Use Case-Based CDP Architectural Design, Explained

CDP 101|8 Minute Read

Use Case-Based CDP Architectural Design, Explained

After securing a customer data platform (CDP), organizations must turn their attention to the types of customer data marketing, commerce, analytics, digital product, and other areas of the business need to execute their respective CDP use cases.

Use cases dictate which data sources and how much data to bring into the platform as well as how that data will be structured. Moreover, they help companies determine how much storage they’ll need to handle the customer data they plan to import.

“So many data projects end up in the graveyard of business priorities because no one ever formally established what it was going to be used for,” BlueConic COO Cory Munchbach recently wrote for Forbes. “Developing clearly defined use cases can help uncover what data you actually need in order to accomplish your goals.”

There’s no one right way to design your use case-based CDP architectural design. But there is a proven blueprint you should follow to best manage and utilize your first-party data.

cdp use cases

Why you must be intentional with first-party data imports in your CDP

Pure-play customer data platforms are built to handle the volume, variety, and velocity of companies’ distinct first-party data sets. Take BlueConic, for example:

  • Our underlying profile database architecture helps businesses scale up by continually adding new profile attributes over time from any channel or source without having to unwind months (or years) of work due to rigid data schemas.
  • We doesn’t saddle our customers with ballooning data storage expenses (like some of the marketing clouds do), leaving them no choice but to arbitrarily delete key data points simply to avoid such costs.
  • Our customers have complete control over the quality and quantity of data stored in their tenants and can easily delete and add data as needed, thus eliminating the potential for any surprise data-overage charges.
  • With BlueConic, the customer data keys are (mostly) taken away from IT and handed over to departments charged with interacting and/or better understanding customers — a move that can lead to far faster time to value for companies.

While our customers can certainly unify all first-party data they collect for their prospects and customers into BlueConic, that’s not always the ideal avenue to take.

Doing so is how many organizations (more specifically, their day-to-day business technology users) often end up with a difficult-to-navigate customer data ‘wasteland’:

  • Innumerable data points for prospects and customers that makes it tough for growth teams to segment and analyze customers and deliver timely and relevant messaging and experiences to individuals across lifecycle stages in an intelligent manner

The end result of such a first-party data ‘dump’ in their CDP is new and/or ongoing inefficiencies for technology users and, in turn, potentially stagnant (or worsening) ROI from their targeted customer engagement efforts.

The key to successful customer data platform utilization is the ability for growth-focused teams to quickly access data and then apply it whenever and wherever they need (i.e., for analysis, segmentation, modeling, and/or activation).

If they’re provided with every data point for customers, they’ll have to spend more time trying to determine which ones are most critical to their key programs and engagement efforts — time they could (and should) instead allocate to improving those crucial activities and initiatives.

A CDP is meant to simplify and streamline growth teams’ customer data utilization and help them (essentially) take over data ownership from IT and/or external agencies from whom they typically have to wait hours, days, or weeks to get custom lists.

However, those teams can’t utilize their data easily or efficiently if it’s not synced into their customer data platform thoughtfully and intentionally. In other words, they need a well-planned data-import and -clean-up strategy that factors in their use cases.

Without such a plan, they will have to play ‘catch-up’ on their customer data quality constantly. That is, they’ll have to regularly audit their data for usefulness and delete data that doesn’t help them achieve the desired return on investment from their CDP use cases.

customer data platform

CDP architectural design 101: Bringing in the MVD required for your use cases

Enhance cross-channel personalization efforts. Create new digital products and experiences. Better manage customers’ consent and federate it across systems and platforms.

Whatever CDP use case(s) related to your growth initiatives you decide to move forward with, it’s best to import and leverage solely the minimum viable data (MVD) you deem necessary to execute those use cases — not all customer data types.

Now, there’s often a chicken-or-egg problem when it comes to defining one’s MVD:

  • A given organization’s use case(s) will determine which MVD they need to import.
  • At the same time, businesses just beginning to build their first-party data sets or unify that data and make it accessible to growth-focused teams for the first time don’t necessarily know which use cases they can execute with that data.

Therefore, BlueConic always recommends our customers identify their ideal customer data platform use cases before importing any data into BlueConic. And the reasons are simple:

  • 1) They can be more deliberate and calculated with the handling of their first-party data.
  • 2) In turn, they can ensure they don’t overwhelm or slow down business users who may find it challenging (or impossible) to understand which customer data is useful for their particular use case and which isn’t.

Consider how retailers that use BlueConic decide on the data they need imported:

  • There are several data types that benefit retailers. Arguably the most valuable type of data for these companies, though, is online and offline transactional data.
  • That’s because this data reveals those organizations’ most valuable customers (i.e., those who purchase high-priced items or buy often) and, therefore, deserve the bulk of growth-focused teams’ attention to maintain their loyalty and entice further purchases.
  • The more purchase data retailers import into BlueConic, the more accurate their models (e.g., our out-of-the-box CLV and RFM models) are, and the more useful customer scores they generate to inform their customer engagement strategies.
  • Teams like marketing and CX can then use that data and those scores to improve the performance of evergreen and/or time-boxed programs (e.g., one-to-one holiday/event promos, premium digital experiences for loyalty program members).

Regardless of their industry, business model, target audience, or strategic ambitions, BlueConic customers may certainly need to bring more data into our CDP as time goes by to better understand and engage customers (and perhaps test entirely new use cases).

But the goal with MVD is to bring in a representative set of data for growth teams to work with immediately following CDP implementation so they can shorten time to value and adapt how they work (i.e., more streamlined and efficient by using the ‘best’ data).

Utilizing MVD isn’t necessarily about doing more with less. Rather, it’s about developing less labor- and time-intensive processes (i.e., so business users can leverage the most critical, pertinent, and timely data that can help them achieve better value-based outcomes.

TL;DR: Select your CDP use cases first, and you can capably identify the MVD necessary to employ them, then figure out if bringing in additional data to your CDP to optimize those use cases (or try new ones) is needed later on.

cdp customer data platform

Where to begin with building your use case-based CDP architectural design

All this leads to the million-dollar question:

  • Which minimum viable data does your particular company need imported into your CDP in order to realize the best business outcomes (and avoid inundating business users with too much/potentially inadequate data)?

The answer lies in each use case, which dictates the data your teams will need.

Want to develop more direct-to-consumer relationships so you can personalize messaging and experiences to engaged individuals who haven’t bought from you before?

Data that reveals their product preferences, recent browsing behavior, and overall interests can help you deliver bespoke messaging to them in real time across channels and touchpoints and foster fruitful, long-term relationships with them.

Looking to better cross-sell and/or upsell to repeat customers and/or subscribers across channels and touchpoints (i.e. accelerate your omni-channel engagement strategy?

Create segments that factor in their transactional data, which shows what products they’ve bought or currently subscribe to, and any other data types (behavioral, interest) that indicates other offerings they’ve researched (e.g., visited the product page for) so you can promote those items to them at the most relevant customer lifecycle stage.

A customer data platform roadmap and the use cases associated with a given roadmap aren’t meant to be set in stone. Instead, they should both be iterative and ongoing.

Continuous optimization is required to enhance the ROI from each implemented use case.

That means you must regularly analyze and, as needed, adjust the data stored in your platform (i.e., your use case-based CDP architectural design) to ensure you can capably execute your use cases, improve key programs’ performance, and accelerate growth.

Watch our “10 Customer Data Platform Use Cases” webinar today to discover how companies use BlueConic to carry out their growth initiatives and realize their desired ROI.

customer data platform use cases

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