How Can You Predict If and When Customers Will Buy?

By Katie Bullard

Most businesses now rely on data-driven decision making, and almost all companies collect customer data in some form. But in this ocean of data, which individual data points actually predict a customer’s intention to make a purchase?

We asked 200+ sales and marketing professionals about 78 data points—as well as “secret sauce” combinations of data points— in a recent survey, with eye-opening results.

The survey uncovered these seven key findings:

  1. Buying likely happens at the confluence of three 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. Most companies are not leveraging Intent or Opportunity data, yet these data points top the “most predictive” leaderboard.
  4. These two specific data points are most predictive.
  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 its buzz, only 20% of respondents use predictive data to fuel their Account-Based Marketing (ABM) efforts.

Our research reveals that behavioral information can only be predictive when it’s combined with well-defined firmographic and demographic criteria that align with a company’s Ideal Customer Profile (ICP). Further, the likelihood of purchase lies squarely in the middle of a Venn diagram made up of three criteria: Fit, Opportunity, and Intent.

Fit criteria

Fit criteria is a staple of both sales and marketing teams. It starts with a clearly identified best-fit company and customer profile as the primary requirement of any kind of scoring or predictive analysis.  If the company itself is not a great fit, all other information, no matter how accurate, will never result in a sale.

Fit data, used by 60% of survey respondents, 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 at predicting a prospect’s likelihood to purchase, according to 85% of the survey respondents? Job title.

This one is simple: a person’s job title is a fundamental part of the Ideal Customer Profile. If the prospect is in the wrong department or doesn’t have purchasing authority, a sale will never happen.

Opportunity data

Opportunity insights are considered favorable conditions.

When layered on top of Fit data, Opportunity becomes a truly predictive piece of the purchasing puzzle. These data points indicate conditions are favorable for a purchase, including:

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

What is the most effective Opportunity data point? Eighty-four percent of respondents said Requests for Proposal (RFPs) and Projects/purchase initiatives are effective or very effective at predicting a prospect’s likelihood to purchase. Sales teams that can get in front of decision makers when a company is issuing RFPs or during the project planning phase have a much better chance of their pitch being reviewed than teams not on the radar.

But surprisingly, only 29% of respondents use both Fit and Opportunity data.

Intent data

Intent data, in short, is information on implicit behavior.

With a foundation of Fit (the right person at the right company) and Opportunity (the right conditions), intent data is the lynchpin for predicting success. Intent data links target buyers and accounts to a solution based on their digital behavior. 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? It’s Companies comparing the products of other vendors in your category. In fact, seven of the top eight 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 the choices have likely been narrowed to a handful.

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

The most significant outcome of the survey? Learning that just 15% of respondents mesh Fit, Opportunity, and Intent data. Why? We think it’s because scoring these three factors in combination tends to be sequential and piecemeal—and not always well understood by sales and marketing teams.

As we unpack this “predictive black box,” the most surprising takeaway is not that any single data point is the magic bullet. There’s no single data point that can replace good selling skills. But what is surprising is that when all three types of data – Fit, Opportunity, and Intent – come together, they are tremendously effective at predicting a purchase.

Which Data Points are actually predictive? You can read the full study here.

Katie Bullard is chief growth officer at DiscoverOrg, where she is responsible for leading the global marketing, product management, and partnership functions at DiscoverOrg. She brings 15 years of marketing, product, and strategy experience in global, high-growth technology businesses to her role at the company.

About Lisa

Editorial Director at SellingPower.com.
This entry was posted in Prospecting and Leads. Bookmark the permalink.

Leave a Reply

Your email address will not be published. Required fields are marked *

*