When it comes to measuring the ROI of your digital marketing campaigns, last-click attribution has long been the industry standard. This model gives all the credit for a conversion to the last touchpoint that a customer interacts with before making a purchase or taking another desired action.
However, last-click attribution has several limitations that can lead to an inaccurate understanding of your marketing performance. For example, it doesn’t take into account the various touchpoints that customers interact with throughout their buying journey, or the impact of upper-funnel marketing activities that may not directly lead to a conversion.
Fortunately, there are more advanced techniques for marketing attribution that can provide a more accurate picture of how your marketing efforts are contributing to business outcomes. Here are some examples:
Multi-Touch Attribution
Multi-touch attribution models take into account all the touchpoints that a customer interacts with throughout their buying journey, rather than just the last one. There are several different types of multi-touch attribution models, including linear attribution (which gives equal credit to all touchpoints), time decay attribution (which gives more credit to touchpoints closer to the conversion), and position-based attribution (which gives more credit to touchpoints at the beginning and end of the buying journey).
For example, let’s say a customer sees a Facebook ad for your product, clicks through to your website, and browses several pages before leaving. Later, they see a Google ad for the same product and click through to your website again, this time making a purchase. With last-click attribution, the Google ad would get all the credit for the sale. But with a multi-touch attribution model, you could give partial credit to both the Facebook and Google ads, as well as any other touchpoints the customer interacted with along the way.
Algorithmic Attribution
Algorithmic attribution models use machine learning and statistical analysis to assign credit to different touchpoints based on their actual impact on conversions. These models take into account a wide range of factors, including the type of touchpoint, its position in the buying journey, and its historical performance.
For example, let’s say a customer sees a display ad for your product, clicks through to your website, and browses several pages before leaving. Later, they see a retargeting ad for the same product and click through to your website again, this time making a purchase. With algorithmic attribution, the display ad might get less credit than the retargeting ad, because historical data shows that retargeting ads are more likely to result in conversions.
By using more advanced attribution techniques like multi-touch and algorithmic attribution, you can gain a more accurate understanding of how your marketing efforts are contributing to business outcomes. This can help you optimize your marketing mix, allocate your budget more effectively, and ultimately drive better results.
In conclusion, while last-click attribution has been the standard in the industry for many years, it is no longer enough to accurately measure the performance of your marketing campaigns. By embracing more advanced analytics for marketing attribution, you can gain a deeper understanding of the customer journey and the impact of your marketing activities. So why not take the next step and start exploring these advanced techniques today?
Also Read: Measuring Marketing Success with Google Analytics
