Acquiring new B2B customers is far more expensive than upselling or cross-selling to existing ones. To unlock wallet share or reactivate dormant customers, sales teams need accurate knowledge of which levers to pull and which customers to target. The problem? While data is abundant, its quality is often severely lacking.

The anatomy of a business customer

To maximize B2B revenue potential, telcos need accurate data on areas like: 

• Company profile

• Key contacts

• Engagement history

• Product usage

• Financial relationship

• Market landscape

• Marketing communication preferences

However, unifying these data points across disparate systems to create an accurate, up-to-date customer profile is a significant challenge.

How Acme Corp has five versions of the truth

It’s far too common to see that a single customer can have multiple, conflicting profiles. For example, a customer called ‘Acme Corp’ has a name that varies in different systems—Acme Corp, ACME GmbH, or it’s a rebranded name which could be something completely different like Axim! This inconsistency highlights a significant challenge of seeing a customer as a single entity with a single truth.

The CRM conundrum and lost RFPs

While essential, CRM systems aren't the cure-all for B2B customer data management. The dynamic nature of businesses—mergers, expansions, downsizing—makes it unrealistic to expect sales teams to maintain perfect data hygiene while focusing on closing deals. 

Outdated CRM data can lead to misdirected pitches. For instance, a sales team might target what they believe is a small retailer, unaware it's now part of a larger conglomerate with different needs. This oversight can result in significant lost revenue. 

Similarly, inaccurate data can derail RFP responses. A proposal based on a company's presence in two markets, when they actually operate in 30, will fail to address the client's true global footprint and miss crucial opportunities.

Pre-sales misalignment and margin loss

Pre-sales teams play a crucial role in aligning technical solutions with customer needs. However, incomplete or outdated customer profiles can lead to misaligned proposals, resulting in lost deals. 

For example, a pre-sales team might propose a cloud migration solution to an e-commerce company, unaware that the real need is managing traffic spikes during flash sales. This misalignment stems from outdated customer insights. 

Similarly, inaccurate data can lead to underpriced quotes, especially for complex customers like large enterprises and public sector entities. For example, if a sales team underprice a high-bandwidth service by say, 40%, this would turn a projected profit into a loss over the duration of the contract. This type of error not only affects margins but also incurred additional costs and penalties.

Evolving beyond MDM and CDP

Traditional solutions like improved data entry and master data management (MDM) fall short in addressing the complexities of modern B2B data. MDM systems, while useful for maintaining basic company information, often fail to integrate crucial details like that add important context. 

Similarly, Customer Data Platforms (CDPs) attempt to address fragmented data but struggle with large volumes and diverse sources as businesses scale. While CDPs can be valuable, they require extensive supporting systems and manual effort to maintain the data.  

The real need is for an always-on, automated solution that unifies scattered data into a single, accurate customer profile. This solution should resolve discrepancies and provide a contextual customer view in just a few clicks, offering a competitive edge in an increasingly complex data landscape. Crucially, it should be able to wrap around all systems where key customer data is stored and integrate external data points, creating a comprehensive and dynamic customer profile. This approach ensures that no existing investment in data systems is wasted, as the new solution builds upon and enhances current infrastructure rather than replacing it.

Revealing the real “Acme Corp” through data clarity

Telco executives need to act swiftly to address this data challenge. As data complexity increases, industries like banking have already adopted a solution called entity resolution to unify customer profiles and enhance processes like KYC (Know Your Customer) and relationship management. Telcos can follow this lead, using entity resolution to gain a clear, accurate view of their business customers. Suddenly, "Acme Corp" is no longer five different entities! 

This isn’t just about a more efficient way to clean up data; it’s about preventing revenue leakage, meeting shareholder expectations, and making better decisions. Quantexa’s Decision Intelligence platform integrates entity resolution, creates a contextualized view of the customer, and uses high-quality data to feed AI models that guide users in decision-making. Moreover, these insights are delivered rapidly. While alternatives to Decision Intelligence exist, they are often neither as fast nor as cost-effective. 

The real challenge isn’t implementing a solution like Decision Intelligence but recognizing its importance in time. As AI and automation evolve, and data volumes grow exponentially, telcos that delay action risk being overwhelmed by the very data they once saw as their competitive advantage.