It is time to wrap things up! This is the last blog post in our series around "Why your main goal should NOT be to build a unified customer journey". With the substantial changes in data availability – what will be needed for the subject of attribution?

This is the last blog post in our series around “Why your main goal should NOT be to build a unified customer journey“. Apart from the intro post, we have been taking you on a customer journey from the topic of building a modern customer/marketing data warehouse to feeding ad platform algorithms. In this last part we will get to the core of marketing, covering what will be needed for the subject of attribution. 

In general, attribution is about understanding what value that marketing is generating. Currently, there are three different types of methods available, and all of them have their benefits and drawbacks. Before analysing how they are affected by the privacy trend, and what will be needed going forward, let’s cover them briefly.

Path-based attribution

Most web analytics software, such as Google Analytics, are measuring each visit to a website or app. Together with each visit, the source is measured, and multiple sequential visits from the same unique identifier are called interaction paths. From a technical perspective, this is done through the use of cookies, where the web analytics software is storing a cookie together with a unique identifier, as well as more information, in the user’s browser. If you accepted cookies when entering this blog post, Google Analytics has stored a cookie in your browser with a so-called client-ID as the unique identifier. Depending on where you came from when finding this blog post, that will also be stored. In the case of Google Analytics, client-ID is unique for each browser, meaning you will get a new client-ID if you visit this post through a different device or browser. This limitation is an example of a drawback that we will come back to later, but there is a possibility to solve for that through additional tracking (implementation of user-ID). 

In its simplest form, path-based attribution is done through analysis of all user (or browser…) interactions that took place before a transaction or conversion and then trying to figure out which interaction should receive what credit for making that happen. Generally, there are two different types of methods: rule-based and algorithmic. An example of a rule-based model is the, sadly enough, commonly used Last Non-Direct Click model (LND) in Google Analytics. In its essence, the LND model allocates 100% of the value to the last known interaction before the event; quite a simplification of reality. In comparison, an algorithmic approach, also often referred to as data-driven, is using mathematics and statistics to decide what interaction should get what credit. All path-based attribution models bring the assumption that the analysed interactions have an effect in the first place, which might not always hold true. Therefore, path-based attribution modelling can never answer the question of how much of the transactions or conversions would have happened anyway. That drawback leads us to the second category of attribution.

Incrementality attribution

The purpose of the incrementality attribution is to figure out how much more (incremental) effect one got from running a specific marketing activity. Therefore, it is designed to answer the question that path-based attribution could not, and in addition that can be very powerful. Simply put, the method is doing this by exposing one group for the ad and one not. After that, the effect is measured and compared to these two groups. The technical process for performing incrementality attribution is either based on an advertiser’s first-party data or third-party data provided by the ad platforms. A benefit of the first-party data method is that you can control the whole methodology, from experiment design to analysis, but a drawback is that it will be limited to users that have previously interacted with your website or app. Another drawback is that it becomes very complex, if not impossible, to analyse cross-channel effects through application of incrementality methodology.

Marketing Mix Modeling

The last category of attribution is often called Marketing Mix Modeling (MMM). The method is a quantitative regression analysis that is aiming to explain the effects on value based on different factors. There are many different ways of conducting that, but in general, it is done by modelling a dependent target variable (e.g. revenue) against a set of independent variables (e.g. media spend, weather etc.). The variance in the independent variables should explain the variance in the dependent variable, and if done right, it can be very powerful. Compared to path-based attribution and incrementality attribution it can answer the question around diminishing returns, and hence also budget optimisation – all without the use of cookies.

So what does marketing attribution has to do with the overall theme around “Why your main goal should NOT be to build a unified customer journey?”. So far, the purpose of building a unified customer journey has not only been about improving the user experience; it has been as much about enhancing the optimisation of marketing investments. When it comes to optimisation of media buying, attribution is critical, and therefore it is related to changes in data availability.

Of the three methods mentioned above, the most commonly used is the path-based attribution. It is adopted widely across the whole industry of digital media buying as the core method for attribution. As for any model, the output will only be as good as the underlying data, and as mentioned, the path-based attribution is relying on data. That means that path-based attribution as a method has become worse. The most immediate effect is that path-based attribution will be able to identify fewer channels in their interactions, and interaction paths will be broken. This used to be a problem due to the user-ID/client-ID problem mentioned above, but with changes in privacy, it is even more significant. If not dealt with, it will lead to relative sub-optimisation of all marketing investments, which is far from preferred.

Next-generation attribution

If you have found yourself stuck with a rule-based attribution model due to data-driven attribution modelling being too complicated, I regret to tell you that things will not become more comfortable. Given the changes in data quality and the increasing demand for improvements in marketing attribution, the next generation of attribution calls for even more complexity. The starting point is that path-based attribution modelling will be based on less data. The output of that, as mentioned, is that paths will be broken and channels that are identified will decrease. The next-generation attribution will have to solve this by combining the different methods explained briefly at the beginning of this blog post. As mentioned, regression analysis is not using cookie data as input, so it is a good place to start. However, there is a lot of innovation needed when it comes to the concept of Marketing Mix Modelling (MMM). The traditional MMM formulation is aiming to explain the spend-return relations of the individual channels, but it was developed in a time where channels were mainly:

  • Push channels: Such as TV, radio, print, catalogues. Channels that, compared to digital marketing channels, have a limited possibility of relevant audience targets.
  • Mostly untracked: Certain actions could not be associated with a channel as a source.
  • Fixed costs / few in-channel parameters: Ad cost was mostly not determined by actions on the part of the consumers, and advertisement in a channel could be viewed as buying more or less of a single product.

This situation lends itself to regression modelling for three reasons: First, there is no data on what happens between the investment in a given channel and the measured total value, so the only way to infer causal impact is (basically) the correlation between the spend and total return. Second, the fixed cost and push nature of channels implies that they behave in a similar way, in particular how the spend interacts with other variables, such as season or variations in demand. Third, the lack of in-channel parameters to control means that the spend and return has a one-to-one relationship, in that we simply buy more or less of the same thing.

It is safe to say that channels have changed, and these three aspects are essential to think about since changes in these also change both the available data for MMM as well as the expected relationship between spend and return. A clear example of the importance in this is to think about the spend-return relationship for linear TV compared to Google Ads (Search):

  • Linear TV: Spend-return relation will be scaled by the return axis by variations in demand, meaning that the ROAS will be lower during periods of low demand, and vice versa. The cost can not be optimised at the time of the purchase. Relatively few in-channel parameters to control, making spend more or less proportional to reach / exposure.
  • Google Ads (Search): Both the spend and return axes will be scaled by variations in demand. Spend is not only decided by your bid, but it is also affected by the number of queries. Tracking implies we know more about the causal links between the input of spend and the output of revenue. A multitude of parameters to control how we spend within the channel.


In other words, modelling Google Ads (Search) in the same way as linear TV is inadequate: in the best case the model will be weak due to discarding relevant (tracked) information, and in the worst it will yield the wrong answer due to different behaviours under varying demand or in-channel optimality. With this in mind, the next-generation attribution should be a combination of path-based attribution and MMM, built on a more modern MMM approach, that is utilising the improved data at hand as well as adjusting for known differences in channel characteristics. Through that approach, attribution will once again be able to answer questions such as:

  • What do we get if we spend X in channel Y?
  • How much more can we spend in channel Y?
  • What is the effect of channel Y if we spend X in channel Z?

 

As with all paradigm shifts, it’s not only presenting challenges. The companies who manage to create the best possible data infrastructure and evaluation will form a competitive advantage. Attribution is likely the foundation of all your media and user optimisation, and therefore it will affect profit. Any incremental improvement of the model, or the underlying data, will unlock high potential for improved profitability, growth or profit maximisation. Having suboptimal attribution will have the same negative effect as setting the wrong target. As complex as it might sound, the solution is found in the combination of path-based attribution modelling and MMM, where MMM is stepping in to surface missing data, and where incrementality attribution is the icing on the cake to improve the final output.

Pierre Rudolfsson Senior Data Scientist