Building an ABM program to grow revenue per customer

The problem

The client is a boutique IT consulting company. Growth from new business had been slow, while its existing clients held more demand than the team had tapped. Leadership wanted to put effort where it would pay, and that raised five questions the company's own data could not yet answer.

• The team needed to know how large its market was. It also needed to see the competitive landscape well enough to judge how much of that market it could realistically win.
• Every account in the market needed deep attributes attached to it. Without them, accounts could not be compared on the signals that predict fit.
• Prioritization had to weigh more than history. Past wins mattered, and so did current signals and how well each account fit the company's future direction.
• Leadership needed reporting it could read at a glance. Every touch point and activity had to appear in a structure that did not require a briefing to understand.
• Marketing and sales could not overlap or duplicate each other's outreach. Two people contacting the same buyer damages a relationship, which meant the work had to be coordinated with sales from the start.

The MotiveGTM approach

Assembling the list took little time, because the client already knew its wish list of new accounts, its key growth accounts and the win-loss history sitting in its CRM. Grading, scoring and ranking those accounts was the harder job, since each one had to be judged on firmographics, technology stack, growth patterns and other market and technology signals. We organized the list into one-to-one, one-to-few and one-to-many tiers, and the work ran in four steps.

1. We rebuilt the ideal customer profile from real buyers. The profile came from the buyers who appear in the CRM and in sales conversations instead of from assumptions, and we then listed the potential accounts that fit it.
2. We mapped the people around each account. That covered decision makers, influencers, former employees and first-degree connections, and we worked with the sales team to build exclusions for accounts and contacts they already handled.
3. We segmented accounts on dozens of variables. These included account type, industry, revenue, company size, technology stack, investment focus, acquisitions and executive movement.
4. We applied custom scoring and weighting. Each account was rated on its win rate and also on its strategic value, projected growth opportunity, key signals and relationship strength.

The commercial and revenue teams reviewed the model and signed it off before anything was acted on.

The solution

With sign-off from the commercial and revenue teams, we turned the scores into working processes.

• Outbound and BDR effort now follows the tier. Each account's score decides how much of the team's time it receives and which motion it gets.
• The marketing technology stack was audited. We assessed which tools performed well and which should be sunset.
• Clay automations distribute and refresh the lead lists. The lists are refreshed as signals change and are distributed to the people who act on them.
• Outreach motions are set by account score. Every tier has its own sequence of touches, matched to the value of the account.
• People step in by hand on the highest-value accounts. A person manages every touch point for those accounts, and again whenever contacts move between decision layers.

The impact

The client came away with a scored account list, a way of ranking it and a system for acting on the ranking.

• The client has a scored list of more than 2,000 accounts. Each one is segmented by how well it fits the ideal customer profile.
• The scoring system reflects where the company wants to grow. It rates accounts on future fit as well as past performance, which keeps the list from simply repeating last year's wins.
• Outreach follows a plan built on the scored data. The plan draws a clear line between what the team personalizes by hand and what it automates.
• The client has a firmer basis for reporting. It can show total addressable market (TAM) and coverage to investors and to its own leadership with more confidence.
• The Clay infrastructure can carry future campaigns and experiments. The team can run them without rebuilding the system.

What has to stay true

A scoring model describes a market on the day it was built. Executive moves, acquisitions and technology changes keep shifting the picture, and the lists need regular refreshing to keep up, which is the job the Clay automations were built to do. The model also depends on sales continuing to record outcomes in the CRM, because win-loss history is one of its inputs, and it works only while the account owners trust the tiers enough to act on them.

This write-up describes the system and the structure the client now has. It does not report pipeline or revenue figures.

Teams in a similar position usually face the same first decision, which is which accounts deserve a person's time and which deserve an automated sequence. Our ABM framework describes how we answer it.