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.

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.

Enable cohort and lifetime value measurement. 

Linking MMM results to customer cohorts turns marketing measurement from a short-term revenue uplift to a forward‑looking view of customer lifetime value. Rather than treating every conversion the same, cohort analysis groups customers by useful traits—for example, how they were acquired (paid search, social, referral), which campaign or creative they saw, the week they first engaged, or the product they bought first. 

These cohorts are then monitored for purchase history, retention patterns and other lifecycle behaviors. It is by following these groups that you convert short‑term sales lifts into projected lifetime value (LTV) and clearly see which marketing efforts are actually building lasting customer relationships.

Support audience‑level modelling and segmentation. 

Audience‑level modelling and segmentation transform MMM from a one‑size‑fits all budget allocation tool into a more nuanced decision system. By leveraging first-party attributes like demographics and churn risk, you can build a segmented MMM to measure how various groups respond to your media and messaging.

This matters, as aggregated findings can hide variation. An overall channel ROI can look attractive, while most of the incremental profit actually comes from a narrow, high‑value segment. 

Conversely, a channel that drives many low‑margin, one‑time buyers may inflate acquisition counts but reduce overall profitability. By modelling at the audience level, you quantify not just volume of incremental conversions but the quality (profitability, retention potential) of those conversions.

Improve long‑term measurement.

Owned data—like email open, loyalty program activity, app usage or brand tracker scores—adds a layer of behavioral context that raw sales and media‑spend data can’t provide. These signals reflect shifts in awareness, consideration and ongoing engagement that often precede sales by weeks or months. 

When you feed them into an MMM, it can become possible to detect customer intent that would otherwise be lumped in with short‑term promotional effects. For example, a sustained rise in loyalty program activity, or improved brand tracker sentiment following a brand campaign, is a strong indicator that future purchase probability has increased, even if immediate conversions remain muted.

Bringing owned metrics into the model therefore helps separate activation from brand building and gives you a clearer view of long‑tail impacts. Instead of attributing delayed sales solely to the most recent tactical spend, the MMM can assign appropriate credit to earlier brand investments that moved customers along the funnel. 

The result is more accurate measurement, better forecasts of future returns, and a stronger business case for investing in brand and retention activities alongside short‑term activation. 

Enable better experimental design and validation. 

Use first‑party data to run and measure experiments (holdouts, geo tests, A/B tests) and feed the results into the MMM as truth or priors. This strengthens causal inference and calibrates model estimates against observed incrementality.

Using first‑party data to design and measure experiments dramatically strengthens your ability to prove what really is moving your KPIs.  With customer and behavioral data, you can undertake holdouts, geo tests and randomized A/B tests on well‑defined cohorts to measure true incremental lift, and then feed those experimental results back into the MMM.

That data can be used as validation points or as Bayesian priors to nudge the model results toward observed causality, reducing reliance on purely observational correlations.

However, the loop goes both ways. The MMM analysis can help prioritize which experiments to run (which channels, segments, or messages look most promising or uncertain). Together they create a virtuous cycle—cleaner causal inference, more trustworthy ROI estimates, faster learning, and better allocation decisions—all while leveraging the identity and engagement signals you already own.

Drive operationalization and activation.

When your MMM uses first‑party signals, its recommendations become more tailored to your business and more actions focused. Instead of saying “spend more on channel X,” the model can suggest which exact customer groups to target or pause, what messages to send, and where to reallocate budget for the biggest incremental impact. 

Those audience‑level suggestions can be pushed straight into your owned channels—email campaigns, app pushes, CRM journeys or loyalty offers—so the right people get the right message at the right time.

That also lets you close the loop. Measure how those actions change behavior, feed the results back into the model, and keep refining both the measurement and the activation rules. This will enable you to make quicker decisions, waste less spend, and build marketing that actually follows through on what the data tells you. 

Get the most out of your first party MMM integrations. 

To maximize the value of your analysis, follow these key steps to ensure your first-party data is MMM-ready.

• Invest in data plumbing and governance. Clean, consistent data is the foundation. Standardize taxonomies (channels, campaigns, creatives), enforce naming rules and put quality checks in place so everyone uses the same definitions.
• Map the customer journey. Link CRM records and purchase histories back to media exposures wherever possible. Knowing which touchpoints led to a sale makes cohort and LTV analysis much more accurate.
• Combine MMM with cohort LTV and survival analysis. Use MMM to estimate short‑term lift, then apply cohort retention and repeat‑purchase models to project lifetime returns and true acquisition value.
• Use hybrid measurement. Complement MMM with experiments and uplift tests on first‑party cohorts to validate and refine model outputs. Experiments provide validation and calibration points for your models, building trust and confidence in its findings.

Build modular models that support audience‑level or channel‑level sub-models so recommendations can be operationalized quickly into owned channels.

In summary, integrating first-party and owned data significantly enhances your MMM. By incorporating these datasets thoughtfully, you can achieve more precise ROI measurement, deeper LTV insights, and more direct activation—all while maintaining a privacy-safe framework. 

Monks previously partnered with TikTok’s Marketing Science Team to uncover that brands are underestimating TikTok’s ROI by more than 50% vs. pixel based attribution models, and that full-funnel ad campaigns drive 13-26% greater ROI than bottom-funnel tactics alone.

However, for retail brands, this is only half the story. The last report’s data focused on the DTC impact of TikTok ads, but understanding true omnichannel sales—including online and brick-and-mortar sales at third-party retailers—is critical to getting the full picture of your media effectiveness.

Now, we’ve partnered with TikTok once more to analyze a leading beauty retailer’s growth using Monks’ Clarity Media Mix Modeling (MMM). Our analysis unlocks a greater understanding of how TikTok ads impact business results off-channel and on-channel, and the interconnected relationship it has with the entire media mix.

By providing stronger data and insights, the CMO and CFO are able to more closely integrate budget changes and pivots as needed, and investment increases have a stronger ability to provide maximum impact alongside greater flexibility. In a time when budgets are under more scrutiny than ever, CMOs need to be armed with the data points to build trust with CFOs that the impact of digital media goes far beyond attributable sales. Using MMM provides a more accurate understanding of the long-term and interconnected impacts that budgeting decisions have from a marketing investment strategy.

At a glance:

Marketing Mix Modeling quantifies how marketing activity drives growth, but it frequently fails when metrics, data, taxonomy or expectations misalign with business goals. To mitigate these risks, align metrics to specific business objectives; ensure consistent, granular data at the right frequency; and combine automation with expert human review to catch nuance. By avoiding these errors, you can turn MMM from a risky exercise into a reliable decision-support tool.

1. Selecting inappropriate success metrics.

If the goal of your marketing campaign is to drive hotel room bookings, then understanding how many people saw your ads can be helpful in optimizing and diagnosing what works and what doesn’t, but it isn’t necessarily a figure you can use to determine success. If a lot of people see your ad, but they aren’t the right audience, then your campaign did not fundamentally succeed in your initial stated goal.

MMMs are the same. Choosing the wrong success metrics leads to models that answer the wrong questions. If you optimize for site traffic when the real business goal is profitable conversions, the model will generate recommendations that miss the mark. It is critical to align metrics with business objectives and validate that modeled outcomes match what stakeholders actually care about. 

Furthermore, these objectives must be tracked consistently. A common failure point is running brand lift campaigns without measuring brand metrics, or setting offline conversion targets that are never captured in the data. If the objective isn't tracked, the model cannot measure it. Ensure campaigns and tracking mechanisms are fully aligned before modeling begins.

2. Insufficient or inconsistent historical data.

For pay-per-click ads, a programmatic marketer might assume that if an ad shows strong conversions for one week, they should expect the same performance for the entire time the campaign is running. However, that’s not always the case, and more data over time is needed to understand a more accurate view of how a campaign is actually performing.

MMM is similar. MMM depends on rich historical records to identify patterns. Datasets that are too short, or records that change methodology mid-stream, reduce model stability and increase uncertainty. Short time series make it difficult to separate the signal from the noise or to estimate long-term effects. Ensure you have a sufficient, robust history to allow the model to learn effectively.

3. Relying on low data frequency.

Monthly or irregular data points can hide important timing effects and reduce the ability to estimate short-lived campaign impacts. Whenever possible, use weekly or even daily data. This granularity allows the model to accurately capture carryover effects and immediate response patterns that monthly aggregates often smooth over.

4. Using an overly broad taxonomy.

If media channels, creative types or campaigns are grouped too broadly, the model cannot distinguish which specific activities drive performance. Aggregating brand-building and direct-response activations together, or lumping key distinct campaigns into generic buckets, destroys actionable insight. A granular, consistent taxonomy is essential to understanding what is actually working.

5. Demanding granularity without model adjustments.

Conversely, asking the model to estimate the impact of very small campaigns or low spend levels without sufficient data support creates unreliable estimates. There is a trade-off between granularity and statistical significance. Models must be structured to match the level of detail the data can actually support; otherwise, you risk chasing noise rather than signal.

6. Simultaneous activity and high collinearity.

When multiple channels and promotions run at the exact same time, it becomes mathematically difficult to attribute causality to any single one. This “collinearity” inflates uncertainty. To mitigate this, plan for staggered tests where possible to help disentangle overlapping activity and isolate the impact of specific channels.

7. Masking local activity in national models.

Localized tests or regional initiatives can be easily masked in a national-level model. For example, a highly successful test in one region may look like a statistical rounding error when diluted across national data, causing the model to recommend cutting a winning tactic. If important activity happens regionally, consider regional models or adding local controls to ensure these effects are properly attributed.

8. Over-reliance on fully automated solutions.

Relying on a fully automated MMM platform without expert oversight is risky. While automation speeds up delivery, it may not spot data idiosyncrasies, incorrect taxonomies, or business-specific context. “Black box” pipelines can hide flawed assumptions that prevent sensible interventions.

The danger of automation without expertise is that you get to the wrong answer faster, but the answer is still wrong. Use automation for efficiency, but always combine it with expert review and customization.

9. Waiting for perfect data. 

Delaying analysis to wait for an ideal campaign schedule or a “clean break” often stalls learning indefinitely. Perfect data rarely exists. It is better to iteratively improve data quality while using pragmatic assumptions and sensitivity analysis to quantify uncertainty. 

While it is true that poor inputs yield poor outputs, waiting for flawless data often yields no outputs at all. “Work in progress” data can still generate useful directional insight compared to flying blind.

10. Lacking a clear strategic brief. 

Requesting exhaustive answers to every possible question in a single modeling cycle dilutes focus and slows delivery. A clear brief that prioritizes the most important business questions leads to faster, more useful outputs.

Remember that markets and media evolve. Running MMM once and expecting permanent answers ignores changing consumer behavior and channel economics. Regular re-runs and model refreshes maintain relevance, capture structural shifts, and prevent strategic FOMO.

Avoiding these pitfalls requires disciplined planning on metrics and taxonomy, alongside realistic expectations about data and timing. Start with a clear brief, align tracking to objectives, use the appropriate level of granularity and iterate rather than waiting for perfection. Doing so turns MMM from a risky exercise into a reliable decision-support tool for the whole business.

Google's Meridian is built upon the foundation of the previously released RBA/LMMM materials. The developments include geo experiments to ingest into the modeling, as well as detail on reach for YouTube. The emphasis on triangulation via A/B testing to enhance MMM accuracy is a strategy we are well-versed in ourselves and offers a good base to start from. However, it is crucial to note that while Meridian provides a step forward in measurement, it remains just a tool—a sophisticated one that requires expert hands to wield effectively. 

At Media.Monks, we pride ourselves on our robust internal platform that is industry-leading in terms of speed and functionality. Meridian gives a step up for brands who are just starting off in their MMM journey, helping them move away from last click to better quantify media uplifts.

From an experienced modeler’s perspective, these are some of the key points to consider with Meridian:

• The methodology behind Meridian is solid and makes sense around the emphasis on triangulation, which enhances the accuracy of the results.
• However, experienced econometricians will be essential for operating Meridian effectively in-house. Brands must ensure their teams possess the expertise to source the right data, build the models to reflect the real world, and translate data insights into actionable ROIs and response curves, or they risk making flawed decisions from the outputs.
• As with all MMM initiatives, data quality remains a critical factor in whether or not you’re adding value or making accurate decisions. Having accurate and full data across all drivers of sales (media, price, promotions, seasonality, climate, etc.) is critical for MMM. Strong data foundations also gives a significant advantage, whether brands are utilizing Meridian or any other technology.
• Effective communication within organizations is key to driving traction and implementation of MMM strategies, and explaining models clearly and effectively is key for any MMMs success.
• The launch of Meridian represents a shift away from outdated attribution models towards a more accurate, incremental media valuation approach. Even if it isn’t the best-fit tool for all brands, it is another step in the industry’s maturation, especially in the wake of cookie deprecation and changing privacy legislation.
• Smaller clients with simpler data structures, such as ecommerce clients spending less than $2 million USD on digital media, will benefit from this tool as an entry point to the world of MMM.
• Some clients may question running their media measurement on a platform from a media owner

In conclusion, Google's Meridian offers a solid starting point for less complex brands looking to enhance their measurement capabilities via a framework. Increasing the usage of MMM can only be good for the industry as a trusted tool to measure and optimize media. That being said, hard work is still needed in attracting econometric talent into the marketing world to maintain model accuracy and increase adoption of these methodologies. At the end of the day, a model is only as good as its modeler: you can have the best model in the world, but if it's not fed with accurate, high-quality data or delivered clearly to key stakeholders, it's not going to be trusted (and therefore, adopted) in an organization.

A good step forward, but still more to do on the talent front. See our post on apprenticeships to learn what we are doing to address this.

For more information on how we can help with your marketing effectiveness measurement or Market Mix Modelling, visit our Measurement page or contact us.

In the past, the idea of providing an automated MMM solution has terrified me. Blackbox solutions, which lack transparency, are as unsettling to me as they are to clients. However, the challenge lies in the fact that clients now demand speed and relevancy to ensure MMM thrives as it should. They require more models across their portfolio, greater granularity to account for channel fragmentation, and faster results and recommendations based on the latest campaigns and market conditions. If MMM cannot keep up with these demands, clients may resort to using easily accessible information, risking the possibility of making incorrect decisions based on inadequate data.

Therefore, I believe it is our role as experienced consultants to harness the power of artificial intelligence (AI) and machine learning (ML) to meet these needs, whilst simultaneously ensuring we still have a thorough understanding of the decisions which are being made within the process. We must guide the machine, set boundaries for AI, and sometimes intervene to provide information that the algorithms may not currently know or be able to find. AI and ML advancements should be utilized to build the core models, streamline hypothesis testing, and handle the heavy lifting. And we need to appreciate that a model itself provides limited insights. We (the consultants) must bridge the gap between the model and actionable recommendations, translating the statistics and the numbers into a language that clients understand and can implement.

The best approach combines the speed of machines with the detailed craftsmanship of the econometrician. It is a fusion of AI capabilities and the expertise of consultants that yields the most valuable outcomes. If you find yourself lacking in either aspect, I encourage you to get in touch with us. We would be delighted to discuss our approach further, ensuring that you benefit from the best of both worlds.

AI and ML offer immense potential for MMM, but they must be leveraged in a thoughtful and supervised manner. By guiding the machines, setting boundaries, and providing human expertise, we can unlock the full power of AI while ensuring accurate and actionable recommendations. Let us embrace the synergy between technology and human insights to drive success in the dynamic landscape of marketing.

For more information on how we can help with your Marketing Effectiveness measurement or Market Mix Modelling, visit our Measurement page or contact us.

Firstly, the importance of data in driving decision-making has become paramount. Businesses recognize the need for robust data-driven insights to navigate the complex marketing landscape. MMM provides a solution by quantifying the impact of various marketing activities, enabling businesses to make informed decisions based on solid evidence.

Secondly, the fragmentation of marketing channels has made decision-making increasingly challenging. With a multitude of platforms and channels available, businesses are seeking ways to measure the impact of their marketing investments accurately. MMM offers a holistic approach, allowing businesses to understand the effectiveness of each channel and optimize their investments accordingly.

Moreover, the rapidly changing economy poses unique challenges for businesses. Factors such as inflation, consumer confidence, political stability, global conflicts, and oil prices can greatly impact business forecasting. In this dynamic environment, clients are eager to utilize the latest intelligence to make intelligent decisions. MMM provides the means to analyze and adapt to these changing circumstances, enabling businesses to stay ahead of the curve.

Structurally, the attribution landscape has been undergoing significant changes. The deprecation of cookies, the rise of walled gardens, and the increasing digital investments have necessitated a more comprehensive and incremental approach to measurement. MMM has evolved to meet these demands, offering agility and granularity that align with the needs of today's market.

Gone are the days when MMM was a slow cruise liner, calmly sailing through the seas of marketing. It has transformed into an agile and adaptable tool, capable of navigating the challenges posed by channel fragmentation and rapid economic changes. MMM allows businesses to quantify what is working, accounting for the latest circumstances and driving data-informed decision-making.

As the phoenix rises from the ashes, MMM has risen to the occasion, demonstrating its resilience and ability to deliver meaningful insights. In a world where marketing strategies must constantly adapt, MMM stands as a powerful tool, guiding businesses towards success in an ever-changing landscape. Embracing MMM means embracing the future of marketing, where data and insights reign supreme, and agile decision-making holds the key to unlocking growth and profitability.

For more information on how we can help with your Marketing Effectiveness measurement or Market Mix Modelling,visit our Measurement page or contact us.

I am here to say that nobody puts MMM in a corner.

MMM not only quantifies the impact of various marketing activities on key performance indicators (KPIs), but also provides a measure of the effect on business performance of other controllable and external factors, such as price or distribution changes, competitors' actions, economic climate, and can even answer the question we all love to discuss, “How much of our performance is really down to the weather?” 

As MMM provides this panoramic view of all the key drivers, it is important to ensure it never operates in a silo if you want it to deliver its full potential. Here's why:

1. Cross-Functional Collaboration: MMM involves analyzing vast amounts of data from various teams. By demonstrating the benefits of MMM in terms of additional insights and recommendations, it encourages greater engagement from teams. This collaboration leads to a more comprehensive understanding of marketing effectiveness and drives better outcomes.

2. Influence Beyond Marketing: MMM has a significant role in shaping commercial decisions, particularly in pricing and promotions. Informed decision-making in these areas, such as identifying optimal price points, understanding price elasticity, and evaluating the impact of promotions on sales and revenue, empowers businesses to strike the right balance between profitability and customer demand.

3. Engaging Finance Teams: MMM often provides budget allocation recommendations across media channels, campaigns, departments, different brands in your portfolio as well as different markets. Involving finance teams ensures that recommendations are implemented and beneficial across the entire organisation. This collaboration quantifies the business decisions in both the short and long term.

In conclusion, MMM should be integrated across the whole business. By breaking down silos, fostering collaboration, and incorporating MMM into the decision-making processes, businesses gain more accurate insights, make better decisions, and achieve improved marketing performance and overall results.

Soon everyone will be holding MMM results and recommendations aloft above their head in the middle of the boardroom.

For more information on how we can help with your Marketing Effectiveness measurement or Market Mix Modelling,visit our Measurement page or contact us.

1. Will the measurement show the incremental uplift of media?

If the MMM models are measuring media but don't include the impact of other factors—such as Covid, seasonality, economic effects, etc.—then it is not providing you with an incremental measure and the media effects will be overstated. 

Always ask what factors other than media will be included in the model, and the sources of the data they use, to ensure your results are as accurate as possible.

2. What period of time does the model cover?

MMM needs at least two, preferably three, years of data to ensure it is deriving an accurate measurement of media and not conflating it with factors such as seasonality or other longer term impacts such as economic movements. If you are getting results with a lookback window of three months, then it's very unlikely to be MMM and therefore it will not be incremental measures you receive. 

Ask how much historical data the provider will require. 

3. What is the KPI that is being modeled?

Ask what the “dependent variable” will be. This is the KPI that is being modeled, and should be the metric on which your business success is judged. A sales metric—such as acquisitions, sales volume, revenue or similar—is ideal, as you can convert uplifts into revenue, then use margin to get to profit which enables you to assess true payback to the business bottom line.

If it is just web visits or digital conversions, alarm bells should be ringing! 

4. How are you dealing with interactive channel effects?

Any model needs to be reflective of how things work in the real world. For example, brand media can drive consumers to search for your products or services, which then drives up paid search. This needs to be accounted for correctly in the model specification, as well as any synergistic effects between channels and media’s ability to drive online and offline sales. If these are not accounted for, it’s probably not proper MMM. 

Ask how interactive media effects are taken into account.

5. How are you testing for causality, collinearity and significance?

These sound like complex terms, but they are not as scary as they seem!

Causality states the directionality of impact, i.e. which way something impacts something else. For example, does brand media drive consumers to search for a brand or does volume of searches impact brand media performance? There are certain econometric tests that can be done, which help determine this and validate your results. 

Ask for a list of all the possible data variables they would like to include in the model as well as the processes they will use to determine causality. 

Collinearity occurs when two factors move in a similar way and it becomes difficult to separate their impact, e.g. if TV and radio were planned with a constant weight over the same four weeks, an MMM model would struggle to determine the impact of each of these separately. Occurrences of collinearity can be tested for and should be flagged by the modeler.

Ask what kind of tests the modeler will use to determine collinearity.

Significance tells the modeler how important each of the factors are in the model. You need to be careful when there is low significance (usually on low spending media channels), as this is where the modeler cannot be confident in the result—which should be flagged to the client. 

Ask at what statistical level media is considered, and how the modeler will flag lower measures.

6. What is your verified forecasting error?

To establish a verified forecasting error, information about how the KPI has performed over a period of time is “held back” or not revealed to the modeler. The modeler needs to then use their analysis to “forecast” what they expect the KPI results to be. The forecast can then be compared to actual sales to verify the accuracy of the model.

The aim should be to have an error no greater than 8%, with a sensible range being between 2% and 8%. Non-incremental models (e.g. last click or attribution models) are poor at forecasting.

Ask if they have any validated forecasts from previous clients.

Hopefully this has helped to give a steer on what you need to be looking for. When in doubt, rely on these simple questions to get a sense of whether your partner is robust and reliable.

For more information on how we can help with your Marketing Effectiveness measurement or Market Mix Modelling, visit our Measurement page or contact us.