From Business Cards to LinkedIn: The Evolution of B2B Data – Marketers’ Guide

From Business Cards to LinkedIn: The Evolution of B2B Data – Marketers’ Guide

It's hard to imagine in today's digital era, but business cards were the OG B2B data.
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Back in the 2000-2004 period, collecting cards was serious business. Trade shows were the premier event to stock up on contacts and fill your sales pipeline.

Everybody looking to generate leads would grab a fishbowl at registration and get to hustling. You'd work the room handing out your card, charming people into taking theirs, and filling your bowl. Whoever collected the most by day's end won bragging rights, prizes, and, most importantly – a stack of sales leads.

Lead Gen Before LinkedIn & Online Directories

Cards weren't just trophies, though. Before LinkedIn and online directories, they were a valuable B2B data source. A business card contained the goldmine trifecta of name, company, and contact information. It would be even better if you could scribble a few notes on the back like "Wants demo ASAP" or "Budget approved."

These cards formed the foundation of sales contact databases and CRMs. After an event, salespeople would manually enter all the new contacts. Some companies hired interns just to type in cards! It was grunt work but a necessary part of lead gen.

This incredibly manual process seems laughable today in our digital era. But it worked at the time because options were so limited. Business cards converted into phone calls and opportunities.

Event Networking

Event networking was the only scalable way to collect B2B data before the internet changed everything. Relationships started with conversations versus LinkedIn connection requests. There was real value in that face-to-face trust building.

Limitations of Business Cards

Of course, business card lead generation had major limitations:

  • Small scale: You could only collect so many cards yourself and hire so many event staff. Boosting volume took a lot of work.
  • Labor intensive: Entering cards into systems required extensive manual work post-event. Mistakes often occur during data entry.
  • Lack of depth: Cards contained limited data, making effective messaging and targeting difficult.
  • Ineffective follow-up: Without emails or digital profiles, following up required cold calling from scratch.

Still, business cards were the top source of B2B data in a pre-digital world. Sales reps eagerly competed to fill bowls because it meant access to warm leads. Little did they know the LinkedIn era would soon turn everything upside down!

The LinkedIn Age – Scraping and Email Patterning

LinkedIn's launch in 2003 was a total game-changer for B2B data. Suddenly, you could access millions of professional profiles with a treasure trove of information. Full names, companies, titles, locations – it was all there for the taking.

Scraping Data

This free data gave rise to entire industries overnight. One of the first was scraping and structuring LinkedIn data for sale. Scrapers created bots to harvest names, companies, titles, and other fields from profiles at a massive scale.

While open scraping raised some ethical concerns, demand was sky-high. Sales and marketing teams realized LinkedIn data could replace manual business card leads. But it was missing one crucial element – email addresses.

What is Email Patterning?

That sparked the rise of "email patterning" around 2004. Patterning uses names, companies, and domains to predict associated email addresses. Data companies generated huge lists by pattern-matching LinkedIn B2B data.

For example, if they found John Smith at Acme Inc., the email might be john.smith@acme.com or jsmith@acmeinc.com based on common conventions. Not always accurate, but it provides a starting point.

Jigsaw pioneered commercial email patterning and was acquired by Salesforce for $142 million in 2010. This demonstrated the enormous value of paired LinkedIn data and patterned email addresses.

However, email patterning had some downsides:

  • Inaccurate predictions: Pattern matching was wrong for many formats, leading to bad emails.
  • Spam risks: Bad emails could cause delivery problems and blacklist risks.
  • Ethical issues: Emailing people without consent based on speculative patterns.

If you aim to run email outreach risk-free, check our 5 strategies for email address list quality hygiene.

Still, the LinkedIn scraping and patterning trend gave birth to many major B2B data companies. ZoomInfo, DiscoverOrg, Apollo, and others got their start mining this data before maturity.

This influx of contact data and new sales tools ignited an explosion in outbound prospecting. But as we'll see next, it was destined to cause significant inbox saturation and engagement declines over time.

The Rise of Sales Tech

The flood of LinkedIn contact data and patterned email addresses spawned a new era of sales technology. Tools emerged to help sales teams capitalize on all this inbound lead volume and continue to benefit from the B2B data they collected.

Around 2011, new software categories arose for managing outbound prospecting at scale. Email sequencing and tracking tools like SalesLoft enabled engaging cadences beyond one-off messages.

CRM giants like Salesforce recognized this sea change and rushed to add functionality for mass outreach. Their acquisition of Jigsaw (later to be known as Data.com) for $142 million in 2010 was an early indicator.

Other engagement tools like Outreach arrived in 2014 to streamline processes around managing scaled outbound emailing campaigns. This rise in sales tech was fueled entirely by the boom in contact data.

Contact Data Providers

Having access to millions of names, companies, titles, and patterned email addresses created incredible demand. Sales teams were eating it up.

However, more competition and commoditization soon emerged. The core ingredients for a basic B2B data company were:

  • LinkedIn scraper
  • Email patterning
  • SMTP email verification

With these pieces, any scrappy startup could compile contact data and edge into the market. This led to an oversaturation of contact data providers.

The Consequences of Scaling

Supply skyrocketed while prices dropped. Suddenly, you could buy B2B leads for pennies rather than dollars. However, this scaling led to consequences down the road:

  • Inbox overload: Sales overwhelmed recipients with outreach volume.
  • Engagement declines: Open and response rates tanked over time.
  • Spam risks: Excess volume caused deliverability issues and blacklisting.

The sales tech revolution enabled by scalable contact data was a blessing and a curse. While output grew at unprecedented rates, it needed to be built to last. The crowded inbox backlash was brewing.

The Crowded Inbox – Saturation and Spamvasion

By 2018, inboxes were overwhelmed, and engagement rates plummeted. The sales enablement rocket fuel crafted from B2B data started to sputter.

All those scraped LinkedIn contacts and speculative patterned emails flooded buying inboxes en masse. Open and response rates declined as recipients couldn't keep up with volume.

Outbound messaging became less effective over time. Expected response benchmarks faded as outreach saturated every corner of the business inbox.

Spam Fatigue

Recipient attitudes also changed. They grew wise to pattern-based speculation and increasingly saw cold emails as interruptive spam.

Sales teams sprayed and prayed so wide that relevance and targeting suffered greatly. Inboxes saw an onslaught of mismatched outreach.

Messaging fatigue merged with technical inbox protection advances. Gmail and Outlook spam filters started growing in sophistication around 2015.

AI Filters and Fake Email Opens

New artificial intelligence capabilities accurately detected and blocked the repetitive patterns of batch emails. Sneaky sales tricks like unusual send times were neutralized.

Senders tried to stay ahead with tricks for bypassing filters through spoofing, unique content, and other methods. But it became an arms race against the relentless AIs.

To make matters worse, Apple Mail on iPhones started displaying fake email opens. Recipients could "open" emails without even viewing them by swiping. Open rates lost trust and meaning.

The crowded inbox brought the scaling contact data boom wave crashing down. Engagement declined, costs rose, and risks amplified. The go-go days were over.

The Search for B2B Data Quality over Quantity

By 2018, it was clear that the spray-and-pray email era had waned. Sending high volumes of minimally targeted messages was losing effectiveness.

Savvy sales teams realized they needed to prioritize quality over the sheer quantity of outreach. The focus shifted to identifying and engaging the most promising potential customers.

With the sales team steering away from quantity, outreach this sparked new interest in intent data signals beyond basic LinkedIn profiles and patterned email addresses. Data providers emerged to track more nuanced buying journey clues.

Advanced Intent Signals: A Shift Towards Greater Context

For example, some companies specialized in analyzing visits to key product or pricing pages on company websites. These indicated early research interest.

Other B2B data companies mined public databases of RFPs and technology keywords mentioned. Active procurement and researching key technologies suggested warmer prospects.

Publishing detailed reviews on sites like G2 Crowd or interacting in relevant LinkedIn groups also provided targeting insight based on demonstrated interest.

These more advanced intent indicators required greater data science investment. It was harder than cold patterning millions of generic emails but provided greater context.

Sales and Marketing Team Alignment

Sales and marketing teams also aligned better to take a full-funnel view. Marketing focused on "warming up" target accounts and contacts before sales stepped in.

This more considered approach was a 180-degree shift from the high-frequency spray and pray style popularized previously. Quality overtook quantity in the maturing data landscape.

The unified B2B data on top, middle, and bottom-funnel prospects can derive tangible benefits from Marketing and Sales collaboration. Read more about the main pillars of Marketing and Sales alignment.

Finding New Channels and Partnerships

Around 2020, forward-thinking B2B marketers realized they needed to find channels beyond saturated email inboxes. Innovation was required to keep engaging audiences.

Some teams doubled down on tried and true tactics like cold calling. Others experimented with new mediums like cold messaging on LinkedIn and LinkedIn Conversation Ads. But these niche shifts only provided limited relief.

B2B Ads on Consumer Platforms

The real breakthrough started when marketers recognized they could reach business audiences on major consumer platforms. Tools emerged to help target B2B ads on Facebook, Instagram, Quora, YouTube, and more.

While B2C platforms didn't allow the volume of a LinkedIn campaign, the hyper-targeted audiences built upon enriched B2B data and lower cost per impression delivered results. B2B marketers realized they had ignored a powerful new channel.

Data Partnerships

Forward-thinking companies formed data partnerships to share contact databases mutually. This allowed partners to identify verified email addresses and enrich profiles without scraping.

Data partnerships opened creative ways to improve quality despite external limitations. As regulations expand, this collaborative approach is likely to increase.

The past decade has seen B2B data strategies mature from speculative email patterning to strategic partnerships. While channels will continue expanding, the cooperative ethos is likely here to stay.

The Future of B2B Data

The past 20 years have seen B2B data strategies evolve enormously—from business cards to data partnerships. As we look ahead, what might the next era of innovation hold?

Several predictions stand out for the future:

  • Commoditization of basic contact data: Name, company, and basic contact info will continue becoming cheap and ubiquitous. This "table stakes" data won't provide a competitive advantage on its own.
  • Continued rise of intent data: Buyer interest clues from search, content engagement, and account profiling will be increasingly valued. Intent signals identify the hottest prospects worth prioritizing.
  • Data partnerships: Regulatory limits on data access will drive more mutually beneficial data collaborations between companies. Frenemies will become cooperation allies.
  • Omnichannel orchestration: Sales and marketing will tightly align around omnichannel messaging—ads, email, phone, social, and events—to surround prospects. Research shows multipronged outreach boosts conversions.
  • AI-enabled personalization: As buyers expect ultra-customized messaging, AI will help tailor outreach and recommendations based on microsegmented factors. The most relevant messaging at scale.

While new challenges and blind spots will certainly emerge, the history of B2B data offers key lessons to build on: prioritize quality, seek new channels, and align strategy to reach customers however they want to engage.

Embracing the Future: Building Strategies for an Ever-Evolving B2B Data Landscape

In the span of two decades, B2B data strategies have undergone remarkable growth, from the reliance on business cards to the rise of LinkedIn and online directories and, eventually, the saturation of email inboxes.

As the landscape changes, it becomes clear that the future of B2B data lies in prioritizing quality over quantity, leveraging intent data to identify the most promising prospects, forging data partnerships to overcome limitations, adopting an omnichannel approach for effective outreach, and utilizing AI for personalized messaging. By learning from the past and embracing innovation, marketers can navigate the winding road ahead and build strategies that align with technology, partnerships, and customer-centricity.

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  • Win higher open rates and respond with personalized 1:1 outbound emails.

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