Remember 10 – 15 years ago, when everyone was talking about “Big Data,” and Bluetooth-operated smart appliances started to appear on shelves? Consumers were the first to feel the effects of mass data collection – far ahead of the business world.
Data proliferation in the B2B world has lead to many different things – and it’s not (usually) perfectly auto-curated Spotify playlists or grocery coupons.
One of the benefits of a large volume of information? Predictive intelligence: In the case of sales, marketing, and recruiting, this means using historical data to identify potential customers, at the moment they are in need of your product or service.
And one of the problems with predictive intelligence? First, we’re talking about a LOT of data. How can we figure out which data actually matters? Which data points – individually, or together in a “secret sauce” – predict buying behavior in B2B customers?
Well, turns out there is a quantifiable answer to the eternal question.
We asked 200+ sales and marketing professionals about 78 predictive data points (and “secret sauce” combinations of data points) to ask: Which Data Points Predict Higher Conversion Rates and More Sales?
Our study did indeed reveal the most predictive data points. Read on to see what you should be tracking, if you can.
Predictive intelligence needs 3 types of data
The likelihood of purchasing lies squarely in the middle of a Venn diagram consisting of three buckets: Fit, Opportunity, and Intent.
Like a puzzle, actionable predictive intelligence is comprised of a few layers: Fit, Opportunity, and Intent: Behavioral information is only predictive when it is combined with well-defined firmographic data and demographic criteria that fit the Ideal Customer Profile.
Fit data is the basics: 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:
- Job function
- Department budget
- Technology stack
- Use of agencies or contract services
What’s the most predictive Fit data point?
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.
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 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
- Pain point
- Hiring plans, Promotions, Layoffs
- Company events
- 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.
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.
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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