Beyond One-Size-Fits-All: How ML-Powered Personalization Is Reshaping Advertiser Growth Programs

For years, advertiser growth programs were built on blunt operational logic. Platforms created broad incentive buckets, routed funds toward visible accounts, and relied on manual rules to determine who should receive credits, discounts, or strategic support. That approach worked when growth programs were smaller and advertiser bases were easier to manage. It becomes far less effective when a platform serves millions of businesses whose needs vary by maturity, technical capability, budget pressure, and readiness to adopt newer advertising tools. What looks efficient at the portfolio level often becomes imprecise at the business level.

That gap matters more now because small businesses are carrying a larger share of economic pressure while being asked to compete inside increasingly automated commercial systems. In the United States alone, there are 36.2 million small businesses, and they account for almost 46% of private sector employment. From March 2023 to March 2024, they created roughly 9 out of every 10 net new jobs. Yet many of the growth systems meant to support them still operate with logic designed for a narrower, more manually managed age.

Stuti Mohan has built her work around that disconnect. As a strategy and operations leader focused on monetization systems and a judge for the 2026 AI Excellence Awards in Business Intelligence, she has argued for replacing broad-stroke incentive distribution with models that allocate support according to where a business actually is in its growth journey. “When support is distributed too broadly, the system rewards scale that is already visible,” she says. “The harder and more important task is building a model that can identify unrealized potential and respond with precision.”

That principle sits at the center of a larger market transition: growth programs are no longer just budget mechanisms. They are becoming decision systems.

Why Manual Incentives Stop Scaling
The weakness of manual incentive programs is not simply that they require more labor. It is that they embed strategic assumptions that become less accurate as advertiser ecosystems expand. Human-managed programs tend to favor fixed tiers, legacy segmentation, and periodic reviews. They often prioritize who has spent before over who could grow next. In a large digital advertising environment, that creates a structural bias toward businesses already fluent in the platform while leaving smaller advertisers under-supported precisely when targeted help could change their trajectory.

Stuti confronted that problem while leading strategy for a multi-hundred-million dollar global incentive portfolio designed to help advertisers, agencies, and partners adopt newer advertising capabilities and improve performance. The challenge was not just budget allocation. It was systemic fragmentation. Programs had accumulated across markets and user types with different structures, thresholds, and objectives, making it harder to see which incentives were truly driving durable business value. Her work focused on redesigning that portfolio into a more unified, customer-first system and building the execution path required to move a global organization toward it. The business impact was substantial: the incentives reached 3x+ more advertisers within 1 year, helping hundreds of thousands of small businesses adopt new solutions to improve the return on their ad spend, while also driving measurable increases in ROI on the incentive portfolio. Those results matter because they show that personalization is not a cosmetic upgrade. It changes customer behaviour in a sustainable manner and shifts how capital performs.

This problem is becoming harder for small businesses to absorb on their own. According to Constant Contact’s 2025 Small Business Now research, just 18% of SMBs say they feel very confident in their marketing, down from 27% in 2024, and 23% say their top frustration is not knowing what is driving results. In other words, the market does not just have a funding problem. It has an interpretation problem. “Most small businesses do not need more noise,” Stuti says. “They need support that is aligned to what will actually move them forward at that moment.” Manual systems do not do that well because they cannot continuously learn, reprioritize, and allocate with enough granularity.

Retail Precision Comes to Ad Systems
What makes Stuti’s approach distinctive is that it did not emerge solely from within the advertising world. Part of its strength came from importing a discipline that retail organizations have refined for years: precision marketing. In retail, promotions are rarely most effective when they are distributed uniformly. The best systems model customer behavior, identify likely response patterns, and tailor offers according to timing, value, and stage. That logic is obvious in consumer marketing, yet much less consistently applied to incentive design for advertisers themselves.

Stuti recognized that disconnect and pushed for a machine learning based incentive engine that could personalize support according to each advertiser’s business needs and growth potential rather than routing them through static program logic. Working across strategy, engineering, and data science, she helped shape a system that could move the organization away from broad promotional structures and toward stage-aware allocation. Instead of treating incentives as a periodic intervention, the new system treats them as a dynamic mechanism for guiding adoption, encouraging better use of advanced tools, and improving the odds that smaller businesses could access the kind of tailored support historically reserved for larger accounts.

It reflects a broader mismatch between what platforms say they want to deliver and the operating models they still use. Stuti’s work addressed exactly that mismatch by translating precision methodology into infrastructure. “Personalization only becomes meaningful when it benefits both parties, and therefore changes how decisions are made,” she says. “Otherwise, customer centricity is just better terminology on existing ways of working”.

Access to Growth Capital at Scale
The most important consequence of this transition is not internal efficiency, though the efficiency gains are significant. It is the redistribution of access. In practice, incentives function as a form of growth capital. They lower the cost of experimentation, reduce the barrier to adopting newer tools, and give businesses room to learn systems they might otherwise avoid because the economic risk feels too high. When those incentives are distributed manually, access tends to concentrate. When they are personalized algorithmically, access can widen.

That is what makes Stuti’s first project especially consequential. By replacing fragmented manual programs with an ML-powered system, the platform expanded its reach to support over a quarter million businesses. It also allowed the platform to personalize incentives for 2x to 4x more advertisers than manual operations had previously made possible. For over 300,000 businesses, this was not just a back-end modernization exercise. It changed who could receive financial and educational support and when. In one reported outcome, participating businesses saw gains such as a 41% reduction in cost. Those numbers illustrate the deeper logic of the system: precision at scale can function as an equalizer when it is applied to access, not just optimization.

The broader market context makes that especially relevant. Constant Contact reported in March 2025 that 52% of early-stage entrepreneurs identified customer acquisition as their top marketing challenge, while 72% said they planned to use AI for marketing in 2025. That combination is telling. Small businesses are under pressure to grow, and they are increasingly willing to adopt advanced tools, but willingness alone does not solve the operational access problem. Someone still has to design the systems that determine who gets support and under what conditions.

Stuti’s work has focused on exactly that layer. As a jury member for the WeRise program, she brings the same logic to initiatives focused on widening access for businesses that have historically had less institutional support. “The point of algorithms is not to make decision-making feel more sophisticated,” she says. “The point is to increase the impact and identify the opportunities that matter”.

The Next Growth Model for Platforms
The next phase of advertiser growth will not be won by platforms that simply offer more credits, larger budgets, or louder promises of AI enablement. It will be won by platforms that can allocate support with discipline, responsiveness, and enough contextual understanding to serve businesses at very different stages without collapsing them into the same operational bucket. That requires treating incentives as a strategic infrastructure layer rather than a patchwork of campaigns.

Stuti’s work offers a clear view of what that future looks like. A fragmented portfolio becomes a unified system. Manual approvals give way to machine-guided allocation. Support expands from a limited population to hundreds of thousands of businesses. Financial efficiency improves, but so does access. The result is not just better monetization performance. It is a more credible growth model for the businesses that depend on these systems to compete.

The broader lesson is larger than advertising. As more sectors adopt machine-led allocation systems, the real differentiator will not be who automates first. It will be who automates intelligently enough to direct resources where they can change outcomes rather than merely reinforce existing scale. “The future of growth programs is not more investment, more people, or more programs,” Stuti says. “It is better decision systems that can recognize who needs support, when they need it, and what form that support should take.” That is the real move beyond one-size-fits-all. It is not a messaging change. It is an architectural one.