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.
We would love to hear from you.
Let’s chat!
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.
We would love to hear from you.
Let’s chat!
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™.
MMM should guide the next media decision, not explain last year's plan. Revology co-designs and builds AI-powered MMM and media allocation models inside your stack.
Most MMM engagements deliver a slide deck once a year. That model is stale by month four. Revology co-designs MMM as an AI-built decision system with your marketing analytics and finance teams: typically Bayesian hierarchical regression with elasticity priors and ROAS-curve estimation, inside your data environment. For mid-market companies ($100M–$2B), the model retrains every campaign cycle so your team allocates media against a live ROI surface. Credibility intervals the CMO can defend to the CFO. Typical year-one outcome is 10–20% media reallocation toward higher-ROI channels.
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.)
How data brokers and identity graphs quietly set personalized prices: what the evidence proves, what it doesn’t, and how pricing leaders should govern the risk.
A Pricing & RGM Analytics Navigator Your Team Owns — Six Modules, One GL-Reconciled Foundation, and the Agent Layer That Follows Most CPG Pricing and
A pharma pricing analytics engine is a governed decision-support system that integrates transaction, contract, channel, market, macroeconomic, regulatory, and competitive data to improve price setting,
This manual is designed for CEOs, CFOs, CCOs, Pricing and RGM leaders, and commercial analytics teams at mid-market CPG manufacturers. It outlines six decision modules,
Build best-in-class pricing analytics & optimization capabilities for your brands.
A practitioner’s field guide to the questions about pricing that actually move margin: what to ask, how to answer them with data, and how to
Centered on proven best practices, Revology Analytics® provides Revenue Growth Analytics advisory services and thought leadership, driving profitable revenue growth for middle-market companies.
Over 225 companies took our scorecard to improve their Revenue Growth Analytics & Management capabilities.
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