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There’s a better way to evaluate campaigns (including digital) than traditional MMM



Marketers have yearned for the data to help them decide how much to spend, and where for many years, data that would give them answers to questions such as:

How much should I spend on advertising?

How do I best allocate this budget to channels, content, markets and over time? What is my real ROI and lift in sales volumes?

More than half a century ago, statistical modeling methods began to be applied to answer questions like these. Over the years, as marketing began to broaden its remit beyond media advertising, these methods began to incorporate other elements of the marketing mix. As a result, the term MMM evolved from Media Mix Modeling to Marketing Mix Modeling. Today, MMM is often, and fairly, criticized for leaving marketers grappling for answers that the techniques themselves are just not suited to deliver.

  • How does MMM help with the details of planning and buying online media?

  • Can MMM measure the long term effect of advertising?

  • Can MMM help us understand the effects of creative?

  • Can MMM incorporate the impact of customer experience?

In short, traditional MMM is too limited in its capabilities to be useful for today’s marketer. To try to tackle these new requirements, Crater Lake has chosen to use a wheel as a metaphor. Each spoke on the wheel represents a channel. In the hub, all channels come together.




If we want to build a model that measures the impact of all channels we are operating in the hub. In the hub, we need to describe each channel using variables that ALL channels have in common.


But this inevitably means some of the data available to an analyst in a spoke is not used.

In the Crater Lake approach, our omnichannel model (hub) uses a unit of analysis that is a combination of geography (small areas) and time (usually day).


In a spoke, the unit of analysis may be different. Consider an email targeting and measurement model; that would use a unit of analysis that is an email address combined with day.


This means that while the “hub” model can certainly be used to measure cross-channel effects such as total lift, and it can also be used to allocate budget between channels, it’s application within a channel (in a spoke) is limited to the data available.


In our view, we compensate by building complementary models for each major spoke, and then solve for optimization across the entire system. When we want to integrate the hub learning with a spoke, we are pushing hub data back out into a spoke model. This means a spoke model (representing a single channel) can incorporate data about the presence and effect of other channels.

Our aim is threefold:

  1. to learn what we can from the totality of the effort.

  2. to push that learning into the data we have available on each individual channel thus allowing us to improve how we optimize effort within a channel.

  3. to optimize, across all channels.

There are many individual hurdles we need to overcome, given the huge quantity of data available on some channels and the comparative paucity out there on others. We believe that there really isn’t any point complaining about the lack of data, or its inconsistency. Over time the situation will improve but for marketers the time is now, and the need is immediate.

We work on the basis that 90% right and on time beats 100% right but too late every time.


We work with what we’ve got, and what we can get.


Crater Lake & Co also has answers to the other questions posed here; for that please download our full paper from the link below. Or, you can ask for a free consultation on how our approach can deliver breakthrough results for your business; contact any Crater Lake partner via our emails on the contact page.


Hub and Spoke blog post Aug 2 21
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