Data is one of the biggest assets a business can have. In a marketplace where the customer has many options, it’s crucial to understand their consumer behavior. Data enables just that. New products, experiences, services, and operations can be personalized. The personalization is based on what data reveals about the customer.
Customer data is actionable, reusable intelligence that can be transferred throughout the business. However, you have to use the info before it becomes irrelevant. Few businesses have the necessary tools to do so.
So, the customer intelligence landscape has changed drastically for multichannel brands worldwide. This is the main reason why the customer data platform (CDP) market has exploded. By 2025, the market size is projected to top $4.1 million.
What are customer data platforms?
Imagine the intelligence power of the CIA within the vein of a business’s customer knowledge. It’s a holistic way to become customer-centric via accessible and readily updated records. A software collection pulls, cleans, organizes, and stores data from multiple sources. Consequently, this creates two invaluable assets:
- A unified customer database accessible to other marketing systems
- A single customer profile
The result is deep and quick customer intelligence and insights that can be utilized immediately.
However, this powerful tool can easily fall short of its promised ROI if it isn’t used properly. Many implementations fail to properly align IT and marketing teams to ensure the setup and function of CDP is up to par.
How? Let’s look at a few CDP implementation tips.
Customer Data Platform Implementation Tips
Get the Data
Data ensures you know your customer from a complete angle. This includes their intent, needs, and likes versus dislikes throughout every single touchpoint. CDP helps you get that data. Therefore, the collection process is first on the checklist.
Customer data platforms offer almost unlimited actionable data. However, that data is floating around in a sea of uselessness. Businesses need to decide what they need to collect in terms of differentiation. This includes in-store interactions, cross channel interactions, mobile app clickstreams, media usage, etc.
The best CDP initiatives use identity strategy to guide collection. That definition varies between brands. So, the signal itself always has four components to guide you on what to collect:
- In-the-moment customer intent
- Expectations and experiences with the brand
- Use case for data to drive an outcome
- Value point of the collected data alongside other previously collected data
Afterward, the data will be collected by a combination of tech design and use experience. Certain questions should be answered in the CDP’s input/output framework. This includes a customer’s primary versus secondary identity definitions, what to collect real-time, and how it should be designed to respect user intent.
Overall, data info is often more valuable when combined with data otherwise sourced. Customer data platforms have the ability to stitch the cumulative data. In turn, this creates a single user profile or customer graph.
Why does this matter? It removes the guesswork in customer identification.
So, think about how many possible digital identities most users carry around with them over a lifetime. How many phone numbers, usernames, and other identifiers might they have had over a lifetime? That’s not to even mention the potential for bots and fraudulent accounts. Moreover, CDPs connect the abundance of footprints to create a single digital identifier for each customer.
After all, stitching is a technical process. Each case needs to be examined to apply an identity resolution method. Essentially, this can be based on either deterministic or probabilistic data. After that, data pipelines stitch the first, second, and third-party data to output a comprehensive view of the customer.
Actionable, Understandable, and Available Data
Using cases to monetize the data will base upon the data being actionable, understandable, and available. Opportunity is often revealed by applying machine learning models to each case. In addition, language is an important factor to naturally share at scale. This means IT and marketing need to understand each other for the data to be usable across the business. A data strategist or translator is often a key role in the line of communication being clear.
Furthermore, a properly designed customer data platform serves as a central intelligence command center for customer info. All users have a platform of a common language and access to what they need and how they need it. Finally, if scale customer-centricity is the goal, then a CDP is the tool to make it happen.