The Difference Between Answering Requests and Creating New Revenue Streams
Once upon a time, I worked at Adobe. Well, in 2005, to be precise (though it does feel like a few centuries ago when I compare it with what my life building AskEnola looks like right now).
At the time, I was working closely with relationship marketing initiatives for Acrobat, and that drove roughly 10% of the product’s revenue (please remember from here on out that all the numbers mentioned here are merely indicative of the actuals). That was the portion of revenue generated by selling directly to people already in our database. It was considered healthy. It was also treated as the limit of what could be done.
The analytics work around it reflected that mindset. We measured email performance, conversion rates, and campaign lifts. We optimized subject lines. We compared month-over-month trends. All valuable. All incremental.
We were marketing to about 2.2 million known Acrobat users, and that was the working universe. The assumption behind every dashboard and every report was simple: this is our base, now improve it.
Meanwhile, one fine day, I noticed that tens of millions of people had downloaded Adobe Reader. Their information existed in our systems. It was stored, maintained, backed up, and ignored. No one had asked what that population represented strategically. It was just there.
That, of course, is how analytics quietly becomes small. Not because the team lacks skill, but because the frame is too narrow.
The Question That Changed the Frame
I was working closely with Acrobat’s product manager at the time, and as we reviewed performance, something about the boundaries felt artificial. Why were we acting as if our addressable base stopped at 2.2 million?
The more interesting question was not how to improve conversion within that group.
It was this: among the millions of Reader users, who looks like a future Acrobat buyer?
That question shifted analytics from optimization to expansion.
I built a lookalike model. We analyzed the behavioral and demographic characteristics of existing Acrobat buyers and scored the broader Reader base against that profile. The goal was not theoretical insight. The goal was to identify a new revenue segment hiding in plain sight.
Several million Reader users surfaced as strong candidates. On paper, the model performed well. At a point like that, most analytics projects quietly conclude. A model is built. Accuracy is reported. The team moves on.
That would have been a mistake.
From Model to Money
A scored list is not a revenue stream.
Once we identified high-potential users, the real work began. Each segment needed a strategy. What offer should they receive? What messaging would resonate? How should the cadence differ from the existing Acrobat base?
This required deep coordination with CRM and marketing execution teams. The model outputs were structured for deployment, not presentation. Segments flowed directly into campaign systems. The teams understood how to act on them.
We tested. We measured. We refined.
The outcome was not a marginal lift. We doubled the relationship marketing portion of Acrobat revenue through this initiative. The impact was visible in the P&L, not just in a slide deck.
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More importantly, the exercise created a repeatable engine. For future launches and ongoing programs, we now had a disciplined way to mine underutilized data, model potential, and activate it. A one-time project became an institutional capability.
The breakthrough was not predictive modeling as a technique. It was the decision to connect modeling tightly to execution.
The Structural Trap of Ad Hoc Analytics
Most analytics teams do not lack intelligence. They lack altitude.
Organizations naturally reward responsiveness. A stakeholder asks for a report. A leader wants to understand a dip in weekly revenue. Marketing needs analysis on an experiment. The analytics team delivers quickly. The cycle repeats.
The work feels productive. It rarely alters the growth trajectory.
When analytics operates primarily as a request-response function, it inherits the assumptions embedded in those requests. The addressable market is fixed. The customer definition is fixed. The revenue streams are fixed.
In the Acrobat example, the narrow question would have been: how do we improve performance within our 2.2 million email contacts?
That question assumes the universe is correct.
The broader question was: what if our universe is incomplete?
One reframed question expanded the revenue pool by millions of potential customers.
Designing Analytics for Business Impact
That experience shaped how I think about analytics leadership.
When I tackled this project, I was not focused on precision metrics alone. I was thinking about revenue concentration, portfolio expansion, and long-term monetization. The work I did existed in the service of financial outcomes.
This orientation changes how analytics work is designed.
First, every initiative should be anchored to a business metric that matters at the executive level. Revenue mix. Lifetime value. Margin. Retention.
Second, the path from insight to decision must be explicit. Who will act differently because of this analysis. What system will change. What campaign will be launched or paused.
Third, deployment is not an afterthought. If an output cannot be integrated into operational systems, its impact will remain theoretical.
In the Adobe case, the modeling and CRM teams worked as one system. The scoring logic was built with segmentation and activation in mind. Feedback from campaigns improved subsequent iterations. Analytics became part of the growth machinery.
That is very different from analytics as an internal consultancy producing elegant decks.
The Quiet Power of Reframing
Looking back, the most important move was not technical. It was conceptual.
A database of Reader users looked like historical residue. In reality, it was a growth asset waiting for interpretation. The organization had not been wrong. It had simply not been curious at the right altitude.
Analytics earns its strategic seat when it reframes opportunity.
Not by producing more dashboards.
Not by answering more tickets.
But by challenging the implicit boundaries around customers, markets, and revenue.
Today, data volumes are larger and tools are more advanced. The temptation to optimize endlessly within existing segments is even stronger. Sophisticated attribution models and experiments can consume entire teams.
The question I still ask, and encourage others to ask, is this:
Where might the next 10 percent of growth already be sitting in your systems?
If your analytics team is primarily measured by how quickly it closes requests, you may be overlooking a deeper mandate. The real mandate is to identify latent revenue, translate it into action, and institutionalize the capability.
In 2005, we did not set out to build a grand transformation story. We asked a slightly bigger question. We connected analysis to execution with discipline. The revenue followed.
Analytics stopped being a reporting function and became a growth driver.
That shift is available to any organization willing to move beyond the comfort of ad hoc answers and toward the courage of strategic reframing.
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