February 7th, 2018 | by

B2B sales pros and marketers swim in an ocean of information.

A large part of our day is spent trying to find data that is relevant to our jobs, and what we can ignore – because we don’t have time to digest it all.

Most businesses agree that data-driven decision making is important, and the vast majority of companies collect data in some form.

But in this sea of data, which individual data points predict purchase intent?

What data matters?

We asked over 200 sales and marketing professionals about 78 data points (and “secret sauce” combinations of data points) in a comprehensive survey, Breaking Open the Predictive Black Box: Which Data Points Actually Lead to Higher Conversion Rates and More Sales?

The results are surprising for several reasons.

In the resulting study, we organized results according to 7 key findings:

  1. Buying magic happens at the confluence of 3 different types of predictive data: Fit, Intent, and Opportunity.
  2. 95% of all respondents find positive revenue gains when predictive indicators are present – and the most common benefit is higher conversion rates.
  3. Opportunity Wasted! Most companies are not leveraging Intent or Opportunity data … yet these data points top the “most predictive” leaderboard.
  4. The most predictive Fit data points are Job title and Department budget.
  5. Sales teams tend to value hiring and personnel signals more than marketing teams.
  6. Knowing your prospect’s tech stack tops the “secret sauce” predictive recipes.
  7. For all of its buzz, only 20% of respondents use predictive data to fuel their ABM efforts.

So how are B2B businesses collecting and using big data for predictive intelligence?

Most (78%) sales and marketing professionals are already collecting data and using it to score accounts on the basis of fit, propensity, or readiness to buy.

With this in mind, let’s look at the most common collection of data points that predict a purchase.

[STUDY] Breaking Open the Predictive Black Box: What Data Points Actually Lead to Higher Conversion Rates and More Sales?

Finding 1: Buying magic happens at the confluence of three different types of predictive data

Fit, Opportunity, and Intent: Behavioral information is only predictive when it is combined with well-defined firmographic and demographic criteria that fit the Ideal Customer Profile.

The likelihood of purchasing lies squarely in the middle of a Venn diagram consisting of three buckets: Fit, Opportunity, and Intent.

Fit criteria

Fit criteria is recognizable to both the sales and marketing side of the house as the right contact at the right company. A clearly identified company profile is the primary basic requirement of any kind of scoring or predictive analysis – basic physiological data upon which Maslow’s Hierarchy of Needs for sales and marketing, as it were. If the company itself is not a great fit, all other information, no matter how effective at prediction, has no value.

Fit data includes basic demographic, firmographic, and technographic information at the account and contact level. These include data points such as:

  • Industry
  • Job function
  • Department budget
  • Technology stack
  • Gender
  • Location
  • Use of agencies or contract services

What’s the most predictive Fit data point?

Job title.

Over 85% of respondents said Job title is effective or very effective at predicting a prospect’s likelihood of predicting a purchase.

This is because Job title is a basic, fundamental part of the Ideal Customer Profile: Even if every other piece of the puzzle is perfect – the right industry, the right time, a perfect pitch – if the prospect is in the wrong department, or doesn’t have purchasing power … nothing else matters.

Without an Ideal Customer Profile that touches on these points, along with fit criteria at the company level, a sales team is likely to spin a lot of cycles on deals that don’t end up closing.

Overall, 60% of respondents use Fit data.

What data are your peers (and competitors) collecting? Download our ebook for COMPLETE “predictive data points” survey results.

Opportunity data

Opportunity insights is considered to be favorable conditions. Sometimes a prospect stumbles upon a solution at exactly the moment they need it … but luck has never been a great sales strategy.

That’s why Opportunity or “trigger” information becomes a truly predictive piece of the purchasing puzzle – when it’s layered on top of Fit and Intent data. These are the data points that indicate that conditions are favorable for a change.

See how DiscoverOrg uses Scoops in Chipotle’s Big Burrito Breach – Malware with a Side of Chips.

Action-based Opportunity signals indicate favorable conditions for a purchase. These types of data points include:

  • Leadership change
  • Funding
  • Pain point
  • Hiring plans, Promotions, Layoffs
  • Company events
  • Merger
  • FCC fine

So what’s the most effective Opportunity data point?

84% of respondents said Requests for Proposal (RFPs) and Projects/Purchase initiatives are effective or very effective at predicting a prospect’s likelihood of predicting a purchase. If a sales team can get a seat at the table when a company is requesting proposals, or during the project planning phase, it stands to reason that those pitches will be selected over those who don’t get in the door in time.

The least predictive Opportunity data point was Company Awards. (While this might not predict purchase behavior in itself, one of our top-performing SDRs would be quick to point out that a company event like an award is a great excuse for outreach.)

Overall, just 29% of respondents use Fit AND Opportunity data.

For complete results, download the new study.

Intent data

The third layer of data that makes up predictive intelligence is Intent data: information on implicit behavior.

With a foundation of basic demographic and firmographic details in place, and favorable conditions present, intent data is the lynchpin for predicting success. Intent data is the behavioral activity that links target buyers and accounts to a solution, solution category, or related topics.

Intent data is implicit digital behavioral signals that link a buyer to interest in a solution from their digital footprints. This includes:

  • Time on website
  • Form-fills / Downloaded your content
  • Comparing your product with a competitor’s
  • Lead source
  • Social media follows
  • Commented or Liked your content
  • Spikes in content on a given topic

So what’s the most effective Intent data point?

Companies Comparing the Products of Other Vendors in Your Category. In fact, 7 of the top 8 most effective Intent data points all involved competitor research and comparison. If a company is comparing vendors in your space – to each other or to your solution – they’re probably not far from making a purchase. And at that point in the Buyer’s Journey, the choices have likely been narrowed down to a small handful.

The information collected by marketing automation systems for an organization is one level of Intent data, but many organizations expand that layer to vast networks of sites and partners that gather intent data from numerous places.

Intent data offers something Fit data cannot: It implicitly signals interest, demand, or urgency related to a particular topic or need.

Overall, just 15% of respondents use Fit AND Opportunity AND Intent data.

What “secret sauce” data-point combination signals intent to purchase? Get our study for complete results.

The sequential, piecemeal nature of the Fit + Opportunity + Intent scoring combination is not always well understood by sales and marketing professionals.


Fit: Kelly is a sales development rep at a company that sells applicant-tracking software. Her best-fit clients are enterprise-size companies in the retail industry – which is always hiring due to a high rate of turnover.

Opportunity: Kelly learns that one of her target accounts is opening 23 new stores in her territory … and Christmas season is just 3 months away.

Intent: Kelly can see that that someone from that same account has visited her company’s website several times, downloaded a datasheet of the integration capabilities of her product, and signed up for a weekly recruitment-tech news round-up. Through third-party intent data, Kelly can see a recent spike in activity and interest in content related to applicant tracking systems and recruiting.

… NOW, there’s a very good chance that prospect will be happy to take Kelly’s call!


As we unpack the proverbial “predictive black box,” the most surprising takeaway is not that any one data point is a magic bullet. There is no single data point that can do the work of good salesmanship.

What is surprising is that when all three types of data – Fit, Opportunity, and Intent – are present, they are tremendously effective.

Over 95% of survey respondents can link growth to predictive indicators. The positive result with the highest correlation is most often higher conversion rates of prospect to qualified lead.

At the end of the day, Business won is truly the #1 data point for sales professionals and marketers alike.

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Katie Bullard
About the author

Katie Bullard

As Chief Growth Officer (CGO), Katie brings 15 years of marketing, product, and strategy experience in global, high-growth technology businesses to her role at DiscoverOrg. She has a bachelor’s and masters degree from the University of Virginia.