Where Are You on the Customer Data Maturity Curve?

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Businesses of all sizes are increasing investments in digital channels and customer data management. In many cases, these companies can point to measurable growth since leaning into customer data for marketing purposes. With tech stacks becoming more powerful and comprehensive, however, it can be difficult for leaders to assess where there is room for improvement. A recent Treasure Data poll surveying roughly 500 marketing and IT professionals across the U.S. and Western Europe tried to examine the customer data maturity curve that has taken shape in a wide variety of industries.

In short, the study tells a story about the haves and the have-nots. Those who use data well are achieving their corporate goals and KPIs, while those who don’t are still in the adolescent stages of customer data maturity. The survey results reinforce that customer data maturity is not only dependent on the technology companies have adopted, but also the strategies they deploy to maximize their data tools. Here’s what those models look like in 2022.

The Four Stages of the Customer Data Maturity Curve

Of the marketing and IT professionals surveyed, there were four common stages of maturity that emerged, giving way to a customer data maturity model that can be broken down by:

The “Getting Started” phase, where there is no customer data strategy yet, and customer data is still fragmented.

The “Developing” phase, in which there may be a semblance of a customer data strategy and some customer data is unified, but the strategy and centralization of the data is limited.

The “Leading” phase, where customer data is centralized, and the strategy is leading to customer insights that help drive KPIs and growth, but still falls short of the final phrase.

The “Visionary” phase, where a strong customer data strategy is bolstered by unified and centralized data that provides a data-driven customer experience (CX).

In the poll, over 60 percent of respondents characterized their companies’ customer data maturity as either Leading (40 percent) or Visionary (21 percent). A not insignificant number of respondents indicated they were still in the early stages, with 10 percent planted in the Getting Started stage and 28 percent in the Developing stage. Overall, only about half (47 percent) of the companies surveyed have a customer data strategy they strictly follow, indicating there needs to be more thought on ways to properly leverage customer data to achieve business objectives.

Key Elements of a Customer Data Strategy

For the 78 percent of the companies using customer data, a majority realize financial gains by boosting efficiency (64 percent) and business growth (57 percent), while 44 percent said they derive value from more actionable insights. In order to unlock this full range of benefits, businesses require a detailed strategy around data management, security, compliance, governance, and personnel.

Above all else, a strong customer data strategy also outlines a cogent business reason for collecting customer data. Whether it’s focusing on improving customer acquisition and loyalty, analyzing customer behavior to improve digital experiences, or assessing customer trends to inform product decisions, companies striving for customer data maturity must have a plan for the management and security of their data. For the Visionary companies, the ones that are most data driven, this can even include establishing a full-time organization devoted to customer data management.

Tech Lagging Behind Data Strategies

Once companies do start connecting, ingesting and managing data, they enter the Developing or, better yet, the trial-and-error phase, where businesses launch campaigns to test their capabilities. This is a consequential stage in a company’s data maturity because it simultaneously proves there is business value in customer data, while also spotlighting the fact that they don’t likely have the data infrastructure in place to maximize that value. According to the survey, even the companies who believe they are on top of their customer data strategies still have much to be gained by expanding their data management capabilities. For example, only 41 percent of those surveyed said they are using artificial intelligence (AI) and machine learning for predictive analytics.

The lack of innovation in some marketing departments does not stop at AI. Nearly two-thirds (64 percent) of respondents cannot pull their customer data into a unified database, suggesting that data silos are still plaguing companies that have already started their customer data journey. Another interesting dynamic the survey demonstrated is that customer data commitments are always changing. Over half (57 percent) of the companies in the poll anticipate changing their customer data infrastructures next year. To make the most of their investments in this area, businesses must marry a sophisticated strategy with modern technologies that can unify and centralize all of their customer data.

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