Imagine walking into your favorite store and being greeted by name, showcasing the products you’ve been eyeing online, and offering a special promotion just for you . Sounds sweet, doesn’t it?
This is the kind of seamless, personalized experience that businesses can create using a customer data platform (CDP). A CDP is like a digital memory bank for your business, collecting and organizing data about customer behavior, preferences, and interactions. With this information at your fingertips, you can create more targeted marketing campaigns, optimize pricing strategies, and more.
Problem? CDPs were intended to be repositories of customer information, but by trying to give quick data access to marketing teams, CDPs created another data warehouse. So, how can you take advantage of the benefits of CDPs without having to deal with multiple data repositories? Import: Combined CDP . 😮
In this post, we’ll dive into six ways that businesses are using traditional CDPs to meet their use cases, and how you can meet those same use cases more effectively using something you may already have: Data warehouse.
Use Case No. 1: Identity Solutions
Do you understand your customers? Are you sure? If each tool in your device has data about your customers, then no tool has the full picture. While CDPs claim that they can help create a unified, 360° view of a customer by identifying all data points that are relevant to that customer and consolidating them into a single record – they can’t.
Sure, they’re storing increasing amounts of customer data, but they don’t have the full picture of their customers because their 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 follow a rigid structure for storage in a CDP, build an aggregated CDP on top of your data warehouse and use reverse ETL to perform “continuous” synchronization between your tools and repositories. This flexibility allows you to have full control over how customer identities are unified instead of a one-size-fits-all approach to traditional CDPs.
Now…
✅ The support team resolves issues more quickly.
✉️ The marketing team will send more personalized messages.
⏱️ The sales team can reiterate the value of the product at the right time.
What should not be loved?
Use Case No. 2: Personalization
Personalization is one of the main benefits of a CDP. By collecting data about customer behavior and preferences to create detailed customer profiles, businesses can build marketing campaigns, product recommendations, and more relevant and targeted customer service interactions. 🎯
For example, if customers regularly buy running shoes, they may be more likely to be interested in promotions for running equipment or events. 👟Personalized product recommendations tailored to customers will keep them coming back.
In theory, customer service interactions could also be improved. Unfortunately, with a traditional CDP, customer service representatives can only access the repository of customer data that the CDP has collected — instead of the new data in your inventory.
That means when they try to access a customer’s purchase history, previous interactions, and any notes or flags, they may not get the most up-to-date data available. Also, when you create your own synthetic CDP using a data warehouse, you won’t be dependent on what the old tool may or may not access.
Use Case No. 3: Segmentation
CDPs can be used to collect data about customer demographics, behavior, and purchase history, and then segment customers based on that data.
By segmenting customers based on purchase history and demographics, businesses can identify high-value audiences and offer them exclusive promotions or, conversely, identify price-sensitive customers and offer them discounts to encourage them to make purchases. 🫰
You can also create targeted marketing campaigns tailored to specific segments. For example, a bank might create a campaign targeting older customers that focuses on retirement savings products, while another campaign targeting younger customers focuses on student loan consolidation products.
But CDPs don’t have a full picture of customers, and segment implementation is expensive and slow. However, using an in-stock data activation platform will give 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 No. 4: Omnichannel Marketing
CDPs can be a powerful tool for organizations looking to implement an omnichannel marketing strategy. With their claim to unify customer data, they’ve promised that you can engage customers across multiple touchpoints and channels with consistent and targeted messaging that drives loyalty and higher conversion rates. 📈
However, CDPs have yet to meet the expectations that many groups place on them. Often CDPs don’t provide the same functionality as a suite of business intelligence tools can, so many businesses find that they still need to use the aforementioned tools alongside their data warehouses or data lakes alongside their CDPs. The CDP will then become another data repository.
Instead, by using your data warehouse as part of a composite CDP, you can create your own CDP solution to do everything you need. The best part? Stored data that was previously only available after using 5 different marketing tools, now remains synced and makes your marketing more scalable and automated.
Use Case No. 5: Predictive Modeling
Predictive modeling is another powerful use case that people often turn to CDPs. CDPs can be used to collect data about 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 churn so that the business can take steps to retain them. Other predictive models can be used to estimate customer lifetime value, identifying which customers are most likely to respond to a particular marketing campaign or which customers are most likely to need assistance from customer service.
However, synthetic CDPs can be used for this same application . In fact, it works even better because you know your customer data is always fresh and accurate. 🧼
Since data scientists often deploy their predictive models in a data warehouse, rather than waiting for the output of those models to sync with a traditional CDP, with a combo CDP, the output will be available in real-time for use in marketing automation tools.
Use Case No. 6: Data Governance
Data governance and compliance is another common use case for CDPs.
Compliance with data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) is critical for businesses. These regulations set strict guidelines on how personal data is collected, stored and used – and failure to comply can result in costly fines and reputational damage. 👎
Unlike a data warehouse that provides a single central location to store customer data, CDPs only have access to a portion of the customer data view. And syncing all available data into your downstream tools not only increases costs — as many downstream tools like Braze charge for data storage — but also simultaneously creates multiple copies of data without management.
So, how can you tell if you’re really complying with the regulations? 😨 That’s exactly why it’s so important to enable original data in the repository .
Native data triggers in repositories are designed for the purpose of synchronizing data from the warehouse to subsequent tools at scale. They have strong support for bulk APIs and give you more control over what data is synced, both ensuring data quality and managing it.
The bottom line: If you want to increase customer engagement and drive growth, a traditional CDP will fail you.
Give your company the advantage of quick data access but don’t buy an expensive CDP solution that has rigid data models, long deployment times, and redundancy across analytics and marketing tools.
A CDP offers many functions that overlap with your company’s existing data platform, so simply turn your existing data platform into an aggregateable CDP. The emergence of data warehousing as the single source of information combined with warehouse-enabled tools such as the census will eliminate the need for organizations to invest in expensive all-in-one CDP solutions that deliver no value over time.
With the application of Saas CDP DataS to the management and analysis of customer data, businesses can create detailed customer profiles and apply them to improve business models for businesses.