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Fit Plus Intent, The New B2B Metric

Sep 10, 2019 | Author John McTigue

This post originally appeared on the Intent Data publication on Medium.

Faced with revenue-centric goals, rather than the traditional vanity metrics, sales and marketing professionals are seeking better ways to deliver on those goals and measure progress. There are plenty of new strategies and tools available, but not much consensus on how to evaluate results.

Yes, we’re still looking at sales process metrics, like lead stages, qualified opportunities, closed won deals, and monthly revenues, but what about additions to pipeline from net new accounts and account team members? How can we reconcile account-level vs contact-level response and make sense out of different forms of demand and lead generation? What’s the best way to prioritize sales contacts and accounts in a world of multiple data sets and complicated tech stacks? The age-old funnel stages, leads, MQLs and SQLs, no longer seem relevant in today’s account-based strategies, and they’re often too loosely defined for highly targeted inbound marketing. We need a better mouse trap, or at least a better way of measuring mouse trap results.

 
Photo by Sharon McCutcheon on Unsplash

Fit Plus Intent

I’m proposing Fit Plus Intent (FPI) as a new key performance indicator for aligned sales and marketing teams focused on both new and existing accounts and account teams. The idea is that prospects that are a great fit for your products and services AND are ready to enter your sales funnel or flywheel are the accounts and members we need to prioritize for ABM teams and/or sales development efforts. Both Everstring and Clearbit have mentioned fit plus intent in their discussions of intent data and its practical use, but as far as I know, no one has yet put together a framework or formula for calculating FPI.

So here goes…

What About Lead Scoring?

Let’s take a look at a familiar metric, lead scoring, as a starting point. Typically, lead score is calculated by summing the number and/or types of interactions a contact has had with your online properties, multiplied by a scale factor based on your view of the importance of each type of input data. For example, X number of website page views plus Y number of visits to your pricing page (multiplied by 2 for importance) plus points for having a C-Suite job title, working for a certain size company, and being in your target industry, etc. Lead scoring can work well if it matches your actual criteria for prospect qualification and fit — and you’re able to collect the right data. If high lead score prospects turn into qualified opportunities and closed won sales on a consistent and reliable basis, you have yourself a winning algorithm.

The bad news is that lead scoring isn’t always relevant, and it’s often ambiguous. If you’re doing ABM, your primary focus is on accounts, not leads. Lead scoring per se doesn’t measure the aggregate behavior of the buying teams in target accounts, so you can’t identify and prioritize them. The other big problem is that lead scoring derives from your own collected data, i.e. first party intent data, so it doesn’t work for leads or accounts that have never visited and converted on your website, ads or social media sites. Another huge problem is that lead scoring tends to be done without rigorous statistical analysis, so the factors you measure and rank have no basis in truth — they’re just educated guesses at best.

So how does FPI solve these problems?

What Do We Want to Know?

In B2B, we typically have complex sales and long sales cycles. It’s no secret that most of the buyer journey happens prior to your first sales call, and that on average, six or seven buying team members are involved in the decision process at some stage. To optimize this journey (for our buyers and for us), we want to know:

  1. Which companies are our most desired customer accounts, and why?
  2. Are they somewhere in the buying process right now?
  3. Are they considering us? Are they considering our competitors?
  4. Who is on their buying team? Which person(s) are most important?
  5. What will it take to win over the individual buying team members?
  6. What will it take to win the account as a whole?
  7. Assuming we win, what should we do to retain and expand the account over time?
  8. How well have we done at optimizing each one of these complex customer journey stages?

Note that each one of these questions involve some measure of FIT or INTENT. Now, let’s break them down into component parts.

Account- Level Fit

If you’ve put together an ideal customer profile, you have this demographic and firmographic information already. If not, you can compile the data from first-party and third-party sources.

Take your top five criteria, for example:

  • Industry
  • Company Size
  • Revenues (or class)
  • Location
  • Growth or Market Share

Now, for each company you collect, score them from 1–5 points for each criteria, for a maximum total of 25 points.

Account-Level Intent

Through a combination of first-party and third-party intent data sources, build a profile for each company with your top criteria, such as:

  • Doing research on industry problems we address
  • Doing research on our competitors
  • Visiting our website and pages, blogs
  • Visiting or converting on our offers, demos
  • Responding to ABM demand gen and/or email campaigns we promote

Again, score companies from 1–5 on each criteria, for a maximum total of 25 points.

Contact-Level Fit

Through a combination of first-party and third-party data sources, build a profile for each contact within your target accounts with your top five criteria, such as:

  • Job Title and/or Role
  • Department or Division
  • Essential Contact Information (Available?)
  • Location
  • Years of Service

For each account contact you collect, score them from 1–5 points for each criteria, for a maximum total of 25 points.

Contact-Level Intent

Through a combination of first-party and third-party data sources, update your contact profiles and evaluate criteria buyer intent, such as:

  • Are they the decision maker? The only decision maker?
  • Are they actively searching? How urgent is their search?
  • Will they be attending relevant events?
  • Have they directly engaged with your brand? How often?
  • Are they an influencer for the rest of the buying team?

For each contact you collect, score them from 1–5 point for each criteria, for a maximum total of 25 points.

Putting Together FPI Scoring

This sounds like a lot of manual updates to your accounts and contacts, but all of this can be automated through marketing automation workflows, provided that you can collect, process, and evaluate input data from a variety of third-party data sources in addition to your web properties. What you do with this data depends on the question you are trying to answer.

  1. Which companies are our most desired customer accounts, and why?
  • Calculate Account-Level FPI = Account Level Fit + Account Level Intent.
  • Chart the data as a bar chart or scatter plot
  • Identify priorities and send a list to your ABM team or SDRs to follow up

2. Are they somewhere in the buying process right now?

  • Calculate Account-Level Intent
  • Calculate Contact-Level Intent
  • Combined-Level Intent = Account-Level Intent + Average(Contact-Level Intent for all Account Contacts)
  • Chart the data as a bar chart or scatter plot
  • Identify priorities and send a list to your ABM team or SDRs to follow up

3. Are they considering us? Are they considering our competitors?

  • Do a search/filter on your third-party intent data looking for us and competitor engagement with content or events
  • Update Account-Level Intent score if activity level warrants it

4. Who is on their buying team? Which person(s) are most important?

  • Calculate Contact-Level FPI
  • Combined Contact-Level FPI = Contact-Level Fit + Contact-Level Intent
  • Do a search filter on contacts within an account
  • Evaluate the Contact-Level FPI and rank order them
  • Identify priorities and send a list to your ABM team or SDRs to follow up

5. What will it take to win over the individual buying team members?

  • Make sure your ABM team and/or SDRs have access to both account-level and contact-level data
  • Train them to view first-party and third-party intent data to see topics, sources, engagements with content and other intent signals that can be used to stimulate demand and nurture deals

6. What will it take to win the account as a whole?

  • Continue to monitor changes in both fit and intent for both accounts and their buying teams
  • Put in place alerts to notify account executives or AMB team leaders when status changes, tempo increases or decreases, or competitor sites are visited more frequently
  • Continue to nurture account primary contacts and teams with relevant, timely information based on their updated profiles

7. Assuming we win, what should we do to retain and expand the account over time?

  • Continue to monitor changes in account-level and contact-level FPI, with a focus on expansion signals (changes in job title, for example) or churn signals (visits to competitor sites, for example)
  • Continue alerts to notify account executives or AMB team leaders when status changes, new research initiatives, or competitor sites are visited more frequently
  • Continue to nurture account primary contacts and teams with relevant, timely information based on their updated profiles

8. How well have we done at each one of these complex customer journey stages?

  • Use account-level and contact-level FPI as a key performance indicator of finding highly qualified, sales-ready prospects
  • Monitor changes in FPI over time to see positive and negative trends, like new product or service additions, or breakdowns due to silos
  • Relate FPI directly to sales data and evaluate ROI throughout the sales-marketing-service pipeline and customer journey

My apologies for the long post, but I wanted to get into the weeds about fit plus intent as a new metric. It’s one thing to describe a need, and another to propose both a process and specific steps for implementing it. I believe that we are heading towards a total confluence of sales, marketing, and customer service, and we need new ways of collecting and analyzing relevant data for today’s combination of account-based and contact-based strategies.

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