How insurers use AI with MMM to fix wasted marketing spend

Insurance carriers spend hundreds of millions of dollars a year across TV, digital, direct mail and agent co-op programs. Despite the immense costs, most still struggle to confidently answer basic questions: How are our marketing investments actually resulting in increases in premium and policies? How many would have renewed or converted regardless?

This frustrating lack of clear attribution represents both real unclaimed revenue. Legacy marketing measurement infrastructure institutionalizes biases driving leaders to inadvertently fail to leverage advances offered by relatively recent AI and data innovations.

That said, a wave of new AI-enabled frameworks for marketing mix modelling (MMM) are quietly becoming the new standard among forward looking marketing leaders, revitalizing the foundational value of statistical attribution. Encouragingly, this MMM resurgence is already producing double-digit returns for the carriers willing to adopt them.

Most measurement methodology was designed for a simple case: a single, short, digital-only path from ad impression to purchase. Insurance is, in most cases, a far more complex business, often involving multiple intermediaries and partners that obscure attribution.

Carriers run marketing dollars through independent agent and broker channels alongside direct digital acquisition, brand and awareness television, and retention campaigns aimed at policyholders who already exist. Personal auto and home purchases can close in a single session. Commercial and life products can take months and multiple decisionmakers. Co-op and market development funds flow to agents in ways that rarely show up cleanly in a digital attribution model at all.

Most carriers still run last-click or rules-based attribution built for none of this. They are measuring a funnel that does not match how their business actually generates premium, and the gap between the model and reality is where budgets get wasted.

Multi-touch attribution depends on identity signal: cookies, device IDs, consented tracking that stitches a consumer's path together. That signal has been degrading for years, and insurance carriers, already operating under some of the country's tightest data-privacy constraints (NAIC, CCCP, NYDFS, etc.), have less of it to begin with than almost any other industry.

The result is a measurement model stack quietly operating on incomplete data while still returning confident looking numbers. Anecdotal observations have seen enterprises across industries running attribution models overstating channel contribution by more than 40%, forcing expensive reallocation decisions based on numbers that were based on faulty foundations. Sophistication without proper data governance therefore produces an illusion of insight and unfortunately, subsequently gets funded with real budgets with declining results.

This is why marketing mix modeling (MMM), an aggregate, privacy-safe, data-driven statistical method with no dependence on individual-level tracking, has moved back to the center of the measurement conversation. A recent TransUnion survey citing July 2025 data noted that nearly half of U.S. marketers now plan to invest in MMM over the next year . This serves as a clear bellwether for our industry considering insurance's privacy exposure and channel complexity. All things considered, the shift seems less optional than overdue.

The statistical core of marketing mix modeling, Bayesian regression with adstock and saturation transforms, has not changed materially in twenty years. What has changed is the technologies and data tools supporting these approaches. Automated data pipelines have cut model preparation time from weeks to days. Open-source frameworks have collapsed the cost of entry that used to make these efforts prohibitively expensive, long and difficult to deliver successfully.

AI-assisted scenario planning now makes model output usable by non-technical users on the marketing team. This means marketers are empowered to do what they do best, largely freed from constant dependence on data science department queues.

The results are not theoretical. Early-stage MMM implementations across carriers suggest meaningful improvements, with gains typically ranging 10% to 30% depending on current state measurement maturity, based on field research and observation. The same marketing budget, reallocated correctly, produces measurably more incremental premium with greater visibility into where the next marketing investment should be spent.

The mistake most carriers make is treating this as a measurement upgrade. At its core, this is a revenue decision. Carriers investing in enhanced MMM capabilities have a material market advantage and will see that market gap only grow over time.

Every dollar currently over-credited to one channel is a dollar under-credited to another, sitting unused in a channel that could have produced more. Enhancements here allow for credible, reliable spend allocations that drive rapid optimization across channel spend.

This is also, increasingly, a conversation the CFO's office is getting involved in. A model that can state, with a defensible confidence range, how much incremental premium a given level of channel investment produced is a model Finance can act on. A marketing department asserting its own success from a self-reported dashboard will increasingly be circumspect by relative comparison.

The good news for insurance marketing leaders is that modern MMM is not a theoretical exercise. Proven approaches and techniques require specific infrastructure, configuration and planning – and have already been battle-tested in real world environments successfully.

None of this is novel methodology. It is available today, at a fraction of the cost and effort it required just two to three years ago. Better data, AI-enabled toolsets and open source frameworks are streamlining the adoption of these innovations while simultaneously making them more cost-effective.

The technology layer is fast evolving. The constraint on MMM has rarely been the statistics; it's more often been the operational overhead of running these models continuously and translating outputs into decisions fast enough and consistently enough to matter.

Agentic AI is closing that gap: agents that monitor incoming media and policy data for quality issues before they corrupt a model, orchestrate model refreshes without waiting on scarce data science capacity, and surface plain-language budget recommendations directly to marketing and finance stakeholders. For carriers, this is what turns a quarterly measurement exercise into a live input on channel allocation decisions – without requiring an army of data scientists to get there.

The competitive dynamic here is not subtle. When one carrier can show, with statistical confidence, exactly which channel and which dollar produced incremental premium, and a competitor is still relying on last-click attribution and gut instinct, the first carrier reallocates budget toward what works while the second keeps funding in the wrong directions. That gap compounds with every policy lost, every renewal cycle, every opportunity lost to a more savvy competitor.

The methodology exists. The cost of entry has collapsed. The only remaining question is which carriers adapt quickly and which get left behind.