At a glance:

Incorporating your own first‑party data with Marketing Mix Modelling (MMM) can make both sets of data far more useful and practical than when they are used in isolation. The combined data set enables marketers to measure customer lifetime value, tailor insights to different audiences, separate short‑term activation from long‑term brand impact and validate results with experiments. When integrated well, MMM enhanced by first-party data delivers more precise ROI measurement, better segmentation and LTV insights, improved long‑term impact assessment, and more direct activation.

1. Anchor decisions away from channel metrics into business outcomes.

Marketing performance is often evaluated through the lens of individual channels, each with its own set of KPIs. While these metrics are useful, they can distract from what ultimately matters: total commercial impact. Optimizing channels in isolation risks improving local performance without improving overall results.

MMM shifts the starting point; it links every marketing touchpoint to a clearly defined business outcome such as profitable sales or contribution margin. This allows decisions to be guided by what drives real value for the business, ensuring that budget is allocated based on impact rather than habit or historical precedent.

2. Account for both short-term response and long-term demand.

Not all marketing activity delivers value on the same timeline. Some channels generate immediate conversions, while others build brand equity and influence demand over the long term. Treating these effects as interchangeable can lead to short-sighted decisions that undermine future performance.

MMM accounts for both immediate response and longer-term carryover effects. By capturing how marketing impact decays over time, it enables a fair comparison between activities that drive short-term sales and those that contribute to sustained growth. This supports more balanced mix decisions that protect near-term results while continuing to invest in future returns.

3. Identify diminishing returns and reset optimal spend levels.

One of the clearest ways MMM supports efficiency is by quantifying diminishing returns. Response curves make it possible to see where additional spend yields little incremental return and where budgets are approaching saturation.

With this insight, teams can reallocate budget away from overinvested channels and toward underinvested activities with higher marginal return, or reallocate spend from expensive brand spots to targeted direct response during promotions. This approach preserves sales while reducing wasted spend, allowing organizations to lower total investment without resorting to indiscriminate cuts that risk damaging performance.

4. Improve efficiency through smarter targeting and stronger creative.

Targeting and creative decisions play a significant role in marketing efficiency. While more selective targeting can reduce wasted impressions and improve conversion rates, it often comes with higher media costs. Without a clear view of the trade-off, teams risk increasing precision at the expense of overall return.

MMM helps clarify where targeting remains efficient and where costs begin to outweigh benefits. By combining MMM insight with structured creative testing, organizations can focus on increasing the effectiveness of the impressions they retain. In many cases, improving creative quality delivers greater gains in ROI than increasing spend or tightening targeting further, allowing teams to drive more sales from the same level of investment.

5. Embed MMM into ongoing planning and decision making.

The full value of MMM is realized when it is embedded into regular planning rather than treated as a one-off exercise. Scenario analysis allows teams to compare expected sales outcomes under different budget levels and media mixes, making trade-offs between risk and reward explicit.

Regular updates ensure recommendations remain relevant as market conditions, seasonality and channel performance evolve. Over time, this creates a more disciplined and confident approach to budgeting and campaign planning, with continuous re-optimization built into the process. Furthermore, presenting both conservative and optimistic outcomes to stakeholders allows decisions to be informed by trade-offs between risk and reward.

At a glance:

Marketing Mix Modelling (MMM) is often treated as the finish line. Data is collected, the model is built, insights are delivered, and the program is considered complete. In reality, this is the point where the value of MMM either starts to compound or quietly fades away.

1. Build trust in the model, the process, and the outcomes.

Trust is the foundation of any measurement program. If stakeholders do not trust the model, they will not use it, regardless of how strong the analysis may be. This trust needs to extend beyond the outputs themselves to include how the model was built, how it is validated and what its limitations are.

Model design and validation should be communicated in a way that it matches the audience's technical understanding and role. Some stakeholders want to understand the underlying theory, while others are persuaded by forecasting accuracy and real-world performance. Data quality checks, clear interpretation guidance and transparent discussion of uncertainty all play a role in making recommendations feel more credible.

Trust is reinforced when insight is followed through. Tracking implemented recommendations and sharing their impact openly, including where results fall short of expectations, drives behavioral change and increases confidence in using the model as a decision input.

2. Agree governance before recommendations are delivered.

MMM insights often cut across teams, budgets and planning cycles, which makes governance critical to successful implementation. Effective programs establish this structure early. Short briefs and workshops with the teams responsible for action help ensure recommendations are understood and practical. Each recommendation should have a clear owner, with accountability embedded into existing monthly or quarterly planning forums. 

Governance also includes creating safe ways to act on insight. Test-and-learn frameworks, such as geo-tests, channel experiments, or creative variants, allow teams to validate impact at a manageable scale. This reduces perceived risk and provides real-world evidence of performance that supports larger reallocations over time.

3. Integrate MMM outputs directly into decision making.

MMM is most valuable when it is woven into everyday decisions. This requires translating model outputs into formats that decision makers can use quickly and consistently. Dashboards that compare recommended versus actual spend, forecast incremental outcomes, and show model confidence bands help stakeholders monitor impact and understand trade-offs. 

Where possible, MMM outputs should inform media planning platforms, bidding rules or budget-setting processes directly. At a minimum, every media plan should be accompanied by a forecast informed by the model, making assumptions and compromises explicit before implementation. When MMM is integrated in this way, it stops being an analytical overlay and becomes part of how decisions are made, debated and approved.

4. Measure outcomes and close the loop.

Measuring what happens after recommendations are implemented is essential to sustaining value and improving future decisions. Clear success metrics should be defined upfront, such as incremental profit, cost per incremental acquisition or lifetime value uplift. These outcomes should be tracked post-implementation and compared with model expectations. Experimental results, cohort analysis and short-term attribution can all be used to validate model assumptions and update priors.

Regular reviews of what worked (and what did not) help turn MMM into a learning system. Feeding these learnings back into the model through regular refreshes ensures insight evolves alongside the market and keeps recommendations relevant over time.

Google’s decision on third-party cookie deprecation—and what is still at risk for your brand.

Google's latest move doesn't signify a step back in prioritizing consumer privacy. Instead, it emphasizes giving users more individual control over their data. Similar to Apple's App Tracking Transparency (ATT) framework that rolled out in 2021, consumers will be given a more prominent opt-in/opt-out choice within Chrome. This functionality already exists within the browser’s settings, but will be surfaced in a “new experience” in the future, according to Google.

For brands who have not made significant progress in mitigating the impact of third-party cookie deprecation, this announcement might seem like a lifeline. However, even without a specific cut-off date from a centralized body like Google, there will still be a decline in use by consumers. With a gradual erosion as consumers opt out, the bigger danger is that many brands won’t realize that the third-party cookie pool is getting smaller and smaller, and therefore less useful for their ad strategy.

We expect the majority of third-party cookie signals to shrink, regardless of Google’s decision.

The digital industry has seen this scenario play out in the past, and the data shows the impact will still be huge, if just gradual. When Google switched to a third-party cookie for Google Analytics over ten years ago, Sayf Sharif, SVP Data, says that his analysis showed “some sites were losing over 80% of their traffic, depending on the industry, due to the adoption of ad blockers.”

This trend has repeated itself over the years; based on the impact from Apple’s ATT rollout, we’d expect to see cookies “capture maybe 15% of the available universe,” according to Liz DeAngelis, SVP Digital Strategy. Even if third-party cookies will continue to exist as an option within major browsers like Chrome, consumers have shown time and again that when made aware of their options, the majority will opt out.

Moreover, third-party cookies have proved increasingly ineffective in today’s digital landscape. Sharif points out, “We still face numerous challenges for measurement, activation and attribution (such as a high use of ad blockers, consent rules and fast cookie expiration), which make a focus on a cookieless approach to measurement and attribution a priority.” This shift to consumer choice underscores the reality that brands should continue to avoid over-reliance on third-party cookies.

Regulatory and consumer influences on third-party cookies helped shape Google’s decision.

The journey to Google's latest decision has been shaped by a blend of regulatory pressures and evolving consumer expectations. “Google has been caught in the crosshairs between evolving global privacy regulations and competition laws in a range of markets, most notably Europe,” says Benjamin Combe, Sr. Director, Data Optimization and Personalization. Similar regulations like the Australian Privacy Act have gained steam elsewhere, reinforcing that this is a global trend, not a regional or cultural one.

Meanwhile, consumer behavior has shifted toward greater consent and control over personal data. The move toward giving users the ability to set their preferences in Chrome, then, is well aligned with the experiences consumers seek online—and their changing attitudes and expectations toward digital privacy. Combe adds, “It merely reflects a more gradual end to a long-running, multi-factored trend. Google will no longer be the executioner, but third-party cookies are dying regardless—and their utility as the foundation of digital advertising’s targeting and attribution capabilities will not return.”

Still, cookies haven't been the only source of scrutiny in recent years. Google's Privacy Sandbox, a privacy-safe alternative to third-party cookie tracking, has faced several challenges since its announcement in 2020: the initiative has struggled with lack of adoption, anti-competitive scrutiny, conflicting industry feedback, mixed testing results and regulatory pressure. “Google’s Privacy Sandbox raised anti-competition issues with the UK’s Competition and Markets Authority (CMA), while simultaneously raising privacy concerns with the European Centre for Digital Rights and the UK’s Information Commissioner's Office,” Combe adds.

In short, both the regulatory landscape and consumer demand for greater data control led us here. So, what are brands supposed to do next?

Your brand’s first-party data strategies still need to evolve, or put your visibility and efficacy at risk.

Google's decision to give users control over third-party cookies rather than enforcing a complete deprecation has different implications depending on where brands stand in their preparation journey.

For businesses who may have used previous postponements of third-party deprecation as an excuse to delay action and conserve their resources, Tyler Stewart, Media Solutions Architect Lead, sees challenges down the line: “Smaller businesses may not have had the luxury of being on the front foot. In the longer term, this may only widen the gap between haves and have-nots as larger enterprises find themselves better positioned to compete in the privacy-first future.” Our advice to them: start prioritizing a cookieless approach now by focusing on first-party data and robust measurement strategies. Investing in AI-powered solutions and privacy-preserving technologies remains critical for future-proofing your marketing efforts.

Brands that have already embarked on their third-party cookie deprecation and privacy roadmap initiatives, meanwhile, have no need to pivot. “Strategies like the judicious use of first-party data, consent management, modeled measurement solutions and conversion recovery mechanisms will continue to be future-proofed strategies worth investing in,” says Stewart.

If you’re in this camp, don’t feel as if your efforts were in vain. “Those that have invested in reducing the impact of third-party cookie deprecation should take pride in being ahead of the curve with respect to utilization of first-party data, increasing compliance with global privacy regulations, innovating in measurement capabilities, and respecting their customers’ preferences,” says Combe. Staying the course will help future-proof your business’s data as the industry standards continue to evolve.

Better solutions for measurement will be customized for your business.

As an industry, the fragmentation and complexity we’re seeing across the digital ecosystem indicates we’re unlikely to move back to a uniform standard. “If you want to reach your customers wherever they are digitally, you need to be looking for new solutions for targeting, buying, and measurement. We can no longer rely on a consistent tactic that the entire industry adopts; brands need to move on from awaiting the next cookie alternative, and work on the solutions that are best for your company,” says DeAngelis.

The right strategy for your brand will depend on the complexity of your digital footprint and the data that’s most valuable for you to capture. To measure efficacy of your marketing activity, an important first step is to establish server-side tracking for your advertising, and take advantage of any event APIs from ad platforms, such as Meta’s Conversions API (CAPI). But in the long run, deterministic (user-level) measurement models will continue to weaken over time. Probabilistic models that assess changes across your entire business and media mix for causal contribution will be a necessity in the future, not an option. Strategies like Market Mix Modeling (MMM), or a Cookieless Multi-Touch Attribution (MTA) model offer viable alternatives to those challenges.

Similarly, identity resolution and user graph technologies are still viable for targeting, but a clear winner has yet to arise across the many providers that brands can choose from. As part of the announcement, Google emphasized that Privacy Sandbox will continue to be supported and developed as brands look ahead toward adapting their strategies beyond third-party cookie reliance—a goal that will remain important should users choose to opt out of third-party tracking en masse.

Move forward with a privacy-first marketing strategy.

No matter where your brand stands on the spectrum of cookie deprecation readiness, the path forward remains clear: continue to prioritize privacy-first strategies and the development of robust first-party data practices.

While third-party cookies have a new lease on life for now, they will never be as functional as they once were. They have already been deprecated in most non-Chrome browsers, and with Chrome indicating it will implement greater user permissions and controls, their availability is likely to continue declining—think of opt-in rates for ATT on iOS as a comparable scenario.

Brands should see this as an opportunity to stay ahead of the curve by continuing to invest in first-party data practices, consent management, and alternative measurement solutions—for teams that need advisory and executional support here, our data experts are ready to talk. The shift towards a privacy-first future is inevitable, and those who adapt proactively will be best positioned to thrive.