The Rise and Fall of Deterministic Analytics

Until recently, deterministic analytics was on the ascendency with its claim of following the journey of actual consumers from their first exposure to the product, in ads and displays, to every exposure along the way to the purchase. Tracking every touchpoint and attributing its value towards getting the sale is the objective of multi-touch attribution (MTA).

The Rise

From the digital certainty, that someone clicked my ad and bought my product, came the religion of determinism. The digerati of the ad world, led by the goliaths Google and Facebook who controlled large swaths of the data, argued that only events that could be directly traced counted. This posed a huge challenge to linear media whose messages were broadcasted to and received by unknown persons. The consequence was television got squeezed and the rest of linear media (newspapers, magazines, etc.) came tumbling down as ad dollars went online. 
 
Directly tracing what people want is a fast and powerful tool to deciding what to create and sell. Google and Facebook taught this lesson to advertisers and direct to consumer sales took off. Netflix taught the TV industry this lesson and television is now transforming.

The Challenge

Tracking digital media and messages is well understood and practiced with cookies, pixels, and device IDs. Since advertisers buy exposures or clicks, they only need to track their advertising message. Complexities arise when the advertiser wants to govern the acceptability of the content its messages show in, but that is another discussion entirely. Determining the journey of exposures of their advertising message requires the advertiser to connect all the touch point identifiers: the cookies, pixels, and device IDs to persons. The challenge is connecting the myriad of cookies and pixels to device IDs and then to persons and transactions. 
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Ecosystems, such as data management platforms (DMPs) and later consumer data platforms (CDPs), rose to connect these data. Google and Facebook built tagging and tracing systems - through their activation platforms - to enable smaller advertisers to gain journey insights without having to finance a DMP or CDP. Google with its urchin tracking module (UTM) tags takes this a step further with their Analytics, letting advertisers track their website traffic and non-Google digital ads too. Advertisers get the journey analytics leading up to the transactions, but not who they are, making the final connection to the transaction probabilistic.

Linear is more complicated. Linear is one way communication. In some cases, there are devices to say it was received, such as cable boxes or smart TVs, and in other cases. In these cases, there is the possibility of connecting linear exposures to journeys. In other cases where there are no devices to say it was received, it is impossible to connect such exposures to journeys. Beyond this leaking of touch points, the challenge of corralling the devices that report receiving linear is immense, as they are owned and controlled by a myriad of competing cable and smart device companies. To get complete coverage on linear, you have to get all this data and then distill it down to the time and channel your advertisement aired to see who was exposed. Alternatively, you can get a large sample of this exposure data, connect it to your journeys, and then model the touch points gaps from the data that you could not get. Getting complete coverage is practically impossible. No one gets it. Consequently everyone models the gaps. 

Then there is the challenge of objective. Do you want to measure Direct Response or Brand Resonance? Last touch attributions are always focused on direct response.

Since the determinist puritans came from digital direct marketing, these challenges are waved aside as yet-to-be-transformed parts of media and marketing.

The Disruption

Privacy is starting to take root and likely to kill determinism for brand marketing. Europe’s legislation general data protection regulation (GDPR) is moving through court cases, defining its scope. The California Consumer Privacy Act (CCPA) is just starting its court cases. ​

Personal information is any information including patterns that can be identified back to a person or household. 

The basic ideas of these two laws are:

1. Personal information can only be stored and used if the consumer actively gives their permission.
2. The personal information can only be used for the purpose permissioned.
3. The personal information cannot leave the control of the company who has the permission.

This means data matching without specific permission cannot take place. A case is moving through the European courts that might make digital activation exchanges illegal as currently constituted, because they deal in personal identifiable information, sharing across dealing companies who do not have specific permission to do so. This may be an edge case but it illustrates the potential scope of disruption.

The Fall

Google and Apple are now starting to champion privacy to their advantage. In Google's case, they are no longer allowing third party cookies, making tracing journeys beyond their walls probabilistic. In Apple's case, they are requiring users to say that they want to be tracked for each app, making journey's that include Apple devices probabilistic.

Probabilities to associate data and draw performance insights are being tapped to deal with the reality that not all data can be connected deterministically anymore.

Viewpoint

Determinism will continue with last touch attribution. Brands will need to find probabilistic paths to handle the increasing data gaps that privacy brings if they want to maintain or grow their resonance. ​

MediaBrain anticipates ad-tech and mar-tech startups to focus on the implications of privacy to transform the ecosystem infrastructure. We expect artificial intelligence to play a role in filling these gaps. Soon we will hear about “machine learning” all over again. This time it will come back as “privacy preserving machine learning”. Think of learning bots that go from one private data source to another to develop an aggregate view of how these sources look and behave according to different characteristics. The learnings are encoded with one way math, so that others cannot reverse engineer the learnings to identify any of the private information. 

The first generation of learning bots will focus on predicted behaviors of common characteristics, such as age, gender, wealth, and location. The second generation will move to deep learning methods that do not presuppose which characteristics are predictive and look to discover which data are explanatory and of what. These multilayer techniques are currently being used to recognize things in photos. Of course the sequencing, that privacy requires, complicates the maths. Google is starting to deploy some first generation privacy preserving learning bots across the Android ecosystem with a technique called Federated Learning. The second generation of privacy preserving deep learning is still in development.

Look for these methods to start transforming the ad-tech and mar-tech ecosystems over the next five years.