The fatal flaw killing your customer data initiatives

Marketers have spent the last decade chasing better customer data. However, for all the investment, most still struggle to get the insights they need to drive real results. Why? Because we’ve been framing the problem all wrong.

Why the customer data conversation keeps missing the mark

Talk of customer data has been the rage in marketing technology circles for the last decade, and we missed the plot. Think of the half-finished projects, churned CDPs and stalled initiatives. The last decade saw billions invested, countless startups started and monumental technological advancements. Yet, when I speak with prospects, I still commonly hear things from marketers like

• “We don’t have basic data in our systems for segmentation.”
• “We can’t do the targeting we want.”

For a long time, I’ve blamed marketers for not being more technical, ceding the essential responsibilities of knowing the customer to cost-conscious and risk-averse IT and enterprise data teams. The reasons are more complex, but the point stands. More recently, I have considered framing customer data as part of the problem.

When discussing data, the conversation drifts to governance, quality, cost and security. We lose sight of what marketers actually need: insights and analysis. The result? A decade of poorly applied investments, and marketers stuck without meaningful access to customer insights.

If we want real business outcomes — growth, loyalty, ROI — it’s time to stop talking about customer data and start talking about customer analytics.

The customer analytics era

Marketers and marketing technologists — it’s time to move away from “customer data” and embrace customer analytics.

Customer analytics shifts the conversation away from governance, data quality, cost and security — important topics that belong primarily to IT. While IT is essential to any successful CDP or data activation effort, overreliance on IT leads to slow, burdensome initiatives with underwhelming results.

Reframing the initiative as customer analytics brings the focus back to segmentation, insights, AI, predictive models and, most importantly, business outcomes.

You don’t need more data — you need insight. And you need to know the basics:

• Who are your customers?
• When did they last purchase?
• What is the customer’s LTV?
• What products or preferences do they have?
• What is the customer likely to do or buy next?
• Do you have consent to message the customer?

You don’t need massive, costly datasets. You need customer 101, predictive analytics and a solid testing and measurement plan. This puts marketers and analytics teams in the driver’s seat, shifting from slow adoption to rapid testing and learning. As an industry, we need to discuss customer analytics, not customer data.

Dig deeper: How to augment market research and glean customer insights with AI

Customer analytics as the initiative accelerator

My company recently onboarded a retail client to a popular CDP. We didn’t call it a CDP — though the CDP Institute says it is. The purpose of the customer data was customer analytics. The client needed basic deterministic identity resolution to build segments for on-site personalization in their CMS. They completed onboarding in 30 days with four live use cases—total cost: under $100,000 in software and professional services.

Another client shifted key responsibilities of a customer data initiative to their analytics team. Within a month, they had dozens of predictive models live and launched high-value use cases just two weeks later.

Contrast the typical CDP onboarding, which involves months-long slogs focused almost solely on data quality. Momentum stalls, stakeholders shift and the initiative gets deprioritized before value is realized. I’ve seen this pattern repeat over the last decade.

But when companies equip analytics teams with the data needed for customer analytics, they can move far faster — and deliver much more — than traditional customer data projects.

Segmentation and scoring: How robust customer analytics helps

Segmentation and scoring are two of the most common marketing-led use cases for customer data. They’re rarely complex, and tools such as AI make them even easier.

For example, we recently completed a customer 101 for a $2 billion B2B2C retailer. Marketing led the initiative, while IT was key in approving the data store’s design and security. The team embedded a natural language AI assistant, enabling business users to run complex SQL queries and create fresh user segments.

Machine learning jobs now score leads daily for common use cases: propensity to buy, churn risk, LTV forecasting, product recommendations and more. An automated k-means clustering algorithm, powered by Google Gemini, even describes the segments it creates.

This happens without burdening IT, and the marketing team moves much faster. In just five months, the project drove a 15% lift in return on ad spend and a 20% increase in conversion rates from select catalog mailings.

Dig deeper: How to categorize customer data for actionable insights

Move faster

Reframe your customer data initiatives as customer analytics. Equip marketing and analytics teams with essential customer 101 data for segmentation and scoring. Enable AI toolkits that let marketers build and describe segments, especially for low-risk use cases. They’ll be able to test, learn and move significantly faster.