No one could have predicted what 2020, had in store for us, from a global pandemic to profound tremors within the digital media industry
The clamor around stricter data protection laws and anti-tracking policies has hit our industry in extraordinary ways.
As a result, the way we are adjusting to a Covid new normal, we must start gearing up for a “digital new normal.”
This digital new normal is two-fold. Firstly, businesses can no longer spend their ad dollars sub-optimally and report incorrect ROI numbers.
We need to attribute conversions to the right media channels and stop basing decisions mostly on intuition or outdated data.
Secondly, due to the pandemic, consumer behavior has changed dramatically.
Consumers are spending more time online now than they ever have before, which compels businesses to reach them in the right place by possibly considering different media budget splits.
Businesses’ longevity will hinge heavily on first-party data to mitigate the new normal risks.
A measurement study gaining attention in line with these trends is the Marketing Mix Modeling (MMM).
A data-driven statistical analysis that quantifies the incremental sales impact and ROI of marketing and non-marketing activities.
It enables marketers to justify budget allocations and optimise future budgets. Highly resilient and using first-party data, MMM is not impacted by current changes.
It does not rely on individual people, identities, or consumer journeys, but aggregates all that data in time series.
However, MMMs are heavily reliant on data quality and accuracy, which implies significant drawbacks: to build them is time-consuming, results are often outdated, and they lack sufficient granularity.
But these flaws and uncertainties can be addressed and the traditional ways of running these studies can be modernised, so that this opportunity can be seised, driving innovation.
Five ways you could adopt contemporary methodologies for Marketing Mix Modelling:
- Shorter models: MMM providers must quickly deliver the most up-to-date media efficiency results that will allow for a close-to-ongoing media-mix optimisation.Shortening the modeled period from the standard two to three years could be a promising way to make MMMs more capable. These shorter models should contain greater granularities.
- Recency variables: Media platforms change over time. A single variable over a three plus years may not precisely capture the next dollar’s true impact historically and looking forward. We believe there is substantial value in splitting variables for recency.For example, instead of considering an entire media X for 3 years, we could split this variable into media X – year 1-2 and separate the recent year media X – year 3 as a different variable.
- Customised decaying effect: Every media channel’s impact wears off after a period once a consumer watches the ad. The decaying effect of a media channel is called ‘ad stock’.Applying a traditional decaying effect means that a media channel’s impact wears off by a set percentage each week. But in the ever-changing digital ecosystem, the way consumers interact with different media channels changes quite rapidly. It is beneficial to test innovative decaying transformations for each media channel, respectively, and not use one-size-fits-all methods.
- Different modeling approaches: MMM providers in the market are using different modeling approaches to drive innovation. This keeps the wheel of modernisation spinning. For example, in most cases, a traditional MMM considers the impact of all variables that influence sales to be static over the modeling period.But this may always not be true, and some variables could be dynamic in nature. State-space modeling is one of the modeling types within the regression family that could be explored. Using the state space modeling approach helps increase the models’ accuracy, overcome data irregularities, and is better suited for model automation.
- Calibrate with experiments: MMMs are inherently complex studies. The insights generated from MMM models obviously depend on the value of the parameters found by running times series modeling.
But how can we make sure these models are accurate?
- One method of validation is to compare the incremental value showcased in the model (incremental cost per KPI) with the platform’s experimental tests.
- Using results from lift studies to choose models. Compare the cost per incremental KPI from a study, such as conversion lift, to the MMM’s contribution and choose the model that minimises the difference.
- The most rigorous and harder way to implement is to incorporate the lift studies results into the MMM models.
Marketing Mix Modeling is a resilient, insightful tool that can help businesses optimize their marketing spends. We need to innovate and transform our methodologies to re-discover it and adapt to changes.
In the immortal words of Don Draper from Mad Men, “Change is neither good nor bad. It simply is.”