and then activating that data with a CDP is a cinch.

1. Decide on a scoring system

Most RFM analyses use a scale of 1 to 5 (1 being the lowest and 5 being the highest) to score customers for each value. Let’s go through some examples of what these scores might look like.

Scoring by recency

Remember, a recency score is how often a customer uses or buys your product. Depending on your industry, you may scale every year, weekly, or in any other timeframe that makes sense for you. 

Let’s say your ideal customer uses your product daily. This would be your 5. A customer that uses your product once or twice a year gets a 1. A customer that uses your product a couple of times a week may be a 4. This scale will all be relatively dependent on other customers’ behavior.

Scoring by frequency

Frequency gauges how often a customer interacts with your product or brand, depending on your goal. If a customer purchases at your store once a week and that seems to be the most active type of customer, that customer is a 5. If another customer purchases at this same store less often, their score drops. 

Scoring by monetary value

Monetary value can have some of the biggest disparities by industry. If you target enterprise business accounts, monetary value may vary widely by individual plan. If you sell one product and a few accessories, you may find monetary value to have less variance.

Even if a customer purchases less frequently, their monetary value may be higher. Maybe they purchase every quarter for a total of $5,000. If another customer purchases every week for a total of $3,000, they’ll receive a lower score than the customer purchasing quarterly.

2. Compile customer data

To start, take raw data about your customer transactions and house it in one spreadsheet, database, or platform. Once you have your scoring system figured out, assign each customer their recency score, frequency score, and monetary value score. You’ll typically gauge these using your scorecard.

3. Calculate the RFM for each customer

A full RFM score has all three values. Your perfect customer has a 555, and your lowest-value customer has a 111. Based on the variance between these scores, you’ll find distinct audience segments.

4. Segment accordingly

Based on RFM score, you can segment your audience into groups similar to the ones we described under the benefits of RFM analysis. This ensures you’re speaking to the right customers at the right time.

5. Strategize for a segmented audience

Congrats, that’s all there is to it! Now, the advantages of RFM are yours. Use it to create personalized email marketing campaigns, relevant ad copy, and marketing content for each audience segment.

If you update an RFM analysis regularly, you can also identify changes in trends, such as customers who don’t shop with you as frequently or customers who are spending less. This kind of data is power.

RFMs and CDPs: Nail your customer marketing goals

Modern systems make RFMs easy. In fact, if you have a CDP like Simon, an RFM report is easy to run and maintain. Because CDPs help you activate your data, many of them have inbuilt RFM features since it’s such an essential report for successful marketing campaigns. If you haven’t already, take a look at your RFM metrics. You may be surprised how much it helps!

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To operate effectively in the modern economy, every business needs a customer data strategy. Customer data informs your digital advertising campaigns; it powers your promotions; it drives segmentation; it identifies your most (and least) valuable cohorts. In short, it’s what keeps the wheels of a modern business turning.

But not all customer data is created equally, and not all customer data is well-suited to the same uses. While first-party data and third-party data both have their uses and offer valuable insights to inform your strategy, we believe that zero-party data is an underutilized resource capable of driving truly effective one-to-one marketing.

Below, we take a look at zero-party data and why it’s so important for modern marketing teams. We also discuss the different ways you can collect or solicit zero-party data from your customers, and offer advice to inform your customer data strategy. 

What is zero-party data?

Zero-party data is any customer data that your customer knowingly and voluntarily chooses to share directly with you. It’s not something that you collect in the background (like first-party data) or that you purchase from a data broker (like third-party data). It’s not something that you collect at all. It’s something that a customer opts to share. 

Examples of zero-party data include a customer’s:

• Name (first, last)
• Contact information (phone, email)
• Location
• Date of birth / age
• Gender
• Budget / income
• Job title
• Interests / likes 
• Etc.

Zero-party data is also sometimes known as explicit data; because the customer is telling you something directly (i.e., explicitly), there’s no need to make assumptions about what the data might mean. When a customer shares information with you in exchange for something — for example, they complete a form in exchange for a coupon code — it may be referred to as solicited data.

Why is zero-party data important to customer marketers?

In the past, customer data strategies relied heavily on cookies and other forms of third-party data. While this was for a long time an effective strategy, it’s become increasingly untenable  — thanks, in part, to Apple sunsetting cookies in 2020 and Google doing the same in 2024. 

A zero-party data strategy offers businesses a means of understanding their customers without having to rely on partners (or technology) that could turn off the tap at any time. 

Some of the other benefits that zero-party data provides include:

• It’s easier to trust: Zero-party data is something that a customer chooses to share with a business. That makes it more likely that the customer is telling the truth, especially compared to instances where a customer may feel like they’re being manipulated into providing information to you.
• It’s explicit: When a customer tells you about themselves, they’re making an explicit statement — there’s no need for you to make assumptions to get at the real meaning behind something. This is an important distinction between zero-party data and first-party data.
• It’s more likely to be accurate: When data comes from an outside partner, such as a data vendor, you hope that it’s accurate — but there’s no guarantee. Because zero-party data is something your customer offers to you, and something you manage internally, it’s more likely to be accurate. 

All of this means that zero-party data empowers more effective personalization and one-to-one marketing.

How to collect zero-party data

1. Make it an exchange

While it’s not necessarily a requirement, customers typically share zero-party data with a business because they believe that it’ll benefit them in some way. As you start designing your zero-party data strategy, think about the different incentives you can leverage to encourage customers to share their information with you.

For example, by answering a quiz, a customer might expect that doing so will lead to better product recommendations. By completing a survey or poll, they might expect that you’ll consider their feedback when making product improvements or when designing new features. 

By submitting a newsletter signup, they might expect to receive valuable or helpful content. By completing a form, they might expect to receive a free trial, promo code, coupon code, or other discount. 

2. Get creative

Forms may be the gold standard for collecting zero-party data from your customers, but they're not the only option. Consider other ways that you can solicit information from your customers, including: 

• Polls and surveys 
• Newsletter signups
• Interactive quizzes and tests 
• Product testing or beta testing opportunities 
• Promotional emails and SMS texting
• Website pop-ups 

The more options you give your customers, the more likely one will resonate with them — and that they'll share valuable data to help you better serve them with segmented messaging. 

3. Pair it with first-party data

Zero-party data can tell you a lot about your customers, but it can't tell you everything. That's why it's important to think of your customer data strategy a little like a puzzle. 

Zero-party data is an important piece of it, but you should still consider how first-party data (and even third-party data) might help you paint a fuller picture of who your customers are and how you market to them. 

4. Don’t abuse it

Finally, it's important to note that any zero-party data strategy is built on trust. Customers share their information with you because they a.) trust that you'll protect it, and b.) trust you won't abuse it. 

With this in mind, it's important to think long and hard about how your customers would feel about the ways you use their information. Think twice before selling their data, anonymized or not, as well as any other strategy that might imply you took advantage of your customer. 

Likewise, ensure your data strategy includes robust security measures and access control to avoid any unnecessary risk that could undermine customer trust and put your entire strategy at risk.

How a CDP enables your zero-party data strategy

Collecting zero-party data from your customers is just one slice of the pie. In order to put it to good use, you’ll first need to combine it with everything else you know about your customer — in other words, harmonizing it with any first-, second- and third-party data you’ve also collected from or about your customers.

This can be difficult to do manually, especially if your data lives in multiple disparate tools or systems. The good news is that it doesn't need to be a manual process. By pairing a customer data platform (CDP) with a cloud data warehouse (CDW), you can automatically reconcile customer data and generate 360-degree customer views to power your marketing efforts. 

It works like this. First, your Cloud Data Platform (like Snowflake) pulls all of your customer data in from the different systems that contain it. That data is organized, cleaned, and structured. Then, your CDP (like Simon Data) converts the raw customer data into usable insights — generating customer profiles, segments, audiences, and more. 

Ready to start collecting and leveraging zero-party customer data? Take a look at our CDP Buyer’s Guide to learn more about what to look for as you evaluate your options.

Interested in learning more about how Simon Data and Snowflake can help you unlock the power of zero-party data? Request a demo today.

The world runs on data. Because of that, there’s more than one way to slice the pie when it comes to market segmentation analysis. 

By now, you’re probably aware that segmentation analysis is yet another way to draw insights from your customer data to drive better marketing campaigns by building more complete customer profiles. In other words, it helps you meet your customers wherever they are on their customer journey. 

With market segmentation analysis on hand, you’re able to provide a personalized experience to an ever-demanding customer base. When done right, it's a great way to make data-driven marketing decisions that yield results capable of driving revenue.

Not sure where to start? This guide will walk you through the ins and outs of market segmentation analysis including the different types of analyses, the benefits, and how you can get started.

What is market segmentation analysis?

Market segmentation analysis is the process of dividing your customers into different groups — known as segments or audiences — that are tied together by similarities. 

This grouping makes it easier for you to make decisions about targeting and positioning your products and services and can even empower you to have multiple messaging strategies depending on which segments you are addressing at any given time. 

There are several different methods for market segmentation analysis, but the most common approach is to use demographic information such as age, gender, location, or income. Other approaches include segmenting customers by their interests, needs, beliefs, or behaviors.

Once you’ve identified your target markets, you can start developing marketing strategies based on segmentation analyses tailored to each of those groups. This way, you can align marketing channels, the audience, and their goals with higher precision.

Pretty straightforward, right? Not completely — market segmentation analysis can be a complex process. But if your business is interested in maximizing results with the resources you have on hand, it’s a must. 

Simply put, without segmentation analysis, it’s harder to make data-driven decisions that help marketers target their audiences effectively.

Benefits of market segmentation analysis

Running a market segmentation analysis helps marketers drive more value. Perhaps the most important benefit is that your business can focus its resources on the most promising segments of your market, or get a better feel for who your audience actually is. As a result, you see an increase in sales and customer satisfaction. 

Some other ways that market segmentation analysis can benefit your business include:

• Uncovering new growth opportunities
• Gaining a more unified view of your ideal customer
• Applying data-driven go-to-market strategies
• Mass customization across your audiences within your marketing campaigns
• Achieving higher return on ad spend (ROAS) for your paid media campaigns
• Securing a better understanding of changing marketing trends
• Appealing to real customer needs, market gaps, and pain points
• Developing new products, features, and services that’ll appeal to each segment
• Increasing your bottom line with better resource management

That’s a long list of benefits —and we could go on. But the bottom line is that any data-driven business looking to optimize its efforts would be remiss not to focus on segmentation analysis as part of its larger marketing plan. To do that though, it’s important to become familiar with the types of analyses you can run.

The types of market segmentation analysis

There are generally four types of B2C market segmentation analysis:

1. Geographic segmentation
2. Psychographic segmentation
3. Demographic segmentation
4. Behavioral segmentation

But there are other types of segmentation as well. Let’s dive in.

Geographic segmentation

Geographic segmentation looks at where customers live or work. You can then use that data to target marketing campaigns, specifically to certain geographic areas that are most likely to result in sales.

For example, a business selling snowboards would want to focus its marketing efforts in areas with high levels of winter tourism or where there is reliable snowfall throughout the season.

Psychographic segmentation

Psychographic segmentation looks at a population’s psychological characteristics, including values, attitudes, interests, and lifestyles.

You can use this type of analysis to identify customer needs and desires that are not always apparent through demographic data alone. For example, a customer interested in buying luxury cars may also be interested in other high-end products and services.

Demographic segmentation

Demographic segmentation looks at a population’s characteristics, such as age, gender, income level, or occupation. 

Understanding these demographics helps businesses better target their marketing campaigns and products/services to appeal to specific groups of people. A company selling baby products may want to focus its marketing efforts on new parents within a particular age bracket, like 25-34.

Behavioral segmentation

Behavioral segmentation looks at how customers behave when making purchasing decisions. Factors such as purchase frequency, product usage, and brand loyalty, are included. Understanding these behaviors helps businesses tailor their marketing campaigns and strategies to better meet the needs of their target customers.

A business selling mobile phones would want to focus its marketing efforts on customers who frequently upgrade to the latest model or those who are loyal to a particular brand.

Other types of segmentation

While the four types of segmentation above are the most common types, that doesn’t mean there aren’t other ways that you can split up your customers. You can also segment your customers based on:

• Lifecycle: Where they are in the customer lifecycle
• Buyer’s journey stage: Which stage of the buyer’s journey they are in
• Persona: Which buyer persona they best represent (if you deal with multiple personas)
• and more

Remember, the groupings above are not set in stone. Don’t be afraid to get creative when designing your segments.

How to get started with market segmentation analysis

First, businesses should identify their overall goals for a market segmentation analysis. What does the business hope to achieve by conducting this type of analysis? Is the goal conversions, brand awareness, or something else entirely? 

Once your marketing goals are clear, it’s time to gather data on your target market. This is where a customer data platform (CDP) makes it easy to gather zero-, first-, or third-party customer data with fewer bottlenecks.

A segmentation analysis usually consists of a few general steps that can be divided into phases, including segmenting, targeting, and positioning. These phases are as follows:

1. Outline who your target market is: Don’t launch segmentation blindfolded. Clearly define your Ideal Customer Profile (ICP). Who are you trying to reach? What are their pain points and desires? What do they care about?
2. Analyze your existing customer base: Now it’s time to get acquainted with the customer data you already have. Analyze existing customer information to identify patterns and potential segments.
3. Create buyer personas: Go beyond demographics by crafting detailed buyer personas that paint a vivid picture of your ideal customers, including what motivates them, their behaviors, and their challenges.
4. Identify gaps, groups, and opportunities to target: Now that you’ve analyzed your data, review your findings. Are there underserved customer groups or unmet needs? Identifying these gaps unveils potential target customers for future growth.
5. Research and define each segment: Sharpen your focus by defining each customer segment with clear criteria based on your research, customer data, and findings. This allows you to tailor messaging and marketing strategies for maximum impact.
6. Test and optimize: Segmentation (and segmentation analysis) is an ongoing process. Here’s where marketers get to experiment! Test your strategies, analyze the results, and optimize your segments for continuous improvement. Remember: the best marketing is always personalized and data-driven!

After you have segmented your target market, you can begin analyzing each segment. You should look at factors such as size, needs, and potential growth of the segment. Through marketing efforts, businesses identify which segments are most promising and worth targeting. 

AI for market segmentation

In traditional market segmentation, there is a human insight that ultimately informs and drives what a particular segment looks like. Someone on your marketing team recognized the fact that people who share certain characteristics behave similarly and then set about creating a segment to capture that audience — whether that is driven by demographics, geography, purchasing behavior, etc.

But today, segmentation doesn’t need to rely solely on human pattern prediction or assumptions. Artificial intelligence (AI) and machine learning (ML) offer potentially quicker and more efficient means of identifying and predicting valuable segments, and can even identify patterns in your customer data that would be difficult — if not impossible — for a human to recognize.

Learn more about how Simon Data can support predictive segmentation for your business. 

Examples of segmentation analysis

Let’s look at some examples of how a segmentation analysis can help you uncover valuable insights for better marketing results.

1. Use customer data to segment your target market

One way to use customer data in a segmentation analysis is to segment your target market according to various criteria. This can include the following criteria:

• Customers on your newsletter who haven’t made a purchase yet
• Specific customer timestamps
• Dates
• Numeric values
• Boolean values
• Statuses (active, canceled, expired)
• Any promo codes used to make a purchase

Once you have these segments identified, you can work to create messaging, promotions, ads, and other creatives tailored to each. A/B testing can be especially helpful here. 

2. Segment your customer base by purchase history

Another way to use customer data in a segmentation analysis is to segment your customer base by purchase history or action. This can help you identify your most valuable customers, set up remarketing campaigns, and use a lifecycle marketing approach to continue to keep repeat buyers.

For example, you might use segmentation to create audiences like:

• Abandoned carts, who can then be served marketing emails with gentle nudges to return and complete their purchase
• Repeat purchasers, who may indicate a particularly valuable subset of your customer base that should be nurtured
• Non-repeat purchasers, who may not be aware of your full offerings and be served messaging that helps them become aware of those other options
• Non-active buyers, who haven’t made a purchase in a certain amount of time and may be lured back with a promo code or some other incentive

3. Use customer data to segment your target market by demographics

A further way to use customer data in a segmentation analysis is to segment your target market by demographics.

What if you want to target adults between the ages of 20-40 who love the Disney brand and live in Florida and California? With a demographic target segmentation, you’re able to target that specific group of people. And the more criteria with which you can narrow down your target audience, the better your chances of getting optimal results. 

Run better analyses with the Simon Data CDP

CDP solutions like Simon Data make it easy for your marketers to segment their market and save and name each segment. With its sophisticated segmentation logic, you’re able to segment your audience with plenty of criteria for GTM motions like email marketing campaigns, brand awareness campaigns, and retargeting strategies.

By using Simon Data, you can get a complete picture of your target market and identify growth opportunities. You can fine-tune and automate workflows, including customer segmentation analysis. Businesses use Simon Data to collect data from multiple sources, including online and offline channels, social media, CRM systems, and transactional data. 

It’s one thing to hear about it — and it’s another to see it in action. Our world-class CDP helps you unlock and manage the insights hidden in your data. Request a Simon Data demo today.

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You’ve probably heard these three acronyms in meetings and stakeholder chats, and sometimes in the same sentence. But what are the distinctions between a CRM, CDP, and DMP? And how do companies use each to manage and act on customer data?

Making the right selection and understanding their differences will determine your business strategies. So, let’s set the record straight.

What is the difference between a DMP, CRM, and CDP?

A data management platform (DMP) stores, organizes, and manages data from first-, second-, and third-party platforms. This data is often anonymized and used for marketing tasks like audience segmentation. Storage is short-term.

On the other hand, a customer relationship management platform (CRM) is designed for sales. It gathers prospect information through the direct contact customers have with your brand.

Like a DMP, a customer data platform (CDP) is designed for marketing. However, the data CDPs gather is typically persistent. A CDP gathers this customer data from several different sources to build a unified view of your customer so your team can precisely execute marketing strategies.

To sum it up:

• CRMs are tools for gathering customer information to initiate direct contact with these customers
• DMPs gather and manage short-term data for marketing activities
• CDPs build persistent profiles of your customers for marketing activities

Now that we have the basics covered, let’s dive into the specifics of CRMs, DMPs, and CDPs, covering how they differ, how they work together, and the best customer for each type of platform. Let’s start with how the three collect their data.

How DMPs, CRMs, and CDPs collect customer data

Every business ingests some form of first-party data — data that’s provided directly by the customer. This may include contact information like phone numbers or emails, job titles, names, purchased products, subscriptions, or even addresses. 

CRM data is usually gathered manually. This means sales development representatives (SDRs) gather contact information through sign-up forms, emails, social media interactions, or surveys. 

On the other hand, CDP and DMP data is collected automatically through avenues such as web tracking, mobile apps, APIs, and integrations.

What’s more, DMP data is rarely first-party, and what first-party information that is gathered is anonymized.

These days, how that data is stored can vary by platform. For instance, Simon Data runs on Snowflake, a cloud data platform that takes all structured and unstructured data and unifies it. 

Not all platforms are created equal!

What are the benefits of a B2C CRM?

Marketing is tough, and competition is fierce. A sales team would have a hard time keeping up with the complexities of modern marketing without a good customer relationship management platform in place. Let’s look at the most significant benefits of using a CRM:

More consistent customer interactions

Have you ever received a newsletter that you only vaguely recalled signing up for eight months earlier? Chances are you hit the unsubscribe button and move on to your next task. 

A CRM helps you stay at the front of your prospects’ minds by constantly staying in touch with them — whether by email, direct mail, or targeted campaigns. With CRM automation, salespeople stay on top of each account, communicating with leads at optimal times and anticipating objections. 

Improved customer loyalty

No customer likes feeling like a number, and a CRM ensures that you treat every customer as an individual. A good CRM will help marketing teams track details about their interactions with each customer so that they can build better relationships.

With a CRM, marketing teams can track customer interactions, preferences, and histories, and use that information to deliver a more personalized experience. By understanding each customer’s unique identifiers, you step up your customer experience.

Improved decision making

A customer relationship management system offers reporting and analytics features for improved, data-driven decision-making. Good CRMs often include near-real-time data capture, guiding your customer journey with a better-informed sales process. 

Who should use a CRM? 

Are you a sales or billing team? Do you offer customer support? Then you’re the best candidate for the features and tools offered by a CRM. 

If you send quotes or work with outbound marketing strategies to contact potential customers who require direct interaction, a CRM is designed for you. CRMs enrich customer-facing roles by arming them with data, automation, and a single view of the customer. 

It’s important to note that CRMs and CDPs can work in tandem. In fact, depending on your marketing goals, it’s often best to integrate both into your workflow. As an example, Simon Data provides a list of all the platforms it integrates with in case you want to connect it to your existing CRM. 

CRM example: Salesforce

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One of the best examples of a CRM is Salesforce. As the leader in the CRM industry, Salesforce helps sales, service, and marketing teams deliver an exceptional customer experience through automation and features that speed up the lead outreach process.

Salesforce enables personalized messaging that meets prospects where they are. As a CRM, Salesforce offers a more organized approach to selling that ultimately drives revenue. 

What are the benefits of a DMP?

Think of a DMP as a couple of tools in a Swiss army knife, but not the whole package. DMPs do some of what CDPs do, and, as a result, they’re helpful for paid advertisers just looking to sort through second- and third-party data or marketers who want to segment their audience. But, many DMPs integrate into CDPs! Here’s how DMPs help marketers:

Better organization with centralized data

Much like a CDP, DMPs centralize your data. The difference is you’ll usually have this data anonymized and for the short term. However, this is still a massive benefit for marketers who bounce between tools to get insights. You won’t need to gather your customer data from multiple sources and cross-reference for accuracy. A DMP does it for you!

Audience segmentation for more personalized marketing

Also like a CDP, a DMP offers segmentation so you can divide your audience into buckets based on real-time data. Rather than sorting for this information yourself, a DMP can do this in seconds and update information regularly.

Better audience insights using second- and third-party data

Maybe you feel like you can sort through first-party data yourself, but a DMP can serve you insights from your third-party and second-party data to help you identify audiences you’re missing or neglecting. Perhaps you’ve been missing an audience of users who visit your site frequently — a DMP can help surface a blind spot in your marketing strategy.

Who should use a DMP?

DMPs are particularly helpful for those working in the paid media space. DMPs quickly serve anonymous data that can help you target new groups. DMPs will usually give you prebuilt targets for your display ads, making your job as a paid media specialist easier.

But if you need more for your martech stack, like persistent data storage of existing and potential customers, a CDP is the way to go.

DMP example: Lotame

There are a lot of DMPs out there, but Lotame is one of the most popular. Lotame does everything you would expect from a DMP: aggregates, analyzes, and activates your anonymous, short-term data in a cookieless landscape.

Lotame even lets you import first-party data as well, giving you a more holistic view of your data. Lotame offers digital marketers a quick way to tap into new audiences with paid media.

What are the benefits of a CDP?

Using a customer data platform for your marketing campaigns determines whether you reach your key performance indicators (KPIs) or fall short. A CDP offers several benefits: 

Removal of data silos 

Data becomes harder to use when it’s siloed inside different sources and applications. For improved messaging and more proactive marketing campaigns, a CDP works to unify this data — whether it’s zero-, first-, second-, third-party, online, and offline data.

Deeper customer insights

A customer data platform enables marketers to build comprehensive customer profiles (i.e., customer 360s) that offer insight into real pain points, interests, wants, needs, and even firmographic data (the equivalent of demographic data for B2Bs). With unified customer profiles, marketing teams can execute new marketing strategies and create hyper-segmented and personalized campaigns. 

Savings through automation

In a data-driven world, automation is your best friend. It takes repetitive yet critical tasks off your to-do list, saving you time and resources so you can focus on higher-ROI initiatives. Automation also allows marketers to establish evergreen strategies that bring in a consistent, predictable stream of new leads and more data, all while building brand awareness.

Increased personalization

Today, marketing is all about personalization. About 75% of consumers are more likely to purchase from a business that offers them a personalized experience. According to McKinsey, even B2B customers prefer an ongoing omnichannel experience that’s personalized to them. 

Personalized experiences are so powerful that customers are willing to switch to providers that offer them. Yet you can’t launch fully personalized marketing campaigns without a platform that gathers, cleans, centralizes, and organizes your data to make it usable. With a CDP, marketers draw data-driven insights about customers' wants and needs. 

Better prediction with ML and AI

AI and ML enhance personalization in marketing campaigns, and the ability to predict the wants, needs, and, in some cases, actions of its customers takes any brand’s marketing efforts to the next level. Brands gain a competitive advantage by using predictive analysis to find new marketing opportunities, customer lifecycle, and better manage resources. 

Predictive modeling can forecast churn, likelihood to purchase, and even products customers might be interested in. Sounds like it does half the work for you, doesn’t it? That’s why ML and AI are important to consider when buying a CDP.

Who should use a CDP?

CDPs combine zero-, first-, second-, and third-party data for more effective behind-the-scenes marketing. Marketing channels like social media, email marketing, SMS, and pay-per-click (PPC) make CDPs ideal for marketing teams who want to grow company revenue, expand market share, and market new product initiatives.

CDP example: Simon Data

The Simon Data CDP is a no-code customer data platform designed to help marketing and tech teams deliver results in the form of new customers and better retention. With the automation and tools needed to create a true omnichannel experience, Simon Data’s customer data platform enables teams to establish ongoing marketing funnels that are hyper-personalized and built with the customer in mind. 

Simon’s CDP’s invaluable features provide the functionality to meet all your marketing team’s needs: 

• Audience management
• Email marketing campaigns
• Customer identity
• Predictive modeling
• Data unification
• Cross-channel orchestration
• Multichannel orchestration & experimentation
• Customer data segmentation

Let’s say you’re trying to build a recurring workflow that sends email messages to specific customer segments about special deals or seasonal offers. Simon Data’s dashboard simplifies the automated workflow creation process by enabling you to set your desired frequency, maximum period, date and time, and messaging.

The Simon CDP combines the automation and detailed data you need to execute highly personalized campaigns that won’t fall flat. Plus, it eliminates the need to work with siloed apps by unifying data collection with robust go-to-market tools.

CRM vs. DMP vs. CDP: Key differences

While both platform types process data for the best sales outcomes, they have some key differences. CRMs shine in the following use cases:

• Increasing customer lifetime value
• Increasing cross-selling and upselling
• Improving customer service
• Gaining insights into customer behavior

DMPs give you these benefits:

• Better organization with centralized data
• Audience segmentation for more personalized marketing
• Better audience insights using second- and third-party data

And on the other hand, here are some ideal CDP uses: 

• Activating real-time, zero-, and first-party customer data you already have
• Executing data-driven marketing campaigns
• Managing the marketing lifecycle
• Unifying and segmenting your customer data
• Used by and designed for marketers

These distinctions can empower you to build a martech stack with confidence. Now, you should have a better idea of what your company needs and who needs each tool. Want to see a CDP in action? Book a demo with our team today.

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Two big things are happening right now at the intersection of data and marketing and they don’t have anything to do with AI: 

1. Data privacy and governance standards are forcing companies to focus on their 1p data strategy in a way they haven’t before (this is nothing new) 
2. Marketing technologies at the intersection of data and customer experience are fighting for relevance

I’m not going to spend much time in this piece discussing #1 because if you found this article, you’re either a victim of LinkedIn’s “more viewers than content” problem or you’re in the target audience for this content and already aware of these trends. 

The second trend is a bit more interesting and merits some discussion. 

I’ve touched on this theme as a supportive context for other articles like these: 

• Twilio isn’t selling Segment. What does this mean for Twilio and the CDP market?
• The CDP Schism: Salesforce vs. Snowflake in the Battle for Your Data

But here’s the real brass tacks: a SaaS recession and wavering macro environment have both forced enterprises to rationalize software spend and have pushed vendors and enterprises to value near-term ROI vs. long-term uncertain payback. Just as higher interest rates focus investors on shorter-term profits, the same is happening in the microeconomics of software investment. 

In simple terms, “nice to have” software must become “need to have” software. Nowhere does this feel more resonant than in the CDP space, which, depending on the context, can fall into either category. 

Shuffling cards when it comes to customer marketing

When I started my career in management consulting (over a decade ago), the trends at that time were… drumroll please: big data, 360 views of the customer, and… machine learning (which we now just call AI even though they’re fundamentally different things). I’ll leave aside the opportunity to joke about how consultants got nothing done in 10+ years (but still mention I had the opportunity and, therefore, make the joke).

The companies (where I sat in a windowless basement for days) talking about how the intersection of big data and machine learning would change their corporate understanding of a customer are the same companies we talk to now a decade later in search of a “customer 360” or a “single customer view across the enterprise.” 

These companies have spent a decade, for lack of a better analogy, shuffling cards. 

Oracle may have sold them a vision of data centralization, and they probably went on a journey with IBM or Informatica. They may have migrated tooling to a more modern MDM provider and moved from an ESP built by someone wearing pleated pants to one built by an engineer who dresses like a barista, but at the end of the day, they have not net increased their likelihood of winning the hand. They’re running the same campaigns in a splashier UI. 

My main caution regarding CDPs that grew up in the #moderndatastack era is that these tools feel like a continuation of this trend. 

Hightouch might deploy inside of your Databricks environment in a way that Segment does not, and that has real business benefits — in the same way that moving from Responsys to Iterable benefits the sanity of your marketing team — you trade breadth of capabilities for user-friendliness, but will it fundamentally improve your customer acquisition or retention metrics by enough to merit the total cost of ownership?

Stacking the card deck

“If you have to ask, you can’t afford it.”

While this term is typically used flippantly, it’s also an accurate answer to the question of whether or not enterprises should invest in a CDP. “Afford it” being dependent not only on the total cost of ownership (which for the wrong CDP can be massive), but also on the measurable value it delivers in terms of key marketing metrics and efficiency.

To deliver measurable marketing outcomes, CDPs are increasing their focus on data enrichment (i.e., stacking the deck) vs. simply data integration and modeling (i.e., shuffling). 

As stated at the top of this article, this is in response to both data privacy and governance trends pushing enterprises to focus more on their 1p vs. 3p data but also has a lot to do with the microeconomics of being a SaaS solution in 2024. 

Just this week, ActionIQ announced a partnership with Acxiom. Zeta Global focused their earnings report on how the intersection of customer identity, data enrichment, and AI can help deliver their customers a “winning hand.” 

These bets have always been core to Zeta’s strategy, and with their recent earnings report, I think they’re on to something. What they’re focused on just simply matters much more to the top and bottom line. 

To show our own hand and torture the analogy a bit further, this has been a core component of Simon’s strategy for years, with our launch of Identity+, Match+, ROAS+, and several other product launches slated for H2 of this year at the intersection of customer recognition and real-time marketing and advertising personalization. 

Conclusions and CDP category predictions

To be relevant in the current MarTech gauntlet a CDP must not only help you move data around, but it must also help you recognize your customer, help you know things about your customer that you don’t know today, and most importantly, it must do that in the service of measurable marketing ROI. 

While CDPs have been narratively focused on this for as long as the category has existed, as I’ve predicted and warned in other articles, now is the reckoning. Hundreds of companies calling themselves CDPs cannot exist in a sustained fiscally austere environment. 

Infrastructure-focused CDPs (Twilio already tried to do this with Segment, and Hightouch is next) will be built to support marketing use cases to get closer to business value. CDPs focused on data management (Amperity already tried to do this by acquiring Custora and ActionIQ’s partnership is more of the same) will both tell a better ROI story around how this impacts downstream customer experience. Some CDP and non-CDP solutions that charge a toll to move data around will be threatened (e.g. Tealium, Liveramp, etc.). 

Accountability for ROI is shared between the enterprise and the platform, but if the platform doesn’t push your thinking around incrementality or offer capabilities in the stacking category, you’re destined to keep shuffling.

As marketers, it can be hard to take a breath and plan for the future. You may be stuck putting daily fires out, so when do you have time left to strategize? 

Predictive analytics can help you build a forecast and devise a marketing plan. It saves you from wading through spreadsheets and finding the patterns and trends yourself — and with advancements in AI and ML, predictive analytics are only getting better and easier to implement.

What are predictive analytics?

As the name suggests, predictive analytics use data modeling to predict the future.

If you’ve used past KPI benchmarks, market conditions, and competitor research to triangulate how you’ll perform this quarter, congrats! That’s a prediction. You took data from multiple sources to make an inference.

Where predictive analytics models improve on this technique, however, is in the scope with which they’re able to perform their modeling. Predictive analytics uses data mining, machine learning (ML), modeling, and the latest AI advancements to create predictions. Some models can give you this data in real time.

You‘re probably already connecting the dots and seeing why this is helpful, but let’s go over why predictive analytics can help you execute marketing strategies that create a personalized customer experience.

Why are predictive analytics helpful for marketers?

Marketers use predictive analytics for all sorts of things (and who doesn’t love a multitool?). They have some key benefits that address most marketers’ pain points:

Less time wading through customer data

Using predictive modeling saves time. Rather than having a human manually gather data from multiple sources — especially from data engineers if you don’t use a cloud data warehouse or a customer data platform — and put together a report (let alone do this regularly), predictive modeling can source insights for you.

Many marketers struggle to find time to gather the right customer data to adapt to customer needs and analyze marketing campaign metrics in real time, so it makes sense to offload this task to a trusted tool to do this for you.

Better data-rich insights

Once you’ve set up your project, predictive modeling pulls from multiple sources (typically sources aggregated in a cloud data platform, such as Snowflake, and activating that data customer data platform (CDP) such as Simon) to power up your decisions with data. Backing your ideas up with the customer data you already have leads to faster action, easy marketing campaign implementation, and real results.

Real-time updates

Trends move fast. Have you ever meticulously planned a campaign only to see the cultural zeitgeist shift before execution? Or try to send out a personalized SMS discount, only to learn the customer prefers emails?

Predictive analytics give you the most accurate customer data when you need it — and as customers’ needs change — to help you strategize the most effective marketing plan quickly and easily.

Are you sold on using your data for better predictions? Then let’s walk through some use cases to activate your data.

How to use marketing predictive analytics for actionable insights 

When it comes to marketing, you can use predictive analytics just about anywhere — after all, you can interpret and assort data for almost any use case. To narrow it down, these are some of the big ones for more effective marketing strategies.

Segmenting audiences

With some handy dandy machine learning, your predictive modeling can use cluster modeling to group users into buckets, typically by:

1. Geographic segmentation
2. Psychographic segmentation
3. Demographic segmentation
4. Behavioral segmentation
5. Other, such as lifecycle, buyer’s journey stage, potential churners, persona, etc.

Once you’ve segmented these audiences, you can activate your data by sending targeted campaigns to the right people on the right channel, at the right time.

Potential buyers

If you’re looking for a way to pinpoint potential customers, identification modeling can use existing data on your customers to pinpoint users like them who are close to a purchasing decision.

Simon Data has an example of this: purchase propensity. Being a CDP that integrates with Snowflake, Simon uses machine learning to find contacts that are primed for conversion — and contacts that are on the fence about buying.

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Another example of this is lead scoring. Predictive analytics can rank prospects by the possibility of conversion based on the prospects you already have. This is a nifty way to determine if a lead is qualified, or if your marketing efforts are surfacing qualified leads to begin with.

Potential churners and cart abandonment

Wouldn’t it be nice to catch customers before they churn and send them an email with an incentive to renew? Or, maybe you want to catch a group of people who aren’t able to fill out your checkout form and complete a purchase. Predictive analytics smartly identifies commonalities between your churners and can predict those likely to churn.

For instance, Vimeo uses segmentation to solve a big problem: free users churning before they uploaded their first video. Vimeo fine-tuned their email reminders to focus on users who abandoned their upload in real time. In this case, Vimeo was already aware free users were most likely to churn if they didn’t upload a video in their first seven days, but predictive analytics could also surface this issue.

Potential remarketing opportunities

Predictive analytics can find a segment of website lurkers. Better yet, you can also target users who are most likely to make a second purchase, or offer personalized discounts to incentivize customers who need a nudge.

Barkbox uses a CDP to run complex A/B tests and gauge whether their discounts are effective. Predictive analytics can take this one step further, finding users who’d be likely to purchase again if you offered a discount.

Staying on top of trends

With real-time data sourced from a wealth of customer information, you can predict and plan for trends faster than anyone else in the industry. Machine learning surfaces these trends from your data and gives you a chance to plan ahead.

For example, your predictive model may pick up on a seasonal downturn that will impact your industry based on the historical data it has. From this, you can prepare to weather the storm, as well as have a better understanding of why sales may be lower in this upcoming seasonal downturn.

Better customer personalization

The better you know your users, the more personalized your content can be. And personalization makes a big difference these days in how customers interact with brands.

Take as an example an industry that needs to be highly personalized: luxury travel. Travel + Leisure Co. successfully uses predictive analytics to offer vacation recommendations by gauging customers’ purchase propensity, perceived value, and urgency. 

As you can imagine, this makes a huge difference over travel sites that serve the same vacation packages to everyone, and even more so for a customer searching for a luxury experience expecting to pay top dollar.

Say a luxury travel site might see a user returning over months, lowering the chance there’s a high level of urgency for their purchase. However, the user might have looked at packages to Fiji several times, thus increasing their purchase propensity. 

And let’s say these packages are expensive, ranking their value highly. It makes sense that the travel company serves up targeted content (travel information, guides, discounts, etc.) to its relevant audience about trips to Fiji, with a departure date that’s not urgent. If you were that customer, you’d probably prefer this personalization.

Machine learning and AI make predictive analytics tick

With these facts in mind, wouldn’t anyone want to use predictive analytics? Obviously. If it came free, and if it were so easy to implement. The challenge is having a team with the skills to create an advanced predictive model, and the ability to house and activate your data in one hub. 

That’s why most of us outsource this complex data modeling to a third party. CDPs can empower you to activate customer data if you’re storing it in a warehouse. The best CDP choices for predictive analytics have AI and machine learning with the ability to deliver these insights in clear, actionable terms.

With technology so new, you can be sure of a competitive edge. Not many tools offer predictive insights packaged neatly, but if you’re interested in finding one, consider booking a demo with Simon Data to learn more about its predictive features.

The silo struggle is real

Figuring out new technology can be a headache. You have to consider whether it’s just a fancy upgrade (i.e., Braze’s “modern” marketing cloud compared to Salesforce’s “legacy” marketing cloud), or if it’s a game-changer.

Back when I started at Simon Data in 2018, CDPs were this new, shiny thing.  

Marketers knew their tech and strategies had to evolve, but there was — and still is — a lot of confusion about what CDPs can realistically do. Half the battle of a CDP sale was challenging marketers to break free from outdated practices.

Wouldn't it be awesome to personalize your emails instead of blasting the same content to everyone? To have customer insights that actually help you target the right people?

Marketers today are in an even tougher spot. Data is exploding, budgets are shrinking, and customers expect way more than they used to. Just keeping up feels like a marathon. But there's a way to turn that chaos into an advantage. A CDP isn't just another tool, it's a superpower for your entire marketing strategy.

Why a CDP is your marketing sidekick

A CDP isn't some boring data warehouse. Think of it as your sidekick — the key to growing your business and making customers feel truly seen. 

Here's what it can do:

• Build a complete picture of each customer: Bring together all that scattered data and make sense of it in what we call a Customer 360
• Segment like a pro: Target the right people with the right message — no more guesswork
• Orchestrate amazing experiences: Make sure the entire customer journey flows across all your channels and supports your buyers even after they become long-time customers
• Find hidden insights: See the patterns in your data that tell you how to truly personalize every single customer experience

Plus, the best CDPs go the extra mile so marketers can:

• Tie the knot between anonymous visitors and known customers: No more wasted ad spend
• Enrich your data for smarter targeting: Reach even more of the right customers
• Build anonymous profiles: Expand your paid reach while still getting insights

But don't just take my word for it. Companies like Vimeo increased trial conversions 300% by optimizing abandonment using a CDP. RCI generated $13MM in revenue through website personalization after implementing a CDP.  Want to see similar results? Check out these case studies.

Convincing your boss you need to implement a CDP

When you pitch a CDP to your leadership team, you should ditch the tech-speak and focus on the results they care about, such as:

• Increased CLTV: Show how personalized experiences make loyal customers who spend more
• Boosted conversion rates: Prove how targeted campaigns turn browsers into buyers
• Reduced CAC: Spend less by finding exactly the right audience (and eliminating the ones that aren’t)

Use numbers, use cases, case studies, arguments about future-proofing marketing, and even quick product demos to make the benefits of a CDP real and apparent. The point is that a CDP doesn't just cost money, it makes money.

Crafting your CDP implementation battle plan

Ok, time to get tactical! Here's how to make your CDP case and drive change in your company.

Be specific about your problems and needs

What's holding your marketing team back? Data silos, bad targeting, clunky customer journeys? Identify these pain points and make sure the CDP can help you solve them.

Do your homework

Explore the CDP options out there, then compare the pros and cons. Is your CDP composable? Does it sit on top of a cloud data platform like Snowflake? Consider scalability, user experience, security and data privacy, and pricing models. The goal is to find the right fit for your company.

Play nice with your other tech

‍The CDP has to make your whole martech stack work better, so focus on integration with your CRM, data platform, and marketing tools. And, if you already use Snowflake, your CDP implementation can be incredibly smooth. A CDP directly pulls data from your Snowflake data warehouse, giving you even richer customer profiles for smarter segmentation. 

It also streamlines the process of sending that data to the email, advertising, web personalization, and other tools you use to connect with customers. With this tight integration, your marketing moves faster and is even more effective.

Think about your budget

Have resources ready to estimate costs and show a clear ROI. Highlight potential cost savings, revenue growth opportunities, and efficiency gains within your teams, workflows, and strategies that a CDP can help deliver over time.

Collaborate with your colleagues

‍CDPs can benefit multiple teams. Typically, using a CDP built on top of a cloud data platform means that the data engineers don’t waste precious time pulling outdated customer data lists for marketers, and marketers can easily activate customer data and respond to marketing strategies in real time. Work with your colleagues to make a compelling case that a CDP can benefit many teams within your organization.

Championing marketing change by addressing leadership doubts

You'll probably face some questions from leadership, so be ready for:

• "This seems complex": Start with a small project to prove it works (a "proof of concept") before advocating for a full-scale implementation. This will help you validate assumptions, fine-tune processes, and showcase tangible results.
• "What about data privacy?": Emphasize security compliance, a cloud data platform, and pick a CDP with strong privacy practices
• "Isn't this expensive?" Show the long-term savings and revenue boost a CDP creates, as well as the streamlined operations that bolster efficiency.
• "Won't this add complexity for IT or data teams?”: The answer to this objection is easy: A CDP can simplify your data landscape. Its built-in connectors reduce the need for custom integrations. Plus, cleaner, more accurate data makes IT's job easier down the line. Look for a CDP with robust security features and a strong track record of data governance.

Ultimately, you need to become the CDP expert in your company. Do your research, share success stories and industry benchmarks, lead training sessions, and prove the value of the CDP constantly and consistently.

By crafting a comprehensive battle plan, addressing concerns head-on, and positioning yourself as a CDP champion, you'll give yourself the best opportunity to get internal sign-off and support. 

The future of marketing is unified, personalized, and data-driven

Marketing's future depends on harnessing technology to solve today's problems and get ready for tomorrow's.

To recap, a CDP gives you: One customer view, killer segmentation, seamless journeys, and the insights to make it all hum. That means happier customers and a fatter bottom line.

What are you waiting for? Now's the time for marketers to embrace CDPs, get ahead of the game, and deliver experiences that wow customers.

Want to take the next step? Check out our marketing strategy guides, case studies, our CDP buyer’s guide, and even request a CDP demo so you can see for yourself how it can transform everything you do.

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You may have seen Snowflake’s major launch of their proprietary LLM called Arctic yesterday. And if you thought to yourself, “Snooze, yet another LLM that’s competitive with industry benchmarks,” you’d be joined by others, such as TechCrunch, who had similar reactions.

But TechCrunch is dead wrong. 

Under the hood, the technology behind Arctic is incredibly impressive. The model has 480 billion parameters, training is optimized by a “Mixture of Experts” (MOE) architecture, and it features a three-part learning curriculum to optimize enterprise intelligence. Snowflake’s press release is incredibly detailed and goes into extraordinary depth on how it works.

This stuff is all super cool, and the technology itself is also somewhat inaccessible — even for someone like myself with a machine learning PhD!

But let’s take a step back and look at the larger picture. This launch effectively propels Snowflake as a key player in the LLM race, an area where they had been lagging for over a year. For Snowflake, it’s not about building a model with 500B parameters, it’s about developing a complete set of capabilities required to drive AI adoption for business applications. The algorithm is only a small piece of the puzzle.

For Snowflake, it’s not about building a model with 500B parameters, it’s about developing a complete set of capabilities required to drive AI adoption for business applications.

With Arctic now in place, Snowflake is poised to affect massive disruption around business processes and drive huge innovation around how LLMs can deploy in an enterprise setting.

Starting simple with AI

Let’s pretend you’re selling socks on the web. Today, you can go to OpenAI right now and ask the following:

>> Write me an email encouraging a customer to buy a pair of socks

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While the message is well-written, clear, and to the point, it’s not personalized, it’s not on-brand, and it has no context of historical effectiveness and what’s required to drive engagement and conversions. It’s also boring and sounds like something a first-year marketing student would write.

With a bit of information about the recipient, you can iterate on this significantly:

>> Now assume the customer is training for a marathon and likes to wear vivid colors

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While this is an improvement, the message lacks a brand-specific tone and is built with zero insight into what sort of messaging is effective in converting sales.

Taking a step back, use cases like things generally require a set of basic enterprise requirements to truly be successful.

Requirement 1: High-quality data 

There’s an adage in data: “Garbage in, garbage out,” and if the context on the customer is inaccurate or incomplete, algorithm quality just doesn’t matter. Understanding customer preference, real-time behavior, and historical content engagement is critical for success. 

In this example, drawing from past messages written by the brand about similar topics, correlating that with historical engagement and conversion rates, and then pairing it with relevant customer data are some of the key data elements required for success.

Requirement 2: High-quality algorithms 

The LLM needs to work for the application and use case, and in this example, there are real challenges around matching brand tone with compelling calls to action to drive outcomes. Without getting too much into details, optimizing context windows can be difficult in use cases like this, so the ability to tune or retrain models to fit brand voice is critical.

Requirement 3: Business application support 

It’s going to be quite some time before marketing gets completely automated away 🤯. Until then (read: in my lifetime), tightly integrated applications are required to guide the algorithm, activate the outputs into the end use cases, and then experiment and iterate to learn what works and what doesn’t. An effective application strategy is much more than a pipe for execution. Rather, it creates a tight feedback cycle to drive business outcomes.

Requirement 4: Fully secure and governed 

Your customer data is the crown jewel of your business. Data security today is more critical than ever — and your customers expect this more than ever.

The mad enterprise scramble

Ever since OpenAI launched in November 2022, there’s been a mad scramble across players to capitalize on opportunities like the use case outlined above.

In Salesforce’s most recent earnings call, Marc Benioff spent the majority of the time addressing Salesforce’s new Data Cloud offering alongside their AI offering, and spoke directly about this opportunity:

“Put your hand up if you're using Snowflake every day…. So many of our customers have islands of trapped data in all of these systems, but the AI is not going to work because it needs to have the seamless amalgamated data experience of data and metadata, and that's why our Data Cloud is like a rocket ship.”

Salesforce is masterful in identifying and positioning high-level business problems, yet their company DNA is rooted in software challenges that are far less technical than the challenges that modern cloud data and LLM-based AI have presented in recent years.

If I were a Fortune 500 CIO, I would not select Salesforce’s Data Cloud as the focal point of a five-year data strategy, if nothing but for the fact that Salesforce is 10 years late to this market.

What Snowflake has uniquely accomplished with the launch of Arctic is a completion of the value triangle:

1. Data quality: Snowflake has been an established leader here for quite some time. Industry benchmarks across performance, cost, and scalability have consistently demonstrated this, and the scale at which Snowflake deployed also shows clear success.

2. Application support: Earlier this month, Snowflake launched its Marketing Data Cloud and featured a set of application partners that are purpose-built to deploy on top of Snowflake.

3. AI support: Not only does Arctic make Snowflake a contender here, but they’ve been incredibly thoughtful around dimensions that matter for the enterprise. A focus on code generation provides huge opportunities for workflow automation that plagues so many marketers today. And training costs that are a fraction of existing solutions open a new world of opportunities to train on first-party data and content.

The beauty of this model is that all of these points are wrapped up in Snowflake’s dedication to end-to-end security, which has long been a focal point and a core value proposition for today’s CIO. Snowflake’s best-in-class tooling also comes with a next-generation of governance tools to fully secure your data while enabling data access and business outcomes.

What’s worth noting in all of this is that Snowflake is focusing on the foundational data components via its Cloud Data Platform strategy. Whereas Salesforce saw a gap and built its own data cloud, Snowflake is not seeking to build a complete set of martech applications (or any other category, for that matter). 

For Snowflake, it’s about creating a secure platform with great data and, as of yesterday, great AI, to enable any application to be built to drive this next generation of use cases.