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Bots Don’t Buy: The Model in Action

As we saw in our last blog post, bots are eating digital ad budgets to an unacceptable degree. Let’s do something about it.

We have developed a predictive model that processes the massive number of signals from the digital ecosystem to predict who will buy….not who (or what!) will click. Buying includes on AND offline purchases (in this case; subscribing to a new service and paying at least one invoice); so this model varies significantly from ones developed on digital data alone.

The model was tested for a telco client for a campaign designed to acquire new customers. Channels included both search and digital display.

We found a very large difference in the activation rates (number of customers divided by households) between groups that scored highly likely to buy (and therefore recommended) and those that scored much lower:

Targeting recommended higher scoring groups led to an activation rate that was 2.1 X higher than recorded for other parts of the audience. But the overall targeting for the campaign, outside of the model, and based on chasing clicks, saw 70% of the budget go to low, out of target groups.

The path to improvement is clear: focus on in-target, high scoring groups. Given the digital inventory available to us, the entire budget could be spent there to secure these much better results.

In the battle against bots, we can have our cake and eat it too. We can keep money out of the hands of unscrupulous bot owners and in the hands of reputable digital publishers. AND we can improve performance, selling to real people who have a real interest in what we have to say. Win-win.

To learn more about how Bots Don’t Buy can help your digital marketing, contact us here.

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