Traditional vs. Modern B2B Data Vendor

When The Yellow Pages first thudded onto desks in turn-of-the-century America, it changed things for the door-to-door salesman.

Here was an alternative to sore feet, slammed doors and lost revenue. Within this directory were thousands of potential customers, attainable by dialing a phone. What’s more, a savvy salesman could guess the financial position of a target based on zip code alone. This directory, in its time, was a rudimentary but unprecedented source of customer data.

Data vendors now use digital delivery instead of paper and glue, but the truth is that they haven’t progressed far beyond The Yellow Pages. They’re selling inert and rapidly outdated lists, contributing to a picture of reality that’s only partially true—if it’s true at all.

But that’s changing with the introduction of "modern" B2B data vendors. These innovators use machine learning to offer data that’s contextually rich, obsessively up to date, and capable of contextualizing the past and predicting the future. It’s data that, unlike a page in a phone book, is alive and multidimensional. Data that is, in a way, thinking.

In this article, let’s consider what the introduction of a modern data vendor means for today’s data buyer, and how these modern vendors are outperforming more traditional models.

The Modern Data Vendor Knows Where The Real Value Lies.

Data is just data. To make it meaningful, someone needs to extract insights and information from it. This was once a relatively easy process, when data was sparse; given today’s volume of data, information extraction is a complex and expensive task. Doing it well requires both data science and machine learning.

Legacy data vendors haven’t adapted to this reality. Rather than insights, they deliver a raw commodity that, with a lot of time and effort, the buyer can mold into something useful. In today’s speed-driven market, all this time spent extracting insights is costing companies their competitive advantage.

A modern data vendor works differently. Using machine learning, it can keep pace with the volume of data available while delivering the next piece of the puzzle: actionable insights and predictions.

The Modern Data Vendor Provides Diversity.

Traditional data vendors offer a static directory. Some update it more frequently than others. Some include more signals than others. But they all, in essence, provide a snapshot of a moment in time.

A snapshot, as we know, stands still. It doesn’t have the ability to consider what came before and it cannot look forward and make predictions about the future. It is, in a way, dead as soon as it's created.

By contrast, modern data vendors provide information that’s alive. With machine learning as a central element of its “always on” engine, the modern data vendor doesn’t sell a snapshot—it sells a perspective on the constantly shifting business context.

To do that, the modern data vendor relies on more than demographic or firmographic data. Where a traditional vendor might offer a baseline collection of, say, company name, website, etc., the modern data vendor offers hundreds of additional signals. This diversity contributes to the “living” aspect of its data; by collecting as much data as possible, and constantly refreshing it, the modern data vendor offers a more nuanced picture of reality.

The Modern Data Vendor Provides Personalization.

U.S. companies produce enough data each year to fill ten thousand Libraries of Congress. To survive in this onslaught, companies need to understand the relevancy of data. Like a quarterback who can scan the field and take action within seconds, companies need to scan the market and quickly determine where their relevant opportunities lie—ignoring the clutter and the costly dead-ends.

But traditional data vendors are in the commodity game, not the relevancy game. The data they provide is typically unranked and unprioritized. This shifts the burden of finding relevant data onto the buyer, which drains that buyer’s time—costing them dearly.

The modern data vendor does things differently. It provides an AI-assisted, quarterback-enabled sidekick that works alongside companies to uncover, rank and accurately score relevant data based on personalized needs, enabling fast and intelligent action.

The Modern Data Vendor Applies Superior Matching Techniques.

Even high-quality, relevant data loses its usefulness without good matching. A wrong name in a personalized email may not seem inconsequential at first, but poor matching carries significant costs. Embarrassment, at best. At worst, an irretrievably lost opportunity. After all, if customers can’t trust you to understand who they are, they won’t trust your offering.

Traditional data vendors, using traditional matching, set companies up for this failure. Modern data vendors, on the other hand, use machine learning to understand and reproduce human decision-making, offering clean, useful, relevant databases at many times the processing speed of a human team. This eliminates costly errors, helping companies capitalize on opportunities with confidence.

Conclusion: Your Business Is Alive. Your Data Should Be, Too.

For its era, The Yellow Pages was a revolution.

But that era is over. Companies are now in a world defined by relentlessly shifting datasets; a static directory, capturing a fleeting moment in time, cannot help those companies to thrive. Instead, they require a modern data vendor with the technology and methodology to enable fast and confident decision-making.