In the age of digitalization, customers find it highly convenient to browse for products and services on the Internet and even make a final purchase online. It is, therefore, imperative for businesses to deploy effective digital marketing strategies to capitalize on such customer behavior. A good way for businesses to drive high profits is to invest in the marketing budget in data-driven attribution.

What is a data-driven attribution?

There are various touch-points in a customer journey before he/she reaches the final purchase decision. For example, if an individual wish to book a holiday package, they may begin by browsing the best holiday destinations online. Once they have chosen a destination, such as Europe, they may refine their search and type holiday packages to Europe. A couple of days later, they may see a display advert for the holiday on Facebook. Following this, they may browse through the retailer’s site and decide that they will book the package from here. During the weekend, the customer finally books the holiday package through the retailer’s site.

The question, which arises, is that which touch-point was responsible for the conversion? The Last Click’s rule model attributes the entire conversion to the touch-point, which the individual interacted with just before making the booking. In the situation mentioned above, the retailer’s site would get the entire credit for the booking. Therefore, applying the Last Click’s rule, the media budget may be pulled from other marketing channels.

However, such an attribution model is not feasible as the journey of the customer involved multiple touch-points. This is where data-driven attribution comes into play.

Data-driven attribution takes into account every click, impression, or touch-point of the user that led to the desired outcome. An algorithm, with the help of machine learning, assigns a value to each touch-point. The algorithm assesses the amount of impact each touch-point had on the customer in their journey towards making the purchase.

Benefits of data-driven attribution

Businesses may know that a particular advert received a particular amount of impressions. However, by deploying a data-driven attribution model, businesses can gain insight into the real impact the impression had on the conversion or sale. This places businesses in a better position to understand their customers.

Data-driven attribution also helps to gauge the performance of each paid media channel of a business, thereby helping to identify the impact of digital campaigns. Based on this, businesses can understand how various marketing channels work together. They may optimize the performance of the marketing channel that worked best for them by reallocating the marketing budget from channels that are not performing well. Data-driven attribution, therefore, helps businesses assign their media budget most efficiently and enables them to make well-informed budgeting decisions.

It should be noted that data-driven attribution requires time and commitment. This is because it involves complex algorithms that analyze high volumes of data to ensure efficient machine learning. However, there is no time like the present to deploy data-driven attribution in business and reap its benefits.


Data-driven attribution uses complex algorithms to assign impact-value to each touch-point that a customer encounters on the journey towards making a purchase. Data-driven attribution helps businesses understand their customers better, identifies the performance of each paid media channel and helps assign the media budget most efficiently by enabling well-informed budgeting decisions.

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