1:B2C brands don’t like to say who their customers are
The first question I asked during the roundtable was: “Who can tell me who your best customers are?” Then I looked around at the participants who were avoiding eye contact with me, perhaps embarrassed that they couldn’t identify their best customers.
How is this still possible? For decades, we’ve made huge investments of time and resources in data, marketing technology, expensive consultants and large teams. Yet none of the people working at leading brands felt comfortable saying they knew who their customers were.
Recommendation to address truth 1: Customer analytics is of utmost importance. Most of our investments have been in systems that enable data. The need for the required data is a given, but these systems – CDPs, messaging tools and trajectory orchestration – have focused on the decision, not on understanding. Of course, brands must continue to collect valuable data about their customers. However, it is time for brands to invest more in their customer analytics capabilities to better understand their customers by:
- Explainable predictive analytics
- Relevant data enrichments
- Constant reevaluation of the segmentation
- AI-enabled classification of content interactions and associated affinities
- And much more.
2: No one measures the ROI of their marketing technology investments.
Martech vendors like to promote ROI. Treasure Data shared a 802% ROI for working with their solution. HubSpot has a handy calculator with assumptions for an increase in key marketing and sales metrics. But having worked with hundreds of brands on customer data-related and martech projects, ROI measurement is the rare exception and far from the rule.
During the roundtable, not a single brand had a strong story for ROI on their customer data. A CMO said to me, “Craig, I appreciate your insistence on ROI, but that’s not how I see it. I’d rather evaluate this investment based on the opportunities it delivers.” They talked about a CDP that was recently rolled out across the organization.
Dig deeper: The marketing ROI problem has its roots in marketing culture
Recommendation to address truth 2: Don’t commit to perfect measurements of your customer data and marketing technologies. Ensure excellent MOps processes so you have the ability to measure their impact. All too often I encounter brands that can’t see their segments in their web analytics, can’t report on their customer universe, and have no idea how their segments are performing.
3: Your measurement is worthless if you don’t use customer data
Signal loss in the digital world is real, regardless of the reprieve Chrome has given to hopeful marketers whose heads were buried in the sand in the age of the impending cookie apocalypse. The multi-touch attribution industry has undergone a serious reckoning, which is why brands are turning to the more complex marketing mix modeling.
Modeling the marketing mix does not necessarily require customer data. Often, MMM solutions use aggregated spend and exposure data across markets to estimate media impact. It’s more reliable than MTA guesswork, but still insufficient for day-to-day optimization – at least without good customer data. The best brands look top-down and use MMM to guide spend allocation, but optimize spend based on its impact on their customer base. This requires good customer universe reporting, essentially knowing where all their customers and users are during 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 into the most important input data for travel reporting. It doesn’t have to be every detail, but it should include important details such as:
- Known / Unknown
- Registrant
- Customer / Subscriber / etc.
- Number of purchases
- LTV
- Recency
You need to understand which customers are converting through which campaigns. Good marketing activities in the recommendation for Truth 2 will help you a lot here.
4: Brands don’t know why their data isn’t ready yet
Composable CDP has been all the rage in recent years. Yet many brands do not yet think they are ready for composable. They often say, “Our data isn’t ready yet.” It has been my observation that brands’ data is much closer to ready than they realize. What’s really holding them back from adopting the simpler, composable pattern for CDPs is that their engineering teams struggle to make important data available. This is usually for one or more of these reasons:
- Not a priority for IT.
- IT is willing to provide data but doesn’t understand the requirements, and marketing struggles to present them clearly or concisely.
- Data is provided, but it is either too simplified to control costs or too crude, placing high demands on the data literacy of non-technical teams.
Recommendation to address truth 4: Move to a modern data stack, but do it quickly and nimbly. My colleague Craig Howard advocates the “Customer 101” approach over the prohibitively expensive and time-consuming Customer 360 approach.
5: Your team can’t use AI at scale until you have the right data
Everyone is talking about scaling AI, and many of us are already using it, whether in our personal lives, within parts of our tech stack, or even through company-wide AI policies. However, the reality is that most AI initiatives do not deliver results. That statistic from MIT – that 95% of AI projects fail – gets thrown around a lot, and for good reason. A large portion of these failures are due to messy data.
Dig Deeper: Before you scale AI, you need to restore your data foundation
I’ve seen this play out myself. We tried to set up a basic context agent to collect information from Fireflies, SharePoint, Google Drive, and Slack. The goal was simple: help new team members or consultants working with multiple clients get up to speed faster. But we hit a wall. Different naming conventions and no standard taxonomy for client or meeting names left the agent unable to make sense of it. It had the potential to save hours of work, but without clean, consistent data, even a simple AI tool could go haywire. It turns out that you can only scale AI if your data house is in order.
Recommendation to address Truth 5: Develop a use case-focused task force on how your organization can use AI. Take tactical actions for operational protocols that allow AI agents to make your teams’ lives easier and unlock incremental productivity.
These five truths may be uncomfortable, but they are also enlightening. They reveal the gaps we have normalized – and the opportunities we have not yet fully exploited. Martech doesn’t need more tools; better practices, clearer priorities and a renewed focus on understanding the customer are needed. Facing this reality is the first step in getting your technology, data, and teams to actually work together. Let’s stop pretending and start fixing.
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