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Testing Every Ad Unlocks Cereal Share Growth

[Insights that Work - Case Study] System1 Research tested the effectiveness of ads for multiple food and drink producers to satisfy clients' planning needs and drive brand growth.

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Strategic communications planning requires accurate prediction. How good is this ad? Which campaigns should I be spending money on? What will the outcome be for long-term growth, given competitor activity?

The relationship between the response to advertising and future brand growth is demonstrated by work like Les Binet and Peter Field’s “The Long And The Short Of It”. Emotional response is key. The more people feel, the more they will buy (in the long term).

But this doesn’t resolve the strategic issues. Copy tests can’t factor in varying levels of media spend. And they can’t monitor competitive activity. The focus on testing single ads limits research’s usefulness.

So our clients had an information gap. To plan for the long-term effectively, they needed a bigger picture of advertising activity which provided a better model for brand growth outcomes. The stakes are huge: as a recent Ebiquity report pointed out, better allocation of spend could boost marketing ROI by $45bn worldwide.


Our solution is a simple one. Rather than testing single ads for single clients, test everything, for everyone. By shifting ad testing to a comprehensive, subscription-based model – a Spotify for ad effectiveness, if you like – the clients’ problem is solved. They can see at a glance competitor ad quality and media spend, and benchmark their own communications activity, adjusting strategy accordingly. Testing at scale allows for much lower costs and higher automation – a classic disruptive solution.

We put this into practice beginning with TV ads in the US and UK in 2017. We used consumer panels to test emotional response to every ad that aired in five major sectors. For UK cereals – a key category for many of our multinational clients – we tested 49 ads with a combined TV spend of £20m.


All this effort would only truly satisfy our clients’ planning needs if it had the potential to drive brand growth. If the test data didn’t improve on the existing relationship between media investment and future (next 6 months’) brand growth, the exercise would have no value.

In our subscribers’ cereals category, we saw a +0.54 total correlation between raw media spend and next-six-months brand growth. When the ad test effectiveness data is factored in, this relationship improves to +0.84. In other words, an emotion-plus-spend model is far more predictive of future growth than a spend-only model and a better tool for communications planning.

This is validation not just of Binet and Field’s work (among others) but of our own intuition that a disruptive, subscription-based testing model would create potential benefits for clients by delivering full-category understanding at low cost.

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