Evolution of B2B Data and Why Marketers Need Personalized Data

Demand Generation
Sales Enablement
Sales Operations
January 17, 2024

Knock, knock. Who’s there? If we go back to the early 1900s, it was likely a door-to-door salesman who drove around town looking for the ideal customer. Back then, that usually meant stumbling upon an expensive neighborhood where he could recalibrate pricing and packaging in real-time. Fast forward to today, and you’ll see companies leveraging digital data to identify their ideal customers while customizing pricing or packaging based on their past purchase history or willingness to pay. History really does repeat itself. But, how’d we actually get here? How’d we go from door knocking to using marketing automation and B2B data? Where is B2B data headed next? First, we have to look at the history of B2B data, from door knocking to web scraping and how easy access to standardized data is causing the push towards hard-to-find personalized and custom data.

The Phone Books: Moving from Door Knocking to Cold Calling

Phonebook was a major revolution to modern-day sales? With its official launch in 1878, the phonebook was the precursor to the B2B data industry, enabling consumers to make appointments or find friends’ numbers. Beyond consumer use, now salesmen could sit at desks and call up potential customers rather than slugging their supplies around town. By 1910, America’s telephone books were keeping track of 7,000,000 phone numbers. It was the very first set of standardized data – first name, last name, phone number. The phonebook kept getting bigger and bigger…but organizations weren’t getting smarter. While phone sales allowed more volume with access to millions of records, it didn’t allow for prioritization. Companies needed more than just this single source of data. They wanted their own databases that were customized to their business needs and where opportunities could be ranked and prioritized based on a certain set of characteristics.

The Call Centers: Collecting “Custom Data” Through Call Centers

With the increasing adoption of dialing for dollars, the B2B data space underwent a transition from data scarcity to data abundance. Sales organizations couldn’t possibly call every phone number and needed a way to easily prioritize and qualify opportunities without wasting their team’s time.This paved the way for outsourced B2B call centers, which were tasked with calling into businesses and gather custom data and ask a series of personalized qualification questions. Outsourcing this work to call centers meant that the organization would now have access to lists of companies that matched their qualification criteria. Individual reps could then call into these businesses themselves and have more informed, personalized conversations. The use of call centers to produce data led to capturing new data points such as intent to buy, information around past purchases, sub-industries and much more. Some of this data began to get modeled for comprehensive coverage. It was easy to provide your custom data requirements to a call center and have them call into businesses and acquire it…over time. Some of the largest data companies of this period used call centers. However, call centers had one major deficiency. They circled around a manual one-to-one process that was too labor-intensive, time-consuming, expensive, and ultimately inefficient. It was only a matter of time before they gave way to a more efficient and scalable process of data collection.

The Internet: Scraping the Web for B2B Information

Then, the late 1980’s and early 1990’s computer boom happened and every company and individual started to come online. Now decision-makers could be reached over email and phone. The rise of the personal computer and internet also brought about the rise of the CRM – which allowed organizations to easily store, update, and manage data of their customers and prospects. Now all companies, big and small, needed data to fill up their CRMs. The B2B data industry needed to switch gears and adapt to these changing times. The initial wave of these high-tech B2B data companies scraped the web for standard data to re-create the phone directory, but using a much more scalable method. They churned through billions of web pages looking for people and company-related information. The next wave of B2B data companies scraping the web looked beyond the basics by exploring job posting and user behavior to re-create some of the custom data around past purchases and intent to buy. Such data was already available using call centers, but the use of web scraping caused a dramatic increase in volume. This caused a wide abundance of data and data related companies resulting in a lower unit price per record and increased efficiency for sales & marketing organization. This abundance of data ensures that even smaller companies take advantage of the efficiency that came from using B2B data. Five years ago, access to accurate company and contact data was coveted. It seriously changed the game — and it came with a hefty price tag. Organizations realized knowing things like the number of employees, estimated revenue, direct dial number of the decision-maker, etc… gave them a competitive advantage. It was worth investing in access to millions of records they could have only dreamed about a few years before. Today, access to standard B2B company and contact data that once provided a competitive advantage is now simply table stakes. If you don’t have it, you’re nearly guaranteed to lose. If you do have it…well, in today’s world, you’re not necessarily guaranteed to win. Why? When everyone has access to something, it no longer provides a competitive advantage. It becomes the new baseline. Everyone has access to millions of records at the click of a button. Today, organizations are drowning in data when, just ten years ago, there used to be a drought. As a marketer, how do you gain back that competitive advantage? It’s about finding the signal through the noise. It is, and always has been, about prioritizing the right prospects and finding those niche pockets of an audience that are interested in your offering. Don’t chase the mainstream, chase the future.

Personalized & Custom Data: From Standardized Data to Custom Curation

The internet is like a vast phone book but with tens of thousands of data points about customers and prospects. Websites talk about their product line, team members, and press announcements. Job postings offer insights into the technology they are using and their strategic intent. Media outlets provide insights into revenue and customer counts. And this is just the start of the data available. Custom data is what cannot be found in a traditional phone directory, company website or a Linkedin record. Access to custom data is the new competitive advantage that enables organizations to think strategically about planning, prioritizing, prospecting, and permeating organizations.

While big contact and company data providers do a great job at providing the standard fields you need, they don’t provide any personalized context to your business or sales process. What exactly does custom data look like? How do organizations figure out what data they need and how to put it to work? Below are some examples of custom data and signals used for prioritization:

  • PoS Company trying to sell into restaurants: Find restaurant ranked by average order size, restaurant hours, service type and cuisine type
  • Large E-commerce software vendor reviewing prospects: Find accounts ordered by number of SKUs, avg product price, and the existence of shopping cart on the website
  • Cloud Storage Company looking to penetrate into hospitals: Prioritize hospitals by number of beds, number of technical staff, IT spend and tech purchase decision-maker at each location

One size fit-all B2B data providers don’t have this kind of information. They don’t work with organizations to build and execute holistic data strategies to drive competitive advantage. They don’t enable a creative marketer to run GTM experiments using personalized data fields.

LeadGenius: Data vendor for the creative marketer

LeadGenius realized that companies need this custom data to get ahead and stay ahead. It’s why we build personalized information extraction tools and teams to collect data individually for every project, a stark difference from the one size fit all datasets. Understanding what custom data an organization needs typically starts with strategic meetings where last-mile knowledge or the information reps use to prioritize and close deals, is extracted. From there, we look at how we can deploy technology and teams to uncover or model this data. From uncovering total available markets to creating ABM lists based on special characteristics, our team and technology have helped leading organizations succeed. Gathering personalized data has always been critical to success — LeadGenius has just figured out how to do it more efficiently. But… what comes next?

The History of B2B Data: From Customization to Always On Signal Tracking

With custom data that reflects key buying indicators, organizations can now prioritize the best opportunities based on unique data points relevant to their individual organizations. But…how do you go deeper? How do you move from reactive sales to proactive sales? How do you know when it’s the right time to reach out? How do you continue to keep updated on an organization throughout the sales process to continually add valuable context to the conversation? In today’s world, data is still pretty static. You may update your CRM data every year or get sent new records every quarter if you’re really lucky. But that’s not enough for today’s dynamic world. Timing and context are of the essence. That’s why LeadGenius is building a Dynamic Signal Tracking (DST) system to keep custom data always updated. With DST, organizations will have always-on signal tracking on key accounts, giving them the ability to understand their target markets and individual prospects as soon as updates are detected. Some examples of Dynamic Signal Tracking includes – employment change of decision-makers or engaged prospects, hiring signals for key roles, change in resource allocation or spend signals to name a few. Instead of completing large custom data projects, organizations will have always-on customized data that is continually updated to keep conversations timely and relevant. This is the future of B2B data. And we can’t wait to build it.

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