Predictive Attribution Model in Marketing
Predictive Attribution Model
Checking the data collected from your campaigns is essential for your business; you won’t know if your marketing efforts are efficient without precise analyses.
One of the big challenges in digital marketing is understanding what channels produce the most revenue. Channel attribution mistakes lead to misleading conclusions. Many e-Commerce brands analysing campaign results without focusing on source channels. Having a deep understanding of how campaigns work within your marketing funnel’s, is essential for success. Without knowing this, it is almost impossible to allocate your marketing budget correctly.
Fortunately, Exposebox incorporates tools that help brands analyse the effectiveness of each channel. The most noticeable of these tools is conversion attribution. The model includes last click, first click, linear, time frame, position, and data-driven attribution. Each attribute has pros and cons; your brand should select it according to the business goals.
Last-click attribution is a straight-forward attribution model. It assigns all the credit for each conversion to the last campaign that the user clicked on. Last click attribution is the most popular method because it is simple to understand and use. Attributing all conversions to the last campaign clicked undervalues other campaigns that the user interacted with before.
Some customers require a more extended decision making than others. That’s why it’s essential to track all marketing channels’ effectiveness, and last-click attribution is not an ideal solution in that regard. Last click attribution can distort the data. Having said that, it doesn’t mean that last-click attribution is worthless. There are circumstances where it is a well-defined choice.
First-click attribution assigns 100% of the credit for each conversion to the first campaign that the user clicked on. Like last-click attribution, this model has some flaws in representing the customer flow. It’s clear why this attribution model would decrease other channels impact.
Linear attribution looks at every user as an individual and all the marketing channels gets equal credit for the conversion. This model has an added value, namely that marketing channels throughout all stages receive the credit. This provides a full view of your allocated marketing budget.
Time Frame Attribution
In time frame attributions, the conversion path’s recent touchpoint receives the most credit, while earlier interactions receive less credit on a sliding scale. The longer the time frame following customer interaction with a channel, the less credit it receives. This manifests a problem because it typically gives less credit to the top-of-funnel marketing channels. These channels are always the most distant from the conversion and therefore, are undervalued by this model.
Position-based attribution allows 40% of the first and last clicks to attribute the total credit, spreading the final 20% equally among the other customer journey touchpoints. Typically, position-based attribution is an excellent way for marketers to focus on lead generation.
The position-based attribution model doesn’t entirely suit the e-commerce industry. It provides most credit to the first and last touchpoints, but very little to other touchpoints. Position-based attribution frequently applied for high-end products that require a direct call before purchasing.
In conclusion, choosing an attribution model is an important decision for any company. It has a dramatic effect on the allocation of your marketing spend and the ROI. The best practice is to decide on the attribution model, the business goals and KPI’s, once. And, ultimately, the model that you select will be the foundation of all marketing campaigns optimisation.