Just like a great CDP, you should listen, think, and speak.

One of the challenges of acquiring a CDP is that it can be unclear who should own the process. The muddle typically happens between Marketing, IT, and Product.

Step 1: Identify the primary stakeholders and assemble a core buying group.

We’ve seen different stakeholder groups champion different deals in our own sales processes. If you’re reading this and recognize some urgent problem areas within your function that a CDP could address, then take up the mantle and mobilize others to join you in your investigation.

Though marketers are the primary end-users, a CDP addresses problems — and can even kickstart a digital transformation — across your org.

Though marketers are the primary end-users, a CDP addresses problems — and can even kickstart a digital transformation — across your org.

The first step is to identify the stakeholders who will form the core of your buying group. This group will include the would-be platform owner — in this case, most often Marketing — and the functions that would be most closely affected, generally IT and/or Product.

Other stakeholders might include:

  1. Data science
  2. Analytics
  3. Legal
  4. Security
  5. Procurement

While we can’t say who doesn’t belong in your buying group, we advise assembling a diverse buying group that comprises the most essential stakeholders. Every voice adds complexity and dysfunction to the decision-making process, which increases the likelihood of a delayed, suboptimal, or abandoned decision.

This doesn’t mean you don’t take others’ concerns and needs into consideration; you just need to contextualize their concerns as existing somewhere further up and lower down on the priority spectrum depending on how close to the day-to-day they will be.

Step 2: Have each stakeholder surface problem areas related to data usability.

Here’s an example of what such a list might look like:

Typical problem statements for marketers

  1. We struggle to drive incremental revenue through better acquisition, conversion, expansion/growth, loyalty, retention, and reactivation
  2. We struggle to access and leverage a complete & actionable 360°-view of the customer for personalization & automation.
  3. We are too often bottlenecked by silos across channels & functions, and these silos prevent us from driving customer outcomes holistically.
  4. We currently cannot scalably launch new campaigns, run experiments, & optimize the customer experience iteratively without relying on a separate engineering/technology team.
  5. We struggle to maximize the value we’re getting out of our existing tech ecosystem.

Typical problem statements for product

  1. We are too often bottlenecked by functional silos, and these silos prevent us from driving customer outcomes holistically.
  2. We struggle to take advantage of the full value of existing investments (including time-to-value)

Typical problem statements for technologists

  1. We struggle with focusing on our department’s initiatives because of other functions’ reliance on engineering, IT, and data for ad hoc or ongoing support
  2. We must build the right tech ecosystem for the future with the ability to handle massive scale & deliver critical capabilities (i.e., single customer view, predictive analytics & data science, etc.)
  3. Focus technical FTEs on strategic rather than tactical projects (e.g., data centralization, data science, etc.)
  4. If we are to prioritize high-leverage internal projects, we must reduce integration pain and level of effort.

Step 3: Look for commonalities of objectives and pain points to build a better argument

Use this as an opportunity to connect cross-functional stakeholders to list out their main objectives.

By writing out and comparing priority lists, you can look for commonalities and potential clashes. From there, you can look at likely pain points in terms of how data fits into the equation.

The lists should next be combined and stack-ranked according to business impact. It’s likely Marketing’s use cases may appear more urgent, if only because their use cases can be tied more directly to revenue. But problem statements from IT’s list likely describe downstream causes to the upstream marketing and product frustrations.

As you review each stakeholder’s list, you may see ways they intersect, like how IT is annoyed with challenges around data activation and integration, which are right downstream from product’s data latency issues.

Step 4. Look at your tech stack and assess where a CDP would fit. What are the key integration areas?

Answer the following questions:

  1. What type of workflow would you like a CDP to support?
  2. What does it need to integrate well with?
  3. What types of data does it need to be able to collect?
  4. What are the requirements around its ability to collect that data?
  5. How will it address each function’s problem areas?

Step 5. Build a structured set of questions to be prioritized to narrow your CDP options

The most common mistake we notice in the buying process is ignoring the key differences between CDP types. Having listed out your criteria by use case, you should be able to determine the best kind of CDP for your needs. Not doing so is setting yourself up for a frustrating apples-to-oranges buying process.

If you went to a car dealership, you wouldn’t ask to test drive “transportation.” You would go in with an idea of how you would use a vehicle and make a decision based on your criteria. The mother of three starts looking at very different vehicles than the grizzly construction foreman.

In other words, the “exercise” of listing priorities will save time, so you don’t find yourself wondering how much lumber you can fit in this seafoam-green Fiat.

Step 6. Run an evaluation of your chosen subcategory

Make a list of vendors from the subcategory that most fit your use cases and run a buying process. Bring ranked problem areas from across functions to CDP vendors to start your assessment. Interview vendors to get a better understanding of the landscape.

Keep in mind that the right CDP should accelerate your tech-stack optimization. It may feel like adding yet another tool, but the point of the hunt is finding the CDP for the long-term play of increasing solution output and/or reducing the total number of solutions in place.

Chapter 11: Additional Resources

Case Studies

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