Discover how Revology Analytics propels mid-market businesses to sustainable, profitable growth by building advanced, in-house Revenue Growth Analytics & Management (RGM) capabilities—fast.
Our senior expert-led, hands-on approach ensures you own the tools, insights, strategy and processes needed to thrive long-term.
Gain exclusive access to the latest insights from over 150 commercial leaders on the state of Revenue Growth Analytics in 2025, based on our expanded Revenue Growth Analytics Maturity Scorecard™.
Discover how Revology Analytics propels mid-market businesses to sustainable, profitable growth by building advanced, in-house Revenue Growth Analytics & Management (RGM) capabilities—fast.
Our senior expert-led, hands-on approach ensures you own the tools, insights, strategy and processes needed to thrive long-term.
Gain exclusive access to the latest insights from over 150 commercial leaders on the state of Revenue Growth Analytics in 2025, based on our expanded Revenue Growth Analytics Maturity Scorecard™.
Marketing Mix Modeling examines how each of your marketing investments – from traditional channels like TV and print to digital ads, social media, and even trade promotions – drives sales and other key outcomes.
By statistically isolating the contribution of each channel (versus baseline sales that would have happened without marketing), MMM provides a data-driven read on marketing ROI.
Our MMM solutions typically involve building a regression or machine-learning model using historical data to quantify how changes in spend (and other factors) impact results. We develop the MMM as a reproducible pipeline, meaning you can refresh the analysis with new data on a regular cadence (e.g. quarterly) instead of relying on one-off agency reports.
This keeps the insights current and actionable.
By seeing the true ROI of each marketing channel or campaign, you can reallocate budgets toward the highest-yield channels. For example, MMM might reveal your paid search ads generate far higher incremental sales per dollar than a particular sponsorship, prompting a re-balancing of spend. This leads to more efficient marketing spend and higher overall returns.
MMM takes into account how promotions and media work together. It can help align your trade promotions with your advertising – for instance, avoiding running a big TV campaign at the same time as a price promotion that would’ve driven sales anyway. By understanding the interplay, you ensure that marketing and promotional efforts complement rather than cannibalize each other.
Once the model is built, you can simulate scenarios like “What if we increased social media spend by 20% and cut back on TV?”. The MMM allows you to predict how such shifts might impact sales or brand metrics. This forward-looking capability means you’re not just learning from the past, but actively using the data to plan future strategy – essentially a marketing flight simulator for budget planning.
MMM provides an objective, quantitative foundation for discussions about marketing effectiveness. It helps CMOs and CFOs get on the same page, as the contributions of marketing to business outcomes are clearly quantified. Teams have clear metrics to justify spend or make tough decisions on cutting underperforming tactics. This transparency can elevate the credibility of the marketing function within the organization.
We gather historical data on sales (or other performance KPIs) along with marketing spend broken down by channel, and any other relevant variables. This often includes promotional calendars, pricing changes, and external factors like seasonality, holidays, economic indicators, or competitor activities that might also influence sales. We rigorously validate and cleanse the data, aligning spend and sales to the same time periods and ensuring data quality (e.g. correcting any misaligned campaign dates or outliers).
Using advanced statistical methods (such as multi-variate regression, time-series analysis) or machine learning techniques, we build a model that attributes portions of sales to each marketing input. We might use approaches like gradient boosting or Bayesian regression to handle complex interactions. The model will quantify, for example, how many dollars in sales are driven by each $1 spent on each channel, after controlling for other factors. We also calculate metrics like diminishing returns and saturation points for each channel.
Instead of delivering results in a static Excel or PDF, we provide the MMM results in an interactive dashboard format. In a Power BI or Tableau dashboard (or a custom web app), your team can adjust spending levels across channels and immediately see the projected impact on sales or ROI. This tool makes the insights far more practical – it becomes a living tool for budget planning, not just a retrospective report.
We train your marketing and analytics teams on how to interpret the model and update it with new data. Because the MMM is built with open, transparent code (and often delivered via your BI platform), your team can rerun the analysis as new months or quarters of data come in. This means you won’t need to hire an external firm each time you want to refresh the insights – the capability is in-house and sustainable.
(By applying MMM, one retail client discovered that while TV advertising had a lower ROI than believed, their digital retargeting ads were significantly underfunded relative to their high return. This insight led to a 15% reallocation of budget, which in the next quarter boosted overall marketing ROI by about 20%. In another case, a consumer electronics company learned that some promotional campaigns were overlapping with periods of strong organic demand, prompting them to reschedule certain promotions and save millions without hurting sales.)
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