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Personalization should be the center of any customer marketing team's strategy. Using personalization within your customer marketing workflows can range from emails addressed to each customer’s first name to different product images being promoted on websites based on the visitor’s web activity.

Similarly, AI also comes in many forms, some of which are extremely useful when it comes to personalization in marketing. With advanced AI and ML becoming more accessible and embedded in marketing tools like customer data platforms (CDPs), every marketer has the opportunity to take advantage of AI to personalize customer experiences at scale.

Not having access to accurate, real-time customer data greatly impedes a marketer’s (and AI’s!) ability to build highly personalized customer experiences. But if your team uses a cloud data warehouse (CDW) like Snowflake and a CDP, the possibilities of AI- and data-driven personalization are endless. 

With a CDP built on top of a cloud data warehouse, you can create 360-degree views of your customer (also known as a Customer 360), which allows you to activate and execute personalized marketing campaigns.

Using AI for customer marketing personalization

Use AI to understanding customer intent and behavior

Similar to how we can pick up on patterns and context in social situations, AI can be used to uncover hidden patterns in customer behavior by analyzing customer data and predicting future behavior.

AI can also help identify customer preferences and purchase intent, which, when applied to an individual level via personalized content, can increase the effectiveness of your marketing campaigns.

Dynamic content personalization

Dynamic content personalization is the use of customer data to deliver personalized content in real time across various channels. For example, you use an AI product to help create a multi-channel marketing campaign promoting a holiday sale on your website. 

Using your customer data, the AI algorithm can segment your customers into groups based on things like purchase history and demographic data, making personalized marketing a breeze. Once your campaign is launched, customers will see marketing relevant to their interests as opposed to a generic canned message.

Let’s say Customer A is a cat owner with recent purchases, and Customer B is a dog owner with no recent purchases. Instead of sending both of them the same message, you could use AI to generate individual product recommendations and offers so that both Customer A and Customer B feel more of a connection with the content and copy.

By utilizing AI, Customer A might receive product recommendations for cats and a 10% discount, while Customer B might receive product recommendations for dogs with a heavier discount to increase the likelihood of a purchase.

Real-time customer engagement

Within the world of real-time customer engagement, AI can automate personalized interactions. 

Continuing with our example from above, let’s say Customer B (dog owner) visits your website after receiving a personalized email about dog products by clicking on one of their product recommendations: a blue dog leash. 

Because we have the data point that Customer B is specifically interested in the blue dog leash, their website experience will automatically be geared toward promoting dog products, and, in particular, the blue dog leash.

After Customer B has been browsing the website for a while, AI can calculate the most opportune moments to trigger personalized offers or promotions such as coupon codes for Customer B, all the while creating an entirely different cat-centered experience for Customer A, our cat owner.

Even after Customer A and Customer B leave your website, AI can ensure that they will continue to have consistent and personalized experiences across all touch points. 

The CDP: A powerful platform for AI-driven personalization

Customer 360s

A CDP helps collect and centralize customer data from various sources to create a single unified view of your customers through the use of identity graphs. Instead of logging into each of your marketing platforms (ESP, SMS, analytics, etc.,) and manually mapping customers to their activity and data, a CDP can automatically do this once it is connected to all of your disparate data sources.

Customer 360s empower marketers and AI to personalize experiences across the different channels and ensure that customer engagement in other channels informs the holistic experience for each customer. 

Data segmentation and insights

AI is only as good as the data it has access to. A CDP keeps all of your data in one place, so, instead of AI being limited to the set of data within just one of your marketing platforms, a CDP with integrated AI can give you results based on your entire marketing stack. 

One great application of AI within a CDP is segmentation. Instead of segmenting on one dimension (e.g., email engagement), AI can group your customers across multiple dimensions like demographics, email engagement, SMS engagement, and purchase history, resulting in more meaningful and specific segments.

Then, AI can take these segments and personalize communication across channels, predict future behavior, and create highly informed segments downstream based on how customers react to the communication.

Real-time data activation

CDPs make real-time data activation possible. The moment a data point is brought into the CDP, it can be used to inform the next steps for a customer’s journey. AI within your CDP can continuously learn and analyze customer behavior in real time, then help marketers optimize their campaigns based on its analysis. 

Activating data in real-time empowers brands to respond quickly to changing customer behavior or trends. 

For example, say a customer is browsing your website and places a product in their cart. For a new customer, this action could trigger a form to collect their email address in exchange for a discount. 

For returning customers, it could trigger a message that includes a discount code through the channel, like SMS, where they have the highest engagement rate.

Here are some real-world examples of AI-powered personalization in action:

• Personalizing movie recommendations based on viewing history and preferences
• Recommending related products to customers based on their past purchases
• Creating personalized playlists tailored to individual music tastes
• Offering personalized rewards and promotions based on customer spending habits

Benefits of using AI for personalization in customer marketing

By accessing and using AI and predictive analytics within your customer data platform, marketers can benefit from: 

• Increased customer engagement and loyalty
• Improved conversion rates and sales
• Enhanced brand perception and customer satisfaction
• More efficient marketing spend and resource allocation
• Greater competitive advantage in the market

Getting started with AI for personalized customer marketing

• Start with clearly defined goals and objectives.
• Ensure data quality, accuracy, and completeness.
• Monitor and measure the performance of your AI personalization efforts.
• Continuously adapt and improve your AI-driven personalization strategies.

Conclusion

Combining AI with your customer data unlocks transformative opportunities for personalized customer marketing, and by using a tool like a CDP, you can significantly optimize your marketing campaigns, streamline processes, save time and resources, and quickly adapt to customer trends to create the enhanced personalization customers crave.

Remember cookies? I do, too. And now, perhaps the most significant shift affecting enterprise marketing teams (besides shrinking budgets, customers’ demand for more personalization, and the emergence of genAI, to name a few) is Google’s deprecation of third-party cookies.

The question on every marketer’s mind is: how do we create innovative, privacy-compliant, and personalized experiences for every one of our customers without being able to use cookies?

My answer is simple: marketers should lean into zero- and first-party data like they never have before.

Thankfully, martech tools like Customer Data Platforms (CDPs) offer features like identity graphs to help identify anonymous and unauthenticated website visitors without relying on third-party cookies.

Using identity graphs can improve personalization in targeted campaigns and turn unknown website visitors into actionable customer profiles and tangible business opportunities.

In this blog post, I'll explain how using identity graphs in a CDP can empower marketers to unlock personalization within their marketing playbook to drive customer engagement.

The importance of identity graphs in customer marketing

An identity graph is a comprehensive database that aggregates various customer identifiers across multiple data sources and touchpoints around what we call a “stable identifier” at Simon Data. An identity graph is not just a collection of data interconnected identities. It also helps provide a unified, 360-degree view of each customer in real-time.

The true benefit of an identity graph is its ability to consolidate many pieces of customer data from several sources into a coherent whole.

Think of it as a puzzle, where each piece represents a different aspect of customer data — identifiers like email addresses and social media handles, devices ranging from smartphones to laptops, and behavioral data capturing every click, view, and interaction on the website.

When these pieces come together, they form a complete picture: a Customer 360, the holy grail for marketers looking to understand and engage with their audience effectively.

Customer 360s help marketers make sense of a customer’s journey across various touchpoints, like website visits, social media interactions, or online purchases, all of which contribute to the narrative of that customer’s relationship with a brand.

This is why identity graphs, which meticulously connect these various dots, can elevate the customer experience, regardless of the channel or device they use.

By using CDPs with identity graphs, marketers can go beyond traditional segmentation and targeting. They can recognize and reach their customers in a more personalized, meaningful, and timely way, delivering experiences that resonate and foster loyalty.

In a world where customers crave relevance and personalization and third-party data is restricted, identity graphs are a fundamental asset in any marketer’s arsenal.

Leveraging identity graphs within a CDP

There are many benefits to using a CDP with identity graphs within your marketing team. For one, composable CDPs sit on top of Cloud Data Warehouses (CDW) like Snowflake to access real-time customer data that marketers can activate. Identity graphs within CDPs then unlock that data’s full potential to drive measurable marketing success.

Identity graphs empower marketers with better personalization

I already mentioned that personalization is more important than ever for crafting experiences that resonate deeply with customers. Leveraging an identity graph in a CDP not only ensures that marketers reach the right people on the right devices in the right channels, but it enables them to deliver messages and offers that strike a chord with individual preferences and needs.

For example, with identity graphs and Customer 360s in place, you can easily craft an email campaign that addresses customers by name, references their recent interactions with their website, and recommends products based on past purchases.

This email will feel conversational rather than transactional by showcasing your customer’s unique journey with your brand.

Another way to leverage identity graphs is through the use of behavioral segmentation, which clusters customers based on their actions, such as purchase history or website engagement events.

This approach allows marketers to tailor campaigns that resonate with the specific experiences and interests of each group. This is where abandoned carts, price drops, or product recommendation emails come into play.

Beyond personalizing individual messages, using an identity graph with your CDP can help you power onsite personalization, serve recommendations across channels, and target specific segments with relevant ads in any of your paid media spend, ultimately helping you spend less while driving customer loyalty and lifetime value.

Using identifier enrichment to amplify paid media performance

Paid media is expensive and competitive, and, without the right tools in place, unsuccessful. In my experience, marketers who leverage accurate, in-depth data, such as hashed identifiers, in their paid marketing strategies drive the most impact.

Hashed identifier enrichment plays an important role when it comes to executing paid campaigns — particularly in amplifying paid social audiences and improving the overall performance of these campaigns.

By syncing additional hashed identifiers for a particular audience, marketers can significantly amplify their reach, which in turn can have a substantial impact on paid media match rates.

Hashed identifiers, such as hashed emails (HEMs) and mobile advertising IDs (MAIDs), act as secure, privacy-compliant ways to match users across platforms without exposing personal information.

When these hashed identifiers are integrated into paid social campaigns, they enable marketers to connect with their audience more effectively, ensuring that the ad reaches the right individuals, on the right platform, at the right time.

Simon Data’s Match+, for example, offers a seamless way to do just that: enrich customer profiles with HEMs and MAIDs and sync them to paid media channels with the click of a button.

So, how exactly are HEMs and MAIDs beneficial? HEMs allow for secure, anonymized matching of users based on their email addresses across platforms and devices, while MAIDs offer a device-specific way to reach users, particularly valuable in mobile-centric campaigns.

These identifiers, when used correctly, respect user privacy (goodbye, cookies!) while providing marketers with the means to create coherent, cross-platform narratives.

One of the most compelling benefits of identifier enrichment is the substantial increase in paid media match rates. Match rates — which indicate the percentage of users from the target list that can be accurately identified on the advertising platform — are crucial for the success of retargeting campaigns.

Higher match rates mean that a larger portion of the existing customer base can be reached, ensuring that marketing spend is strategic. Ultimately, it minimizes waste by ensuring that ads are delivered to the customers most likely to engage and convert.

Using identity graphs to de-anonymize unknown website visitors

Anonymous website visitors can be the bane of a marketer’s existence. But, if you’re able to identify them, they can quickly become a boon to your business. The de-anonymization process transforms these unknown entities into recognizable, and, more importantly, actionable individuals.

Identity graphs can help achieve this by piecing together fragments of data that visitors leave behind, like digital breadcrumbs. When integrated with a CDP, these scattered data points coalesce, forming a clearer picture of who these visitors are.

In Simon’s case, we leverage our trusted partner’s identity graph to power Identity+ to enable our users to reach more known, unauthenticated customers on their website, then plug these interactions directly into their existing abandonment campaigns.

What’s the big deal? Well, not only can marketers activate who they thought were anonymous customers, but they also have the opportunity to put more customers into existing abandonment journeys, further increasing ROI.

By integrating identity graphs into your martech stack, you can enhance the base of your first-party data foundation and increase the size of the prize, so to speak.

Here’s what I’ve seen: on average, our Identity+ clients experience a 37% higher abandonment campaign send volume than non-Identity+ clients.

This means that these clients can reach 37% more people with their most lucrative, high-intent campaigns because an identity graph enables them to identify existing customers who are browsing and adding items to their carts in an unauthenticated state.

Linking anonymous visitors with hashed identifiers via identity graphs

While identity graphs enable marketers to activate existing customers who are browsing the site but are not logged in, CDPs can help take de-anonymization a step further.

Similar to hashed identifiers, a CDP uses an identity graph to link anonymous visitors with various identifiers such as HEMs and MAIDs. This connection empowers marketers to target these previously unreachable audiences by creating hyper-personalized ad campaigns, bridging the gap between unknown visitors and loyal customers, all the while amplifying match rates in downstream marketing channels.

Here's a quick example. Imagine you could capture 90% of the anonymous visitors that land on your website, create a personalized site experience for them, and sync them to paid media channels like Google Adwords, TikTok, and Meta. This would improve ‌customer experience, ROAS, conversion, and critical metrics like CLTV and CAC.

Conclusion

Being a marketer in today’s world is extremely challenging, but it doesn’t have to be. Using identity graphs within CDPs can help marketers create data-driven, customer-centric strategies that impact the success of a brand’s customer experience and overall success.

The insights and strategies outlined above go beyond concepts — they are strategies I’ve seen succeed during my time at Simon. With identity graphs, marketers can better identify and cater to customers, turning every interaction into an opportunity for engagement, loyalty, and growth.

If you’d like to learn more about identity graphs within the Simon CDP, book a demo today.

It’s no secret that companies ‌often incur costs to attract and convert customers. Advertising, marketing, promotions, etc. — they all add up, and they are a regular part of doing business.

But how do you know if the costs you paid to acquire a customer were actually worth the return on investment? To answer that question, you’ll need two pieces of data: Your customer acquisition cost (CAC) and your customer lifetime value (CLTV).

Understanding CAC and CLTV

Customer acquisition cost is exactly what it sounds like: the average cost associated with acquiring a new customer. It’s calculated by dividing your total sales and marketing expenses over a given period by the total number of new customers acquired during that same period.

Lifetime value, on the other hand, is a measure of the total revenue that an average customer will contribute to the business during their time as a customer (i.e., their lifecycle). Importantly, this extends beyond the initial transaction that first brought them to a business. It’s calculated by multiplying customer value (average purchase cost and frequency of purchases) by the average customer lifespan (how many years a customer remains active).

Measuring these two metrics together gives you insight into the total ROI you can expect from your sales- and marketing-related expenses, and they can help you understand whether or not certain expenses were justified.

Once you’ve begun measuring your CAC and CLV, you will form a baseline understanding of where your business is — and you can begin thinking about ways to optimize both metrics so that you’re getting the greatest return on investment at the lowest cost. This will typically involve lowering your CAC while boosting your CLV.

Key strategies for driving down CAC

Lowering your customer acquisition costs will, in most cases, involve incremental optimization of your existing processes. Below are three optimizations that can help you lower your CAC.

Improve targeting and segmentation

Targeting can make or break your marketing and advertising campaigns. Target too broad an audience, and you risk wasting campaign funds advertising to people other than your ideal customer — people with a low risk of converting. Target too narrow an audience, and you risk leaving potential customers out of the picture.

The key to improving your targeting is to have a clear sense of who your ideal customer is and what they care about. Information about their demographics, income, location, interests, and browsing history can all help you build a targeted campaign with the highest potential for getting in front of the right eyeballs.

Does your product or service potentially appeal to multiple audiences? Segmenting your customers into different groups can also help you better tailor your marketing efforts to improve conversions — for example, by presenting a different message to each segment that best aligns with their motivations.

This is where having the right tools, such as a Cloud Data Warehouse (CDW) and a Customer Data Platform (CDP), can help, thanks to a CDW’s ability to collect real-time, first-party data, and a CDP’s ability to use that data and create unified customer views. Really nailing your targeting and segmentation can help you reduce wasted funds, lowering your CAC.

Reevaluate marketing channels

Where you choose to deploy your marketing and advertising efforts will have a big impact on the cost of your campaign — and, as a result, your CAC. This is due to two main reasons:

• Costs vary by channel
• Some channels are more or less effective at reaching and converting your ideal customer

When deciding what channels you will target, it’s important to start with an understanding of where your customer spends their time online: What websites they visit and how often, what online communities they belong to, and what social media platforms they use. Once you have this information, you can build campaigns to get in front of them in those places.

But just because your customer spends time on a certain website or platform doesn’t necessarily mean that that’s the best place for you to spend your campaign funds. Some channels will simply be better suited for converting your customers, and other channels won’t be.

Given enough time and data, you can reevaluate the performance of these channels. Dropping channels that don’t convert well allows you to reallocate funds to better-performing channels, improving your cost efficiencies and lowering your CAC.

Automate processes where possible

While ad and marketing spend are an important part of your CAC, they’re not everything. Another important bucket to be aware of is the personnel costs related to actually executing the campaign, which can be significant. Luckily, there are ways that you can lower these costs as well.

One extremely effective way of controlling these costs is to automate tasks wherever and whenever possible — especially if those tasks are repetitive or low-value. Doing so frees up your staff to perform higher-value tasks. And of course, it can also lower the personnel costs associated with your campaigns.

What, exactly, you choose to automate will depend on the types of campaigns you are running and the tools you have at your disposal. That being said, it’s possible to automate large parts of the campaign process, including:

• A/B testing
• Email nurturing
• Social media marketing
• Advertising
• And more

Strategies for maximizing CLTV

Maximizing customer lifetime value boils down to one thing: Getting them to spend more money throughout their time as a customer. Below are some methods for doing just that.

Reduce customer acquisition friction

Friction is the enemy of conversion. Whether it’s during the initial customer acquisition process or subsequent transactions, reducing friction increases the chances that a customer is going to spend money with your business. The more they come back, the more transactions they complete, and the more they spend — the higher your CLTV and the easier it is to justify your customer acquisition costs.

The good news is that there are many opportunities to reduce friction or other roadblocks that might be preventing conversions. Some examples include:

• Offering multiple paths or channels to conversion (phone, email, direct sign-up, etc.) so that your customer can choose the path that appeals to them
• Eliminating unnecessary steps in the sales process that gets in between your customer making a purchase
• Offering free trials or a freemium model that lowers perceived risk that might otherwise prevent them from signing up
• Providing other incentives — such as promotions and referral awards — that provide value to the customer
• Providing excellent customer service right from the initial interaction to begin building trust and loyalty early in your engagement

Develop a strong customer onboarding experience

Once your customer has made their first purchase, it’s important that you do everything you can to help them realize value from it. Doing so increases the odds that they will come back to you for additional purchases or, in the case of subscription businesses, renew or upgrade their subscription in the future. It also makes them more likely to evangelize your product by referring other customers.

As mentioned above, you can do this by fostering a positive first impression during the initial conversion stage or during onboarding (where applicable). You might, for example, offer:

• Training on how to use the product or service
• Configuration or integration services
• A dedicated account rep that is available to answer any questions

Providing educational materials — such as how-to videos, step-by-step articles, FAQs, and guides — can also go far in helping your customers hit the ground running using your product or service.

Promote customer engagement and loyalty

The more engaged a customer is with your business, and with your product or service, the more loyal they are likely to be — and the less likely they are to potentially leave you for a competitor.

You can promote customer engagement and loyalty in several ways. Some effective strategies include:

• Regularly communicating with customers through email, social media, and other channels
• Implementing loyalty programs and reward programs to incentivize repeat purchases
• Creating a sense of community and belonging among your customer base

The longer a customer stays with your business, the greater the chances that they will make more purchases, boosting their CLTV.

Upsell and cross-sell to existing customers

Once you’ve gotten over the hurdle of the initial conversion, it should be much easier to convince a customer to make additional purchases — especially after they’ve started realizing value from your product or service. That’s why upselling and cross-selling to your existing customers is such an effective means of increasing CLTV.

Examples of upselling and cross-selling include:

• Offering product recommendations relevant to your customers, based on what you already know about them. This might be additional products or services that complement what they’ve already bought, or potentially those that address different (but related) needs
• Creating product bundles and package deals that help your customers save if they spend more
• Providing exclusive offers and discounts to loyal customers, especially if the customer has expressed cost as a concern or a reason they have considered moving to a competitor.

Measuring and optimizing CAC & CLTV

The strategies outlined above can help you optimize your customer acquisition costs and lifetime value. As a final note, here are other steps you can take to boost the overall effectiveness of your sales and marketing campaigns:

Track other key metrics: CAC and CLTV are, of course, important KPIs. But they’re far from the only ones you should care about. Return on ad spend (ROAS), customer churn rate, satisfaction score, and average order value all provide additional insights.

Let the data lead you: Leverage cloud data warehouses, CDPs, and A/B testing to glean insights and identify trends that you can use to personalize the customer experience and improve CLTV and CAC.

Continuously iterate: In your journey toward improving CAC and CLTV, there’s no guarantee that the steps you take will have the effect you expect. That’s why it’s so important to regularly benchmark your metrics — so you can look at the data and see what, if any, effect your efforts have. Continuously iterate as you work toward optimization.

Two sides of the same equation

While they measure different things, your customer acquisition costs and lifetime value both offer valuable insights into the overall effectiveness of your marketing and advertising efforts.

Optimizing campaigns with either metric in mind can help you boost ROI; optimizing campaigns with both metrics in mind can go far in turning your marketing team into a profit center for your business.

Improving CAC and CLTV requires a significant amount of customer data. On the CAC side of things, customer data makes it easier to adjust your targeting and segmentation, for example, or to reevaluate your marketing channels. On the CLTV side, customer data and insights make it easier to provide real value to your buyers and to upsell or cross-sell them other products they might enjoy.

A customer data platform (CDP) that integrates with your martech stack can help. These platforms aggregate data in real-time from all the sources so that you can see it all in one central location — empowering you to draw insights and turn them into action.

Interested in learning more about how a CDP can help you achieve your paid media goals? Request a free demo today.

Welcome to 2024. Some things, like my inability to stick to a New Year’s resolution, have not changed. But there are some things, like customer expectations, that do change and are ever-evolving in the world of customer experience (CX).

Customer loyalty is important to a brand’s strategy. It is why marketers track metrics such as Customer Lifetime Value (CLV) and Lifetime Value (LTV). Loyal customers become advocates who drive business growth through repeat purchases and word-of-mouth, a timeless advertising form.

They’re profitable; they’ll continue to buy new products or services and typically are a cost-effective subset of customers to market to in comparison to acquiring new customers.

Lastly, these committed customers not only care about your brand but actively engage in providing valuable feedback. This fosters genuine organic brand advocacy through reviews or social media – a practice we can all admit influences our purchasing decisions as we navigate the digital marketplace.

In this post, we’ll cover the best ways marketers can build customer loyalty and drive value from it.

What customers now expect from brands

In this era, consumers are equipped with instant access to information and an abundance of products and services to choose from. Talk about information overload.

But this means customers are empowered to want more from brands. Your brand can break through the digital noise by adapting your strategy to what these customers are looking for:

• More personalized interactions and higher-quality experiences
• Authenticity and transparency
• Trust when it comes to their data

With the noticeable movement in customer preferences, it’s key for businesses to understand the trends and adapt their strategies. Our advice? Throw your generic marketing tactics into the trash, and do it fast.

The shift toward personalized customer experiences

You’ve probably seen the buzzword “personalization” headlining the top marketing blogs for years, and 2024 is no different. Brands have direct access to their customer data. Some businesses let this data sit and collect the proverbial dust, but those who use it to personalize marketing communications will have a competitive edge and more effective marketing campaigns. Personalization is a key component of driving customer loyalty.

Customers in 2024 not only want — but also expect — businesses to cater to their individual needs. Consumers appreciate brands that engage with them on a personal level, acknowledging their specific preferences and history with the brand. This could be through targeted birthday emails or category-specific discounts based on their favorite items.

Brands that take the time to understand individual needs create a sense of connection and relevance. This is what nurtures a meaningful customer experience, which is the foundation for ‌turning a one-time shopper into a loyal and life-long customer.

Building customer loyalty with authenticity and transparency

Consumers are naturally interested in the quality of products or services. They’re also becoming increasingly conscious of the values and ethics upheld by a brand. This is evident with the amount of sustainability-focused brands that have emerged from the market.

Do a quick search on “green marketing” to see for yourself. To cultivate customer loyalty in this competitive landscape, brands should prioritize building trust with their consumer base.

Transparent communications about business practices, authentic brand storytelling, and social responsibility and sustainability efforts are a few things that contribute to developing a trusting brand-to-consumer relationship.

Let’s pretend, for example, that your business uses eco-friendly ingredients and zero-waste shipping material. If you educate your customers on your sustainability efforts, the customers who support this initiative will not only understand the importance of purchasing from your brand over a competitor’s, but you’ll also begin to strengthen your loyal customer base.

How technology and data influence customer loyalty

While technology facilitates the collection of customer data, it is equally important for brands to prioritize ethical and responsible data practices. Brands use technology to collect customer data, ranging from online browsing behavior and purchase history to demographic information.

These same tools can give brands an advantage by allowing them to leverage customer data, like advanced analytics, artificial intelligence, machine learning, etc. This wealth of information empowers marketers to deliver personalized and targeted experiences.

Respecting customer privacy, obtaining consent for data collection, and ensuring data security are fundamental principles of building customer loyalty. Transparently communicating ‌how your customer’s data is used will strengthen ‌customer-brand trust and reinforce your company’s commitment to ethical conduct.

Strategies to build customer loyalty in 2024

There are so many different aspects of marketing and CX – it’s easy to put a dedicated customer loyalty strategy on the back burner. Here are some strategies to build customer loyalty that you can implement within your organization this year.

Exceed customer expectations: Go above and beyond the ordinary customer experience. Provide exceptional support for your customers, proactively address their concerns, and ensure that issues are resolved promptly.

Personalize the customer journey: Use all the data you’ve gathered to tailor your customer experience. Remove those batch and blast emails from your marketing calendar and set up tailored promotions based on actual behavior and individual preferences.

Build strong emotional connections: Engage with your customers on a personal level to cultivate positive brand perception. Consider creating communities of brand advocates, which will foster a loyal like-minded community with a sense of belonging.

Reward loyal customers: Your customers want a beneficial experience with your brand. Implementing a loyalty program will celebrate your loyal customers and reward repeat business. If you already have a loyalty program, make sure you’re offering exclusive experiences to incentivize continued participation with your brand.

Prioritize customer feedback: Actively welcome customer feedback. Loyal customers will be honest with what they think about your product or service. Use that to improve.

Embrace transparency and authenticity: Honesty is the best policy; in 2024, brands should follow this golden rule. Acknowledge any mistakes your organization makes and hold your business accountable for any issues customers experience. Customers will appreciate ‌open communication.

Be socially responsible: Publicly share ‌important social issues that align with your brand. Causes that resonate with your customer will lead to and promote a genuine customer-brand relationship.

Leverage technology for better engagement: There’s a myriad of marketing technology platforms in this world that support marketing efforts. Use these tools to automate your personalized communications. You can take it a step further and analyze customer data to gain insights and optimize your existing strategies, such as your loyalty program.

“Customer loyalty” isn’t just another buzzword

via GIPHY

Remember: customer loyalty is a strategic necessity that requires an understanding of evolving consumer expectations and a commitment to meeting them. As we dive deeper into this competitive market, brands must adapt and evolve with their customers by focusing on understanding what their customers want and exceeding those expectations.

As we look ahead, keep the leading customer trends top-of-mind. Meet your empowered customers’ expectations. Be authentic, be you. And lastly, use the tools and data at your disposal, but do so ethically and responsibly.

Brands that prioritize their customers’ wants and embrace these strategies will weather the ever-changing customer experience landscape.

Jeff Bezos, the visionary founder of Amazon, often emphasizes the concept of “customer obsession” as the center of Amazon’s remarkable growth strategy.

It’s easy to assume that every brand has adopted this principle, but the reality is that delivering on customer obsession demands more than mere acknowledgment — it requires relentless execution and unwavering focus.

The essence lies in meeting and surpassing customer demands at every step of their journey with a brand.

Navigating customer needs in an unstable landscape

Macro-environmental shifts have significantly influenced shopping behaviors, particularly within the retail sector. The onset of the “retail apocalypse” in 2010, which reached its peak during the pandemic, marked a pivotal moment in today’s world of retail.

Amazon played a substantial role, but equally impactful were ‌digitally-native brands like Casper, Allbirds, and Harry’s.

These disruptors reshaped the landscape by bypassing traditional middlemen, enabling direct shipments from manufacturing plants to consumers. The consequence was an influx of brands into the market, and big box retailers found themselves struggling to adapt quickly.

This transformative period coincided with the evolution of marketing technology. Brands now have toolkits brimming with thousands of SaaS applications specifically designed to enhance various facets of the consumer experience.

The goal was clear – to convince customers not just to make a purchase but to foster a relationship that would endure through repeated transactions.

via GIPHY

The so-called “retail apocalypse” signaled a departure from the days of direct competition between big box retailers and department stores.

A new wave of brands, with lower barriers to entry, surged ahead by strategically aligning themselves with this idea of customer obsession. But, the question lingering was whether this trend would persist. Has it?

In many ways, I think the answer is yes. Digitally-native brands continue to flood the market, now with even lower barriers to entry. Internet and TV personalities are leveraging their influence to establish brands, meeting their target customers wherever they are.

Examples like Logan Paul & KSI’s Prime energy drinks and Kylie Cosmetics showcase the immense success achievable.

However, the landscape is not without its challenges, and for retail brands to remain relevant, they must continuously innovate and adapt to customer needs.

Building customer resilience through proactive engagement

In the current market scenario, brands cannot afford an unlimited budget for acquiring new customers. So, how can a brand efficiently evolve its customer engagement strategy and endure amid the chaos?

Consider the SaaS applications I mentioned earlier. Though not the most glamorous anecdote, leaning into owned channels is one of the most cost-effective means of engaging buyers.

Channels such as email, text, mobile push, and direct mail provide brands with complete control over who they reach, when they reach them, and the content of their messages. However, the efficacy of these tools hinges on having a robust data strategy in place.

I’ve always believed that the key to an effective customer marketing strategy starts with the cloud data warehouse. It enables brands to adopt the best cloud data strategy that aligns with their business requirements rather than forcing them into a rigid data structure that doesn’t work for their organization.

When you look at retailers, major players like Spotify, Netflix, and Apple excel at using data within their marketing strategies, thanks to their vast resources. Surprisingly, smaller brands like Yeti, Resy, and Equinox have also showcased proficiency in executing database marketing strategies that delight their customers.

What do these strategies have in common? They:

• Leverage real-time engagement data collected on the website to guide customers down the sales funnel
• Incorporate customer support data to address users in dispute
• Understand the customer lifecycle to deliver thoughtful, personalized messages at every touchpoint

Fostering community and loyalty through value-based strategies

While these strategies might seem commonplace, brands must go beyond the basics for growth and differentiation in the competitive retail landscape.

Tools like Klaviyo and Shopify have undeniably made it more accessible for smaller brands with lean teams to execute effective strategies, but it isn’t enough to drive customer loyalty.

Consider the prevalence of anonymous web traffic — a recurring theme across many clients that Simon Data works with today. Addressing this challenge is crucial, especially as third-party cookies are going away, making it harder to track users across the internet.

So, how can brands proactively engage with customers who are browsing anonymously? Some tools help marketing teams identify anonymous users. Take, for example, Simon’s innovative solution that includes an identity graph that helps brands determine whether an anonymous browser is a known customer within their database.

Ultimately, this enables proactive engagement while respecting user consent, which plays a crucial role in today’s privacy-focused landscape.

For those identified as known customers, engagement becomes a personalized and meaningful interaction. For those who haven’t given consent, there are still ways to leverage the data by enriching it with HEMs and MAIDs to improve match rates in paid media channels.

Empowering marketing teams in-house with these capabilities facilitates a natural breakdown of silos, offering more control over the broader customer marketing strategy and significantly increasing the likelihood of conversion. This is the epitome of innovating for the customer.

Conclusion

Today, customer retention can make or break businesses. Jeff Bezos’ visionary concept of “customer obsession” is the fundamental strategy that has fueled Amazon’s success.

Brands must commit to understanding and fulfilling customer needs by delivering 1:1 personalization and building long-lasting relationships founded on trust (especially when it comes to data privacy) and value throughout the entire customer journey, delivering the right message via the right channels at their preferred time.

Accomplishing this requires access and centralization of real-time data, proper campaign orchestration, personalized engagement, active listening, and a commitment to delivering value consistently.

As I wrote in an earlier article, the benefits of composability have often been expressed in terms of the architectural principles of data management and how this benefits data teams.

While these benefits are important, I’d argue they’re not the primary benefits of composability.

Businesses ultimately invest in CDPs to drive marketing and customer experience performance metrics – improving customer acquisition efficiency, increasing customer lifetime value, driving up purchase frequency, etc.

Expressing the benefits of composability purely in architectural terms has limited conversation around how it benefits the above business outcomes expected of a CDP and the customer marketing teams that invest in the process.

Martin Kihn at Salesforce, the way less cool version of Don Cheadle, recently published Salesforce’s future vision for their Data Cloud, a term they now apparently use interchangeably with CDP.

While I won’t go into their CDP approach in this piece (and I mostly expressed my opinions on where Salesforce’s Data Cloud is headed in my previous article), Salesforce’s vision for the capabilities a CDP should offer (if you look past the vaporware and marketing jargon) underscores a few key business benefits of composability, even though this is clearly not the approach Salesforce is taking with their Data Cloud.

First, let’s talk about the composable trend and why it’s important.

Hightouch started creating noise around this term in 2022 with aggressive claims that the CDP is dead and this piece explaining the benefits of a composable CDP vs. a traditional CDP.

So, what exactly is Hightouch’s stance?

Hightouch was founded by a group of former Segment engineers who attached Hightouch’s vision to data warehouse centricity and the #moderndatastack. It’s also worth mentioning that Segment seemed to miss this data-warehouse-centric trend and now Twilio’s business is feeling the pressure.

I agree with just about everything Hightouch has to say about traditional CDPs. Traditional CDPs predominately come from an era of SaaS where the application wants to become the source of truth for whatever data it controls.

Salesforce pioneered this approach by creating a cloud for everything that stores an authoritative copy of your sales, marketing, advertising, support, and [insert data for any use case here] data. Data Cloud is now their response to the problems this approach creates.

This approach means that the data marketing teams are working with is often siloed from investments being made on the data side for other use cases.

A great example of this is that enterprises will often have analytics and data science teams interpreting data to gather insights and that same work is rarely being used or seems to seldom be available to support marketing use cases.

At Simon, we’ve always embraced data warehouse centricity and the myriad benefits centralizing data within a data warehouse creates for a business, beyond CDP use cases.

Hightouch is a product built by engineers for engineers (like Segment) and so the vision and product marketing around it have always focused on the benefits for engineers. My goal here is to translate some of these benefits into the things that matter most for the teams that use data to power the customer experience.

If the Cloud Data Warehouse is the center of your data strategy, and the CDP is fully connected to your CDW, there are many benefits for customer marketing teams beyond the benefits for data teams that Hightouch and others have expressed.

1. Auto-optimizing experiences

If the CDP is fully connected to all customer data — think ML models trained on historical data, engagement data, real-time data from web/app, etc., — the CDP can auto-optimize the customer experience.

Why is this different in a composable approach? The answer is simple: the optimization is driven by the highest quality dataset (i.e., the complete customer profile).

When real-time and historical data come from different sources and the CDP is organizing this (a classic traditional CDP use case), ML models may take into account something like the customer’s historical lifetime value, but it cannot fully incorporate real-time data in training the models that predict what experience to serve the customer.

2. Data availability

One of the things I hear most often from CRM teams is that they spend a significant amount of time wrangling and structuring data to support their various marketing use cases.

This is primarily driven by the rigid data structures of marketing automation platforms (I said I wouldn’t continue to talk about Salesforce in this article, but SFMC data extensions or PET tables in Oracle Marketing Cloud are probably the best examples of this).

With a composable approach and well-structured data in the data warehouse, the CDP can function as the “brain” of marketing automation, with the downstream platform simply executing the messaging.

3. Identity management

Customer identity is the biggest blocker to achieving fast time to value with a CDP. For enterprises with complex data structures (e.g., accounts, households, multiple brands, etc.), this is especially true.

While engineering-focused rETL tools talk about this benefit, and composability provides significant benefits in enabling teams to leverage complex identity structures, these benefits should also be extended to CRM teams.

Getting identity right means not only being able to structure data to reflect the customer context but also being able to personalize the customer experience against it (and leverage relational data tied to the customer context).

For example, we have many clients who have multiple brands, and being able to personalize the experience based on the context of a given brand, while also being able to understand the customer holistically across brands, is an important use case that comes to mind.

With a traditional CDP, and Salesforce Data Cloud may now be the most prominent example of this, the quality of your identity resolution, data science model outputs, and the blocking and tackling of your CRM program — segmentation, personalization, orchestration, and the like are dependent on the data quality within your traditional CDP.

Why composability matters

With a composable approach, there is no divide between investments designed to solve these problems at an enterprise data level and the business outcomes that CRM teams are driving toward.

Further, composability means that achieving these goals can be accomplished through credentialization of the CDP into the data warehouse vs. a months- or years-long implementation of the CDP and ongoing maintenance and integration work as data changes.

With GenAI advancements, GPTs and the like can interpret a dataset to, for example, generate the right email content for 1-1 personalization. GenAI could do this regardless of where the data lives, but the quality of the output is going to be determined almost entirely by the quality of the dataset and not the quality of the model itself — something that is certainly improved with a composable approach because the GPT can interpret the entire corpus of customer data.

I predict that the more vendors, data, and CRM teams adopt composable approaches and the more they can express the business value or use cases that composable approaches enable, this trend will move from the collective industry navel-gazing that composability seems to have so far represented and will eventually become a requirement of CDP vendors.

In today’s cookie-less modern marketing world, navigating customer data is both an exciting opportunity and a challenge. Many brands are striving to stay ahead, and Customer Data Integration (CDI) is more important than ever when it comes to driving effective decision-making.

Yet, many marketers find themselves struggling to provide a personalized customer experience due to inaccurate and fragmented customer data.

Let’s explore how the adoption of CDI, coupled with the capabilities of Customer Data Platforms (CDPs), can revolutionize the way marketers harness and interpret customer information.

What is Customer Data Integration (CDI)?

Customer Data Integration involves seamlessly bringing together several data sources, ensuring the data collected from them is accurate, consistent, relevant, and actionable for marketing teams.

The key components of CDI are:

1. Data ingestion
2. Data cleansing
3. Data Transformation
4. Data Storage
5. Data Activation

Together, these stages function to create a comprehensive and unified view of customers.

This unified customer view, known as a Customer 360, is a game-changer for enterprise marketing teams because it breaks down barriers between various touchpoints, thus enabling holistic customer understanding, empowering decision-makers with a 360-degree view of customer behavior, and helping to create personalized customer experiences.

This, in turn, allows marketers to craft targeted and effective strategies to optimize their marketing efforts and focus on what matters most: increasing customer engagement and loyalty.

The benefits of CDI extend far beyond streamlined processes. At Simon, we’ve seen our clients experience a shift in their ability to comprehend, engage, and retain customers across diverse end channels after implementing CDI.

The dream of real-time campaign optimization becomes a reality once marketers access accurate, up-to-date insights. If, for example, a marketer notices that customers are more likely to abandon a cart on Thursday at 7pm, they can use that data to send email incentives on a Wednesday to encourage the customer to purchase sooner.

Essentially, the more accurate your first-party data is, the more personalized the customer experience becomes, meaning they’ll be more likely to purchase from — or refer — your brand in the future.

Another benefit to implementing CDI and a CDP is saving time and money. You can improve marketing ‌spend while increasing conversion rates, showcasing the tangible return on investment that effective CDI can deliver.

Common challenges with Customer Data Integration

Implementing CDI is not without its challenges, however. At Simon, we’ve witnessed our fair share of data silos, poor data quality, and even technological limitations within marketing teams.

One of the initial challenges our teams have encountered when implementing CDI is the existence of data silos. This is usually a symptom of decentralized data across disparate systems. This is where CDPs can be extremely effective in bridging data siloes, ensuring a cohesive and unified approach to data management.

Poor data quality is another formidable obstacle when it comes to customer data integration. The repercussions of inaccurate information are far-reaching — they lead to flawed insights and compromised decision-making.

Technological limitations often become stumbling blocks in the search for seamless data integration. Here, CDPs serve as a technological enabler, providing the necessary tools and infrastructure to navigate and overcome integration challenges effortlessly.

But the journey of implementing CDI with a CDP is not just about overcoming challenges — it’s about transforming them into opportunities for growth. It’s also important to use a solution that provides comprehensive customer support and can guide you through the intricacies of CDI implementation with confidence and expertise.

We’ve mentioned that a Customer Data Platform can help improve your customer data integration experience. Let’s look at how to approach CDI within your CDP.

How marketers can approach Customer Data Integration with a CDP

CDPs are designed to address the challenges of data integration, with data cleansing and enrichment as the pillars of data quality. Some CDP companies, like Simon, are hands-on with their tools and platforms to actively ensure that every piece of information retains its quality.

For example, Simon Data assists in seamlessly connecting data sources, ensuring a holistic view for enhanced decision-making and targeted marketing strategies. Instead of focusing solely on silo elimination and redundancy, a CDP sits on top of a cloud data warehouse (CDW) like Snowflake to help build an accurate data foundation.

By doing so, marketing teams can ensure clean, enriched data within their CDI implementations. The platform’s tools work diligently to eliminate inaccuracies and redundancies, guaranteeing a foundation built on reliable and high-quality data.

A CDP then takes the clean, real-time data and curates a Customer360, allowing them to personalize customer messaging for every individual.

Additionally, CDPs help marketing teams activate their real-time data to ensure that insights are both accurate and timely. Simon, for example, offers AI-powered insights that add a predictive dimension to marketing strategies. Through the use of machine learning and predictive analytics, marketing teams can anticipate and respond to customer behavior proactively.

With a scalable and secure CDP, marketers won’t be data-strapped when their business scales. Additionally, CDPs ensure that data collection adheres to ever-changing privacy laws and that data is secure, assuring customers of the safeguarding of their information.

Setting up CDI for success

When looking at CDI, marketers should have a strategic and meticulous approach. Start by establishing clear goals to ensure seamless alignment with broader marketing objectives. For example, many of Simon’s customers want to improve customer lifetime value (LTV), drive customer loyalty, and improve customer retention, which is accomplished by catering to customer needs and experience.

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Next, identify all of your data sources. At Simon, our customers often use a cloud data warehouse such as Snowflake, Redshift, Bigquery, or even sources such as Shopify. Then, conduct thorough assessments of your data quality. Is it accurate and relevant to your marketing needs?

Once CDI is successfully up and running, continue to train your marketing team and foster a data-driven culture. This will allow your organization to continually evolve and leverage the full potential of integrated data.

Finally, implement continuous monitoring and measurement to provide feedback loops that help marketers make agile adjustments in response to evolving customer preferences.

Conclusion

Marketing teams that employ CDI and CDPs benefit from access to real-time, high-quality data from disparate sources that can be easily activated for campaign and orchestration initiatives.

Beyond setting up personalized marketing campaigns, AI and analytic tools within your CDP can help you deliver the right message to the right person at the right time, helping you promote brand awareness and drive significant revenue growth.

In the continually evolving landscape of customer-centric marketing, the combination of cutting-edge technologies has paved the way for unparalleled personalization opportunities.

At the forefront of this revolution are Customer Data Platforms (CDPs) and Cloud Data Warehouses (CDWs), with Snowflake emerging as a powerhouse when it comes to marketing data management.

In this article, we’ll discuss how CDPs like Simon Data integrate with a CDW like Snowflake to unlock infinite personalization possibilities that ultimately lead to increased customer acquisition, retention, and satisfaction.

Understanding Snowflake and CDPs

The benefit of CDPs

By now, you’ve probably heard about Customer Data Platforms, so let’s quickly define what a CDP is and its use cases. A CDP is a martech tool that sits at the center of your marketing strategy. CDPs aggregate and unify all types of data from basic customer contact information to all-time purchase history, to purchase propensity determined by an AI tool, to a customer’s website page view from five minutes ago.

The data that CDPs compile is stitched together to create a complete, accurate, unified view of your customers. This single customer view serves as the basis for effective personalized marketing. From there, mark‌eters can use this data to create granular customer segments to deliver the right message, at the right time.

What is Snowflake?

Snowflake is a powerful Cloud Data Platform that can store all of your customer data and serve as your single source of truth. Snowflake’s scalability and ability to handle massive datasets make it a perfect fit for your business as it grows and acquires more data.

With your CDP sitting on top of Snowflake, your marketing team has direct access to this data. Your data team works diligently to collect and maintain this data, and by having your CDP sit on top of Snowflake, marketers get easy access to the wealth of quality data that is already available.

The connection between a CDP and CDW reduces the duplication of data outside of your warehouse, improves data cleanliness, eliminates data silos, and allows marketers the freedom and ability to quickly set up high-quality, data-driven marketing campaigns.

Now that we’ve defined CDPs and Snowflake, let’s explore how these two powerhouses work together to achieve hyper-personalized marketing campaigns.

Empowering Customer 360s with Snowflake

Without having real-time, centralized data, it’s nearly impossible for marketers to effectively manage their marketing workflows and tailor their campaigns to their customers.

This is where using a CDW like Snowflake comes into play. Snowflake excels in storing and aggregating customer data collected from various sources and customer touchpoints, such as customer website behavior, order history, and customer service interactions.

This centralization creates the foundation for a unified customer view, which is instrumental in understanding the complete customer journey.

Benefits of centralizing customer data

Rather than setting up data routing from disparate sources and sending them to your CDP, focus your energy on getting that data into your data platform so that it remains your source of truth, enabling your marketing teams to get access to the most reliable data to build their campaigns.

When all of your data is housed in your CDW, it not only can be used for marketing campaigns, but for campaign analysis, historical tracking, and any other data use your business may have as well. Working toward your data platform as your source of truth is an investment in your company’s long-term success.

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Let’s look at some use cases. You’ll probably want to use a customer’s email clicks and opens to create personalized customer experiences across many different channels like social media, onsite personalization, SMS, and further email campaigns.

Instead of sending email behavior data from your ESP to tens of different locations, route that data to one place: your data platform. Then, your CDP can use that data to segment customers and orchestrate a unified, omnichannel customer experience.

So, if your customer opens an email and clicks on an image of a red pair of sneakers, you can store that information in Snowflake and use a CDP like Simon Data to send that information to channels like Meta, Attentive, Dynamic Yield, and more.

You can ensure that the next time your customer goes on your website or Meta, those red sneakers are the first thing they see. And, when your next SMS campaign goes out, an image of those sneakers is sent along with it.

When your data is centralized in your data platform, all your channels can use the same reliable data so that your customers have one unified, great experience.

A powerful CDP can also send data back to you. With Simon Data, for example, we can set up a Snowflake share so that all metadata related to campaigns orchestrated through Simon can be shared directly back to you — making Simon CDP the connective tissue that helps keep Snowflake as your source of truth.

Scalability: Empowering growth with Snowflake and a CDP

Snowflake’s scalability is a game-changer, especially in the era of big data. As businesses accumulate massive datasets, the ability to scale becomes paramount.

Snowflake’s architecture allows for seamless scalability, accommodating the growth of customer data without compromising on performance. It achieves this by separating storage and computing, allowing an almost unlimited amount of queries to be run simultaneously.

Snowflake can scale both vertically and horizontally and has powerful auto scalability capabilities. This scalability is key to supporting the growth and continued optimization of your marketing campaigns.

Ensuring data quality for accuracy and precision

Every good marketer knows that a single customer view is only as valuable as the data used to create it. Snowflake allows you an environment to easily implement and automate various data cleaning processes that will standardize and deduplicate your data.

By having your CDP sit on top of Snowflake, you have complete control over the quality of your data used to create your unified customer view.

In addition, Snowflake’s commitment to data security and quality ensures that the information stored is not only comprehensive but also safe and secure. By consolidating all of your data in one place, you strengthen its security and allow enforcement of data governance policies established by your business. Snowflake provides a secure location to manage customer data responsibly.

Clean and trustworthy data is crucial to crafting personalized experiences that resonate with individual preferences. When a CDP sits on top of Snowflake, your marketers get access to accurate, holistic, and centralized data.

Incorporating AI and Machine Learning for personalized insights

With a high-quality cloud data platform in place, the stage is set for the integration of artificial intelligence (AI) and machine learning (ML) tools. Snowflake’s compatibility with these technologies empowers CDPs to capitalize on these meaningful insights from customer data and build highly targeted campaigns that can strategically pivot based on customer needs.

Seamless integration with AI and ML tools

Snowflake can integrate with a variety of AI and ML tools, and its compatibility spans popular tools and frameworks, empowering data scientists and analysts to leverage their preferred AI and ML tools without constraints. Performance is crucial when running intensive ML algorithms. Due to Snowflake’s ability to scale, it is perfect to support robust ML queries needed to effectively derive insights from customer data.

Using AI insights for customer marketing

When your CDP sits on top of Snowflake, you can easily infuse your AI insights into all customer journeys to create a 1:1 personalized customer experience. Marketers have access to all of the customer data and insights that they need to build data-driven, tailored communications.

Real-time insights can be implemented across all channels to create smarter campaigns that nudge customers with the right message at the right time. These insights can be used to automatically adjust a customer’s experience based on their behavior.

Some of the most useful AI insights for marketers are churn propensity, purchase propensity, and product recommendations. If you’re looking for robust predictive insights, check out Simon Predict.

Targeting your customers at the right time can make or break your campaign success. Models such as churn propensity can help catch customers at crucial moments in the customer lifecycle.

When your CDP has access to powerful churn models and customer data, you can instantly detect when their churn risk changes and take action before it’s too late. You can even set up a journey so that customers receive proactive communication when their churn risk increases.

This journey could provide them with a special offer or send them a personalized message from your customer service team showing them that they are a valued customer, and helping build brand affinity, loyalty, and revenue.

Driving personalization across the entire customer journey with a CDP

Armed with a unified customer view and AI-driven insights, CDPs leverage data in cloud data platforms to personalize interactions across diverse touchpoints. Here are some of the benefits our customers see.

Tailored website and app experiences

Websites and apps serve as primary interfaces for customer interaction. Fueled by your data platform, CDPs can help personalize these onsite experiences so that they resonate with your customers.

With a CDP, you can sync over all customer information to your onsite personalization tool so that it is fresh every single day. This information can include past purchases, recent email clicks, demographic data, communication preferences, and more. Because your CDP is powered by your CDW, you can be assured that it is the freshest, most accurate data.

Dynamically syncing this data to your onsite personalization tool allows your customers to see tailored product recommendations that they are most likely to purchase and enjoy. The result? An immersive, 1:1 customer experience.

Elevating email marketing campaigns

Emails remain a crucial and effective part of marketing strategies, and personalization here is non-negotiable. CDPs built upon CDWs give marketers the data they need to craft email campaigns with targeted messaging and offers, aligning with individual tastes and behaviors.

For example, while marketing automation tools may be able to execute simple abandon carts and browse emails, orchestrating abandonment through a CDP allows you to take advantage of all the customer data you already have and further personalize abandonment.

Your AI tool may have predicted that sending an abandonment email for a pillow is best 30 minutes after it’s abandoned, versus an email that is best sent three hours after for a mattress. With a CDP, you can use those insights to effectively choose the right content and timing that would best resonate with your customers.

With CDP-powered emails, customers’ inboxes become a personalized feed worth their time rather than a generic broadcast channel.

Smart post-purchase campaigns

Access to customer data and AI insights allows you to offer highly personalized product recommendations in the post-purchase customer journey. If a customer has recently bought a camera, AI algorithms can analyze their preferences as well as items frequently bought together and suggest complementary accessories like specific lenses, tripods, or camera bags that fit with their purchase.

This tailored approach not only enhances the customer’s experience by introducing relevant items but also increases the likelihood of additional purchases. You can harness upsell and cross-sell opportunities that make sense for your customers. This level of personalization could not be achieved without a CDP that is powered by your cloud data platform.

Rethinking real-time customer support

In the realm of customer support, real-time personalization matters more than ever. Customer information can be sent over from a CDP to any customer support tool.

When that data is powered by your CDW, you are sending over the complete, accurate customer profile. With more relevant information, support members can be better empowered with context to provide recommendations and solutions that align with the customer’s history, fostering a sense of individualized care. This leads to better and quicker support interactions, and ultimately more satisfied customers.

Conclusion

The combination of a CDP with the robust, real-time data stored in Snowflake has the potential to bring about transformative improvements in personalized customer experiences. The combination of these technologies centralizes data and creates a complete customer view, which can be activated to foster increased engagement, customer loyalty, and satisfaction.

The seamless integration of a CDP and Snowflake ensures that all of your marketing campaigns are using the most accurate, freshest data available. When CDPs are built on top of your CDW, it eliminates data silos and duplication of data and gives marketers the tools they need to re-imagine touch points across the customer journey, infusing data into every customer interaction from your brand.

Explore the boundless hyper-personalization possibilities that unfold when a CDP like Simon Data is combined with a powerful cloud data platform like Snowflake.

If your marketing team has decided to invest in a paid media campaign, it’s important to treat it like you would any other investment. That means having a plan in place to measure its performance.

Once you have a measurement strategy in place, it becomes possible to perform calculations that can help you understand whether the campaign is performing the way you want it to — or if changes need to be made to reach your company goals. The return on ad spend (ROAS) formula is an important calculation that can help you tie your marketing campaign directly to what matters most to your business: Revenue.

Below, we take a closer look at what the ROAS formula is, how it’s calculated, and the different factors that can affect it. We also provide advice you can use to improve this critical metric within your paid marketing campaigns.

What is ROAS?

ROAS, or return on ad spend, is a KPI that marketing teams use to quantify the performance of paid media campaigns. This can include anything from paid search engine marketing (SEM) and social media advertising to email marketing campaigns. It also applies to print, TV and online display, and direct mail campaigns.

The metric directly ties revenue generated by ads to the cost of those ads. This makes it easier to see whether a campaign is effective and profitable, or if it may in some way be missing the mark.

In this way, knowing your return on ad spend empowers you to iterate. Then, you can double down on the campaigns and distribution channels that show the highest returns while abandoning (or shelving) those not performing as you expected.

With enough historical ROAS data, the metric can even be used to inform your paid media advertising and marketing budgets, as well as future revenue projections.

Calculating the ROAS Formula

The formula used to calculate ROAS is a fairly simple one: ROAS = Revenue / Ad Spend

In this formula, revenue refers to the total revenue that can be attributed to a particular paid media campaign, and ad spend refers to the total cost of running that campaign.

An easy rule of thumb for remembering the formula might be to think of it as “revenue over ad spend.”

ROAS examples

To help put ROAS into context, let's calculate it for an ambitious, multi-channel paid media campaign.

In this example, a marketing team ran a single campaign across four channels: Paid search, paid social, email marketing, and online display. They spent $2,000 on each channel. Paid search brought in $5,500 in revenue. Paid social brought in $4,500 in revenue. Email marketing brought in $4,250. And online display brought in $1,750.

To calculate the ROAS for the entire campaign, we would add up all of the revenue the campaign generated, then divide it by the total costs:($5,500 + $4,500 + $4,250 + $1,750) / ($2,000 + $2,000 + $2,000 + $2,000) = $16,000 / $8,000 = 2This means that for every $1 spent on the campaign, the business brought in $2.

But it’s also possible to calculate ROAS for each individual channel to glean more granular insights about how each performed.

When we do this, we see that the ROAS for paid search, as a channel in this campaign, was 2.75 — almost 50 percent higher than the ROAS for the entire campaign. We also see that the ROAS for online display as a channel was 0.875 — meaning it didn’t even break even.

With these insights, the next time the marketing team runs a paid media campaign, it might choose to abandon online display as a channel altogether and allocate those funds to other, higher-performing channels, like paid search, instead.

Recognizing the limitations of ROAS

While ROAS can be an incredibly helpful metric in evaluating the performance of a paid media campaign, it isn’t the only metric you should consider. Other relevant metrics, like customer acquisition cost (CAC) and customer lifetime value (CLTV) are also important parts of developing a holistic view of how your campaigns perform.

Likewise, it’s possible for a campaign to fail in its revenue goals while succeeding in achieving other goals, such as brand recognition. Returning to the example above, ROAS shows us that the online display channel didn’t earn a positive ROI, but it tells us nothing of that channel’s reach, or how it could have potentially contributed to the success of other channels.

Additionally, marketing efforts like events and influencer or sponsored media should also measure engagement and perception instead of ROAS.

With this in mind, it’s important to look beyond the immediate returns your campaign generates and to consider the long-term impact of your paid ad efforts.

Factors affecting ROAS

Many factors can affect how effective your ad campaign performs — and, by extension, the campaign's ROAS. If a campaign performs significantly better or worse than initially expected, it can be helpful to consider these factors as you optimize them:‍

Targeting: Your campaign's targeting determines who your ads are ultimately presented to. The more relevant your product or service is to a particular audience's needs, the more likely they are to convert on those ads — and the more revenue they will contribute, which will directly influence the campaign's ROAS. With more customers demanding a personalized experience, targeting is particularly important in online channels like paid search and paid social.

‍Channels: How you choose to distribute your ads will have a material impact on whether or not they arrive effective. Channels can be broad, such as paid search, paid social, and online display ads. They can also be more specific, like which social media platforms you are specifically leveraging for paid search or which websites your display ads appear on.

The channels that make the most sense to you will be those where a.) you know your audience spends its time, and b.) there is an obvious path to conversion or purchase.

‍Creatives: A lot goes into making an ad that converts. An eye-catching design, memorable copy, and overall “production value” can mean the difference between a campaign that generates revenue for your business and one that flops. Creatives that are of a low quality or that are not properly aligned to the expectations of the target audience can adversely affect your campaign's ROAS.

‍Landing page experience: Just because an online ad is successful in generating clicks doesn't mean it will contribute revenue. If your landing page is not properly optimized for conversion or presents a poor user experience, it could lead to drop-offs before purchases are made — again, leading to a lower ROAS.

‍Bid strategy: Other than making your campaign itself more effective, the only other way to improve ROAS is to lower costs. Optimizing your bid strategy for online ads to ensure that you're not overpaying for exposure and distribution can go far in stretching your campaign's budget and improving its ROI.

Creating successful marketing campaigns with ROAS

To get the most value from your marketing campaigns and accurately measure ROAS, you should have several key elements in place:

1. Clearly defined goals and objectives. What do you want to achieve with your campaign? For example, are you increasing brand awareness, driving leads, or boosting sales? No matter your marketing goals, have specific, applicable, and measurable goals in place to measure your success.
2. Accurate targeting and segmentation. To reach the right audience with your message, you need to understand your ideal customer profile (ICP), their needs, and behavior on various platforms for efficient ad spend and relevant messaging.
3. Effective campaign tracking and data collection. Set up proper tracking mechanisms to measure campaign performance and understand ROI and accurately calculate ROAS. This includes using analytics tools, UTM parameters, landing page conversion tracking, and CRM integration.
4. Robust data analysis and reporting. Don't just collect data and measure ROAS; gather insights and actionable takeaways. Analyze your campaign data regularly to identify what's working, what's not, and where you can optimize. Visualize key metrics using dashboards, reports, and AI for easy communication and decision-making.
5. Agile optimization and iteration. Don't be afraid to adjust your campaign based on your data insights. Continuously test different elements, optimize your spending, and refine your targeting to improve performance over time.

From these, marketers can gain valuable insights from their campaigns, iterate, and ultimately achieve their strategic marketing goals. Remember, ROAS is just one metric to help marketers understand the effectiveness of their paid media.

It's essential to look beyond the numbers and understand the broader impact of your campaigns on brand awareness, customer engagement, and long-term value.

Conclusion

Once you regularly and consistently start tracking the ROAS of your campaigns, you'll suddenly have much more information at your disposal — data you can use to plan future campaigns, adjust your budget, and more accurately forecast future returns. It will also give you insights into ways you can adjust and optimize your currently running campaigns to make them more effective.

Of course, more data will only be helpful to your team if it is easily accessible. The more integrated your data is with the rest of your team's workflow, the more likely they will actually leverage it in making the decisions that matter most.

This is where a Customer Data Platform (CDP) — which sits on top of a cloud data warehouse, integrates with your martech stack, and compiles all relevant information about your customers into one central location — can be especially helpful. CDPs help marketers access real-time data to create Customer 360s and orchestrate campaigns to deliver a personalized customer experience.

Interested in learning more about how a CDP can help you achieve your paid media goals? Request a free demo today.

The impact of AI in customer marketing workflows, continuously tightening privacy regulations, and a cookie-less future are the talk of the town for every enterprise marketing team. Now more than ever, a brand’s ability to create personalized and streamlined customer experiences is at the heart of effective customer marketing.

But without access to first-party data and the right tools in place, marketing teams can’t orchestrate impactful workflows that resonate with their audiences. To overcome this, marketers must optimize their data, martech stacks, current customer workflows, and analytics.

In this article, we’ll cover the ins and outs of customer marketing workflows, including their importance, strategies for streamlining them, tools like Customer Data Platforms (CDPs) to consider implementing, and the common challenges associated with traditional approaches to customer workflows.

What are customer marketing workflows?

A customer marketing workflow is the creation, execution, and analysis of marketing campaigns directed toward customers. It encompasses everything from real-time data analysis and segmentation to campaign execution and post-campaign evaluation.

The value of these customer marketing workflows lies in their ability to cultivate lasting relationships, drive customer loyalty, and ultimately contribute to business growth — meaning they require a strategic and iterative approach to develop and execute.

The challenges of customer marketing workflows

Traditional customer marketing workflows often grapple with inefficiencies that hinder their effectiveness. Manual processes, data silos, and disjointed communication between marketing teams and within their martech tools can impede the seamless execution of campaigns.

Another challenge is the implementation of stricter privacy laws when it comes to third-party data collection and storage. Now it is more difficult than ever to identify users, understand their customer journey, and have a complete picture of each potential customer — especially when many databases have duplicate or conflicting customer data records.

Even when marketing teams can identify users, the lack of a unified view of customer data (also known as a Customer 360) makes it challenging to craft personalized and timely messages, resulting in missed opportunities for engagement and ineffective marketing campaigns. This is why marketers must ensure their first-party data is accurate, implement streamlined workflows, and track metrics against their business goals.

The advantages of streamlined customer marketing workflows

Streamlining your customer marketing workflows offers a multitude of advantages, benefiting both your business and customers. These benefits run the gamut, from increased efficiency and productivity and lower operational overhead costs to improved delivery of hyper-targeted and personalized content for your customers.At Simon, we’ve seen many customers reap the benefits of streamlining their workflows by using a Customer Data Platform (CDP) and Cloud Data Warehouse (CDW), with many citing the benefits below as the most impactful advantages to having efficient customer marketing workflows.

Enhanced efficiency

Streamlining customer marketing workflows provides a myriad of advantages, ‌and enhanced efficiency usually tops the list. Automation is key, as it frees marketing teams from performing repetitive, time-consuming tasks. This is also where AI can significantly improve customer marketing workflows.While automation tools can handle email scheduling and customer segmentation, marketers can focus on strategic planning and creative endeavors. Moreover, automation minimizes the risk of manual errors, ensuring that marketing campaigns are executed consistently. This enhances your brand’s voice and helps maintain integrity across diverse channels.

1:1 personalized customer experiences

A streamlined workflow has a direct and positive impact on the customer experience. By automating routine tasks, marketing teams can dedicate more time to understanding customer behavior and preferences, which, in turn, enables them to curate personalized content, offers, and recommendations throughout the entire customer journey.

For example, marketing teams can build campaigns that recommend specific products based on a customer’s past purchases and browsing history, or initiate real-time push notifications offering relevant promotions based on specific interests. This improved responsiveness and personalization — and the ability to have tools like chatbots available 24/7 that cater to customer needs — ensures that customers feel valued, fostering a deeper connection with the brand.

Predictive analytics enable marketers to develop a 1:1 customer relationship by anticipating a customer’s next purchase or desired channel and offering relevant suggestions in real time, as well as tailoring loyalty or exclusive experiences.

They can even help reduce customer churn by providing opportunities for personalization. Building churn models can help identify customers at risk and proactively engage them with retention offers or support the moment they are flagged for potential churn.

Increased ROI and boosted marketing performance

Optimizing customer marketing workflows is not just about efficiency, it’s also about driving tangible results. Targeted campaigns, made possible through customer marketing workflows, significantly improve conversion rates and boost customer lifetime value (LTV).

By leveraging data-driven insights, marketing teams can allocate resources effectively, ensuring that every dollar spent contributes to a positive return on investment.

With the right customer workflows in place, data-driven decision-making becomes the cornerstone of success because campaigns are optimized based on real-time analytics. This strategic approach maximizes marketing spend and sets the stage for continuous improvement and adaptation to changing market dynamics.

Streamlining your customer marketing workflows

So, how do you go about streamlining your current customer marketing workflows? First, thoroughly review your current workflows to identify bottlenecks and inefficiencies. Pinpoint areas with redundant tasks, data silos, and manual processes, and prioritize improvements based on their impact on efficiency and feasibility of implementation.

Next, look for opportunities to automate repetitive tasks. Automation is the engine that powers streamlined customer marketing workflows, and, depending on your needs, there are plenty of automation martech tools you can consider to execute on these tasks more efficiently. (Hint: our customers use the combined power of a CDP and CDW to keep their real-time data clean, connected, and actionable.)

Consider where you can build out automated email campaigns triggered by customer behavior, such as a discount promotional code when a customer abandons their cart without purchasing from your site, or personalized content delivery based on segmentation.

Finally, focus on automating tasks that are predictable and rule-based, allowing your team to redirect their energy towards more strategic initiatives.

Breaking down data silos within workflows

One of the primary challenges in traditional workflows is the existence of data silos — isolated pockets of customer data scattered across various platforms. To overcome this, implement a robust data integration strategy that unifies customer data from disparate sources into a centralized platform. This is where using a CDP, especially one that sits on top of a cloud data warehouse, proves invaluable.

Utilizing a CDP enables the creation of a single, holistic view of each customer. This consolidated data repository acts as a treasure trove, empowering marketers with a comprehensive understanding of customer preferences, behaviors, and interactions across channels. This not only facilitates personalized communication but also enables more informed decision-making throughout the customer journey.

The best part? Marketers don’t have to rely on data teams to access and use this data, empowering them to access Customer 360s and optimize their customer campaigns in real time.

Activating customer data within marketing workflows

With a unified view of customer data in place, the next step is leveraging this information for data-driven decisions by personalizing campaigns based on customer preferences: Do your customers prefer receiving SMS, push, or email notifications? Is there a particular day or time that customers seem more engaged and likely to purchase?

With these insights, you can schedule and personalize content delivery across various and preferred channels based on individual preferences.

Another way to streamline customer marketing workflows is to segment audiences strategically. Marketing teams can leverage a CDP's segmentation capabilities to automatically group customers based on specific criteria, such as demographics, interests, and purchase behavior, ensuring your customers have a consistent, seamless experience.

Finally, predict future behavior using advanced analytics. CDPs provide centralized analytics dashboards and reports that offer actionable real-time information into campaign performance.

Marketers can use website and app analytics to monitor user activity, like page views, clicks, and geolocation to understand immediate customer interests.

When it comes to campaigns, using a CDP’s analytics tools can identify spikes or dips, engagement, and purchase patterns.

With this data at your fingertips, you can track key metrics, analyze customer behavior, notice patterns, and identify areas for improvement within your customer marketing campaigns. Ultimately, CDPs enable marketers to continuously improve their customer marketing workflows and campaigns based on real-time feedback.

Fostering collaboration and communication

While a platform like a CDP can centralize data, enable teams to work cross-functionally, and improve scalability, streamlining workflows isn't just a technological transformation; it's also about fostering collaboration and communication within the org.

Ensure clear communication and shared goals among all teams involved in the customer journey, including engineering, IT, security/compliance, customer success, marketing, and sales. Look for cross-functional objectives and pain points, as well as ways to optimize processes, within your workflows to understand how they fit into your business goals.

Conclusion

Streamlining customer marketing workflows requires a strategic and multifaceted approach combining technology, automation, and a customer-centric mindset. First, consider the state of your customer data. Is it siloed? Is it accurate? Most importantly, can you access it?

Next, review current marketing workflows and identify areas for both improvement and automation, revamping as necessary. Once your data and processes are in place, build out personalized campaigns powered by the data. Don’t be afraid to experiment!

Review the metrics and outcomes to determine what’s working and what isn’t, then iterate.

By embracing the advantages of streamlined workflows and harnessing the power of CDPs, businesses can unify real-time customer data, automate campaign management, understand critical insights, and elevate their customer marketing strategies to power the personalized experience that customers crave.

To learn about how Simon Data can help you streamline your customer marketing workflows, check out our resources or book a demo.

Imagine peering into a crystal ball and seeing every single one of your customers in full detail, not just in fragments. That's the transformative power of a unified customer profile, also known as a Customer 360 (C360).Today, customers are more empowered than ever before. With access to a plethora of information and options, they demand a personalized and seamless experience from the brands with whom they interact.

In response, businesses turn to Customer Data Platforms (CDPs) for help. These platforms help them stitch together a detailed view of customers within a Cloud Data Warehouse (CDW), aiming for that all-important personal touch across every interaction.Creating a C360 is like putting together a complex puzzle of your customers, but the right CDP can make the process easier. Let’s dive into how you can transform your scattered customer data into an accurate, unified C360 using a CDP.

Decoding the Customer 360 Dream

What is a Customer 360?

A Customer360 (C360) is a unified and comprehensive view of a customer that combines data from various sources and touchpoints, providing a 360-degree view of the customer's interactions, behavior, and preferences. It includes data from both online and offline channels, including website visits, social media interactions, purchase history, customer service interactions, and more.With a C360, businesses can gain a better understanding of their customers, their needs, and their journey, enabling them to provide a personalized and seamless experience.

Benefits of unified customer profiles

While it takes time to implement a CDW and CDP, providing marketers access to real-time data via a CDP helps marketers gain a comprehensive view of their customers — empowering them to make data-driven decisions to improve their products, services, and marketing strategies. Below are some of the benefits of building Customer 360s.

Gaining a deeper understanding of customers

A C360 provides marketing teams with a holistic view of their customers, including their preferences, behavior, and interactions, allowing them to gain a deeper understanding of their customers.For example, a bank can use their customers’ transaction data to offer personalized financial advice or product suggestions, such as a higher interest rate savings account or a credit card with benefits that align with the customer’s spending habits. They can also send alerts and advice relevant to the customer’s recent transactions, enhancing the feeling of a bespoke banking experience.

Providing a personalized customer experience with a CDP

With a C360, businesses can harness the power of their customer data to deliver highly personalized experiences. A clothing company, for example, can connect past purchase history to browsing behavior on its website to recommend new arrivals that match the customer’s style preferences.

Additionally, the company can use website browsing behavior to power abandonment or price drop campaigns that include links to relevant items and additional product recommendations. Layer in customer service interactions, and this clothing company can be more helpful and efficient in addressing customer concerns in a personalized manner.

By leveraging data from various touchpoints, businesses can tailor their marketing messages, product offerings, and customer service interactions to meet the specific needs and preferences of each customer and on devices relevant to past and present interactions.

Improved customer engagement and increased ROI

Unified customer profiles can help businesses engage with customers in a more meaningful and contextual way, while also helping to identify cross-selling and upselling opportunities. If done thoughtfully, it often leads to increased ROI, ROAS, and CLTV.

Because a C360 consolidates data from various customer touchpoints — including online and offline purchase history, customer service interactions, and website behavior — it enables businesses to:

‍Apply analytics to this data and predict future buying behaviors. If a customer frequently purchases high-end skincare products, a cosmetic company might predict their interest in a newly launched luxury facial serum, presenting a lucrative upsell opportunity.

‍Personalize emails or ads to suggest products or services that complement previous purchases (aka cross-selling). A sports store can craft and send a personalized email to a customer who recently bought a tent and might also be interested in outfitting the rest of their camping gear, like sleeping bags or camp pillows.

‍Engage with their customers at the right time. If the data shows that a customer tends to buy during certain seasons or after specific intervals - flowers for Valentine’s Day, for example - businesses can time their offers accordingly, increasing the likelihood of a sale.

‍Better tailor their upselling and cross-selling campaigns by segmenting customers based on their behaviors and preferences. High-value customers might receive offers for premium products, while cost-sensitive audiences might be targeted with bundle deals, for example.

‍More efficiently spend their advertising dollars. With a better understanding of customer profiles and behaviors, businesses can allocate their advertising budget more effectively. By targeting those most likely to respond to upselling or cross-selling offers, or suppressing customers they know don’t fit the criteria for a particular campaign, marketing teams can improve ROAS.

The challenges of building a Customer 360

While the benefits of a C360 are clear, constructing one can be challenging, especially when it comes to gathering the real-time, accurate data required.

Data is often scattered across various systems, such as CRM, marketing automation, and social media platforms, sometimes making it difficult to get a complete view of the customer. Beyond breaking down data silos, inaccurate or incomplete data can lead to incorrect insights and decisions — which is why businesses must have a robust data quality management system.

The data challenges don’t end there. Integrating data from various sources can be a complex and time-consuming process, and customers using multiple devices and channels can be difficult to identify and track. To do so, businesses must have comprehensive and accurate technology, like a CDW and a CDP with identity resolution and products such as Simon’s Identity+, in place.

To add to the list of challenges marketers must face in 2024, data privacy laws will require strict standards when it comes to data governance, collection, and security. Brands must ensure they are compliant with ever-evolving privacy regulations to avoid hefty fines and damage to their reputation. Using a CDP to build Customer 360s can help ensure marketing teams are staying compliant.

Using a CDP as the Customer 360 architect

Implementing a CDP as the foundation for customer data

A CDP is a customer-centric system that collects, unifies, and enriches data from various sources, creating a unified customer database. But some CDPs, like Simon, take it a step further and do this all inside of a company’s own data warehouse.CDPs enable businesses and their marketing teams to create a single customer view by breaking down data silos, resolving identities, and maintaining data quality. By leveraging a CDP, businesses can build a C360 that acts as the backbone for their customer-centric strategies.

Common data integration strategies for building C360s

Data integration is a critical aspect of building a C360 — it’s how marketing platforms and CDPs collect data from various sources, such as website analytics, CRM systems, social media interactions, loyalty program engagements, and other relevant platforms.

Batch integration vs. real-time data integration

There are several ways to collect and integrate data into your CDP to build Customer 360s. The two primary integrations are batch and real-time.

Batch integration involves collecting data from various sources, such as CSV uploads, and integrating it into the CDP either one time or at regular intervals, usually daily or weekly.

A specific example of this would be a retailer throwing a launch party for a new product in stores and asking attendees to provide their email when they arrive. This list of emails needs to be added to the data warehouse so these new customers can be sent a thank you email and further marketing communications.

Real-time integration, on the other hand, integrates data as it is collected — think web events. This provides businesses with up-to-date information for real-time decision-making. In the case of Simon’s Identity+, this also enables businesses to deanonymize known customers in real-time, which in turn increases the size of the baseline audience for high-ROI campaigns such as abandoned browse and cart.

No matter how customer data is integrated, all of the relevant data must reside in the same place to unify customer data in a meaningful, strategic way.

Identity resolution within a CDP

Identity resolution involves identifying and linking customer data points from various sources, devices, and channels to create a Customer 360. It is a critical aspect of building a C360 because it helps businesses understand customer behavior and interactions across various touchpoints and throughout the customer journey.

Identity resolution also resolves duplicate customer profiles and fragmented data points. By deploying identity resolution techniques (such as deterministic, identifier-based matching), businesses are empowered to craft singular, unified identities for each customer.

Some CDPs can also help businesses make deterministic identity matches by linking anonymous online actions to known users to unlock incomplete or partial first-party data, as is the case with Simon’s Identity+.

The result? More revenue, easily — it’s that simple! It allows marketers to reach more known, unauthenticated users on their websites that plug and play directly into their existing abandonment campaigns. Not only can marketers then activate what they thought were anonymous users, but they also have the opportunity to put more users into existing abandonment journeys.

Understanding the building blocks of Customer 360s

Master data management (MDM)

Master data management (MDM) is the process of creating and managing a single, authoritative source of all customer data. It establishes a unified customer ID system and ensures the data is cleansed, standardized, and enriched where possible.

In addition to identity resolution strategies deployed within the CDP, MDM is an important process that typically happens upstream of the CDP to ensure data consistency and accuracy. It also helps businesses avoid duplicates and inconsistencies in customer data.

Customer segmentation

Once all customer data is unified within a CDP, it can be used to build audiences for downstream marketing workflows. Customer segmentation divides customers into different groups based on their characteristics, behavior, and preferences — and can only be as accurate as the data that’s used as input.

An accurate C360 allows marketers to tailor their marketing efforts to each segment's needs and preferences. With a C360, businesses can accurately segment their customers based on various factors, such as demographics, purchase history, interests, and the channels on which to reach them.

Customer journey mapping

Understanding and mapping customer journeys across different interactions is vital in realizing the full potential of a Customer 360. A CDP can uncover patterns and insights in customer behaviors and present marketing opportunities for personalized interactions and improved customer engagement.

By tracking a customer's interactions with your brand – from initial engagement through various points of contact like website visits, social media engagement, and purchase history — CDPs provide a comprehensive view of the customer journey.

Marketers can extract meaningful insights from this real-time data to better understand customer behaviors, preferences, and patterns, which are crucial for tailoring personalized experiences.

For example, an eCommerce retailer may notice a significant uptick in searches and purchases of eco-friendly clothing lines on Friday evenings. This insight reveals not only a general growing interest in sustainable fashion among their customer base but also indicates that customers are more likely to shop online at the end of the work week.

Armed with this data, retailers can tailor their marketing efforts by scheduling email campaigns featuring eco-friendly products on Friday afternoons to boost engagement and sales.

Activating Customer 360s within a CDP

CDPs leverage real-time customer data to populate Customer 360 profiles, enabling marketing teams to trigger personalized campaigns and experiences. Here's how the process unfolds.

1. Real-time data collection: CDPs continuously gather real-time data from various customer interactions across multiple channels. This includes browsing behavior, purchase history, social media engagement, and customer service interactions.
2. Updating C360 profiles: The collected data is fed into the Customer 360 profiles, which are dynamic and get updated as quickly as the underlying data in the CDW updates — reflecting the latest customer interactions, preferences, and behaviors. ‍
3. Behavioral triggers and segmentation: The CDP identifies specific behavioral triggers based on the updated C360 profiles. For example, a customer adding items to a cart but not completing the purchase can trigger a specific abandoned cart marketing campaign. Customers can also be segmented based on their offline interactions and data, such as in-store purchases, allowing for more targeted and relevant marketing approaches.‍
4. Personalized marketing automation: Businesses can leverage data sourced from various platforms all made available in a unified customer profile to create targeted and relevant marketing campaigns, which can lead to higher engagement, conversions, and customer loyalty.‍
5. Omnichannel delivery: Personalized, contextually relevant campaigns are delivered across the customer's preferred channels, whether it’s email, mobile, web, or social media. This omnichannel approach ensures that customers receive a consistent and personalized experience regardless of how or on what device with which they interact with the brand.‍
6. Insights and optimization: A CDP’s ability to provide real-time insights into customer behavior is fundamental to maintaining an accurate C360. These insights are the driving force behind continuous campaign optimization, ensuring that marketing efforts are not only responsive but also highly personalized and effective.

Creating a sustainable Customer 360 with a CDP

In today’s digitally-driven business landscape, building a sustainable C360 is more important than ever, and this is where using a CDP that sits on top of a CDW can help marketing teams shine.

However, using the combined power of a CDP and CDW to create robust Customer 360s involves more than just collecting and analyzing customer data. It requires a strategic approach that includes strict adherence to data governance and security protocols, continuous management of your data quality, and future-proofing your CDP to adapt to evolving market trends and technological advancements.

Adhering to data governance and security protocols

To build a sustainable 360-degree view of your customers, you’ll need robust data governance and stringent security measures ‌in place. Ensuring data privacy and compliance is not just a legal obligation, but also a cornerstone of customer trust.

The underlying cloud data warehouse for your C360 should prioritize secure data handling and adherence to privacy laws, ensuring that every piece of customer information is treated with the utmost care.

Continuous data quality management

Creating a sustainable 360-degree view of each customer also requires a continuous commitment to data quality monitoring and cleansing so that marketers can access reliable and accurate C360s. As the saying goes, garbage in, garbage out.

Here are a few ways to ensure your real-time data is accurate:

‍Implement automated data quality tools: Employ automated tools within your CDW to regularly scan and clean data. This ensures ongoing accuracy and reliability of customer information.

‍Establish data quality metrics: Define specific metrics for data quality within your organization. Regularly measure your data against these benchmarks to identify areas needing improvement.

‍Create a data cleansing routine: Schedule regular data cleansing cycles to remove duplicate records, correct inaccuracies, and update outdated information.

‍Involve (and train!) stakeholders: Educate your team about the importance of data quality. Encourage them to take an active role in maintaining data accuracy.

‍Create a feedback loop for continuous improvement: Establish a feedback loop where insights from data usage are used to refine and improve data quality processes continually.

‍Leverage AI and machine learning: Utilize AI and machine learning algorithms to predict and correct data errors, improving the overall quality of your customer data over time.

Why you should future-proof your CDP

When you’re in the market for a CDP, consider purchasing one that’s adaptable and scalable so it can accommodate future data growth and your evolving business needs.

Remember: as businesses grow, so does the volume of customer data. A scalable CDP can efficiently manage this increasing data, ensuring that C360 profiles remain comprehensive and up-to-date, regardless of data volume.

Equally important is a CDP’s ability to integrate with and offer the latest emerging technologies, like AI, and play well within a marketer’s tech stack. This also helps marketing teams tailor their tech to meet specific business requirements, such as campaign execution and analytics reporting, as well as save time, resources, and cost when it comes to tooling.

Ultimately, CDPs are designed to elevate the customer experience by breaking down data silos within marketing and data teams, developing comprehensive Customer 360s through identity resolution and real-time data, and enabling marketers to provide customers with a personal message at the right time through their preferred channels.

CDPs are the key to delivering a personalized customer experience

Customer data platforms are the key to creating accurate, real-time Customer 360s that can be easily activated by marketers looking to build a personalized experience for their customers.

The accuracy and usefulness of Customer 360s are intrinsically linked to the quality of the data it contains. Having high-quality data leads to insightful analytics, better customer segmentation, and more effective personalized experiences, all of which contribute to stronger customer relationships and business growth.

Marketing teams need to access real-time data, break down data silos, and leverage their CDP to its full potential to deliver cohesive and personalized customer experiences.

By implementing a tech stack that uses the combined power of a CDP and a CDW like Snowflake, marketers ensure access to real-time data quality, the ability to deliver a customer-centric experience, and the analytics required to adapt campaigns to drive revenue more effectively.

‍Take the first step by booking a demo today. Leverage Simon Data to dismantle data silos, unify customer information, and embark on your journey toward building effective and dynamic Customer 360s.

As we approach 2024, economic uncertainty, tightening data privacy laws, and the impact of AI are at the forefront of every brand’s mind. But amid these challenges lies new opportunities to innovate, personalize, and redefine the customer experience.

In this blog post, we tap into the expertise of Jason Davis, Simon Data’s Co-Founder & CEO, and our partners to learn what trends lie ahead and how marketers can adapt and implement new strategies to succeed next year. Our panel of experts includes:

• Steven Aldrich, Co-Founder & Co-CEO, Ragnorak
• Brant Cebulla, Co-Founder, Scalero
• Onil Gunawardana, Head of MarTech and Customer Data, Snowflake
• Will Pearson, Co-Founder & Co-CEO, Scalero
• Sara Varni, CMO at Attentive
• Scott Zakrajsek, VP of Data Intelligence, Power Digital

Navigating marketing budget constraints

Across all industries, marketing budgets will continue to shrink, so companies will seek to streamline their operations and force marketers to develop innovative solutions that have the same impact on the business as they would with a large budget.

One way to streamline processes and stretch the marketing budget, argue Will Pearson and Brant Cebulla of Scalero, is to reduce the number of software platforms marketing teams use in 2024, creating an opportunity for platforms that offer comprehensive solutions and eliminate the need for multiple tools — like CDPs — to shine.“The expectation in marketing tools, including CDPs, is that the more they can do, the better. Consolidation streamlines their workflows and makes budgeting simpler,” says Brant.

In addition, marketers should also look to leverage AI within marketing operations, campaigns, and execution to help smaller budgets go further.

“CDPs like Simon that have a warehouse-centric approach are the real lifeline here. Budget pressure isn’t just on marketing — it’s across all functions, and showing ROI for all areas of investment is critical,” says Jason at Simon. “Zero-copy warehouse approaches win on both sides by aligning marketing and data tech investments for cost reductions, as well as bringing a next-generation of personalization to drive incremental conversion rates & ROI.”

In 2024, the keys to marketing success will be simple: Focus on profitability, make smart financial decisions, and be prepared to provide strong financial cases for whatever you need next year.

Data privacy and compliance: A rising tide of regulations

The tightening grip of data privacy regulations, such as GDPR, is another critical trend that will continue into 2024. Europe and consumer expectations in particular are driving significant change when it comes to data privacy, and gone are the days when CDPs and SaaS platforms stored their own data in siloed environments.

So, what does the future look like? Jason’s take is this: “Architectures will need to enable businesses to fully control their data when it comes to how it’s locked down, where it lives, and what is (or more importantly, isn’t) stored.”

Companies that fail to comply with these regulations may face hefty penalties and damage their brand’s reputation, according to Scott at Power Digital. “Data governance and compliance have been creeping up for years, but most brands are way behind. 2024 is the year when brands will need to get serious about how they collect, store, and handle their customers’ first-party data,” he says.

Sara Varni at Attentive agrees. She believes that stricter regulations and consumer expectations will require marketers to find innovative ways to personalize campaigns while respecting privacy boundaries.

Beyond these restrictions, customers are also getting smarter and more empowered when it comes to how their data is managed. Moving forward, brands will need to collect only the minimal data required to make it simple and easy for customers to view, update, and opt out of data sharing, all while ensuring transparency around what and how that data is collected.

Measuring success beyond vanity metrics

In an era of economic uncertainty, marketing efforts must demonstrably contribute to the bottom line. “It sounds obvious, but is your marketing spend driving profit? The cost of capital at the moment is too high for marketers to be operating otherwise,” says Will of Scalero.

Experts emphasize the need for marketers to focus on profitability, thoroughly understanding unit economics, and measuring success through metrics like customer lifetime value (CLTV) and return on investment (ROI) next year.

Of course, customer loyalty and retention will be the name of the game, and Scott at Power Digital suggests that brands will steer away from using deterministic attribution models and “look toward experiment-driven approaches that measure incrementality and provide a more accurate picture of marketing effectiveness.”

“Businesses are going back to the basics. In today’s economy, it’s all about the bottom line. If you run an ecomm business with 20% margins and your acquisition strategy relies on coupons for 15% off, you’re cutting profit on these sales by 75%,” says Jason. “This change is fundamental relative to how marketing measurement works today, and it will require a shift in the data and systems used for measurement.”

Next year, marketers must overcome the challenge of not having access to real-time and granular data by building trusted partnerships or using comprehensive platforms if they want to succeed.

AI: The powerhouse driving efficiency and innovation

AI is no longer just a buzzword, argues Sara at Attentive, and, in 2024, it will revolutionize the way marketing teams work by automating mundane tasks, optimizing campaigns, and generating personalized content.

AI will empower marketers to achieve greater efficiency and effectiveness, with those who embrace it gaining a significant competitive advantage over those who don’t.

Attentive has already seen the impact of AI within its customer marketing efforts. During BFCM, over 218 million text messages were influenced by AI, and just over 10% of all SMS campaigns were created using Attentive’s AI copy assistant. The benefits far outweigh the downsides, and Attentive has found that:

• AI allows brands to deliver 1:1 messages to each customer, personalizing the experience based on individual behaviors
• Generative AI can be conversational and act like a real person because it looks at a customer's purchase history and preferences and responds with personalized and accurate recommendations using customer data
• Customers are comfortable engaging with AI — 45% of Attentive concierge conversations were powered by AI, delivering a 2.4x higher conversion rate than those powered by humans alone

Looking ahead, AI will provide marketing teams with the opportunity to differentiate and innovate. “In the past, marketers have had to rely on data scientists to pull insights from customer data. But now, with predictive AI marketers can spot key trends, like who is most likely to make a purchase, and send them the right incentive,” explains Sara.

Gen AI is a disruptive force, and businesses are looking to innovate and purchase products that offer AI. “We’ve shipped some foundational features in 2023 that allow our customers to leverage Gen AI for code generation to affect some incredibly powerful personalization,” says Jason at Simon. “The next frontier in Gen AI is 1:1 personalization, which represents the intersection of current content creation workflows that we’re familiar with, coupled with deep access to customer data."

However, embracing AI creates another challenge marketers will need to tackle in 2024: reconciling data privacy regulations with AI ethics to ensure data is being used responsibly. Sara predicts that CMOs and CIOs will need to work together to incorporate AI into their marketing strategies.

Powering the personalized customer experience

In customer marketing, one truth remains unchanged: brands need to know their customers to reach them. In 2024, success will hinge on the ability to access high-quality, real-time data and deliver a 1:1 personalized customer experience.“​​With the growth of the CDP category over the last 2 years, we have seen the underlying quality of data used for marketing is generally not good enough to power deep personalization use cases,” says Steven Aldrich at Ragnarok.“

In 2024, especially with the growth of AI requiring great data to leverage effectively, we expect to see marketers drive change by bringing more transparency to how data is collected organization-wide and to structure it so that it is more actionable for personalization at scale,” he suggests.

Brands that harness AI for personalization power will be able to replicate the in-store sales associate experience online by answering shopper questions and suggesting products as if it were an in-person sales associate. AI is also available 24/7, improving global reach and the potential for more sales, unlike a physical store with limited hours.

When it comes to AI, Onil Gunawardana, Head of MarTech and Customer Data at Snowflake, expects it to impact personalization the most. “We expect AI to have the most significant impact on customer 360s first. Building an intelligent, privacy-first Customer 360 — the first foundational step in the end-to-end enterprise marketing lifecycle — remains one of the biggest challenges for marketers,” says Onil.

Incorporating a CDP into marketing strategies to collect real-time data and ingest it from multiple sources, then provide semantic unification, entity resolution, and fine-grained consent management will significantly improve the personalized experiences marketing teams need to deliver to customers.

Using the combined power of customer data and delivery will also drive cross-collaboration within the business. Steven at Ragnarok expects marketing teams to have increased pressure to deliver more when it comes to the customer experience but will result in more collaboration and shared goals with the product team.

But even with AI rapidly changing how customer marketers work, understanding your customer and crafting compelling brand narratives remains paramount.

Scott at Power Digital stresses the need to differentiate your product through targeted branding and creative, catering specifically to your ideal customer profile (ICP). This requires marketers to go back to the basics of customer marketing, conducting in-depth customer research, and developing a deep understanding of customer needs, desires, and online behavior.

Data quality can be a limiting factor in powering personalization, and that challenge is often unrecognized. Today’s businesses are fully omnichannel, but the data collection, identity modeling, and centralization process is complex.“Ultimately, personalization is an outcome, but it’s limited by the data that’s a core input. Winning businesses will align people and technology in a way that sets their data strategies to drive the right personalized outcomes,” argues Jason at Simon.

Preparing for a new era of customer marketing

As we navigate the dynamic landscape of 2024, marketers must accomplish more with less, adapt to emerging technologies, and focus on delivering measurable results in an increasingly challenging and noisy environment.

By focusing on profitability, harnessing the power of AI, and cultivating 1:1 customer experiences, marketing teams can drive innovation and thrive in 2024.

‍Learn how Simon Data can power customer marketing teams in 2024.