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Improve Customer Lifetime Value with a Unified Data Platform


A recent survey from Havas Group found that a staggering 75% of brands could simply disappear overnight and no one would notice or care. That statistic should shake companies and their marketing teams to the core.  

Customer loyalty is low, and the age of personalization has arrived. Businesses hoping to survive and thrive in the long term have put dynamic customer experiences at the top of their priority lists. The goal? Increase customer lifetime value (CLV). 

CLV is a critical metric, but companies have experienced challenges implementing it correctly in the past. Big data — especially high quality first-party data — can enable robust CLV computations, but companies need a robust customer data platform (CDP) to wrangle and use this data once and for all. A unified data platform can improve CLV, and this is what all companies need to know, whether leveraging data to calculate CLV at the time of measurement or projected from acquisition to attrition. 


The Challenges of Measuring and Implementing CLV as a Mission-Critical KPI 

CLV is vital to long-term business success. Companies need to know what their loyal customers are worth over the course of the relationship and (hopefully) continue to see that value rise. There are several CLV concepts that companies measure: 

  • Average CLV per individual customer 
  • Average CLV per specific marketing campaign 
  • Average CLV per customer demographic 
  • Overall CLV (all returning customers) 

Some businesses like to look at CLV to-date to target and cultivate customers who have already been valuable. Some like to target those projected to be worth the most over time, even if they aren’t valuable yet. Others use a combination of the two.  

Computing CLV to-date is a simple matter of totaling up each customer’s activities over time. Computing projected total CLV requires more complicated formulas and various assumptions, and there are a wide range of computations in use in the market. 

However, despite a multitude of CLV formulas and regardless of how they decide to compute CLV, companies have struggled to measure a meaningful CLV and implement what they’re measuring into a customer experience strategy that works. Why is CLV still such a challenge if the math is all there? 

In data science, “garbage in, garbage out” is a quick way of saying the quality of the data determines the quality of the answer. Customer data has traditionally been siloed, inaccessible, and nearly impossible to use in real-time.  

Deciding How to Calculate CLV Is Itself a Challenge 

Since each company has to decide for itself how to calculate CLV, inconsistency is the first challenge. The data that companies use and any assumptions that get utilized can produce different results, affecting how marketing and sales departments view customer segments. This complexity can produce a skewed view of customers and adversely influence decisions, despite those decisions being “data-driven.” 

CLV Is Not Static 

Traditional wisdom tells companies to focus on customers with the highest CLV. However, this is a mistake in a world where customer behavior changes quickly thanks to disruption and global connection. CLV can become a self-fulfilling prophecy for businesses that are used to viewing data as a static, historical asset rather than a dynamic one. CLV must be continually updated by making use of the most recent data. Customers that looked highly valuable recently may not look valuable at all now if their behavior changed, the company’s business model or strategy changed, or the company’s product mix or cost structures changed. Refreshing the CLV computation with clean, up-to-date data is the only way to identify this. 

Some Companies Use CLV to Justify Poor Customer Experiences 

The partner to the previous challenge — focusing solely on customers with the highest CLV — can lead companies to justify overall poor customer experiences. Again, it’s a self-fulfilling prophecy. McKinsey noted way back in 2006 that 70% of customers make purchase decisions based on how they’re treated. Statistics consistently show that even one negative experience with a company can turn a customer off for life. 62% of customers will share that one bad experience with others, and 33% of customers have ended a relationship with a company not because of a bad experience but merely a lukewarm one, i.e., one that wasn’t personalized enough. So, businesses must still treat low CLV customers well even as they treat high CLV customers even better. 

Past CLV Calculations Relied Too Heavily on Third-Party Data as a Proxy 

The days of plentiful third-party data are numbered given current trends in regulation. This is a good thing for digitally transformed companies that are ready to take advantage of real-time, first-party data. Third-party data has long been a proxy for companies whose first-party data is a mess, but it causes significant blind spots because: 

  • It’s old, out-of-date, and often of dubious accuracy. 
  • It relies heavily on assumptions about customers based on demographics or other indirect measures, not actual customer behaviors. 
  • Soon, third-party data may not be readily available given the increasingly tough regulatory environment. 


A Unified Data Platform Answers These Challenges 

One of the biggest reasons why companies don’t make the most of their first-party data and can’t seem to settle on the right CLV metric is their data infrastructure. The data will provide answers of course, but only if companies can leverage all the data they gather in a timely manner. A unified data platform could provide the first glimpse into what data can be in production. 

A Single Source of Truth 

Data silos prevent a coherent approach to marketing and can affect the outcomes of queries depending on who has access to what data. A unified data platform creates a single source of truth through which all stakeholders access the data they need when they need it. 

This gives marketers the data they need to personalize and target marketing to eliminate irrelevant offers. It allows customer service to build holistic and thoughtful customer experiences that anticipate customer needs. It gives product teams a deep understanding of what their customers need so that products and services evolve and even anticipate what customers will want. 

This approach could help companies increase CLV by offering the personalization customers crave. 65% of customers in the United States believe customer experience is even more influential in their decisions than advertising. And by the same survey, 73% of global customers believe experience is one of the most important factors in their decision-making process, behind only price and product quality. 

Evolved Data Access 

Even with a single source of truth, trying to get access to data is a nightmare for many departments. Users must request permission to access specific rows or columns, which can take weeks to earn. If there’s a change, it only moves the timeline back further. IT is overwhelmed building brand new pipelines for each query instead of reusing them, and business users don’t know what data is even available. 

A unified data platform does more than just unite data sources. It democratizes data so that all users can access it for the queries they need, regardless of technical expertise. It should use outcome-based engineering — meaning a user can choose the outcome they need and the system will build the right process for them while ensuring that the process is compliant with all security and governance policies.  

In addition, searching for data much like a standard search engine speeds up insights. Users don’t need to request data, find out it is not the right kind, re-request, and watch as their initiatives fail. Instead, they know exactly what data is available. Using an attribute-based governance system, the company can balance the need to free data with the need to remain compliant with privacy regulations. 

This solves one of the most pervasive challenges of CLV: getting quality, timely data for both the CLV equation itself and creating better customer experiences based on first-party data. Companies move from “garbage in, garbage out” to “quality data in, actionable insights out.” The data evolves as customers do. 

A Connective Tissue for the Entire Company 

This is where a unified data platform shines. Data becomes actionable as a connective tissue — not a collection of point solutions or mere fuel for analytics. Right now, analytics tools may power dashboards, but the process is static. Companies must think about data dynamically. 

Real-time insights give companies the power to pivot quickly to fulfill customer demand, reduce friction at common customer service or onboarding intersections, and evolve products, pricing, and services intelligently. It can inform every decision a company makes to optimize CLV. 

Unified digital channels alert the business to changing customer behavior, and the company can adapt quickly and effectively. No business wants to cause dissatisfaction, but not being remembered at all can be just as detrimental. With unified data as a connective tissue, the company can be flexible and, most of all, attuned to the needs of real customers — not avatars.  


A Unified Data Platform Is a Data Operating System From Modern 

The Modern Data Company (Modern) created the only end-to-end data operating system on the market, fulfilling every capability that companies need to unify and then free their data. DataOS simplifies data while balancing access with security. It integrates easily with existing tools and data formats and transforms data infrastructure without disrupting day-to-day operations. 

As companies compete for visibility in a noisy online world, simplifying data use will allow them to know their customers inside and out. And as third-party data access disappears, this could transform how companies optimize operations to increase CLV. It’s time to become part of the 25% of brands that no one can ever forget. 


Learn more about DataOS in our solution brief, Modern DataOS Overview. 

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