WORK
Equifax Workforce Solutions | The Work Number
$6M+ in influenced revenue over 20 months
When I joined Equifax, one of my first assignments was to take over a retention campaign for The Work Number, part of a roughly $20 million line of business.
What I inherited had run only a couple of times. The idea was sound, but executing it required help from nearly every part of the marketing organization.
Data & Analytics built the audience. Brand helped write the copy. Creative Services built the email in Stensul. Legal approved it. Marketing Automation loaded the audience into Salesforce Account Engagement and sent the campaign. Afterward, Data & Analytics calculated the revenue associated with customers who engaged with the email and subsequently transacted.
I was initially expected to keep the campaign running.
Instead, I started by pulling the whole process apart.
I met with the people involved in each part of the campaign to understand how the audience was created, how the email was built and sent, and how influenced revenue was calculated.
That led me to an important operational detail.
Inactive accounts in The Work Number could eventually be suspended for compliance reasons. But the inactivity milestone Marketing was using to identify customers for outreach wasn’t aligned to that suspension timeline.
That changed how I thought about the program.
Revenue was important. It was how senior leadership determined whether the campaign was worth continuing. But the customer problem was bigger than generating another transaction.
We had an opportunity to reach customers before inactivity resulted in account suspension.
I redesigned the program around the roughly 120-day inactivity window that preceded suspension.
Instead of treating inactive customers as one audience, I created three stages based on how close an account was to suspension: approximately 30 days, 15 days and five business days.
The messaging changed with the customer’s state. Someone early in the window didn’t need the same treatment as someone approaching suspension.
If a customer transacted, their activity clock reset and they naturally dropped out of the program.
What had started as a recurring email was becoming an always-on lifecycle system.
The next problem was making the system practical to run.
I learned Stensul so I could build the emails myself. I took over the copy and legal-review process. I worked alongside our Marketing Automation Manager to learn how audiences, campaign tracking and workflows were managed in Salesforce Account Engagement.
Then I worked with a partner in Data & Analytics on the upstream dependency.
Initially, getting a new audience required submitting a request and waiting for Data & Analytics to manually pull it. Together, we turned the underlying SQL queries into self-service reporting in Looker.
Now I could access the current inactive population myself.
There was still manual work. I exported the data from Looker, cleaned and transformed it, separated it into the three inactivity segments, and uploaded and tagged those audiences in Salesforce.
But once they were loaded, workflow automation handled most of the recurring execution, including the initial treatment and follow-up logic.
That mattered because I’d already learned what happened when I tried to scale the program through manual effort. At one point, the segmentation and resend strategy had me manually managing as many as nine emails per week. It wasn’t sustainable, and the pace increased the risk of errors.
Automation allowed me to move the program back to a weekly cadence without rebuilding that operational burden.
Audience creation wasn’t the only Data & Analytics dependency.
Reporting still required another request.
So we built a second self-service process in Looker. I could take customers who engaged with the Salesforce campaigns, run those accounts through Looker over a defined date range, and calculate the verification revenue they subsequently generated.
Senior leadership agreed to a 120-day attribution window. If a customer engaged with the program, subsequently transacted and generated verification revenue during that window, I reported that revenue as influenced by the program.
That distinction matters.
This was attribution, not proof that the campaign caused every dollar of subsequent revenue.
Over roughly 20 months, the methodology produced more than $6 million in cumulative influenced revenue.
I also saw the eligible inactive population decline substantially over the life of the program. Individual campaign audiences that had started in the low 20,000s were typically around 11,000 by the time I left.
I can’t claim the program alone caused that decline. But it was an encouraging operating signal alongside the influenced-revenue results.
I eventually wanted a stronger answer than attribution could provide.
Would these customers have transacted anyway?
I began introducing holdout groups so I could compare treated and untreated customers and look for incremental lift.
That turned out to be harder than simply withholding an email.
Customers moved through the program as their inactivity increased. Someone held out at the 30-day stage could later enter the 15-day stage and eventually the five-business-day stage. A valid control needed to remain untreated across that entire progression.
Maintaining those persistent holdouts across changing customer states added substantial manual complexity to the remaining data process.
I experimented with it, but I never produced evidence strong enough to claim statistically significant incremental lift.
So I don’t.
The result I can support is the one we measured and reported: $6M+ in influenced revenue over 20 months.
By the time I left Equifax, the pilot I’d inherited had become a recurring system spanning customer-state segmentation, differentiated treatments, workflow automation, self-service audience creation and attribution reporting.
More importantly, it didn’t depend on me to keep running.
During my final two weeks, I documented the program, trained the email marketers who would inherit it and transferred its operation to the team.
That’s the part of the work I value most.
I inherited a campaign. I learned the system around it, found the customer problem underneath it, removed as many operating dependencies as I could, built something repeatable, ran it long enough to prove its value, and handed it off.