Customer intelligence remains a fragmented, costly operation rather than a strategic asset. Despite massive investments in various tools and hiring talent, most operators still struggle to create a unified, contextual view of their customers. This isn’t because they lack the talent or the individual tools to capture customer data. It’s because they have to devote too much time deriving intelligence from siloed and disparate customer data. 

Over the past 15 years or so, the telecommunications business model has been disrupted by radical, data-first companies practicing radical customer-centricity. These companies don't just collect customer data really well—they have mastered efficient ways to transform it into actionable intelligence with minimal operational friction. 

Management consulting firm Oliver Wyman frames this challenge precisely: True customer-centricity requires shifting the unit of analysis from products or Revenue Generating Units (RGUs) to actual customer entities—whether they’re individuals, households, businesses, or public sector organizations. This shift means telcos can use their time, talent, and tooling to unify data to operate with real-world knowledge of customer relationships, wallet share, and unmet needs. 

This means that you should consistently answer critical questions, such as: 

• “Who are our genuinely high-value customers, and what do they truly need?” 

• “What's the optimal next action across products, channels, and service management?” 

• “How much of our customers' total spend are we capturing versus our competitors?” 

Answering these questions wont just bring you incremental improvement, but will also be the foundation for building a sustainable competitive advantage.​​​​​​​​​​​​​​​​ 

Quantifying the hidden OpEx burden 

Here is a formula that gives us a conceptual view of some of the unit economics behind decision-making, and why telcos pay too much for too little.  

OpEx of Decision-making = (Time + Tools + Talent) / Actionable Insight  

If you examine this equation closely, you can see the problem. Many telcos have made significant investments in the numerator (time, tools, and talent), but still struggle to increase the denominator (actionable insights) at a corresponding rate. The result is an increasingly unfavorable return on the operational investment in data for decision-making.  

One obstacle to this is when your existing customer data ecosystem cannot automatically and instantly recognize that "Acme Ltd" (with 50 mobile lines), "Acme Furniture Ltd" (with premium broadband services), "ACME Delivery Ltd" (using field service MPLS), and "YANG Ventures" (a potential new customer) are the same customer, despite having common attributes. If your data ecosystem doesn’t recognize these commonalities, it won’t recognize Acme as a strategic enterprise customer with potential to grow.  

Without an automated and highly accurate system to resolve and match customer entity data, your sales teams might see multiple small accounts instead of one major enterprise opportunity. From there, marketing will send disconnected messages. The service desk will treat each interaction in isolation. Overall, a lot of true value will be missed, and you’ll be leaving money on the table. 

The Quantexa advantage 

Quantexa's Decision Intelligence Platform addresses these data challenges by creating a contextual understanding of customers and their networks. The approach includes: 

• Matching and resolving entities across systems: connecting fragments of the same customer relationship 

• Mapping relationships with external data: revealing hidden connections and real-world context 

• Operationalizing single-customer views: enabling different business functions to see the same unified customer view and act on the same truth  

For example, Quantexa's work with Vodafone Business delivered significant improvements in how the business sales teams gained access to high-quality customer data, as well as resolving post-merger data fragmentation challenges that had persisted for over a decade. This shift not only accelerated sales cycles, but enabled the business to uncover hidden opportunities previously buried in fragmented records.  

Leading with a high ROI customer intelligence engine 

The economics of a successful telco needs to create room beyond the network infrastructure or product portfolios. It must factor the cost of transforming fragmented customer data into actionable intelligence with minimal operational friction. 

To do so, let’s revisit our conceptual formula:   

OpEx of Decision-making = (Time + Tools + Talent) / Actionable Insight 

When data and business intelligence teams spend weeks manually reconciling records just to establish a baseline view of customer value, the numerator (the investment in time, tools, and talent) balloons—but the denominator stays fixed. For all the time and effort we’ve expended, we don’t get more or better actionable insights. This creates an unsustainably high cost per insight—an operational expenditure that erodes margins and slows decision-making precisely when agility matters most. 

By investing in entity resolution and contextual intelligence, we can increase the efficiency of the time, tools, and talent, getting us actionable insights at a fraction of the operational expense. In this way, telcos can reduce the time and resources the process requires, while simultaneously increasing the quality, quantity, and business impact of the insights generated. 

Get in touch to find out more.