Why CDPs Fail: A Tale of Three Unfulfilled Expectations
All insight and no execution is cause for frustration, errors, and money down the drain. Learn why Customer Data Platforms are often hard-pressed to keep their promises
Some weeks ago, we had a meeting with a respectable Private Equity investor, discussing the state of the MarTech industry. He was doubtful and perplexed as to why—for all the buzz about Customer Data Platforms and other predictive analytics and customer segmentation tools—he had not quite found a company who proudly acknowledged they found the perfect solution.
A Customer Data Platform or CDP (AKA Winner of the “Buzzword of The Year 2018” award) is defined as “a marketing system that unifies a company’s customer data from marketing and other channels to enable customer modeling and optimize the timing and targeting of messages” (Source). A CDP makes it easier for marketers to gain visibility into their customers’ behaviors, differentiators and needs, and thereby build and execute targeted, data-driven strategies at scale. The end result is a ‘Single Source of Truth’ for direct access and use that allows personalization and the speedy adjustment of marketing messaging across all channels.
CDPs are, without a doubt, a powerful and necessary tool for any marketing team wanting to evolve their strategy to a customer-centric approach, execute campaigns that tell a single and harmonious story, and increase metrics as CLTV, conversion, repeat purchase, and loyalty.
That said, it is true that the road to realizing the above results is often one full of hurdles which bring the marketer (and, clearly, our P.E. friend) more pain than joy.
Here are some thoughts on the three main issues that prevent CDPs from bringing their promised benefits and returns:
The lack of true personalization
Beyond creating a unified 360◦ view of the customer, one of the goals of a CDP is to provide the relevant segmentation, surfacing distinct personas companies can later address with separate, personalized communications. Many CDP segmentations are pre-templated and focus on RFM analysis and purchase categories, which are often high predictors of consumer’s future value and behaviors.
The reality is that there are several other segmentation layers that can bring actionable and valuable insights. Think about understanding behavior based on purchase platform, percentage of returns, product ratings, customer service calls, and discount affinity. Each company has a unique data set and the most valuable insights come from those unique attributes (a quiz on the site, a dual subscription / ecommerce model, a free trial period etc.). At Optimove, we have seen that the most ROI-generating segmentation layers are ones that are unique to each brand. Not to mention that personalizing segmentation beyond traditional attributes is costly, resource consuming, and often not scalable for a technology company. It is rarely done, and it is a failure point.
Also, consider that not every business model lends itself to successful RFM analysis. For luxury, or generally expensive and high-consideration products, customers may only purchase once a year, sometimes less. For these companies, an off-the-shelf CDP segmentation will fail due to its inability to take into account engagement and brand awareness (in addition to purchase volume or frequency) as metrics for powerful segmentation.
The lack of seamlessness into execution
The goal of a CDP is twofold: first, aggregate all customer data and surface valuable and actionable insights, and second, accelerate the marketer’s access to data and marketing workflow.
Having insights and predictions about your customers on an evolving basis is wonderful. However, let us ask this: how does one bring out the value of customer insights if, in spite of all of the knowledge acquired, one still has to build segments, export lists, import them into several execution channels, manually test, manually download the lists for reporting, and do this all over again, every day, always?
Most CDPs traditionally stop at providing insights, and one of the biggest frustrations for a smart and busy marketer is to be left with the time-consuming task of juggling between their CDP and the three to ten other execution and reporting systems that bring their strategy to life. Marketing personalization is about moving at the speed of your customers, which the above friction points effectively prevent from happening.
It is exceedingly difficult to get the full value out of a CDP unless it is seamlessly connected with execution channels or, better, a campaign orchestration system that allows the marketer to manage an infinite number of unique customer journeys, at speed and at scale.
The inability to measure ROI and attribution
Lastly, and perhaps the crown jewel of all CDP failures, is the very difficult task of reporting true ROI. A CDP represents a budget anywhere between $40,000 to over $1M a year, and any CMO or executive decision maker needs to concretely understand the dollar value generated by this tool in return.
A CDP generates insights, later transformed by marketers into personalized campaigns across email, social channels, push notifications, banners, SMS, direct mail, clienteling, call centers and more. Campaign effectiveness across channels is traditionally measured through each channel’s relevant metrics: open rates, click rate, click-through, impressions, ROAS and so on.
These metrics are valuable to an extent as they tell the story of the customer’s responsiveness to a marketing cue. What is missing, however, is the ability to measure, across channels, (i.e with no overlap or competition) the total incremental uplift generated by all marketing campaigns across KPIs such as net revenue, order volumes, repeat rate, AOV, CLV etc.
That incremental uplift (or in other words, the value added by CDP-driven campaigns), can be measured through a rigorous and intelligent use of Test vs. Control analysis which can unify reporting into one metric across campaigns and channels There are intricacies to the use of control groups which are beyond the scope of this piece, however, we believe and have proven that this methodology provides marketers with an accurate picture of the ROI of their Customer Data Platform.
Our discussion partner was right to acknowledge the mixed reception of Customer Data Platforms amongst marketers. There are many claims made towards “send the right message through the right channel at the right time.” which never come to fruition. This is mostly because the proliferation of CDP tools exposed a gap in the market for a solution that enables the end-to-end, seamless translation of customized insights into concrete, measurable value, generated in marketing campaigns. This does not in any way diminish the need for a CDP, which is the starting point to more emotionally intelligent marketing strategies, but certainly increases the need for transparency and accuracy, everywhere from how a CDP is built, to how its returns are measured. This is a gap that Optimove’s vision and technology have come to solve.