Marketing mix modelling has been around since the 1960s. It was developed by econometricians to help large consumer goods companies understand which combination of product, price, promotion, and distribution was driving their sales. For decades, it was firmly in the realm of big corporations, large consulting budgets, and months-long projects. 

That world has changed. AI-driven platforms have reshaped who can access MMM, how quickly it delivers results, and what it can tell you. Today, it is one of the most important measurement tools available to marketers operating across multiple channels, and it is worth understanding properly. 

So here is the plain-English version. No econometrics degree required. 

The Simple Version 

At its core, marketing mix modelling is a statistical method for understanding how different factors combine to produce your commercial results. Those factors include your marketing channels, external conditions like seasonality or economic climate, and tactical variables like pricing and promotions. 

It works by looking at the historical relationship between changes in your marketing investment and changes in your sales. If you increased your television spend in Q3 and sales went up, MMM can quantify how much of that lift was attributable to television, how much was driven by a concurrent promotional offer, and how much was simply seasonal demand. 

The output is a breakdown of the incremental contribution of each marketing input to your overall performance. From there, you can model future scenarios: what happens to revenue if you move 20% of search budget to video? At what investment level does a channel start producing diminishing returns? 

MMM answers the question every CMO eventually faces: which parts of our marketing investment are actually driving our business, and which parts just look like they are? 

How It Differs from Attribution 

Marketing mix modelling and marketing attribution are often used in the same conversation, but they measure fundamentally different things. 

Attribution models, including the last-click, linear, and data-driven variants you find in Google Analytics, focus on individual customer journeys. They trace the path a customer took through your marketing touchpoints and assign credit for a conversion to some or all of those touchpoints. 

The challenge is that attribution can only measure what it can see. It cannot capture the effect of a television ad on a customer who later searched your brand name. It cannot account for the billboard that built brand familiarity over three months. It assigns credit based on digital signals, which means click-generating channels consistently look more important than they actually are. 

MMM does not follow individual users. It analyses aggregate data across your full marketing investment, including offline channels, and separates each channel’s contribution statistically. WARC’s Future of Measurement 2025 report is direct on this point: results from incrementality experiments consistently expose clear flaws in platform-based attribution, which routinely miscalculates advertising ROI. The recommendation is that attribution handles tactical optimisation; MMM informs strategic investment decisions. 

Source: WARC, Future of Measurement 2025 

What Data Does It Actually Need? 

One of the most persistent myths about MMM is that it requires years of data and a dedicated data science team. This was true of traditional approaches. It is not true of modern AI-powered MMM platforms. 

At a minimum, a useful model typically needs: 

• Your weekly or monthly marketing spend by channel, across the major categories you invest in 

• Your corresponding commercial results, whether that is revenue, leads, store visits, or another metric that matters to your business 

• Known external factors, such as promotions, pricing changes, or seasonal events that influenced your market in the relevant period 

The model becomes richer with richer data. But the threshold for getting meaningful, actionable insight is lower than most marketing teams assume. Telstra built its ML-powered MMM capability progressively, scaling complexity as confidence in the approach grew. That is a sensible model for most businesses. 

Source: CMO Australia, Telstra machine learning marketing mix modelling 

What You Can Actually Do With the Outputs 

The results of a marketing mix model are most valuable in three contexts. 

Budget allocation: Once you understand the marginal return on each channel at your current investment levels, you can identify where incremental spend generates the most additional revenue and where you are past the point of diminishing returns. This is the conversation that changes in a budget review. 

Scenario planning: Before you commit a budget for the next quarter or financial year, you can simulate the likely revenue impact of different allocation strategies. ‘Based on our modelled data, increasing video investment by $500k is likely to generate between X and Y in incremental revenue’ is a very different conversation from ‘I think we should invest more in video.’ 

Finance team credibility: One of the most consistent challenges for Australian CMOs is justifying marketing investment to their CFO or board. An MMM output, framed in commercial terms rather than click-through rates and impressions, is often the most effective tool available for that conversation. ANZ’s marketing team specifically cites mix modelling as what enabled more informed investment trade-offs within their centre of excellence. 

Source: AMI, ANZ commercial mix modelling case study 

Is MMM Right for Your Business? 

Honestly? For most growth-stage and established Australian businesses spending meaningfully across multiple channels, yes. The question is less ‘should I use MMM’ and more ‘what kind of model suits my stage and budget right now.’ 

MMM is most valuable when you are running activity across more than one or two channels simultaneously, have at least 12 to 18 months of marketing and sales history, and are making allocation decisions significant enough to justify the analytical investment. 

Very early-stage businesses with a single channel and limited history may find the timing is not quite right yet. But for the majority of Australian brands in growth mode, the barriers that historically made MMM inaccessible, including cost, timelines, and the need for specialist expertise, have been substantially reduced by modern AI-native platforms. 

IAB Australia’s 2025 landscape report identified twelve active MMM vendors operating in Australia, and noted accelerating adoption among major advertisers. The businesses driving that growth are not all enterprise-scale. Some of them are brands that found a path to MMM that fit their current stage and moved faster for it. 

Source: IAB Australia, Market Mix Modelling Landscape Report 2025 

Curious about what MMM could reveal for your business? We’d be happy to walk you through the platform