Imagine walking into your favorite store and being greeted by name, shown products you've been eyeing online, and offered a special promotion ✨ just for you. ✨ Sounds pretty sweet, right?
This is the kind of personalized, seamless experience that businesses can create with a customer data platform (CDP). A CDP is like a digital memory bank for your business, collecting and organizing data on your customers' behavior, preferences, and interactions. With this information at your fingertips, you can create more targeted marketing campaigns, optimize pricing strategies, and more.
The problem? CDPs were intended to be a repository of customer information, but by trying to give fast data access to marketing teams, the CDP created yet another silo of data. So, how can you reap the benefits of a CDP without fighting against multiple data silos? Enter: The composable CDP. 😮
In this post, we'll dive into six ways businesses are using a traditional CDP to satisfy their use cases, and how you can fulfill those same use cases more effectively using something you probably already have: A data warehouse.
⚠️ Believe the CDP hype, but don't believe you need to go out and buy one ⚠️
As a concept, CDPs are great, but as another silo for your data, buying a CDP should be a hard pass. 🚫 You likely already have many of the components of a CDP inside your company's data platform.
Why not leverage these building blocks to build your own composable CDP? You already collect and transform data, so all that you need is a way to activate that data to make it accessible across your company. This is where a reverse ETL tool like Census comes in.
If you’d like to learn more about how Census can help you build your own CDP solution out of your existing data infrastructure, read The best CDP is already in your data warehouse.
Use Case #1: Identity resolution
Do you understand your customers? Are you sure? If each tool in your stack has data about your customers, none have the full picture. While CDPs claim they can help create a unified, 360° view of a customer by identifying all the data points that are related to that customer and consolidating them into a single profile – they can’t.
Sure, they are storing increasing amounts of customer data, but they don’t have the full picture of the customer because marketing automation and support systems are still disconnected and have limited activation capabilities. 🚫
But here’s the good news: Your data team already maintains a central nervous system for storing information – your data warehouse. So, instead of forcing customer data to conform to a rigid structure to store in a CDP, build a composable CDP on top of your data warehouse and use reverse ETL to do “continuous” syncs between your tools and your warehouse. This flexibility allows you to have complete control over how you unify customer identity rather than the one size fits all approach associated with traditional CDPs.
✅ The support team gets to resolve issues more quickly.
✉️ The marketing team gets to send more personalized messaging.
⏱️ The sales team gets to reiterate product value at just the right time.
What's not to love?
Use Case #2: Personalization
Personalization is one of the key benefits of a CDP. By gathering data on customer behavior and preferences to create detailed customer profiles, businesses can build more targeted and relevant marketing campaigns, product recommendations, and customer service interactions. 🎯
For example, if a customer frequently purchases running shoes, they may be more likely to be interested in promotions for running gear or events. 👟Personalized product recommendations that resonate with customers keeps them coming back.
In theory, customer service interactions could be improved too. Unfortunately, with a traditional CDP, customer service reps can only access the silo of customer data that the CDP has collected – instead of the fresh data in your warehouse.
That means that when they try to access a customer's purchase history, previous interactions, and any notes or flags, they may not be getting the most up-to-date data available. Alternatively, when you create your own composable CDP using a data warehouse, you’re not at the mercy of what a legacy tool can or cannot access.
Use Case #3: Segmentation
A CDP can be used to gather data on customer demographics, behavior, and purchase history, and then segment customers based on that data.
By segmenting customers based on their purchase history and demographics, businesses can identify high-value audiences and offer them exclusive promotions, or conversely, identify customers who are sensitive to price and offer them discounts to encourage them to make a purchase.
You can also create targeted marketing campaigns that are tailored to specific segments. For example, a bank can create a campaign targeting older customers with a focus on retirement savings products, while a different campaign targets younger customers with a focus on student loan consolidation products.
But CDPs don’t have the full picture of the customer and are costly and slow to implement for segmentation. Using a warehouse-native data activation platform in its place, however, gives marketing teams access to 360° customer data for segmentation. Marketers can create targeted audiences that are more likely to convert with a complete view of customer attributes and interactions.
Use Case #4: Omnichannel marketing
CDPs can be a powerful tool for organizations looking to implement an omnichannel marketing strategy. With their claims of unifying customer data, they promised that you could engage customers across multiple touchpoints and channels with consistent and targeted messaging, driving higher loyalty and conversion rates. 📈
Yet CDPs haven’t delivered on the expectations that many teams have of them. Often the CDP doesn't offer the same functionality that a suite of business intelligence tools can, so many businesses find that they still need to use said tools in conjunction with a data warehouse or data lake alongside their CDP. The CDP then just becomes yet another silo of data.
Instead, using your data warehouse as part of a composable CDP, you could create your own CDP solution that does everything you need it to. The best part? Previously siloed data that was only available after going to 5 different marketing tools now stays in sync and makes your marketing more scalable and automated.
Use Case #5: Predictive modeling
Predictive modeling is another powerful use case that folks often turn to a CDP for. A CDP can be used to gather data on customer behavior and purchase history, which can then be used to create predictive models.
A predictive model can be used to identify customers who are at risk of churning so that the business can take steps to retain them. Other predictive models can be used to estimate customer lifetime value, identify which customers are most likely to respond to a specific marketing campaign, or which customers are most likely to need assistance from customer service.
But a composable CDP can be used for this same application. In fact, it works even better since you know your customer data is fresh and accurate. 🧼
Since data scientists typically deploy their predictive models within the data warehouse, instead of waiting for the output of those models to sync to a traditional CDP, with a composable CDP the output is available in real-time for use in marketing automation tools.
Use Case #6: Data governance
Data governance and compliance is another common use case for a CDP.
Compliance with data protection regulations such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) is crucial for businesses. These regulations set strict guidelines for how personal data can be collected, stored, and used – and non-compliance can result in costly fines and reputational damage. 👎
Unlike a data warehouse which provides a single, central location for storing customer data, a CDP only has access to a partial view of customer data. And syncing all the available data into your downstream tools not only drives up costs – since many downstream tools like Braze charge to store data – it also simultaneously creates multiple copies of the data with no governance.
So, how would you know if you’re really in compliance with the regulations? 😨 That's exactly why warehouse-native data activation is so important.
Warehouse-native data activation tools are purpose-built to sync data from the warehouse to downstream tools at scale. They have robust support for bulk APIs and give you more control over what data gets synced, both ensuring data quality and data governance.
Bottom line: If you want to increase customer engagement and drive growth, a traditional CDP will fail you.
Give your company the advantage of fast access to data, but don’t fall for buying an expensive CDP solution that has rigid data models, long onboarding times, and redundancies across analytics and marketing tools.
A CDP provides a lot of overlapping functionality to that of your company's existing data platform, so just turn your existing data platform into a composable CDP. The rise of the data warehouse as the single source of truth paired with warehouse-activation tools like Census takes away the need for organizations to invest in expensive all-in-one CDP solutions that don’t provide time-to-value.
With data activation and reverse ETL democratizing access, the data warehouse is best positioned to become the system of customer record that powers not just your marketing technology, but the entire business operations of the company.
👐 If you’d like to learn more about how Census can help you, schedule a demo, and we’ll talk you through it.