Marketing mix modelling: a fuel to win the privacy-centric game?

Published: June 14, 2023

min read

In today’s digital arena, marketing dollars are gradually moving away from conventional upper-funnel media such as linear TV in favour of performance-based channels at the bottom of the funnel. This is an area where more apparent methods of measurement provide greater chances for provable returns for brands.

On the other side, privacy is predominantly taking centre stage as consumers become more and more conscious of how brands use their data. This, in turn, pushes marketers and brands to experiment with various new approaches to target the right customers at the right time in a privacy-safe manner.

Although experimenting with new techniques is undeniably important, dusting off some old yet significant marketing practises and incorporating them into the marketing strategy also makes sense.

In that regard, marketing mix modelling (MMM) is one of the prominent practises that marketers can consider. And, as a way of reiterating it, DAC, the international performance marketing agency, has released some myths around MMM based on its hundreds of MMM analyses across dozens of industries.

Here are the myths and facts on MMM by DAC:

MMM results can be utilised in developing forecast models, but they are not forecasts themselves. In other words, it can generate optimised expenditure splits throughout channels by using historical data, but it can't foresee which budget strategies and tactics will work in the future.
Marketers believe that developing MMM capabilities necessitates a six-figure investment and enormous amounts of data in an enterprise-level data warehouse. However, with a minimal amount of historical data on brand expenditure and conversion metrics, they can develop a marketing mix model for a relatively low investment.
Brands do not need to complicate themselves in order to benefit from an MMM study. With a simple data set, regression models can generate an initial estimation of a mix that either contradicts or endorses previous budget decisions.
Machine learning and artificial intelligence developments present enormous advantages to marketing mix modelling. However, AI can't resolve every MMM challenge.

What does it mean for brands and chief marketing officers?

In this privacy-centric world, where tech giant like Google are making fast moves by implementing privacy sandbox, brands are even ready to boil the ocean to come up with an effective and statistical method to efficiently target their audiences and determine the effectiveness of marketing campaigns in a privacy-centric way.

Statistics show only 8–10% of retail revenue from sales is spent on marketing. The most pressing concern for CMOs is determining how to optimally allocate funds to a broad spectrum of marketing activities. This will probably get even harder in a cookie-less world.

At this point in time, in our opinion, DAC’s new insights on MMM will indeed be an eye-opener for many brands and CMOs to reconsider this old-school practise and add it as part of their marketing strategy to efficiently face the cookie-less world.

And here are a few reasons why we think brands should no longer ignore the practise of MMM:

As MMM does not rely on visibility into the consumer journey or require user-level data to run the model, marketers can easily embrace this for analysing overall trends and patterns and planning campaigns accordingly. In other words, it will help marketers minimise the risks associated with personal data handling and pave the way for a privacy-compliant way to enhance the campaign's effectiveness.
By determining the impact of marketing activities accurately on sales, profitability, and other factors, MMM can help marketers find the most effective channels, campaigns, strategies, etc. This way, it will enable them to measure the return on investment (ROI) of their advertising spend precisely. As a result, marketers can use this understanding to allocate budgets thoughtfully and maximise their marketing ROI.
In this uncertain economic situation, marketers are already under tremendous pressure to get the most out of every penny they put into the marketing bucket. Thus, it is incredibly important for them to allocate their marketing budgets carefully, limit budgets to underperforming channels, and redirect them to ones that generate the highest returns. In this regard, MMM will be a huge helping hand for them as it provides detailed insights on marketing activities. Besides, using the MMM results, marketers can also find effective strategies, tactics, and channels that deserve increased investment so as to plan the campaigns accordingly.
In this competitive marketing arena, it is crucial for marketers to know what is trending in their industry, their competitors’ strategies, what gains more traction among the target segments, etc. As MMM provides a holistic view of market dynamics, marketers can use it to figure out how external factors such as competitive activities, economic conditions, etc. can affect their sales and other performance metrics. This, in turn, will assist them in setting feasible targets and developing effective marketing plans.

Overall, DAC’s insights on MMM are indeed an eye-opener for brands. Considering the significance of privacy-centric methodologies in today’s digital landscape, we believe it is the right time for marketers to consider or reconsider the practise of MMM to drive their marketing performance in an effective way.

Author

Sarah Johnson

Sarah is an analytical marketing expert with a passion for data-driven insights. She has a keen eye for detail and a talent for turning complex information into actionable strategies. In her free time, she enjoys yoga, travel, and trying out new recipes in the kitchen.