The Good, The Bad, The Missed Consent | DMA

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The Good, The Bad, The Missed Consent


Marketers, before you retrain to be accountants, teachers or, heaven forbid, data compliance officers, I have something that might cheer you up.

A melancholic mood pervades marketing departments these days because changing data volumes mean revenue plans are unlikely to impress the boss. The volume of consented marketing data is now predicted to drift downwards when most were expecting it to drift upwards.

The gloomy forecast is based on announcements from the ICO and respected experts who say the “direction of travel is towards more explict or unambigous consent”.

Let’s be honest here. For far too long marketers have gained legal marketing consent by either hiding the opt-out box or confusing repondents. The result has been datasets described as “legally consented”, but containing many respondents who did not actually intend to consent. This game is now drawing to a close. Hence the gloom.

Figure 1. shows the data generated from two statements from a well known brand.

Statement A generated 34% of opt-ins compared with statement B’s 30%. On this evidence alone the inevitable conclusion would be that statement A is the preferred data set because it generated 4% more consents, which would probably generate a similar amount of additional sales. But this is not the whole story.

To help concientious brands to prepare for change, fast.MAP has developed a research process that can assess the proportion of “good”, “bad” or “missed” consents for each statemement.

By going back to the same respondents, armed with the knowledge of whether they consented or not to a particular statement, and carefully re-interview them using very clear language, it is possible to ascertain whether they actually intened to give or withhold consent. The two sets of data are then compared. They are never the same and the difference is normally caused by people misunderstanding or being confused by the permission statement.

Figure 2. reveals more information about the data set in Figure 1. It shows statement A generated more “Good” consent, 21% , than statement B, 18%, even though statement B achieved more legal consents. Once consents have been collected, brands cannot separate the “good” from the “bad”. In some cases mixing bad consenters with good consenters could make the resulting concoction unprofitable to contact.

As well as the higher amount of “good consent” in statement A, there is an additional reason why it is likely to perform better. The proportion of “good” consent for statement A is 70% rather than 53%. This proportion affects the performance of the data set because the “bad” consent will reduce the performance of the overall file.

It’s also worth noting that the use of a misleading statement will eat away at a brand’s reputation. Consumer logic is likely to go something like this: “If a brand is deliberately confusing me with this consent statement, why should I trust what it says about its products or services?”

Measurement is King

Despite the fact that many brands have survived profitably for years on the far higher levels of bad consent generated by opt-out statements, they need to be prepared for a largely legislation-driven move towards opt-in. The status quo is no more. Consumer attitudes, legislation and codes of practice are changing rapidly.

In a perfect storm, as well as the flexibilty to alter course the ship’s captain needs to make acurate calculations.

Marketers must understand the environment has changed fundamentally and a different route will be needed. But they must also be equipped with the best measurement tools to enable them to plan that route.

To plan properly marketers should be using more than the size of the legally-consented data file to compare statements. Rather than relating everything to database size, many brands are using this more sophisticated process to asses the impact of a change from opt-out to opt-in and to apply more objectivity to the resulting effect on revenue.

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