The raison d’etre for the majority of Market Mix Modelling agencies is to calculate the marketing return on investment, or MROI. Gain Theory takes a different approach – knowing the ROI is nice, but does little to solve the pain points of our customers –how to optimize marketing investment, the allocation between media channels and to ensure that all touchpoints are working coherently.
As one Marketing VP recently said in an independent CMO survey: “MMMs provide some insight into making better investments, but that is still fairly one dimensional”.
Because we start from a different position – dynamic improvement rather than static reporting – our approach is also different.
The common approach to media impact
By far and away, the most common approach to estimating the impact of media is to use an ad stock. This may largely be seen as taking the ratings that your target audience had an opportunity to see and then decaying them. Using a decay allows the media to be tested for an immediate effect, as well as an impact over the medium and longer term.
Now, this approach is fine if you are only concerned with identifying the ROI.
But it says nothing about wastage and it gives little insight into weekly phasing or even where diminishing returns begin to set in. This is because a rating has little definitive to say about the chances of your target audience hearing or seeing an ad.
Ratings are a trading currency. Nothing more.
Consider a simple example. What do 20 TV ratings actually represent from a viewing perspective? It could mean that 20% of your target audience have had an opportunity to see (OTS) one exposure. Or it could mean that 10% are at 2 OTS. Or some other combination. On its own, it is impossible to say. As ad stocks are based on this rather ambiguous metric, they have little to say about the level of reach or frequency required to drive improvements in your ROI.
Our tried and tested AdModel approach is much more forward thinking and considers five key parameters that provide key information for all stakeholders.
- Effective Frequency
How many exposures need to be seen to trigger a response?
- Recency Frequency
How many exposures need to be close to the purchase?
- Recency Window
What do we mean by ‘close’ – a week? 2 months?
- Memory Decay
How long is the exposure remembered?
Is there repeat purchase?
The fifth parameter – Habit – describes the long term impact of converting new users – basically a measure of trial and repeat. They may be completely new to the brand, or they may be existing users for who marketing has helped them discover new opportunities for use.
Going beyond ROI to help marketers take action
Identifying the first four parameters defines the budget required, the phasing strategy, and optimal investment. For example, knowing that for your brand consumers need to see 4 exposures, 2 of them in the last 7 days throws up a completely different approach than if consumers just need 2 exposures, just 1 of which can be seen anytime within 2 weeks of the purchase decision.
Planning on norms, benchmarks and experience can lead to some serious inefficiencies. Let’s look at the following simple example.
The base plan from the agency seemed OK – continuity was deemed important, and running with a broadly constant level of weight. However, the AdModel tells us that, to trigger a response from those active in the market, we need:
Using this insight we can deliver a 30% increase in the ROI. Based on science, not hunch.
As you can see, some big improvements in efficiency, just by understanding how media is working for you. And this is for just one channel – the same efficiencies can be seen across the media and marketing mix.
This approach has been tested across all verticals and across all continents. If you want to move away from reporting just an ROI to dynamically improving your marketing investments, it’s time to rethink your approach.