The Ideal Customer Profile: Why is “Fit Data” so Important?

All organizations, even global brands, are limited: by size of their sales and marketing teams, by budget – and, of course, by the finite number of hours in day.

A clear Ideal Customer Profile (ICP) allows sales and marketing teams to prioritize both outreach and follow up based on a person or account’s “Fit” – so that those precious dollars and hours are spent working on accounts that have the greatest likelihood of becoming customers.

Outbound demand gen campaigns can target the right leads with messaging that are statistically more likely to become customers. And sales follow-up can be prioritized based on how closely a lead fits an ICP.

The Ideal Customer Profile also creates alignment between sales and marketing around what a “good lead” looks like. It provides objective criteria for determining the quality of the leads being generated.

Lacking a clearly defined ICP, you have the perfect storm for low conversion, SDR burn-out and high turnover, poor customer retention, and stagnant growth.

Fit Data ideal customer profile

This focus on Fit data is the first of a 3-part series about predictive data, that will include Fit, Opportunity, and Intent data – the trifecta of predictive purchase intent in the B2B sales and marketing space.

Wait – what is “Fit” data?

Fit data is the information that informs the Ideal Customer Profile: What kinds of customers are a good fit for your product for service?

sales and marketing alignmentIn the B2B world, this often refers to demographic (personal) and firmographic (company) data, including:

  • Prospect Age
  • Business Model (B2B, B2C)
  • Company Budget
  • Company Revenue
  • Department Budget
  • Employee Count
  • Gender
  • Growth Rate
  • Industry
  • Job Department
  • Job Function
  • Job Level
  • Job Title
  • Technology Stack
  • Location
  • Ownership Type (Public, Private, Government)
  • SEC filings (Changes, Specific Classifications, etc.)
  • Use of Agencies or Contract Services

What Fit data should I focus on?

How do you actually identify an Ideal Customer Profile? What data points are important in determining a prospect’s Fit for your solution and organization?

A good starting place is: your customers.

The characteristics of your best previous customers can be leveraged to predict your next best customers.

How you define “best” is really up to you and your specific organization. It could be based on the size of the contract, but there are several other ways that you could identify your best customers – longevity, satisfaction scores, the most upsell/ cross-sell potential, etc.

Company (firmographic) data

Look the company data on your best customers: What industry are they in? What is the revenue size of the company? Are they public or private-sector? How many employees does the company have?

Where are they located? What does their technology stack look like? Are they tech-savvy with a lot of cloud-based applications, or are they more traditional?

Individual (demographic) data

Do a quick assessment of your buyers at those companies: What was the buyer’s job title? What was their role? What was their biggest pain points? What was the role of your customer champions?

All other information means nothing if the company or buyer are not a great fit.

So – what does YOUR ideal buyer look like?

Get our Ideal Customer Profile worksheet

Laser focus on Fit

“No sports team in existence aims to get near the goal; why should your business?” says Dion GeBorde, Senior Account Exec at DiscoverOrg. “A focus on your Ideal Customer is a focus on the win.”

Account-based marketing ABMBefore we go farther, consider this underlying assumption: It’s tempting to sell to any warm body that shows a spark of interest; however, companies see higher conversion, bigger deal sizes, and better customer retention if they prioritize prospects and companies that fit their Ideal Customer Profile.

That means being proactive about going after good-fit accounts. It also means treating inbound leads that do not match your Fit criteria with somewhat less urgency.

Your sales team can only reach out to a finite number of accounts each day. Any time spent following up with companies who are not a good fit, or spinning cycles with tire-kickers who don’t have the ability to actually sign the check, is time NOT spent on activities that are most likely to generate revenue.

If your sales reps are spending their time calling and emailing individuals who don’t have purchasing ability, or who won’t have budget to spend until next year, or who’s tech stack is incompatible with your product – a sale will never happen.

Inbound leads that don’t initially match your Fit criteria could still be potential customers, and they still deserve follow-up; just make sure your sales isn’t neglecting good-fit prospects in order to respond to these poor fits. Consider putting these leads into a nurture email campaign with the goal of qualifying them down the road.

This is why a data-based approach to identifying this foundational level of information is so critical for success. It allows your team to skip straight to the accounts that matter most to your bottom line.

Which Fit data points predict a buyer’s purchase intent?

DiscoverOrg recently surveyed 280+ sales and marketing leaders to see how many people were actually taking a data-based approach to account scoring. We asked which data points these professionals found to be most predictive, and how often they took this data-based approach.

Despite the fact that using a good-fit strategy is extremely effective at predicting a purchase, just 60% of respondents use Fit data.

The most predictive Fit data points, in order:

  1. Department Budget
  2. Job Function
  3. Technologies That Enhance the Value of Your Offering
  4. Technologies That Integrate Well with Your Offering
  5. Company Budget
  6. Job Department
  7. Job Level
  8. Technologies That Compete with Your Offering
  9. Growth Rate
  10. Lack of a Particular Technology
  11. Industry
  12. Company Revenue
  13. Location
  14. Business Model (B2B, B2C, B2G)

Four of the top 10 most predictive Fit data points involve the prospect’s tech stack. At the very least, consider your prospect’s installed technologies as part of your Ideal Customer Profile and account scoring process.

Get the ebook: Breaking Open the Predictive Black Box: Which Data Points Actually Lead to Higher Conversion Rates and More Sales?

Fit data and predictive intelligence

It’s so basic. So how does Fit data fit into the larger picture: focusing efforts on those accounts most likely to purchase?

predictive data points black boxPredictive intelligence is often illustrated as a mysterious “black box” of data and algorithms, mixed with a lot of assumptions that might work: if that the data that is used is accurate. If the data sample is large enough and representative of the whole. If the algorithms are reliable.

… That’s a lot of “if’s”! And the results must be taken at face value.

It’s a numbers game. “Predictive intelligence” is just a way of describing the strategic steps we take to improve our odds.

It’s probably safe to assume that future customers will have a lot of the same traits that great past customers have. By being intentional about establishing (and sticking to) Fit criteria, you can improve conversion and retention rates.

This is compounded when you combine other types of data, like Opportunity and Intent data.

A recipe for CRUSHING IT = Fit + Opportunity + Intent

The likelihood of purchasing lies squarely in the middle of a Venn diagram that includes three types of data: Fit, Intent, and Opportunity.

how startups can generate leads fast

Opportunity data refers to favorable conditions: Is the right time? Leadership changes, hiring, layoffs, promotions, partnerships, product launches, seasonality and time of day or day of week – these address the element of timing that is so critical to sales success.

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 buying signal becomes a truly predictive piece of the purchasing puzzle. These are the data points that indicate that conditions are favorable for a change.

Intent data refers to implied behavior: behavioral activity that links target buyers and accounts to a solution, solution category, or related topics. With a foundation of basic demographic and firmographic details in place (Fit data), and favorable conditions are present (Opportunity data), intent data becomes very useful for predicting success.

Intent data includes mostly web-based insights like web form-fills, downloads of content on a particular subject, event registrations, page visits, time on page, number of visitors from an account, and similar digital footprints.

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

Look for more ways to incorporate Opportunity and Intent data into your strategy in the coming weeks.

But first, take some time to nail down your Ideal Customer Profile. Take a step back, reassess what you think you know about your customers, and use that data to prioritize future outreach. Hitting your growth goals doesn’t take magic, and it’s not rocket science.

It just takes good data.

Which Data Points are ACTUALLY predictive?Get the complete study

As the Vice President of Demand Generation at DiscoverOrg, DeAnn and her team are responsible for sourcing 50% ..read more