A good use case provides specificity that is crucial when choosing among customer data platforms.
Late last year, I was preparing a customer data platform (CDP) onboarding workshop for a large direct-to-consumer brand — the next phase of a multi-brand rollout for the parent company — when we started talking about use cases. We asked for input from that brand’s own marketing, ecommerce, data science and web teams so we could tailor the workshop for their business goals. Reviewing a draft agenda from the project team, a brand marketer with the amazing title of Director, Consumer Lifecycle Management responded with the following:
“I think this is still too theoretical. I would go very basic with this — you could potentially have people fill out a survey monkey and ask for up to 3 of their highest priority use cases. However, you have to explain what a 'use case' is. In my conversations over the past weeks, this term has been confusing to people.”
Turns out that this uncertainty about what exactly marketers (and their technology partners) mean when they talk about use cases is quite common. If you were to ask a hundred marketers to craft use cases about customer acquisition, I’d wager you’d get an enormous range of responses (and this is a safe bet, based on the daily conversations I have with marketers).
When it comes to choosing a customer data platform in particular, the lack of shared understanding can cause big problems. It leads to a lack of clarity about what is important: to the team and in the solution(s) they use to address those priorities. Often, it results in a one-dimensional assessment of what it will actually take to get results, pinning it all on choosing a technology and leaving out the people (resources, skillsets, bandwidth) and the processes (governance, workflows, campaign calendars) vital for success.
What Is a Use Case?
If you search the phrase, “What is a use case,” and check out the Wikipedia page, you’ll immediately see part of the problem: “In software and systems engineering, a use case is a list of actions or event steps typically defining the interactions between a role (known in the Unified Modeling Language (UML) as an actor) and a system to achieve a goal.” Yikes. Talk about a niche, vague and generally unhelpful term if you’re a marketer.
To bring some utility and common ground to this discussion, I propose the following definition for a marketing use case:
A use case describes the current state, target outcome, supporting activities and relative complexity required to successfully reach your [marketing or] business goal.
This definition includes the following:
A point of origin for the effort to clarify what pain point this work will relieve or untapped opportunity it will create.
An ultimate objective that matters to the business, which can be tactical (“improve click-through rates for loyalty program emails”) and/or operational (“decrease time between an in-store transaction and a follow up email”).
An explicit accounting of what it will take to achieve that goal, both in the solution itself and to adequately support it with time and resources.
A complexity curve that maps all marketing activities. What is wildly difficult to one organization will be standard operating procedure to another. Accommodating your level of readiness in the definition helps to set appropriate expectations.
Anchoring the use case in a broader end-state conveys both a level of importance for the work and a bigger picture context for marketing and/or the business overall.
In this format, a use case becomes an actual map: where we’re starting, where we are going, how we’re going to get there and how getting to the result impacts the business.
Why Are Use Cases So Important in Selecting and Implementing a CDP?
A good use case provides specificity that is crucial in choosing among customer data platforms. CDPs support an almost unlimited number of use cases because every company using a CDP has an entirely bespoke, first-party data set to support their unique business contexts and priorities. For example, we frequently see vague goals like “improved web personalization” as use cases. Extrapolating that into our proposed use case structure would yield very different use cases:
“Our website only has personalization based on onsite behavior. We want to use their customer status from our CRM to create more relevant experiences and increase engagement scores. In turn, we want to use onsite behaviors to understand customer interests that the sales team can then use to tailor outreach to prospects at large enterprise software firms.”
Or:
“Our website personalization is very extensive, but since our web personalization solution is separate from our email system, we can’t use the same segmentation definitions in both systems. This lack of consistency is inefficient for our marketers to manage and results in customers seeing conflicting messages across channels. We want to have a single system to define segments in order to eliminate the inefficiency and inconsistency and save our large marketing team time while also providing an omnichannel experience to our customers.”
Or:
“Our website personalization omits any personalization based on predictive customer scoring, which means the output of our data science team’s scoring and modeling for propensity to buy or churn can’t be used on the web channel in a timely way to increase conversions or retention from our marketing programs. We want to apply that data in the web channel for our 100m monthly active uniques.
Increasing use case precision this way creates significantly better technical requirements for selecting a CDP, and ultimately a more efficient, pragmatic, and successful implementation.
What Variables Affect the Use Cases?
One can come up with a lot of fairly generic marketing use cases, but it’s helpful to also consider the following:
Industry or business model: How do your business needs refine your use cases? For example, when declaring a goal of increasing average order value, noting that the business is ecommerce-only vs. a brick-and-click retailer adds meaningful detail and refinement to your use cases.
Resource availability: What internal and/or external capacity can you apply to the project? Your answer determines a lot. For example, if you lack data science resources, then you’ll likely be limited to the out-of-the-box models and UI-based configuration your CDP-of-choice provides. That’s fine — but something helpful to know early.
Timeline: Do you need results quickly or do you have some runway? Some companies need to prove value in the first three months because it’s tied to a planned marketing initiative. Others take a longer-term, more strategic approach and can show results over a few quarters. Either is OK — but if your CMO wants to see results in weeks, you shouldn’t develop use cases that require an extensive data foundation as a prerequisite. It’s all about striking the right balance.
Any brand can be successful with a customer data platform. The most successful ones not only carefully plan and prioritize their use cases to guide vendor selection and implementation, but continue to review those use cases on an ongoing basis. Follow their lead and you’ll be well on your way to maximizing the value of a customer data platform.
This article was originally published in CMSWire on May 4, 2020.
