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.
So here goes…
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?
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:
Note that each one of these questions involve some measure of FIT or INTENT. Now, let’s break them down into component parts.
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:
Now, for each company you collect, score them from 1–5 points for each criteria, for a maximum total of 25 points.
Through a combination of first-party and third-party intent data sources, build a profile for each company with your top criteria, such as:
Again, score companies from 1–5 on each criteria, for a maximum total of 25 points.
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:
For each account contact you collect, score them from 1–5 points for each criteria, for a maximum total of 25 points.
Through a combination of first-party and third-party data sources, update your contact profiles and evaluate criteria buyer intent, such as:
For each contact you collect, score them from 1–5 point for each criteria, for a maximum total of 25 points.
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.
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 each one of these complex customer journey stages?
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.