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The pandemic has changed the world in more ways than one. When before, many of us saw technology in retail and other similar industries as just a gimmick, today, we all rely on it.

We use technology to do our groceries, to order food, to shop for clothes, or even to workout.

One thing that’s become much more widespread since COVID hit is the use of QR codes in our everyday shopping experience.

And this is where connected products come into play… which brings us to today’s episode.

In this week’s episode of Data Unlocked, Jason sits down with Cindy Brown, Chief Performance Officer at MERGE, and Keith Turco, Chief Growth Officer at MERGE.

Before taking her expertise to MERGE, Cindy was the founder and former CEO of Blue Moon Digital, a digital marketing agency.

She’s a digital marketing expert, and today, she’s using her knowledge at MERGE.

As for Keith, he is a seasoned marketing and advertising, and technology executive who started his career nearly two decades ago at AT&T.

Today, he is MERGE’s Chief Growth Officer.

For those of you who won’t know, MERGE is a company that merges storytelling with technology in order to make a meaningful difference in the human experience and promote health, wealth and happiness in the world.

In this episode, Cindy, Keith, and Jason discuss connected products, how they work, why they’re changing the market, and more.

Ready to learn?

Let’s dive in.

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Most savvy businesses understand that a Cloud Data Warehouse is the foundation of modern data architecture. It enables the next generation of data capabilities, offering better speed, scalability, and accessibility to the broader organization. Ironically, once an organization invests in a Cloud Data Warehouse it often goes vastly underutilized.Many marketers, for example, view a data warehouse as only an analytical solution — either to feed into a business intelligence tool or something the analytics team can leverage to draw conclusions about marketing campaign efficiency or customer behavior. While those are valid applications, they only scratch the surface of what it can achieve for a growing business. Take a marketing ecosystem, for example. There are dozens of tools marketers rely on to both collect customer data and power customer experiences. Yet in many cases few are actually connected to the company’s data warehouse.

Put your head in the cloud

Why would an organization need to feed its marketing systems with data from its data warehouse though? Marketing tools connect with each other anyway, so what’s the point? There are three fundamental reasons: data completeness, data quality and data access.Every marketer strives to put the right message in front of the right person at the right time through the right channel. But this is easier said than done. Doing it effectively requires data from dozens of siloed sources: channel engagement metrics, product inventory, transaction history, profile attributes, loyalty interactions, site/app visits, attribution. The list goes on. Typically, each of these datasets lives in a different tool. Rather than focusing on connecting these tools in a tangled web of data streams and APIs, the most successful modern marketers shift their focus to connecting each of their systems to their data warehouse and subsequently connecting the data warehouse to their marketing channels. This gives them the highest quality data to work with, a complete view of their customer information and, most important, access to all of it.Trying to build powerful, relevant customer experiences across multiple channels without having easy access to a complete and accurate source of truth for your customer data is like trying to cook a spaghetti dinner while blindfolded. Sure, you might end up with a finished product to serve, but you used cinnamon instead of pepper to season the dish, the heavy cream you thought you poured into the sauce was actually orange juice and the “spaghetti” you cooked was just a bunch of really thin carrots. Edible? Sure. Satisfying? Probably not. (Although I haven’t tried orange carrot spaghetti, so I can’t say for sure.)So how can marketers take off their “blindfolds” and get full access to customer data when building campaigns? They need to turn to their data warehouse.

Take off the blindfold

Let’s break down the three major benefits of connecting your Cloud Data Warehouse to your marketing channels: Data Quality: Your marketing campaigns are only as strong as the data powering them, just as a dish is only as tasty as its ingredients. The best dishes have the highest quality ingredients. Running back and forth between platforms, pulling and merging CSVs, or creating dozens of API connections between them is a common practice for marketing teams, but this creates a high amount of risk in the quality of data that ends up in a marketing communication. A data warehouse should always serve as a business’ source of truth for customer data. Connecting marketing channels to the source of truth drastically reduces the risk of creating duplicate or incomplete profiles, ingesting inaccurate customer attributes, or using incorrect data types. Data Completeness: Sometimes a main course can be enhanced by a perfectly chosen side dish. Creating a complete and unified view of your customers is an essential prerequisite for communicating with them. Once that complete view exists, it’s easy to start leveraging triggers and personalization from a variety of data sources. This will create a more powerful downstream experience for your customers. Our 2022 state of customer data report found that the top 40 percent of campaigns, when measured by number of data points used, performed 2.9x better than the bottom 20 percent. Data access: Serve your meal while it’s still hot. Take advantage of a Cloud Data Warehouse’s ability to provide access to real-time data. The more timely and relevant a message, the more likely a customer will convert. A Customer Data Platform is a great way to take real-time data that exists in a data warehouse and make it usable and actionable to non-technical marketers. Build sophisticated segments leveraging 100 percent of customer data, and easily sync them to marketing channels.

The bottom line

Don’t fall into the trap of serving your customers orange carrot spaghetti. There are more ingredients out there than ever before. Take off your blindfold and see for yourself!

The last several years have seen an explosion in a new world of cloud-enabled data infrastructure. Snowflake made this official the other day with the release of their inaugural “Modern Marketing Data Stack Guide.” This new world is unlocking a new set of opportunities, but also a new set of complexities. This FAQ is designed for today’s marketers to acclimate to this world of cloud-enabled data infrastructure.

What is a Cloud Data Warehouse - and why is the “cloud” aspect of this important?

The “Cloud Data Warehouse” is just that - a data warehouse that can deploy on today’s cloud-enabled infrastructure such as Amazon Web services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. Popular Cloud Data Warehouses today include Snowflake, Databricks, and Google’s BigQuery.Predecessors to these systems were deployed on-premises and in data centers. This limited both storage and compute capacity. As with everything before cloud computing, scaling these systems was both expensive and slow - and common challenges would include the warehouse running out of storage or getting overloaded due to too much simultaneous compute.With cloud-enablement of the data warehouse, these limitations are virtually gone today. Storage can scale to virtually any data that businesses want to retain, and cloud compute allows for many applications and end users to simultaneously query and use the data warehouse at the same time.Cloud Data Warehouse applications started out as primarily Business Intelligence (BI) and analytics tools such as Tableau or Microstrategy. These applications are quickly evolving to other spaces including security applications, marketing applications, and much more.

What are the benefits of connecting a Cloud Data Warehouse to my marketing systems?

The Cloud Data Warehouse has enabled the next generation of data capabilities. Whereas historically, data warehouse capacity was limited, the cloud model has opened this up to house a magnitude of more data and can support usage by many more people and teams.The rise of the data scientist and huge investments in data teams more broadly has resulted in massive efforts by many modern businesses today to build and maintain their core data infrastructure and Cloud Data Warehouses. These investments aim to develop a data asset that can provide a competitive advantage across the entire business.For many organizations today, the Cloud Data Warehouse represents the highest quality and most complete view of the customer and the broader business. Connecting this data repository into your marketing systems can represent a shift in the potential to drive a next-generation of data-driven segmentation and personalization.

I already have a Customer Data Platform (CDP) - why do I also need a Cloud Data Warehouse?

Historically, many CDPs have roots in tag management & data collection. Segment’s core product for example is designed to collect data from your website and mobile applications - and then aggregate, unify, and stream this data directly to your marketing applications.While these systems may be sufficient for businesses that are completely web and mobile-based, most businesses are not. For example, you may purchase a pair of shoes on your iPhone, but the shoes may take three weeks to arrive (data provided by a shipping provider), may arrive damaged (data submitted via a call to a support center), or may get returned (via a third party provider that handles returns). In this example, critical data required to stitch together the true customer 360 wouldn’t be available from a tag manager. And furthermore, the business logic required to properly account for total revenue (including returns), average shipping times (including lost or delayed packages), and other factors like per-product margins - all require a degree of complex bookkeeping and transformations which are best maintained in a Cloud Data Warehouse.So for many businesses, a CDP on its own will not be able to scale to the data needs and complexities across key customer touch points - and this is where the Cloud Data Warehouse comes in.

How is cloud data related to the death of cookies?

As third-party cookies deprecate, publishers who previously shared behavioral and demographic data over cookies in the browser are now starting to store this very same data in the cloud, and specifically in the Cloud Data Warehouse.Initiatives such as Unified ID 2.0 that standardize customer identity coupled with the emergence of cloud storage-based marketplaces, including Snowflake’s data marketplace, have enabled many use cases that were formerly cookie-based to now to operate in the cloud and the Cloud Data Warehouse.In the coming quarters and years as cookies continue to deprecate, this new world of cloud-enabled data will not just support existing use cases, but open up a new world of machine learning-powered applications that leverage compute capabilities of the Cloud Data Warehouse - compute capabilities that previously weren’t available in a browser-based third-party cookie world.

What does it mean for my data to have a “single source of truth” and why does this matter?

For many MarTech systems and architectures today, data is fragmented and siloed across multiple products. This makes it very difficult to find the right information and to know which data points and fields are correct and which ones are not.A single source of truth is an organization-wide investment to create a single, accurate, and maintained view of key data points that describe the business, customer behavior, and everything in between.Many organizations today have defined their Cloud Data Warehouse as the central source of truth for data in the business.

How can a Cloud Data Warehouse help provide better compliance across emerging privacy laws including GDPR and CCPA?

A core requirement of GDPR and CCPA is the ability to delete customer data on request. Yet at the same time, if your customer data is fragmented across multiple MarTech tools, it can be incredibly difficult to know which data lives where much less be able to delete this data across a complex set of systems.With centralized data models, this problem can be simplified greatly - all data on your customers is tracked in a single place and just as you have visibility over your customer 360, you also have visibility across the sources that create your customer 360.

What’s special about Snowflake and how is a Cloud Data Platform different from a Cloud Data Warehouse?

In recent years, Snowflake has emerged as a winner in the competitive Cloud Data Warehouse space. Historically, the Cloud Data Warehouse along with their predecessors (the “enterprise data warehouse” which is deployed on-prem in a data center) existed primarily for reporting purposes.Snowflake’s move to so-called “data applications” has been accompanied by a rebranding of their database as a “data platform.” With this has come an expanded set of functionality and partners to enable these new applications.Marketing and MarTech systems are one class of applications where Snowflake is investing in developing further. As mentioned, in October of 2022, they published a comprehensive report called “The Modern Marketing Data Stack” which overviews their platform capabilities and preferred set of partners.

What’s “Reverse ETL” and how does it fit into all of this?

Reverse ETL is a simple way to move data from your Cloud Data Warehouse into your MarTech tools. The category today has gotten quite a bit of attention from data engineers and analysts since it creates a simple way to integrate data using SQL instead of custom code that integrates via APIs.We’re seeing some of the larger marketing players integrate reverse ETL functionality directly into their platforms - including Salesforce (with their proposed Snowflake integration), and Braze.A big restriction of reverse ETL is data limitations of destinations. Most MarTech systems can’t accommodate even a fraction of the data in your Cloud Data Warehouse. So while reverse ETL may make initial integration easier for your technical teams, it will not solve your problems around data still being extremely hard to use and access from your end systems.

Brought to you by ScaleroThe holidays are a time when customers are bombarded with more messaging and emails than they can possibly handle. So how can you cut through the inbox noise to make sure your message comes through as clear as a Christmas carol? Scalero, the experts in email marketing, share some examples, tips, and tricks for creating emails that won’t turn your customers into the Grinch.

Don’t overthink Black Friday. Consumers are bound to have an influx of emails leading up to BFCM. Make yourself available and make it easy for your subscribers to click and see what you’re offering.

Rely on high-quality images. In this email, Good Eggs lets their products do the talking - the food looks and is amazing and gets you into the holiday spirit (and might make you a little hungry too!).

This email is smart. There is a to-the-point promo in the header, clever copy in the body, and beautiful product shots throughout. There's also a can't-resist deal front-and-center!

Subscribers will enjoy a break from cliche holiday emails and advertisements. The Perfect Jean is able to pull off the humor and a clear call to action in this email. Short, sweet, and to-the-point is bound to win this season.

Don’t forget New Year’s after the holiday rush! If you have interesting data on your subscribers and the data is mostly harmless, subscribers will be delighted to see a recap of their year.To learn more about ways to improve your email and lifecycle marketing, visit www.scalero.io.

Technology has made it much easier for brands to create and execute great marketing campaigns.

It is now more common (for big and small companies alike) to collect data, analyze it, and use the results to make more profits.

However, this only works if the data is accurate. And to make sure that’s the case, brands have to use the right technology.

What does that mean exactly? This is the question this week’s guest answers in today’s episode.

In this week’s episode of Data Unlocked, Jason sits down with Pratik Kodial, the founder and managing partner of Tapasya Investment Partners.

Before embarking on his hedge fund manager path, Pratik actually had a completely different career.

He was a data analytics and ecommerce expert as well as the VP of Marketing Effectiveness at JCPenney. Before that, he was a Senior Director of Pricing Analytics at Best Buy.

Pratik has had a long career in the data analysis industry, and is a subject matter expert in the space.

In this episode, he and Jason discuss how to execute a marketing strategy, taking campaigns from data to creative, how technology enables process, and more.

Ready to learn?

Let’s dive in.

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From website visits to emails, reviews, purchase history, psychographics, support tickets, and more, it’s likely that your business accrues a massive amount of customer data every single day. 

That data can be a rich source of actionable insights for your company — or it can go to waste. In order to put it to use, you’ll need to first collect it and then segment and manage it so that you can distill those insights. 

If you don’t yet have a customer data strategy (or if it’s been a while since you’ve revisited it) we know that it can be difficult to know where to start. That’s why we have pulled together answers to some of the most common questions business owners and marketers tend to have about customer data. This includes a review of the different types of customer data that exist, how you can collect it, the different ways that you can begin putting it to use, and more.

What is customer data?

At the most basic level, customer data refers to any information you know about your customers, whether they be individuals or other businesses or organizations. This includes contact information, demographic data, customer preferences, online interactions, and more. 

Collecting and analyzing customer data helps you understand your target audience more deeply. It reveals their needs, wants, and even the beliefs that drive those needs and wants. This empowers you to incorporate more data-driven decisions into your marketing, sales, and promotional strategies to attract and retain more customers.

Types of customer data

There are four main types of customer data:

1. Demographic data
2. Psychographic data
3. Behavioral data
4. Transactional data

Here’s a closer look at each one.

1. Demographic data

Demographic data refers to characteristics by which you can identify your customer base, such as their age, gender, income, and education level. 

This type of data is useful for understanding who your customers are and what their needs may be. It can also be used to segment your customer base for marketing strategies and full-blown campaigns.

For example, if you’re selling an anti-wrinkle cream, you may want to focus your marketing efforts on the age groups most likely to purchase your product. Gathering demographic data on your customer base will make your campaigns more targeted and improve results.

2. Psychographic data

Psychographic data — made up of a customer’s values, personality traits, opinions, attitudes, beliefs, and lifestyle — can shape your selling approach and messaging. 

It’s no longer enough to target a part of the market with demographic data. Think of psychographic data as the next step up. It’s simple: while demographic data may help you appeal to a customer's logical or analytical side, psychographic data helps you appeal to the emotional component that goes into a buying decision.

Consider how Bryan Kramer, marketer and author of Human to Human: H2H, puts it: “The fact is that businesses do not have emotions. Products do not have emotions. Humans do. Humans want to feel something.” 

What motivates your customer? What kind of messaging will they respond to? What brand voice or attitude will push them to hit the buy button? All these questions are easier to answer once you’ve gathered enough psychographic data to paint a more comprehensive view of the customer.

3. Behavioral data

Behavioral data includes anything from purchase history to website interactions or opting in to your newsletter in exchange for a discount. In other words, it’s the actions and behaviors that your customers take. 

This type of data helps you understand how your customers have interacted with your business in the past, which may provide insights as to their wants and needs in the future. 

For example, continuing with the wrinkle cream scenario above, users of a certain age that land on your homepage and click through some of the featured product pages show some level of brand awareness as well as buyer intent. Buyer intent insights strengthen when you track user behavior data and see that they came back a day later to sign up for your brand newsletter. 

That example is just a small scenario illustrating how analyzing behavioral data helps you further optimize the touchpoints throughout your customer journey. You’ll learn as you begin to understand what actions your customers are most likely to take, when they’re likely to act, and most importantly, why they’re doing what they do. This will also improve your lifecycle marketing efforts as you re-engage return customers.

4. Transactional data

Transactional data is information about the transactions your customers have completed with your business. This can include data points like purchase amounts, purchase frequency, how long it takes the customer to make those purchases, and how many items they’ve returned. 

This type of data helps marketers understand spending patterns and trends among your customers. It’s especially important as market forces shift, new technology is introduced, and new variables enter the buying equation.

How do you collect customer data?

There are many potential ways to collect customer data. We offer a handful of options below. Keep in mind: Customer data is only useful if it is accurate and up-to-date, so factor those points into your collection strategies.

Website forms

Website forms give your customers a means of communicating with your brand even if they aren’t ready to make a purchase. Examples can include a form which must be completed to sign up for a newsletter, receive a promo code or discount, or download an asset like an ebook. In this way, you can use forms to collect a customer’s name, date of birth, contact information, and other key data. 

Surveys and focus groups

Another way to collect customer data is by sending surveys. You can either give customers a paper survey or use an online survey tool like Survey Monkey. Old-fashioned focus groups can also be a great way to get detailed feedback from your target audience.

Collecting direct feedback

You can collect data by speaking to your customers directly. This could be done over the phone, through email, or in person. If you speak to customers directly, you will better understand their needs and wants. This approach can also uncover objections, attitudes, or behaviors that weren’t so apparent before. Ultimately, it’s all about getting closer to your customers and creating a brand that appeals to more than just their logic.

Other data sources

It should be noted that you can also collect a lot of customer data without your customer actually needing to do anything. That’s because customer data can live in many of your business’s systems, passively collected as a customer interacts with your brand. Examples include:

• Transaction data, living in your CRM, accounting software, or POS
• Behavior data, living in your website and CMS tracking software

A note on compliant customer data collection

We’d be remiss not to touch on a few best practices for data privacy in this guide. 

Now more than ever, customers are wary about sharing their personal data. Marketers must respect customers’ privacy and ensure their data collection processes comply with data collection laws and regulations. Even in the absence of these regulations, respecting and protecting your customers’ data helps you build brand trust which can translate into greater sales.

Here are three essential practices to follow:

1. Right to opt out: Provide customers the option to opt out of providing their data.
2. Legal compliance: Ensure your data collection practices comply with all relevant laws and regulations, such as GDPR and CCPA.
3. Transparency: Be transparent about how you will use any customer data you collect. Make sure your website’s Terms and Conditions page explicitly details how you collect customer data, what you do with it, and how visitors or users can opt out.

While the conversation about ethical customer data collection goes well beyond the scope of this post, following these best practices will set you on the right track to collect valuable customer data without crossing any lines.

How do you segment customer data?

There is no one-size-fits-all way to segment customer data. Ultimately, how you segment your data will highly depend on your end goal. Let’s look at a few examples:

Segmenting by demographic data

The most common way to segment your customer data is by using demographic data such as age, gender, location, or income level. 

With this information at your fingertips, it’s easier to make assumptions about what products or services interest your target market.

Middle class income couples who have a driveway, are between the ages of 23-60, and live in southern California, for example, won’t be interested in buying a high-quality snow blower—it doesn’t snow in southern California. That’s an example of why segmenting by demographic is essential if revenue is the goal.

Segmenting by behavioral data

Another way to segment your customers is by behavioral data — which includes information on what kind of customers are more likely to make a purchase, how often they purchase, and what products they tend to buy.

For example, let's say you know a customer has recently searched for “best crockpot” on Google. Based on that search history, you might conclude that the customer has a high intent to buy — and that they might be primed to make a purchase with a gentle nudge in the form of an email or promotion.

Behavioral data can be a key part of shaping how, when, and why you’ll run certain marketing campaigns, like paid ads.

Segmenting by psychographic data

You can also segment your customer data by using psychographic data. This includes information on your customers' values, beliefs, and attitudes. 

This type of data can be helpful in understanding what motivates your target market and how best to appeal to their needs.

Suppose, for example, you run a website that sells holiday cards, but you don't cater to one particular religion. If you know what religion a customer practices, you can tailor your promotional emails, product recommendations, and even messaging so that it aligns with the values that matter to them. Then, when a Christian holiday like Easter comes around, for example, you can send a promotional eblast to customers you know are Christian, who may have an incentive to make a purchase — without accidentally alienating customers who practice other religions and who might be offended or turned off by receiving said promotion.

Segmenting by transactional data

What if you wanted to run a remarketing campaign? This is where segmenting by transactional data can be useful. 

By segmenting your audience by customers who have made several repeat purchases in order to target them, your remarketing campaign has a bigger chance of creating more revenue. 

Why? Because it’s more targeted to an audience that’s familiar with your brand and primed to buy. This is infinitely better than running a remarketing campaign that includes customers still on the fence about making their first purchase (or haven’t at all).

Validating and analyzing customer data

Once you’ve created your customer database, you need to validate it and ensure it’s usable within your data systems. 

It’s critical to note that data analysis can’t happen before data validation. To validate data, you'll need to run it through a set of rules against your existing database. The rules and even your approach to data validation will depend on the solutions you use as part of your marketing data collection and management process. 

For example, with a data platform like Simon CDP, all the data you ingest — whether it’s first- or third-party data — goes through a validation process that checks for items like the following:

• Syntax
• Empty data fields
• Accurate timestamps
• Unique field names
• Appropriate identifiers
• Unauthenticated users

Any errors or omissions detected during this process will either be flagged or updated automatically, depending on the validation rules you’ve set. Once the data is validated, Simon CDP analyzes it for marketing insights. For example, you can match website visitors to first-party data to create unified customer profiles. You can also pinpoint customers with unique identifiers that plug them into the right conversion campaigns.

Benefits of customer data analysis

One of the most important benefits of customer data analysis is that it helps businesses to understand their customers more deeply. 

Customer data analysis enables businesses to group their customers into segments, understand their needs and wants, and develop more sophisticated selling strategies. 

Additionally, customer data analysis helps businesses identify trends and patterns in customer behavior, which improves the products and services they offer. And there are many other benefits:

• Accurately gauging customers’ satisfaction with your products or services
• Gathering accurate metrics to measure the health of your business
• Increasing revenue and profit margins
• Improving customer retention rates
• Developing more successful marketing campaigns
• Increasing customer lifetime value

Do more with Simon Data

At Simon Data, we know that customer data is an important part of understanding your target market, meeting their needs, and providing a stellar customer experience. That’s why we enable marketing teams with the tools and features they need to create a better-rounded view of their customers. 

Simon CDP centralizes, unifies, and manages all customer data so you can easily track your customers' interactions with your business. You can gather contact information, demographic data, customer preferences, and more. 

Whether you’re looking for a more comprehensive way to execute data orchestration or you want to improve your lifecycle marketing efforts, Simon Data helps you get there. Request a Simon Data demo today.

In today’s market, staying on top of your competition is key to a successful and healthy business.

How can you achieve that goal?

To answer that question, we’d like to introduce you to the CDP, or Customer Data Platform.

As you know, the only way to stay competitive today is to use the right data, and a CDP can help you do just that, since it’s a platform that collects, unifies, and activates customer data.

Just the thing you need, right?

And who better to discuss this in more depth than today’s guest on Data Unlocked?

In this week’s episode of Data Unlocked, Jason sits down for a second time with Michael Katz, the co-founder and CEO of mParticle.

Before founding mParticle, Michael was the CEO of interclick, a data-driven advertising network, before selling it to Yahoo for $270 million.

Today, he’s at the head of mParticle.

mParticle is a customer data platform for multi-channel consumer brands. Their work helps the world’s best brands unify data in order to optimize marketing outcomes and CX.

In this episode, Michael discusses the future of CDP, the technology and market changes that are affecting today’s CDP landscape, the driving forces shaping the CDP category, and more.

Ready to learn?

Let’s dive in.

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Marketing teams use several marketing technologies to wrangle ever-increasing volumes of customer data from a myriad of channels, including mobile apps, desktop applications, website browsers, email, and SMS.

In the past, to create a unified customer profile, marketers had to request the help of IT and data teams to consolidate the data from all these disparate data sources. But now, this data can be easily managed in a customer data platform (CDP).

A customer data platform is marketing technology that unifies customer data from disconnected data sources to create a single profile of each customer that can be used to create more targeted, personalized marketing campaigns.

Why are customer data platforms important today?

CDPs break down data silos, unifying customer data from all your user touchpoints into a single place and making it easier to create a 360-degree view of each customer.

This unification of data is important for several reasons:

Improved personalization

Cookie-cutter marketing messages sent to every customer and prospect are no longer acceptable. A first-time customer and a repeat customer expect different messaging based on which stage of the customer journey they are in.

With a CDP, marketers can associate user interactions with specific users to see who is making purchases, the frequency of those purchases, the channels they use, and how campaigns have led to those purchases. With this information, you can customize your marketing campaigns based on actual customer behavior.

Better omnichannel engagement

More and more companies are interacting with their customers through a variety of channels, including websites, social media, email, and ads. If a customer is having disjointed experiences across channels, they may fail to connect with your brand or make a purchase.

Statistics show that 94% of customers who give a brand a “very good” rating for customer experience are more likely to continue purchasing from that brand in the future.

Using a CDP with dynamic identity resolution capabilities, you can combine data from disparate sources to create a consistent experience, no matter how customers interact with your brand.

Moving away from third-party data

In the past, marketers have relied on third-party data collected from website cookies and trackers. But with ever-changing privacy regulations, third-party cookies are becoming obsolete. The focus has shifted to zero-party and first-party data—information which is collected directly from customers.

By unifying data from customer interactions, preferences, purchases, quizzes, and surveys into a CDP, you’ll be able to offer personalized messaging and interactive customer experiences without violating customer privacy.

Remaining compliant

GDPR, CCPA, and other data privacy laws give consumers the right to access any personal data a company has stored and to have that data erased (the right to be forgotten). To comply with these regulations, organizations need to ensure that customers have access to every platform where their data is stored and that data can be erased from each platform when requested.

CDPs make customer data management simple since all the data is stored and accessed from one place. They also help improve compliance by only collecting the data that is essential to your marketing efforts.

What are the different kinds of CDPs?

Depending on the size and goals of your business, different CDP platforms will offer different benefits. Let’s look at the different types of CDP to help you decide which one is right for you:

Data streaming

Also called data integration CDPs, data streaming CDPs sit on top of existing databases and focus on the fundamentals of customer data management, such as gathering, organizing, ingesting, and centralizing customer data.

Their strengths lie in data manipulation, governance, tag management, and data streaming collection. However, data streaming CDPs can be complex to implement and maintain, and they often lack the automation and visualization features that marketers need.

Orchestration

Orchestration CDPs, also referred to as campaign CDPs or smart hubs, focus on campaign orchestration and personalization. These CDPs specialize in building unified customer profiles and segments. They also offer capabilities for timing and targeting responses based on user behavior and events.

Orchestration CDPs can ingest real-time data from a data streaming CDP for immediate integration into a campaign. Some also come with their own streaming data functions, such as data ingestion and centralization, and may even include some of the marketing convenience features found in automation CDPs.

Automation

Automation CDPs, also known as delivery CDPs, offer a high level of marketing automation for developing and executing campaigns faster. They provide features such as data assembly, enhanced segmentation, and analytics for better message delivery via channels such as email, mobile apps, and websites.

Automation CDPs are great if you’re looking for “set it and forget it” functionality to launch preset campaigns automatically. But they may not be the best choice if you want to use real-time data to adjust messaging.

Marketing cloud

Marketing clouds aren’t technically CDPs. They often begin with a focus other than unifying customer data, such as providing email marketing services. These multi-channel marketing solutions can integrate customer data from various channels, but they don’t provide the features necessary for handling the complexities of modern customer data.

CDP vs DMP vs CRM

If you’re already using a customer relationship management tool (CRM) or a data management platform (DMP), you might be wondering if you need a CDP. Here’s a quick look at how they are different and how they work together:

CDP vs DMP

Like CDPs, data management platforms also centralize and organize customer data. But while CDPs focus on first-party data, DMPs collect anonymized third-party data to use with paid digital advertising and marketing platforms.

DMPs are used only in advertising and don’t collect the customer behavioral data that’s necessary for personalization.

On the other hand, CDPs push audience data with personally identifiable information (PII) to DMPs, which then pass it on to demand-side partners and advertisers. When customers click on downstream advertisements, the CDP then ingests that data for further analysis and segmentation.

CDP vs CRM

Customer relationship management tools are used to track business interactions such as purchases, emails, logs, and customer support communication with existing customers. They help businesses organize and manage customer-facing interactions—but they don’t collect behavioral data or perform analysis on how customers interact with your products or services.

CDPs can ingest data from CRM tools to draw out this behavioral data for audience segmentation. Segmentation helps you personalize your messaging to improve your marketing ROI.

What to look for in a customer data platform

A good customer data platform brings together four key capabilities: data collection, profile unification, segmentation, and campaign orchestration. But the choices and variations between platforms can be overwhelming. To help you find the best CDP for your business, here are some key factors to consider:

Business type

Are you a small or mid-sized company or a large enterprise? Is your business mainly B2B or B2C (or both)? Identifying your business type will help you refine your needs.

Integration

Are you already using a CRM? Does your data come from a range of sources like data warehouses, marketing engagement platforms, website activity, social media engagement, customer service tools, call centers, and ecommerce platforms? For seamless data management and analysis, you will want to find a CDP that integrates with all the platforms you are using, now and in the future

Users

Is your data being managed by data engineers or do non-technical users need to be able to access and use the data? Simplicity and ease of use are essential to eliminating inefficient workflows between teams, and ensuring marketers have access to the data they need to create the experiences they want.

Analysis

The right CDP will help you make sense of your data to turn it into actionable insights. The goal is to maximize the ROI of each customer interaction—so make sure you have access to all the data you need, segmented in a way that makes it easy to analyze and put to work for your particular use case.

Compliance

Depending on what regions you are serving, you may have different privacy measures to adhere to. Any CDP should be GDPR and CCPA compliant. The country or countries you are operating in may also have their own laws regarding customer data, so make sure your CDP will meet all your privacy and security requirements.

Whatever your needs, here are some essential features to look for in a CDP:

• Ease of use
• Omnichannel engagement
• Advanced, real-time customer data analysis
• Predictive and actionable insights
• Out-of-the-box integration with your existing CRM, databases, and marketing tools
• GDPR and CCPA compliance

Since the 1970s, organizations have recognized the benefits of the ETL (extract, transform, load) process for consolidating data from disparate sources into a centralized platform such as a data warehouse.

Traditionally, this data has been used to power business intelligence (BI) dashboards for reporting. But today, it is more valuable to analysts, business teams, and marketing teams if it’s accessible to the frontline tools that drive their day-to-day operations, such as HubSpot and Salesforce.

This is where Reverse ETL comes in. Reverse ETL technology moves cleaned, optimized data from a data warehouse into third-party frontline tools. This gives you access to accurate, real-time customer data to power your workflows and marketing campaigns.

Here we’ll discuss the ins and outs of Reverse ETL and how it transforms marketing strategies and decision-making.

What is Reverse ETL?

Reverse ETL is a process by which data is extracted from a data lake or warehouse and sent to SaaS applications, like customer relationship management (CRM), marketing automation, advertising, and customer experience tools.

Data in a data warehouse is an aggregation of an organization’s most accurate, up-to-date data. It serves as a “single source of truth.” But this data is often only accessible to technical users who know how to write SQL and Python scripts.

The Reverse ETL process replicates data from the data warehouse into the third-party systems employees use daily. This process allows them to get the data and insights they need faster, instead of relying on IT teams or data engineers to process the data.

Why Reverse ETL?

Without Reverse ETL, valuable data and insights can remain locked away within your data warehouse. This is data that marketers need to create personal, data-informed strategies to improve your customer relationships.

A Reverse ETL solution offers three main benefits:

1. Operationalizing data: Reverse ETL allows you to operationalize relevant data by sending it to SaaS applications—for example, importing consolidated customer data from the data warehouse into Salesforce. A Reverse ETL pipeline is the key to improving your sales processes and making your data usable for marketing campaigns.

2. Reducing reliance on IT: Reverse ETL technologies offer no-code, easy-to-use plug-and-play features. This allows sales and marketing professionals to access the data they need without relying on IT or data teams.

3. Improving the customer experience: Reverse ETL transfers data in real time. Having access to the most up-to-date data enables sales and marketing teams to make the best decisions based on current customer needs.

The ETL process

To better understand the Reverse ETL process, let’s first take a step back and look at ETL.

ETL is a data integration process that allows you to extract data from different applications and import it into a data warehouse. The process takes raw data from several tools and data sources, transforms it into a usable format, and loads the transformed data into the target data warehouse.

The three steps of the ETL process are extract, transform, and load:

1. Extract

The first step in the ETL process is to import and consolidate structured and unstructured data into a single repository. Data is extracted manually or with automated tools from a wide variety of sources, including existing databases, legacy systems, sales and marketing applications, CRM systems, and analytics tools.

2. Transform

The transformation phase of the ETL process ensures data quality, integrity, and accessibility. During this process, the data can go through several stages:

• Cleansing: Removing inconsistencies and missing values
• Standardization: Conforming to specified formatting rules and naming conventions
• Deduplication: Excluding or discarding redundant data
• Verification: Flagging anomalies and removing unusable data
• Organization: Sorting and grouping according to data type

3. Load

This step in the ETL process loads the transformed data into a new data repository such as a data lake or data warehouse. Data can be loaded in full or incrementally. With full loading, the transformed data is stored in unique records in the data warehouse. Incremental loading compares incoming data with existing data in the data warehouse and only creates additional records if it finds unique information.

Advantages of ETL

There are several advantages for organizations using the ETL process to consolidate data:

Timely access to data: ETL merges data from multiple sources into a single repository and transforms it into a useful format. As a result, it improves access to valuable information that drives strategic and operational decision-making. Instead of piecing together siloed data, business leaders have access to useful data aggregated from multiple sources.

Data quality: The ability to remove data inconsistencies, redundant data, and unusable data during the transformation stage makes data more consistent. Data has increased quality and integrity.

Ease of use: Most ETL applications are no-code and provide graphical user interfaces (GUIs) that allow business users to create ETL processes without programming knowledge. Users operate a drag-and-drop interface and specify rules to map the flows of data in a process.

Capacity for big data: Today’s ETL tools handle massive amounts of big data without compromising process and sync speed.

What does this have to do with Reverse ETL?

Reverse ETL is how you make use of all this data. Where ETL prepares the data and moves it into a central location, Reverse ETL moves it in the opposite direction—from the source (e.g., data warehouse) into third-party business tools and applications.

This means you can create a data pipeline from modern data platforms like Amazon Redshift, Snowflake, and Google BigQuery to Salesforce and other frontline applications. With Reverse ETL, you can make use of both structured and unstructured data.

Common use cases for Reverse ETL

Here are some ways businesses are taking advantage of Reverse ETL:

Customer service

Customer service employees represent your company’s brand and significantly impact customer satisfaction. But they need access to the right information to deliver personalized customer experiences.

A Reverse ETL pipeline syncs customer data from your data warehouse to various support channels, such as Zendesk and Help Scout. This gives customer success teams access to important metrics related to customer profile data and service history. With up-to-date, actionable customer data, support teams can more successfully engage with customers.

Sales

Sales teams often have to navigate multiple tools and platforms to gather product usage data. This makes it more challenging to get the detailed information they need to nurture potential leads and identify high-value accounts.

With a Reverse ETL solution, sales teams import aggregated customer data from several data sources into their CRM software. Having all the data in one place enables them to make more meaningful connections and increase their conversion rates.

Marketing

To deliver a great customer experience, marketers need to bring together information about the customers’ behavior through the entire marketing funnel. Using Reverse ETL, marketing teams can send customer data from product, sales, and customer support to their own marketing automation tools, like HubSpot.

With all this data gathered into one platform, they can identify which content or channel triggered the customer journey, view customer purchase history, and segment leads based on demographic and behavioral data. A holistic view of the customer journey enables marketing teams to create more personalized and effective campaigns.

Why you still need a CDP

At first glance, Reverse ETL may appear like the perfect solution to completing your data stack. You have a warehouse that stores data and, now, a solution that moves all that data back and forth to your tools.

However, Reverse ETL does not provide an interface to activate your data into personalized, cross-channel customer experiences. This gap is where a customer data platform (CDP) comes into play.

A CDP unifies all your customer data and consolidates it into a single profile view for each customer. This results in downstream marketing orchestration and business analysis that are targeted, personalized, and effective.

How does Reverse ETL interact with your CDP?

Reverse ETL and CDPs have some overlapping functionality, but they complement each other in organizational tech stacks. Their aligned capabilities make customer data bi-directional and consistent across tools and sources.

Besides facilitating the bi-directional flow of data between data warehouses and various end channels, CDPs uniquely provide strong customer identity models. By creating single customer views, they combine all the user data that lives across disconnected tools into easy-to-access customer profiles.

Without a CDP, you would have two options:

1. Maintain separate customer profiles in each end channel, risking data fragmentation.
2. Build that logic into your data warehouse, sacrificing significant time and engineering resources.

Though some Reverse ETL tools allow marketers to filter data, this solution still ultimately requires the work of an engineering team. CDPs make data accessible to marketers with no-code workflows, improving overall time-to-value and efficiency.

In addition, CDPs provide powerful, dynamic orchestration functionality that allows marketers to create highly personalized, cross-channel customer experiences. All the actions are defined in the CDP and built on single customer profiles, based on each customer’s behavior. This translates to optimized customer engagement and ROI.

Simon CDP: Your complete cross-channel marketing platform

Simon Data enables marketing teams to transform data into outcomes. Powered by an industry-leading CDP, Simon integrates real-time and historical data into unified customer profiles that enhance sophisticated identity models and predictive capabilities. Through more targeted messaging and personalization that are built and scaled with ease, Simon Data customers get better results.

Request a customized demo to find out how Simon Data can help you align your CDP with Reverse ETL capabilities for consistent, bi-directional customer data across all your tools and data sources.

‍

Since the 1970s, organizations have recognized the benefits of the ETL (extract, transform, load) process for consolidating data from disparate sources into a centralized platform such as a data warehouse. Traditionally, this data has been used to power business intelligence (BI) dashboards for reporting. But today, it is more valuable to analysts, business teams, and marketing teams if it’s accessible to the frontline tools that drive their day-to-day operations, such as HubSpot and Salesforce.This is where Reverse ETL comes in. Reverse ETL technology moves cleaned, optimized data from a data warehouse into third-party frontline tools. This gives you access to accurate, real-time customer data to power your workflows and marketing campaigns.Here we’ll discuss the ins and outs of Reverse ETL and how it transforms marketing strategies and decision-making.

What is Reverse ETL?

Reverse ETL is a process by which data is extracted from a data lake or warehouse and sent to SaaS applications, like customer relationship management (CRM), marketing automation, advertising, and customer experience tools. Data in a data warehouse is an aggregation of an organization’s most accurate, up-to-date data. It serves as a “single source of truth.” But this data is often only accessible to technical users who know how to write SQL and Python scripts. The Reverse ETL process replicates data from the data warehouse into the third-party systems employees use daily. This process allows them to get the data and insights they need faster, instead of relying on IT teams or data engineers to process the data.

Why Reverse ETL?

Without Reverse ETL, valuable data and insights can remain locked away within your data warehouse. This is data that marketers need to create personal, data-informed strategies to improve your customer relationships. A Reverse ETL solution offers three main benefits:

1. Operationalizing data: Reverse ETL allows you to operationalize relevant data by sending it to SaaS applications—for example, importing consolidated customer data from the data warehouse into Salesforce. A Reverse ETL pipeline is the key to improving your sales processes and making your data usable for marketing campaigns.

2. Reducing reliance on IT: Reverse ETL technologies offer no-code, easy-to-use plug-and-play features. This allows sales and marketing professionals to access the data they need without relying on IT or data teams.

3. Improving the customer experience: Reverse ETL transfers data in real time. Having access to the most up-to-date data enables sales and marketing teams to make the best decisions based on current customer needs.

The ETL process

To better understand the Reverse ETL process, let’s first take a step back and look at ETL.ETL is a data integration process that allows you to extract data from different applications and import it into a data warehouse. The process takes raw data from several tools and data sources, transforms it into a usable format, and loads the transformed data into the target data warehouse.

The three steps of the ETL process are extract, transform, and load:

1. Extract

The first step in the ETL process is to import and consolidate structured and unstructured data into a single repository. Data is extracted manually or with automated tools from a wide variety of sources, including existing databases, legacy systems, sales and marketing applications, CRM systems, and analytics tools.

2. Transform

The transformation phase of the ETL process ensures data quality, integrity, and accessibility. During this process, the data can go through several stages:

• Cleansing: Removing inconsistencies and missing values
• Standardization: Conforming to specified formatting rules and naming conventions
• Deduplication: Excluding or discarding redundant data
• Verification: Flagging anomalies and removing unusable data
• Organization: Sorting and grouping according to data type

3. Load

This step in the ETL process loads the transformed data into a new data repository such as a data lake or data warehouse. Data can be loaded in full or incrementally. With full loading, the transformed data is stored in unique records in the data warehouse. Incremental loading compares incoming data with existing data in the data warehouse and only creates additional records if it finds unique information.

Advantages of ETL

There are several advantages for organizations using the ETL process to consolidate data:Timely access to data: ETL merges data from multiple sources into a single repository and transforms it into a useful format. As a result, it improves access to valuable information that drives strategic and operational decision-making. Instead of piecing together siloed data, business leaders have access to useful data aggregated from multiple sources.Data quality: The ability to remove data inconsistencies, redundant data, and unusable data during the transformation stage makes data more consistent. Data has increased quality and integrity. Ease of use: Most ETL applications are no-code and provide graphical user interfaces (GUIs) that allow business users to create ETL processes without programming knowledge. Users operate a drag-and-drop interface and specify rules to map the flows of data in a process.Capacity for big data: Today’s ETL tools handle massive amounts of big data without compromising process and sync speed.

What does this have to do with Reverse ETL?

Reverse ETL is how you make use of all this data. Where ETL prepares the data and moves it into a central location, Reverse ETL moves it in the opposite direction—from the source (e.g., data warehouse) into third-party business tools and applications.This means you can create a data pipeline from modern data platforms like Amazon Redshift, Snowflake, and Google BigQuery to Salesforce and other frontline applications. With Reverse ETL, you can make use of both structured and unstructured data.

Common use cases for Reverse ETL

Here are some ways businesses are taking advantage of Reverse ETL:

Customer service

Customer service employees represent your company’s brand and significantly impact customer satisfaction. But they need access to the right information to deliver personalized customer experiences. A Reverse ETL pipeline syncs customer data from your data warehouse to various support channels, such as Zendesk and Help Scout. This gives customer success teams access to important metrics related to customer profile data and service history. With up-to-date, actionable customer data, support teams can more successfully engage with customers.

Sales

Sales teams often have to navigate multiple tools and platforms to gather product usage data. This makes it more challenging to get the detailed information they need to nurture potential leads and identify high-value accounts.With a Reverse ETL solution, sales teams import aggregated customer data from several data sources into their CRM software. Having all the data in one place enables them to make more meaningful connections and increase their conversion rates.

Marketing

To deliver a great customer experience, marketers need to bring together information about the customers’ behavior through the entire marketing funnel. Using Reverse ETL, marketing teams can send customer data from product, sales, and customer support to their own marketing automation tools, like HubSpot.With all this data gathered into one platform, they can identify which content or channel triggered the customer journey, view customer purchase history, and segment leads based on demographic and behavioral data. A holistic view of the customer journey enables marketing teams to create more personalized and effective campaigns.

Why you still need a CDP

At first glance, Reverse ETL may appear like the perfect solution to completing your data stack. You have a warehouse that stores data and, now, a solution that moves all that data back and forth to your tools. However, Reverse ETL does not provide an interface to activate your data into personalized, cross-channel customer experiences. This gap is where a customer data platform (CDP) comes into play. A CDP unifies all your customer data and consolidates it into a single profile view for each customer. This results in downstream marketing orchestration and business analysis that are targeted, personalized, and effective.

How does Reverse ETL interact with your CDP?

Reverse ETL and CDPs have some overlapping functionality, but they complement each other in organizational tech stacks. Their aligned capabilities make customer data bi-directional and consistent across tools and sources.Besides facilitating the bi-directional flow of data between data warehouses and various end channels, CDPs uniquely provide strong customer identity models. By creating single customer views, they combine all the user data that lives across disconnected tools into easy-to-access customer profiles.Without a CDP, you would have two options:

1. Maintain separate customer profiles in each end channel, risking data fragmentation.
2. Build that logic into your data warehouse, sacrificing significant time and engineering resources.

Though some Reverse ETL tools allow marketers to filter data, this solution still ultimately requires the work of an engineering team. CDPs make data accessible to marketers with no-code workflows, improving overall time-to-value and efficiency.In addition, CDPs provide powerful, dynamic orchestration functionality that allows marketers to create highly personalized, cross-channel customer experiences. All the actions are defined in the CDP and built on single customer profiles, based on each customer’s behavior. This translates to optimized customer engagement and ROI.

Simon CDP: Your complete cross-channel marketing platform

Simon Data enables marketing teams to transform data into outcomes. Powered by an industry-leading CDP, Simon integrates real-time and historical data into unified customer profiles that enhance sophisticated identity models and predictive capabilities. Through more targeted messaging and personalization that are built and scaled with ease, Simon Data customers get better results.Request a customized demo to find out how Simon Data can help you align your CDP with Reverse ETL capabilities for consistent, bi-directional customer data across all your tools and data sources.

Simon Data partnered with eTail to survey over 500 U.S. consumers and learn how they intend to shop this holiday season. Unlock key consumer insights to help you hit your Black Friday and Cyber Monday goals and build stronger customer relationships.

Download the report to learn:

• 6 key takeaways to use this data to maximize holiday performance
• The consumer’s POV on cart abandonment, why they do it and what sparks reengagement
• The latest insights on online shopping and what factors can make or break a sale
• Which marketing messages are the most preferred on each channel

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Last spring, Kim Larsen did some analysis around the NBA finals and posted it on LinkedIn.

He was predicting probabilities and trying to guess how things would go, and, as it turned out, his predictions were more accurate than you might think. His posts got a lot of attention because of this.

In fact, his numbers were so accurate, he was able to predict who was going to win.

Of course, this wasn’t very surprising to us. After all, we work in the data industry, and we know it can make some pretty accurate predictions.

And, because we wanted to dive further into this, we thought we’d bring him on to discuss the subject!

Yes, you’ve guessed it; in this week’s episode of Data Unlocked, Jason will be sitting down with Kim Larsen, SVP of Business Data Science at WHOOP.

Kim has been working in the data science industry for the last two decades. He’s done consulting and data work for B2B and B2C companies all over the world, such as Uber, Stitch Fix, Charles Schwab, and more.

Today, he’s lending his expertise to WHOOP.

WHOOP is a tech company whose mission is to unlock human performance and help people reach their ultimate physical potential. They do this through services such as WHOOP APP and products like WHOOP 4.0 band.

In this episode, Kim discusses WHOOP’s work, using data to predict and model sports outcomes, the difference between sports predictions and customer predictions, and more.

Ready to learn?

Let’s dive in.

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