As multi-device usage increases, brands must move away from the out-dated and simplistic last-click attribution metric. By understanding the impact that marketing efforts have across all devices and channels during a consumer’s path to purchase, marketers will be empowered to create more effective advertising campaigns and increase conversion rates.
Most consumer paths to purchase involve multiple online and offline touch points – including awareness, consideration and conversion – with almost 4 in 5 (78%) of all purchases involving a connected device at some point. For example, the consumer purchase journey could start with the consumer clicking on an interactive ad on a smartphone, followed by a retargeted display ad across another devices, and culminate in the consumer clicking on a sponsored listing on a laptop to purchase. Last-click in this instance would attribute 100% of the conversion value to the last media touch point – the sponsored ad.
This method of measuring the effectiveness of an advert can be misleading and brands relying on this model will not have a clear understanding of how consumers are interacting with their marketing campaigns. This lack of understanding often means brands misjudge where to invest marketing budgets to achieve maximum ROI.
So what are the alternatives to last-click attribution?
1. Linear attribution
This marketing measurement model is – aside from last-click – the most simplistic. In this model, each touch point in the purchase journey is accredited equally for the transaction. For example, if there were five touch points, each would get 20% of the credit.
2. Position based attribution
When using the position based attribution model 35-40% of credit is given to both the first and last touch points, with the remaining 20-30% distributed evenly to other interactions in the middle. For example, if the first and last touch points are assigned 35% credit each, the middle three receive 10% each.
3. Time decay attribution
This measurement model assigns the most credit to touch points closest in time to conversion and, the previous touch point receives less credit, and so on down the line.
4. Algorithmic Attribution
This machine learning model is based on the analysis of every attribute of every touch point experienced by converting and non-converting consumers across all online and offline channels and devices. The algorithmic attribution can also analyse combinations of different touch points to quantify the actual impact that each one has on the final conversion.
The marketing industry as a whole has slowly accepted that last-click is out-dated and inadequate. As brands adopt advanced algorithmic attribution and replace the flawed last-click metric with attribution-informed optimisation they will have a clearer view of their consumers’ path to conversion. This insight will enable brands to make informed decisions about where to invest marketing budgets and how best to maximise ROI.