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Rethinking Marketing Mix Modelling for Direct-Response Brands

In the constantly evolving landscape of marketing, understanding the impact of media and other business drivers on sales and profitability is crucial. Consumer brands, especially Consumer Packaged Goods (CPG) companies have utilized Marketing Mix Modelling (MMM) for many decades as a valuable tool to analyze and dissect these factors. CPG companies have a rich history of employing MMM and the conventional approach has been fine-tuned over the years, providing a

reliable framework for understanding customer behaviour and optimizing marketing strategies. In recent years however, the rise of digital or direct-response brands, such as e-commerce and app-based services, has brought to light the need for a differentiated approach to MMM for such brands. This article explores how digital brands are different and why a distinct MMM methodology may need to be considered for them compared to their CPG counterparts.

The rise of direct-response brands such as e-commerce and app-based services has made it necessary to adopt a differentiated approach to MMM for such brands.

CPG vs. Digital:

Fundamental differences in customer journeys


Digital direct-response brands operate in a fundamentally different way compared to CPG companies. In CPG, the customer buying process is flat and non-stratified, influenced by various factors both before and during the in-store experience. Factors like advertising, brand awareness, brand positioning, income, lifestyle etc. play an initial role in influencing customers. Once they are in-store, another set of unrelated stimuli like product placement, competitor pricing, offers and promotions additionally influence them till they make a final purchase decision.

The CPG buying process is nonstratified, influenced by factors before and during the in-store experience. E.g.Advertising, income, lifestyle, product placement, competitor pricing, offers & promotions.

It is interesting to note that through this entire purchase journey, traditional CPG brands do not “know” their individual customers directly – their identity, their purchase history, or how to contact

them. I may have been buying the same brand of shampoo every month for decades, but the brand doesn’t know me or the fact that I am a loyal longtime customer. And it has no way to reach me directly.

Digital brands, on the other hand, have a two-step customer journey. In the first step, factors like media advertising, activations, brand proposition and pricing build brand awareness leading to customers visiting an e-commerce site or downloading an app. They then register their details or make an initial purchase. From the moment of this first interaction, the company possess valuable customer information such as customer names, contact details, and purchase history, opening up direct (and unpaid!) channels for personalized communications. Once a customer is acquired, in the second step, their focus turns to maximizing revenues from these customers using a different set of marketing strategies and tools.

Digital brands have a distinct two-step customer journey divided into Customer Acquisition and Revenue Maximization.

The better MMM approach for digital brands

Dual Model MMM Approach Key Performance Indicator (KPI) Business Drivers
Model 1 Customer acquisition (Website visit, app download, registration) Advertising, brand building, activations, referrals, macro factors
Model 2 Revenue maximization (LTV) from existing customers Email offers, in-app notifications, coupons, promotions, app events

Our experience building MMMs for digital brands reinforces our hypothesis that they should adopt a dual-model approach with at least two separate MMMs — one for new customer acquisition and another for existing customer revenues. The models must consider not just the overall ROI but also the cost of customer acquisition. Knowing which media channels contribute to acquiring new customers allows brands to tailor their strategies more effectively. Simultaneously, understanding the impact of different marketing efforts on existing customer revenues is vital to increase loyalty, purchase frequency, and average basket value, thereby maximizing LTV.

A key additional point to keep in mind is that with digital brands, customer LTV varies significantly depending on the business model.

While customers at subscription-based services are likely to spend a predictable amount every week or every month, it is less predictable with, say, “freemium” digital business models where most customers may never opt for paid services. So, just estimating the cost of customer acquisition of different media channels is enough. It is also critical to measure which media channels are most effective in acquiring customers driving the highest revenues. A good way to do this is use “30-days revenue” of new customers as the KPI for the first MMM. This can help identify which channels are driving acquisition of high-value customers and help acquisition teams to optimize their marketing spend accordingly.



The traditional MMM model, perfected by CPG companies over the years, falls short when applied directly to digital direct-response brands. To unlock the full potential of their marketing strategies, digital businesses must embrace a dual-model approach—one for customer acquisition and another for existing customer revenues. Customer acquisition models must also measure which channels are driving acquisition of high-value customers. By doing so, they can more successfully tailor their marketing mix to reflect the unique customer journey of digital consumers, and in this way, optimize both acquiring of new customers as well as the maximizing revenue from their customers.

About Analytic Edge

Analytic Edge is a global analytics company that leverages technology and advanced analytics to help companies make data-based marketing decisions. The company’s flagship platform Analytic Edge Qube offers a suite of marketing analytics solutions with a Software as a Service (SaaS) model. The solutions include DemandDriversTM for always-on Marketing Mix Modeling (MMM), SynTestTM for AI powered Test and Learn, PriceSenseTM for pricing and promotion analytics, and PowerViewTM for analytics visualization. Analytic Edge works with clients across industry verticals such as e-commerce, mobile apps, gaming, consumer packaged goods, retail, automotive and many others. The company has offices in Singapore, India, US, Mexico, Brazil, UK, China, Japan, South Korea, UAE and Australia.


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