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.

Bots Don’t Buy: A better approach to digital display ad planning and buying

The digital ad display market has been plagued with issues, but it should still be an important channel for all marketers, both to build sales and to build brand.

The challenge is to overcome 5 problems:

1. Bots eating ad budgets

· According to Google, 56% of ads are never seen by a human

· A study by Forrester found 69% of brands spending $1 million per month reported that at least 20% of their budgets were being lost to digital ad fraud

2. Lack of respect for people’s privacy

· an industry focused on surveillance has created the largest consumer boycott in history…ad blockers

· at the same time, new privacy regulations in Europe, the US and Canada demand transparency and restrict the use of personal data

· Google will deprecate the third party cookie in Chrome browsers in the near future and Apple’s approach to email and ad permissions will greatly restrict some common practices

3. Bad, unreliable measurement and attribution

· digital attribution tools impose a rule on clickstream data, vs using that same data to intelligently determine cause and effect.

· some digital attribution tools pretend offline media doesn’t even exist!

4. Targeting without context

· When marketers don’t develop media plans with synergy in mind…how channels can reinforce each other, vs cannibalize…budgets are wasted

· cross-channel impacts can add a double-digit boost to performance…added impact without added budget

5. No forecast capability

· common digital planning tools are not predictive at all, let alone predictive of the incremental lift digital can have in the presence of offline channels

Navigation ME is pleased to announce we are launching a solution to these problems. Since bots don’t buy, our models will act as filters to avoid ad fraud. These models focus on signals (plentiful in the digital ecosystem) that differentiate human traffic, associated with purchases, from click traffic from bots…that never buy. These patterns allow us to focus digital ads on target audiences that are disproportionately driving sales volume lift and avoid audiences that will spend little to nothing with your brand.

In addition, our new predictive and optimization models are designed to drive incremental sales from digital ad buys. Since we will use privacy compliant data, we avoid making the problem of invasive tracking worse. And since our models don’t need personal tracking to determine cause and effect, we can help you avoid regulatory problems…and keep a cleaner conscience. Most importantly, these models integrate data from offline media, so that we can quantify digital ad lift without making attribution errors. We also, in this way, take full advantage of the synergies that exist between channels that are hard to measure but powerful in effect.

We believe the digital display marketplace can be rehabilitated; and turned into a reliable growth engine for your brand. To find out more, contact us here.