Analytics seem overly complex and not fully trustworthy. 

Perceptions around analytics can be a hurdle. Many view it as a black box, lacking transparency and trustworthiness, and may think it’s merely retrospective with no actionable insights.

This is not the case. In fact, analytics can be highly effective when approached correctly. What you need is the right supplier. Look for one that offers transparency, expertise in developing robust MMM, and the ability to translate these models into actionable recommendations. Ultimately, the value lies in insights and recommendations rather than the models themselves.

Insufficient budget or resource allocation. 

The cost associated with MMM can often seem overwhelming. Many businesses do not allocate sufficient funds in their annual budgets, which can stall or completely halt the implementation of an MMM program.

To overcome this, start small and focus on your main objectives initially. Use this to build a case for increased budgets over time. Leverage existing resources and identify any open-source tools to build MMM, and work with suppliers to provide data in a pre-agreed format, which can often help reduce fees.

Uncertainty of when is the right time to start.

Timing is critical. With new campaigns always on the horizon, it's tempting to delay MMM analysis to include the next campaign, wait for full-year numbers, or incorporate the upcoming product launch. There is always something upcoming and this can lead to a cycle of postponement.

In short, don’t delay. Begin the process as soon as possible. Early insights and recommendations can lead to impactful business changes. Over time, your MMM will evolve and you can always update the analysis to include future activities. The value from initial findings often outweighs the reasons for waiting.

Concerns about data quality and accuracy. 

Quality data is the foundation of any successful MMM program. Questions about data availability can cause hesitation, as the adage "garbage in, garbage out" holds true.

Conduct a data audit, as the quality of the data remains unknown until it’s reviewed. Set up checkpoints during the analysis to determine data fitness, and if it falls short, create a plan to collect fit-for-purpose data. Focus on using the data that is available. Generate initial insights with whatever data you have, then enhance the MMM with more data over time. Don't wait for perfection to begin gaining insights. Look for continuous improvements, and remember that better data leads to better decisions. Improving data quality is an ongoing process.

Fear of undesirable results.

Fear of discovering negative outcomes can be paralyzing. Concerns about campaign performance or potential budget reallocations may deter businesses from pursuing an MMM program.

Acknowledge the risks and treat this as an education. Working in ignorance is one thing, but remaining deliberately uninformed is unacceptable. Embrace the process and be prepared to make data-informed decisions.

Addressing these common blockers is essential for the successful implementation of an MMM program. Finding the right scope to meet your budget and a trusted MMM partner are the best next steps to creating valuable insight and actionable recommendations to drive business performance.

This article will walk you through how MMM works, why it’s valuable and how to apply its insights to your business. Whether you’re striving to optimize ad spend, improve campaign performance, or better understand your audience, MMM can provide the clarity needed to make smarter decisions and achieve better outcomes. At its core, MMM helps organizations allocate resources more effectively and drive sustainable growth.

Define your objectives.

The first step in effective Market Mix Modeling is setting clear, actionable objectives. For example, let’s say your goal is to optimize marketing spend across digital and traditional channels. You might want to determine which channel drives the highest ROI or how seasonality impacts sales. Defining such priorities ensures a focused approach that delivers practical and valuable results.

Collaboration across teams is critical to this process. Marketing, finance and operations stakeholders must align their expectations and ensure they are realistic and achievable. This alignment sets a solid foundation for meaningful insights and actionable strategies.

Strategically collect relevant data.

Once objectives are clear, the next step is gathering the right data. For instance, if your goal is to optimize ad spend like mentioned above, you would collect historical data on marketing investments across TV, digital and print channels, as well as metrics like customer conversions, click-through rates and revenue impact.

Don’t overlook operational data such as pricing strategies, distribution channels and promotional campaigns. Additionally, include external factors like seasonality and economic conditions, as these can significantly influence outcomes. A skilled consultant ensures that this data is accurate, relevant and ready for analysis.

Translate data into insights.

With the right data in place, the next step is building an analytical model to generate insights. Suppose you’re analyzing a seasonal campaign’s effectiveness. MMM can help identify whether increased returns during the holidays were due to ad spending or external factors like market demand. A/B testing or controlled experiments can also supplement analysis. Tests can confirm that the model’s assumptions are validated to ensure alignment with business goals.

A good model evolves over time. For example, if new data reveals a shift in customer behavior, such as higher engagement with digital ads, the model should be adjusted accordingly. This adaptability keeps the insights relevant and actionable.

Transform insights into action.

Once the model is complete, focus on analyzing its outputs to develop actionable strategies. For example, if you discover diminishing returns from a saturated marketing channel, you could reallocate resources to a channel with higher growth potential. Look for patterns, such as the point at which additional ad spend no longer drives meaningful results.

Effective recommendations don’t just present numbers; they provide a clear narrative. If digital ads outperform TV during specific months, for instance, then translating this insight into a seasonal budget shift can maximize ROI and align with business goals.

Implement your MMM insights.

The final step in leveraging MMM is implementation. Let’s say you’re rolling out a new budget allocation strategy based on MMM insights. Develop a detailed roadmap with clear responsibilities and timelines for team members to ensure smooth execution.

But your work isn’t done yet: you’ll want to regularly monitor performance to identify opportunities for improvement. For example, if one channel underperforms despite increased investment, reassess its role in your strategy. Clear communication is key—sharing updates and outcomes with stakeholders builds trust and ensures alignment throughout the process.

Fuel future growth with MMM.

Market Mix Modeling is more than a tool—it’s a strategic approach to driving growth. By setting clear objectives, gathering meaningful data and translating insights into action, businesses can thrive in a competitive marketplace.

This structured process helps organizations allocate resources more effectively, improve marketing strategies and achieve sustainable success. Companies that embrace MMM don’t just react to change—they lead the way forward, shaping a brighter future for their 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.

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.

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.

Predicted Black Friday spend is on the decline.

Online searches for Black Friday have shown a decline in recent years, with a significant drop in global search volumes of 70% in 2023 compared to 2019. Additionally, there is a 30% decrease in consumer searches for Black Friday in 2023 compared to the previous year.

This is also backed up by data from finder.com, which looks at spending in the UK and again predicts a 23% drop (2023 versus 2022) in consumer spend on Black Friday—dropping from £3.9 Billion in 2022 to £3 Billion in 2023.

According to the data, this reduction is primarily attributed to a decrease in planned spend per person, possibly influenced by factors such as inflation and the cost of living, i.e. people having less disposable income to spend on more discretionary parts for their budgets.

We use MMM to understand how Black Friday impacts our clients’ business.

By using Market Mix Modelling (MMM), which is a statistical evaluation technique which takes into consideration all factors which impact on sales including media, price, economy, seasonality, and promotional discounts, we are able to quantify the impact of Black Friday on our clients' businesses. You can find a sample analysis for a major retailer below.

The pink bars in the chart show that seasonality hits a peak in November each year—which from our weekly models we can determine is the impact of Black Friday—and has a seasonal decline in December. The green bars show the further impact of changes to discounting over the years.

There are a few conclusions we can draw from this for the client:

• Black Friday has a “brought forward” sales effect: MMM uncovers a brought forward sales effect caused by Black Friday discounts. Following the peak in sales during November, there is a subsequent decline in December. This indicates that customers trained to buy during the discounted period may have pulled forward their purchases, affecting profitability. Evaluating the incremental profit from Black Friday against the potential loss in December sales at full margin is crucial for assessing Black Friday's overall effectiveness.
• Consistent Effectiveness: Effectiveness of Black Friday is not declining over time. The pink bars in November are not diminishing over the years, meaning this client is not seeing a drop in effectiveness for Black Friday.

So we are not seeing a drop in effectiveness for this retail brand, but what about others?

When we compare this to a selection of other brands we work across in the table below, you can see that generally the uplifts from Black Friday are consistent across time, and not diminishing. In the table below, three brands show a static uplift from Black Friday over the years, in pink. In the case of our manufactured goods brands, the uplift increases—though this is in line with the brands' base sales growth.

What all these brands have in common is that not only are they supporting Black Friday with discounts—more importantly, they’re doing it along with media investment. The media is working to remind customers that the offer is out there, and also to entice away from other competitors in the market.

So whilst we may see a general drop in consumer spend, our analysis shows that brands that support Black Friday with media can see a consistent, healthy impact.

Recommended Strategies for Success:

To continue benefiting from Black Friday, brands should consider the following strategies:

• Support Black Friday with media: Utilize media to set your brand apart from competitors and maximize visibility during the event.
• Assess incremental profit: Evaluate the incremental profit gained from Black Friday against any potential loss in sales during subsequent periods.
• Maintain consistency: Consistently support Black Friday over time to sustain its effectiveness and capitalize on customer engagement.

The effectiveness of Black Friday for your business remains dependent on various factors, including media support and the ability to manage “brought forward” sales effects. Brands that strategically leverage media and evaluate the profitability of Black Friday can continue to succeed in this dynamic retail event.

For more information on how we can help measure drivers of growth for you business, check out our Measurement expertise.