Data-driven attribution can be described as a statistical model for assigning conversion credit.
Data-driven attribution is a highly efficient and comprehensive statistical model for attributing conversions, or sales, to different marketing touchpoints in an individual customer’s conversion path. These touchpoints encompass various online marketing channels like organic search, paid search, display ads, email marketing, social media, among others. By assigning credit to specific touchpoints along this conversion path, it aids marketers in understanding and determining the actual value or influence each touchpoint has and optimally allocate resources for maximum efficiency.
Suppose a customer journey for purchasing a product includes the following touchpoints: a Google search, a display ad click, a marketing email, direct website visit before the final purchase. A data-driven attribution model may ascertain that the email marketing was most effective in driving the purchase. Thus, it will assign maximum credit to it, and the rest of the credit is distributed among other touchpoints based on their effectiveness evaluated by the model.
Understanding the role of different channels in conversion events is crucial for efficient marketing budget distribution. By relying on a data-driven attribution model, businesses can identify their most successful channels and devise their marketing strategies around them instead. This reduces wastage of resources on less impactful channels and aids in maximizing the Return on Investment (ROI).
The effectiveness of data-driven attribution is directly linked to a variety of e-commerce metrics. For instance, it can significantly impact metrics like Cost Per Acquisition (CPA), Customer Lifetime Value (CLV), and ultimately the Return on Investment (ROI). By understanding the attribution of conversions, businesses can optimize their marketing spending leading to a lower CPA, increase repeat purchases improving the CLV, and thereby boost overall ROI.