How much of your marketing budget is actually working — and how much are you simply hoping is working? In most of MENA, the honest answer is that nobody knows, because almost nobody is measuring it properly. Only a handful of companies in the region use econometric modelling to understand what actually drives their business performance. Everyone else is running on instinct dressed up as strategy.
That gap is not a footnote. It is the difference between a marketing function that can defend its budget with evidence and one that defends it with conviction alone — and conviction does not survive a downturn.
The Deceptively Simple Question
Return on investment, in theory, is almost embarrassingly simple: how much did I sell, and how much did I spend to earn it? But that simplicity is precisely the trap. Treating ROI as a two-variable equation — spend in, sales out — ignores the multitude of factors that actually determine whether a campaign succeeds: distribution, pricing, seasonality, competitive activity, and a dozen others moving simultaneously. Isolating the true effect of media spend on performance requires a statistical analysis of key business drivers, one that can separate signal from noise across every variable acting on the business at once.
This is exactly the gap that rigorous marketing mix modelling is built to close, and it is not a niche concern globally. A 2025 Harvard Business Review Analytic Services survey of 547 marketing professionals found that only 22% of organizations are genuinely effective at both extracting insight from marketing mix modelling and converting it into timely action — while 42% remain largely ineffective at turning their modelling into real results. That is a global finding, not a MENA-specific one — which makes the region’s near-total absence of adoption even starker by comparison.
What Rigor Actually Looks Like
Econometric modelling is not a marketing exercise with a statistical veneer. It is a scientific discipline. The people who practice it are experts in mathematics, statistics, advanced data analysis, trend extraction, and forecasting — and they build their models from data sets that go far beyond a simple correlation between media spend and sales.
The output, when done properly, is genuinely powerful. Clients use it to sharpen marketing strategy, forecast sales, and feed customized scenario-analysis software that lets them stress-test decisions before committing budget. When the underlying model is robust, its reach extends across the entire product cycle — from production planning through to returns. I have seen cases where the variance between forecast and actual sales was close to zero, and the worst-case variance across a full portfolio was still only 5%. That is not a rounding error; that is a level of precision most marketing organizations in this region have never experienced.
The financial upside compounds from there. In the real-world scenarios I have observed, applying statistical modelling to budget allocation across communication channels increased efficiency by as much as 20%. This finding tracks with what’s now being documented more broadly in the industry: Circana reports that brands applying marketing mix modelling to strategic decisions have seen average annual ROI increases of 10% to 15% — a figure that likely understates what’s achievable in markets, like ours, where the starting baseline is closer to zero. The end result is not incremental improvement. It is prioritized investment that maximizes ROI while minimizing wasted spend — the outcome every CFO asks marketing to prove and almost none can.
The Real Obstacle Isn’t Belief — It’s Infrastructure
Here is where I need to correct a common assumption. The biggest obstacle in this region is not that executives fail to understand the importance of this analysis. Most do, at least in principle. The real constraint is data availability and data infrastructure.
Thorough econometric modelling demands a substantial volume of data to test the many hypotheses required to separate one driver’s effect from another’s. Take a typical FMCG brand: a complete model needs data spanning distribution, seasonality, new product development, PR, competitive activity, pricing, promotional activity, trade activity, macroeconomic indicators such as consumer confidence, inflation, consumer attitudes, in-store positioning, and advertising activity — among others. That is a long list, and the uncomfortable truth is that most clients in this region either don’t have this data or have never tracked it consistently over time.
This changes what the job actually is. It is not enough to arrive with a modelling methodology and ask for access to a data warehouse that doesn’t exist. The real work starts earlier: building the tools and disciplines clients need to collect this data accurately, and doing so with a planning horizon that extends well beyond the standard twelve months. A one-year lens cannot capture seasonality effects, competitive cycles, or macroeconomic shifts that unfold over multiple years. Clients who buy into this approach are, in effect, buying into a horizon longer than their own reporting cycle — and to their credit, many do, understanding that the payoff will be felt in the future rather than the next quarterly review.
From Measurement to Strategy
None of this matters in isolation from creative and strategic execution. Marketing and business planning should take a holistic, media-neutral approach to advertising and campaign design. When consumer insight, creative engagement, and advanced scientific analysis are combined, the result is category-specific, target-group-driven creative strategy that competitors relying on instinct simply cannot match. Modelling doesn’t replace strategy — it makes strategy defensible.
The Only Question That Matters
Strip away the discipline-specific language — advertising, media, finance, sports — and the underlying principle is the same everywhere: providing accountability for the results you deliver is the end game. Everything in this article, the forecasting precision, the efficiency gains, the data infrastructure work, is in service of one thing — the ability to stand behind a number and prove it.
So the real question for marketing leaders in this region isn’t whether econometric modelling is worth the investment. The evidence, both mine and the broader industry’s, already answers that. The question is whether you’re willing to build the data discipline it requires — or whether you’ll keep defending your budget with conviction, in a market where your competitors are starting to defend theirs with proof.
