The Untapped Goldmine: How Measuring the “View of the Customer” Can Transform SaaS Growth

For the past two decades, SaaS and tech companies have obsessed over the voice of the customer — collecting, parsing, and analyzing text feedback from surveys, reviews, and social media.

But there’s a problem: people don’t read anymore. They recognize. And the internet is now dominated by visual content — imagery powering trillions of dollars in e-commerce, ads, and brand experiences. Yet most companies have no system to measure how those visuals perform before launch.

Jehan Hamedi, founder of VIZIT, saw the gap. His insight: text analytics tools report on the past, but brands need predictive visual intelligence to win in an “infinite scroll” economy.

Lesson 1: Find the Next Unmeasured Data Frontier

Hamedi’s background was in quantitative social science, building platforms to analyze online conversations. But when consumer behavior shifted from text to images, he realized:

Visuals drive most purchase decisions.
No one was measuring them at scale.
Brands were guessing — and often getting it wrong.

For SaaS founders, the takeaway is simple: look for high-impact business inputs that remain unmeasured or unanalyzed in your category. Being first to quantify them can become your moat.

Lesson 2: Move from Reporting to Predicting

Social listening tools mine historical text data. Hamedi wanted to predict how an image or design would perform before spending millions on campaigns.

VIZIT’s approach:

Mine oceans of existing visual interaction data.
Use AI to simulate target audiences.
Predict real-world performance in real time, during the creative process.

For SaaS, this is a reminder that predictive insights beat historical reporting in competitive markets. If you can shorten the “idea → decision” loop for your customers, you win adoption.

Lesson 3: Focus by Following the Customer’s Pain

Visual intelligence could apply to hundreds of use cases — packaging design, digital ads, product imagery, social posts. The risk? Spreading too thin.

VIZIT narrowed its focus by asking:

Which problems are customers willing to introduce us to their peers over?
Where did our tech directly produce measurable business lift (e.g., sales)?

That narrowed the path to early high-ROI case studies, which became the growth engine.

For SaaS teams facing endless feature ideas: follow the pain that customers will talk about publicly. That’s your wedge.

Lesson 4: Prove Outcomes Before Scaling Product

Early VIZIT engagements looked more like “tech-enabled consulting” than a SaaS self-serve platform. Why?

The IP (their proprietary visual dataset) was valuable but raw.
The interface wasn’t fully productized.
Customers didn’t care about the tech yet — they cared about sales lift.

By embedding the AI into solving a single urgent problem, VIZIT earned the right to expand and build full product features later.

For SaaS founders, this is the land-and-learn model:

Sell outcomes now. Productize later.

Lesson 5: Hire for Mission, Not Just Skills

Hamedi’s early team came largely from trusted past colleagues — people who shared both the skill set and appetite for tackling unsolved problems.

In AI (and SaaS generally), this matters:

You need builders who thrive without a playbook.
Cultural fit in the early team compounds over time, attracting like-minded talent.

Lesson 6: Keep Feedback Loops Human, Even in AI

Despite building cutting-edge AI, VIZIT’s best customer insights came from direct human conversations, not dashboards or surveys.

At their stage, they avoided over-engineering feedback collection and focused on real-time trust-based discussions with decision-makers.

For SaaS: Don’t hide behind your product analytics. Pair data with direct, unfiltered conversations to shape roadmap priorities.

Consulting Takeaway for SaaS & Tech Leaders

VIZIT’s story offers a repeatable playbook:

Spot an unmeasured, high-impact business input.
Shift from reporting on the past to predicting the future.
Narrow focus using customer-validated pain.
Prove ROI with outcomes before building all the features.
Build a mission-aligned early team.
Use human feedback loops to sharpen AI outputs.

In a world drowning in data, the biggest growth opportunities still lie in what no one’s measuring yet. Find that — and you can own your category.