One of the primary outputs of any Market Mix Modelling (MMM) project is quantifying the incremental drivers of a KPI and how these change over time. This information in itself is incredibly useful and enables us to optimize media and other marketing levers to maximize returns for future activity. However, for particular industries and clients, we can take this a step further and utilize aggregated customer cohort data or loyalty purchase data to identify the best media laydown to acquire customers who are more valuable in the long term—such as revenue from today’s new customers that are most likely to repeat purchase in the future.
Understanding cohort data.
The use of customer cohort level data allows us to examine and predict the value of repeat purchases over time. A cohort level analysis typically seeks to answer a question along the lines of, “If I bring a new customer in with these characteristics, how much value will they bring to my business in terms of continued purchases?” Typical characteristics that may be factored into the analysis could include type of goods or services purchased, payment method, device type and time of year the purchase was made.
One such example can be shown in the chart below, where we see new customers that enter in Month 1 (initial purchase month) and the value that we gain from repeat purchase of that cohort of customers over time.
This allows us to apply an average multiplier to any future new customer revenue based on the characteristics provided—showing us that for instance, new customers driven at a particular time of year, or through a particular category or payment type, are more valuable than others. One such example is shown below, where regardless of category, driving credit customer revenue is much more valuable than customers paying upfront in full. We can also see that it pays off much more in the long term to drive new customer revenue through category A in Q1 and Q4, whereas we drive more long-term value from new customer revenue through category B in Q2.
Leveraging MMM to drive profit.
These insights are interesting themselves, but drive meaningful action when combined with media costs, effectiveness and profit margins to optimize a media laydown. Rather than simply spending on media when our traditional “busy periods” are and adapting to that, we can instead optimize our media laydown to take advantage of customer lifetime value instead.
This allows us to not only run scenarios that seek to drive profit but also scenarios to maximize long-term customer value in the most cost effective way.
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