To Build or not to Build a Customer Data Platform?

Customer expectations around the kind of experience they expect from brands, digitally, has significantly transformed in recent years. This transformation has resulted in a very crowded marketing technology landscape — with platforms focusing on narrow niche capabilities within the larger customer experience and lifecycle management. Every platform within the marketing technology landscape wants to be the gatekeeper of the invaluable customer profile data along with the legacy systems and repositories that organizations have been using for a long time. This need for customer data in order to enable personalized digital experiences for customers by every platform has made most brands grapple with the question around should they build this rich customer data repository or should they invest in a platform that can solve their needs to a large extent out of the box?

Data is at the core of any personalized experience.

In order to be able to present personalized experiences to your customers for a lasting impression and potential loyalty, you not only need a lot of data to generate key valuable insights on your customer’s interests, but you also need that data to be high quality. The insights you seek about your customers: buying patterns, propensity to buy, etc… are best served with data that is rich with insights and purposeful for your needs. As the old adage goes “garbage in — garbage out”.

Organizations need to answer the following questions to assess their data readiness to support these personalized experiences for their customers:

1. What data do we already collect on the customers and prospects? Is it just transactional, behavioral, 3rd party, firmographic, ethnographic, etc...
2. Is this quality data: how much history do we have and is it the right data?
3. Is this data in a single data repository?
4. If there are multiple data repositories, is there a way to link the data in these repositories to a customer or prospect?
5. Do we have well defined KPIs and user journeys to map the lifecycle of the customer or prospect on our owned channels? Are these consistent across our data repositories?
6. Do we have an enterprise customer taxonomy “unified profile” to define a customer and their various attributes needed to customize their experience with the brand?

As organizations gather their responses to the above questions, they would quickly arrive at the understanding of how well defined their data foundation is to support their future needs of personalized experiences for their customers, prospects, and to differentiate from their competitors. This understanding only covers the 1st Party data that is collected by the organizations on their owned channels and limited to only their own customers and prospects. The overall understanding of what their customers are doing on other channels (i.e. on competitor channels, searches, complementary brands, etc…) is needed to complete the full profile of the customers and prospects.

In order to get this full picture on the customer, organizations need to create a marketing technology solution architecture that consists of 2 key foundational components (as shown in figure 1):

1. A Holistic Customer Data Profile Repository, a.k.a. Customer Data Platform (CDP)
2. A Data Management Platform (DMP) to complement the 1st party data with 2nd and 3rd party data and serve as a platform to re-target customers and prospects to create personalized experiences that keep bringing them back to the organizations owned channels in a meaningful and contextual way.

Each of the above components is supported by a crowded field of 3rd party platforms. Which brings us back to the key question that we set out to answer:

Should organizations build or buy the above components of their marketing solution to support personalized experiences for their customers and prospects?

The answer is not a simple one and needs to be arrived at by assessing the following aspects of the organization’s makeup and readiness:

1. Assess how the organization has approached past technology decisions around Build vs Buy. Are they more geared towards building or buying?
2. Do you have the necessary talent and scale to support a complex build of this customer data platform?

3. How mature is the industry vertical that your organization is in (refer to the maturity model in figure 2 for reference)? This is a key question to answer, in order to understand the agility with which the organization needs to respond to customer needs and their changing expectations around digital experience with the brand.

4. Are you innovating on your marketing approach faster than the 3rd party products can keep up on their roadmaps?

5. Is your organization operationally ready to take on the upkeep of a custom-built solution?

6. Is personalization key to differentiating your organization from your competitors?

7. Are there industry regulations around gathering customer data which require special handling around collection and activation of this data? Will this add additional complexity to the overall solution?

8. Following the 80–20 rule, can 3rd Party systems provide 80% of the organizational needs with relative ease, requiring 20% custom effort?

9. How much personalized marketing content does the marketing team create? This is equally, if not more important, than understanding your customer behavior as content is the way you can create engaging experiences based on insights generated from the customer data.

10. What integrations are needed for other marketing solutions like:

• Content management
• Marketing automation
• Lead management
• CRM
• Brand website
• etc...

Content creation and authoring is equally, if not more important, than customer data as the activation of insights generated from customer data can only be done through targeted and personalized delivery of engaging content.

There’s a lot to consider for any organization looking at their own CDP. At Slalom, we approach this assessment with our marketing technology experts working in conjunction with our customer strategy and data analytics teams to help our clients in answering the above questions and recommend a path forward on the build vs. buy front.