An old proverb states that it’s “Better to be hurt by the reality than comforted with a lie.” A roundtable of martech experts I recently hosted proved the reality of that, validating many things I had long feared to admit to myself. Here they’re: Five truths we must address to move forward more effectively.
1:B2C brands aren’t comfortable saying who their customers are
The first query I asked on the roundtable was, “Who can tell me who your best customers are?” I then looked around at participants who avoided eye contact with me, perhaps embarrassed that they couldn’t discover their best customers.
How is that this still possible? We have spent a long time making enormous investments of time and resources into data, marketing technology, high-priced consultants and large teams. Yet, not considered one of the people working at leading brands felt comfortable saying they knew who their customers were.
Recommendation to address Truth 1: Customer analytics is paramount. Most of our investments have been in systems that activate data. The need for the requisite data is a given, but these systems – CDPs, messaging tools and journey orchestration – have focused on the choice, not the understanding. Of course, brands must proceed to collect precious data about their customers. However, it’s time for brands to invest more of their customer analytics capabilities to higher understand their customers through:
- Explainable predictive analytics
- Relevant data enrichments
- Ongoing segmentation re-evaluation
- AI-enabled classification of content interactions and corresponding affinities
- And far more.
2: No one measures ROI on their marketing technology investments.
Martech vendors love to promote ROI. Treasure Data shared an 802% ROI for working with their solution. HubSpot has a handy calculator with assumptions for lift in key marketing and sales metrics. Yet, having worked with tons of of brands on customer data-related and martech projects, ROI measurement is the rare exception and removed from the rule.
At the roundtable, not a single brand had a powerful story for ROI on their customer data. One CMO said to me, “Craig, I appreciate that you’re at all times pushing for ROI, but I don’t see it that way. I prefer to evaluate this investment based on the capabilities it unlocks.” They were talking about a CDP recently rolled out across the complete organization.
Dig deeper: The marketing ROI problem has its roots in marketing culture
Recommendation to address Truth 2: Don’t commit to perfect measurement in your customer data and marketing technologies. Commit to outstanding MOps processes in order that you have options to measure their impact. All too often, I encounter brands that may’t see their segments of their web analytics, can’t report on their customer universe and do not know how their segments are performing.
3: Your measurement sucks if you’re not using customer data
Signal loss in digital is real, whatever the reprieve Chrome granted hopeful marketers whose heads were buried within the sand in the course of the time of impending cookie apocalypse. The multi-touch attribution industry experienced a serious reckoning, which is why brands are turning to the more complex marketing mix modeling.
Marketing mix modeling doesn’t necessarily require customer data. Oftentimes, MMM solutions use aggregated spend and exposure data across various markets to approximate media impact. It’s more reliable than MTA guesswork, but it surely’s still insufficient for day-to-day optimization — a minimum of without great customer data. The best-in-class brands are looking top-down, using MMM to guide spend allocations, but optimizing spend based on its impact on their customer file. This requires great customer universe reporting – principally knowing where all their customers and users are inside their journey with the brand.
Dig deeper: Why MMM makes marketers nervous — and why you should use it
Recommendation to address Truth 3: Simplify your customer data to the important thing inputs for journey reporting. It doesn’t have to be every detail, but it surely must include key details equivalent to:
- Known / Unknown
- Registrant
- Customer / Subscriber / etc.
- Number of Purchases
- LTV
- Recency
You must understand which customers are converting from which campaigns. Good marketing operations within the advice for Truth 2 will help you tremendously here.
4: Brands don’t know why their data isn’t ready
Composable CDP has been all the fashion for the previous couple of years. Even so, many brands don’t think they’re ready for composable. They often say, “Our data isn’t ready.” It has been my remark that brands’ data is far closer to ready than they realize. What really blocks them from adopting the simpler, composable pattern for CDPs is that their tech teams struggle to make key data available. This will likely be for a number of of those reasons:
- Not a priority to IT.
- IT is willing to provide data, but doesn’t understand the necessities, and marketing has trouble stating them clearly or concisely.
- Data is provided, but it surely is either overly simplified to control costs or is just too raw, placing high demands on data literacy amongst non-technical teams.
Recommendation to address Truth 4: Move to a contemporary data stack, but do it quickly and with agility. My colleague Craig Howard advocates for the “Customer 101″ approach over the prohibitively expensive and time-consuming Customer 360.
5: Your team can’t use AI at scale until you get your data right
Everyone’s talking about scaling AI, and lots of us are already using it—whether in our personal lives, inside parts of our tech stack, and even through company-wide AI policies. However, the fact is that almost all AI initiatives fail to deliver. That stat from MIT—that 95% of AI projects fail—gets thrown around quite a bit, and for good reason. A giant chunk of those failures is due to messy data.
Dig deeper: Before scaling AI, fix your data foundations
I’ve seen this play out myself. We attempted to arrange a basic context agent to gather information from Fireflies, SharePoint, Google Drive and Slack. The goal was easy—help latest team members or consultants juggling multiple clients stand up to speed faster. But we hit a wall. Different naming conventions and no standard taxonomy for client or meeting names meant the agent couldn’t make sense of all of it. It had the potential to save hours of labor, but without clean, consistent data, even an easy AI tool got tripped up. It seems that you can’t scale AI until your data home is so as.
Recommendation to address Truth 5: Develop a use case-focused task force for a way your organization can use AI. Follow through with tactical actions for operational protocols that can enable AI agents to make your teams’ lives easier and unlock incremental productivity.
These five truths could also be uncomfortable, but they’re also clarifying. They reveal the gaps we’ve normalized—and the opportunities we’ve yet to fully seize. Martech doesn’t need more tools; it needs higher practices, clearer priorities and a renewed give attention to understanding the shopper. Facing these realities head-on is step one toward making your technology, data and teams actually work together. Let’s stop pretending and start fixing.
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