I don’t think there is a single soul in the media industry who does not buy into the promise of data targeting. The huge attention on how to leverage data in advertising is obviously strongly fuelled by the shift to automated buying and selling of media inventory. According to Forbes, data transcends pretty much all of the 10 marketing trends that is expected to define 2016 for marketers. So, there is no rest for data companies in the coming years.
The triple win
For publishers, data targeting means that they can do a better job in terms of reaching desired audiences and decrease impressions wastage, leading to higher yields. From an advertiser point of view, they are given an opportunity to primarily communicate to/with their target audiences in contrast to pushing out advertising to people who are unlikely to engage in their creative or buy their product. Obviously, this is a benefit for the users as well.
So, the promise of data driven advertising is a triple win! However, to deliver on this promise, publishers need a data partner who can say something meaningful (precision) about a big portion of their available page impressions (reach). Fact is, far from all data companies are able to deliver both reach and precision, and there is a lot of confusion about this in the market place (my point in writing this post).
The need for deterministic data
The reason is, nobody knows everything about everyone everywhere (facebook come closest). And therefore data companies use algorithms to predict users behind the multiple devices and screens. Prediction is great and a necessity, but it is also a source of inaccuracy.
The more deterministic data (stuff you know) you have as a training set for your algorithms, the higher combination of accuracy and reach can theoretically be achieved, leading to more impressions you will deliver on target.
But after you train a probabilistic model, you also need to validate if the model was successful? Or it requires more tweaking. Without a large volume of deterministic data to validate your model up against, you are flying blind. This is why trying to predict audience segments based on behavioural data alone or small pools of first party user data (e.g. 1000 user surveys) makes it very hard to generate reach without compromising on precision.
In other words, you can have all the behavioural data the internet has to offer, but without a solid base of deterministic data you are unlikely to deliver precision in your predictions. Many publishers will nod in disappointment to this, as they have experienced how their data products/partners were unable to help their business in the way they expected.
We help publishers predict with precision
This is probably the biggest myth in the advertising industry, because data companies without first party deterministic data will avoid waving around this fact. They will however tell you how their algorithms with small amounts of deterministic data can predict accurate audiences at scale, and therefore they don’t need much first party data in the first place. But as with everything else; your output depends on your input.
For that reason, our publishing partners integrate to our data store of predicted profiles based on deterministic panel of one million surveyed profiled to enrich their own user profiles. By doing this, we help everyone achieving the mass they need to build reach around demographic audience segments without loosing out on precision.
With this approach publishers can on average predict ‘something’ (e.g. gender, age, income, household size, kids, occupation, interest) on about 60% of their page views, and with precision ranging between 70-90%. To some this might sound limited, but reality is that this is miles ahead of industry averages.
If you would like to know more about your possibilities as a publishers, you are welcome to contact by email: firstname.lastname@example.org