Attribution Modelling

The importance of data-driven planning in digital advertising has become a significant factor for marketers. Advancements in data collection technologies have provided marketers the prerequisites for thorough analyses of the impacts of digital marketing activities and most often attribution models are used to evaluate the performance.

This is a fast moving, competitive and innovative industry, where tiny improvements in the clickthrough rate (CTR) and conversion rate can lead to large improvements in the effectiveness of campaigns.

Given the large amounts of digital advertising spend, and the competitive nature of the industry, it is increasingly important to make sure each advertising dollar is spent in the right way. In the marketing world, this question of where to spend money is often referred to as attribution, since the question is how to attribute value. In other words, how much did each interaction with an advertisement contribute to sales conversion?

But for many media agencies it still is a challenge to attribute value, and as a result, it’s not uncommon for companies to use the easiest of all models, where all value is attributed to the last click. There are many reasons why the attribution analysis is tough to implement. Some of the reasons are follows

  •  It’s difficult to get the digital marketing data required for attribution out of the social media, search properties, and digital devices that consume much of our attention, and this creates significant blind spots for attribution analysis.
  • Large data volumes require the use of modern big data platforms. And the talent required to unlock the digital marketing value in that data is limited.
  • The value of different digital marketing channels varies by campaign, so every attribution model (whether determined algorithmically or via simple rules) must be unique to each campaign on which it is being applied.

But the fact that the attribution analysis is tough is no excuse not to do it, and keen marketers recognize that some measurement is better than no measurement.

The benefits are clear; better spending, channel, and strategy decisions lead to dramatic increases in campaign performance.

There are many different approaches to digital marketing attribution, ranging from the overly basic to the incredibly sophisticated.

Statistical Modeling

Statistical attribution models examine customer conversion paths and use techniques such as logistic regression or estimators to conclude which media elements are driving the most consumer response. Since massive sample sizes are required to conclude causal relationships, these models categorize interactions into larger groups, such as by channel or by campaign.

As a result, Statistical modeling techniques can only provide high-level directional insights into the contributions of each channel or campaign, with no granular level of optimization ability at the publisher, placement or keyword level. Since accurate attribution relies on having large amounts of granular-level data to determine how channels and touchpoints work together to produce outcomes, much is left to be desired when it comes to the accuracy of this approach.


Path-based algorithmic models compare the behaviors of consumers who took similar conversion paths, with one touchpoint added or removed. This approach enables marketers to see how a particular touchpoint impacted consumer behavior, and translate that into a fractional amount of credit given to that touchpoint.

As with Statistical models, a massive data set must be used in order to understand how individual placements, sizes, creatives, etc., impact consumer response.

Path-based attribution also leads to questions about accuracy when optimizing media based on the recommendations it provides. The consumer-decision journey may consist of millions of interactions, yet path-based models are based on the behaviors of consumers who took similar conversion paths. Since you can’t force a consumer to take a pre-defined journey or interact with media in a certain way, the optimization recommendations that are put into market may not drive the anticipated results.

Embarking on digital marketing attribution modelling for the first time can be discouraging. It is essential to have the following in place:

  1.  The right technology that is flexible enough to capture data from all the multiple channels by which your customer interacts.
  2.  The analytics to explore the end-to-end journey from the customer’s perspective, with data stored centrally, and the capability to apply weightings and relevant rules based on your business needs.
  3.  A flexible algorithmic attribution tool that offers a portfolio of different models to manipulate the data and take the guesswork out of rethinking your digital marketing strategy and programmes.

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