Yesterday I got my new headphones delivered! The old ones were heavy and with multiple hours of Zoom calls I was looking for something comfortable for the ear. Having been remote working for more than a decade now, I prefer a better listening device to be the best listener.

The journey to the purchase was multi-day , multi-channel and multi-intended. I started at the nearby mall browsing through the available options. Followed by more research on Google, Amazon, Kogan , eBay and checking their reviews across blogs, Twitter and YouTube.

I believe it is the same story for every online consumer today. For digital leaders it is impossible to keep up with the zig zag customer behavior. They have realized that customer journey is coming close to a myth and it is important to make every touchpoint ‘shoppable‘.

Enter : Customer Data Platforms.

What is Customer Data Platform (CDP) ?

Think Customer Data Platform or CDP as a massive database that aggregates information about the customer from various platforms and across various touchpoints. After aggregation, the database & profiles are accessible to other systems for marketing campaigns, customer service & other customer experience initiatives.

Sounds similar to a CRM or DMP or Personalization Engine? I’ll cover that on a separate blogpost.

How to Implement Customer Data Platform (CDP)?

Yes, implementing a CDP irrespective of product is another digital transformation initiative and yes, it has failures. According to McKinsey, “just 16 percent of executives say their company’s digital transformation efforts are succeeding”. Additionally, Gartner reports “Seventy-two percent of strategists say their company’s digital efforts are missing revenue expectations”.

To prevent failure I recommend to achieve the following enterprise values across the implementation timeline. A value approach helps define outcomes for each phase and the next phase can build upon the progress made, instead of starting from scratch.

Value 1 : Compliant

Outcome : User Privacy Protocols

Consumer trust is earned and maintained based on how the enterprise uses their data. No one wants to meet, interact or invest with brands that appear creepy. Privacy & compliance teams should lead discussions on how user data would be used. Outcomes of these discussions would be a set of protocols on how each customer data source is treated. These outcomes have to implemented in the CDP to ensure that customer data follows the same directions.

Outcome : Data Access Security

Customer data leaks, open S3 locations – there’s no stopping to bad news about customer data and you don’t want your brand to be there. Instead of implementing CDP as a silo, it is best to bring your data team to the same table. To prevent any data loss/negative impacts ensure that your CDP ingestion practices are inline with internal data practices. In case of conflict between required protocols and capabilities available, there should be a middle ground solution that meets the requirements of both teams. Thus de-risking the brand.

Value 2 : Use Cases

Defining use cases help achieve actual realizations and outcomes that help drive return over CDP investment. Based on your current role, you would definitely have a wish list of capabilities that you would want to achieve from your marketing technology investments. After defining your use cases you would require time & investment to achieve these use cases.

To help find the most optimal outcomes, I have grouped use cases as below :

Value 3 : Enterprise-Wide Scalability

If you are on this journey, at this time you have defined your use cases that should describe the data sources or data that you need to achieve your use case. But your CDP implementation investment should be one-time with later increments that enhance value and/or achieve additional use cases.

Here’s a template to group your data sources based on your use case need and feel free to add your data sources

Value 4: Augmented Data

At this time you have acquired and integrated all of the data sources participating in your use case with your CDP. Now lets enhance the data to overachieve your use cases with Data Science and Machine Learning models. Each every enterprise have their own inhouse model to define and design the workflow to enhance data. It can be generalized into the below workflow which are continuous steps of profile unification, data mining & segmentation.

Value 5 : Omnichannel Activation

Here’s the last and final step in our CDP implementation journey. Customer data is now augmented and segmented based on the known and unknown attributes. Based on your CDP’s capability they would require to be mapped to all of the endpoints customers interact with the brand. Again they can be real time and/or batch based on how the endpoint is capable to ingest this data