Multi-touch Attribution for Marketers: What Brands Should Keep in Mind?

Before leading to a conversion, a buyer’s journey involves a variety of devices and touchpoints. Marketers need to know the touchpoints and messages a consumer encounters that lead to favourable action to optimise campaigns and provide more tailored consumer experiences. The objective is to determine how to distribute your marketing budget. Marketers can more efficiently allocate dollars to similar touchpoints in future media plans and redirect money away from unsuccessful channels to successful ones when they understand specific touchpoints’ role in a conversion.

Multi-Touch Attribution: What is it?

Multi-touch attribution is a technique for measuring marketing that considers all the consumer touchpoints and assigns a particular percentage of the credit for each channel so that marketers can evaluate the importance of each touchpoint in generating a conversion. Let’s take the case of a buyer debating buying a fresh pair of shoes. Nike advertisements are directed at them after performing some studies. They first notice and disregard a display advertisement. They then encounter a native advertisement on Instagram that grabs their attention and sends them back to the Nike website. Lastly, customers receive a promotional email with a coupon code that prompts them to purchase.

Each of these advertisements reflects a stage in the purchasing process. With the help of multi-touch attribution, marketers may link the transaction to the native advertisement and the email marketing campaign. They can then decide to change their strategy after realising the display advertisement was useless. Marketers may use several multi-touch attribution models to examine user-level data, i.e., the impact that user-level events (clicks, impressions) have on the result. These approaches all scale advertising performance in unique ways.

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What variations of multi-touch models are there?

Remember that there are various models to choose from when considering the advantages of multi-touch attribution. Each model is unique depending on how you assign a given touchpoint’s importance. The models that work well for the types of attribution that various companies encounter are listed below.

1. Linear

The linear multi-touch attribution model is the most straightforward method to implement. Each touchpoint in the process of moving towards a final sale in this model is given equal weight. Each touchpoint is given an equal amount of weight regarding revenue. Selling a pair of athletic shoes to a man in his early 20s would illustrate a linear multi-touch attribution model.

2. Time Decay

The touchpoints closer to the ultimate sale are given more weight by the time decay model. Customers who purchase an enterprise-level SaaS solution after receiving a cold call, visiting the solution provider’s stand at a trade show, attending a webinar, and participating in a demo will be given more excellent sales credit for those activities. A cold call may be given 10% of the credit, a trade show booth visit could be worth 20%, a webinar 30%, and a demo 40%.

3. U-shaped

The U-shaped multi-touch attribution model evenly distributes credit to all subsequent touchpoints while giving higher weight to the initial and last touchpoints. If there were five engagements altogether, the first and last engagements would each receive 40% of the credit, while the other three would each receive 6.66%.

4. W-shaped

The initial touch, lead generation, and opportunity creation touchpoints are the three focal points of the W-shaped multi-touch attribution model, like the U-shaped model. The big three receive 90% of the credit (each touchpoint receiving 30% each), but there may be more touchpoints between those three; if so, they each receive equal credit divided among them.

5. Full Path 

Compared to the previous models we’ve spoken about thus far; the complete route multi-touch attribution model is more complicated. Because it gives the same three touch points equal weight, plus one additional in the form of the lead creation touchpoint, it is a near relative of the W-shaped paradigm. The remaining touchpoints split the remaining 10% evenly, while each of the four touchpoints receives a share of 22.5%.

6. Custom

The bespoke multi-touch strategy is the most complicated attribution model. The organisation implementing the concept develops its algorithm to determine and weigh each touchpoint rather than relying on a determined weight. This strategy may be most effective in sales cycles with more extended periods and account-based marketing strategies where it may be challenging to determine which touchpoints are most important. Even though this technique has a high likelihood of success, developing the attribution plan does call for a sophisticated marketing attribution solution.

7. Algorithmic

The algorithmic multi-touch attribution approach is the most efficient attribution model available. The algorithmic model describes a prospect’s path and assigns credit based on constantly changing data instead of allocating weighted credit to several touchpoints based on a predetermined or custom model.

A corporation can use multi-touch attribution to comprehend the role played by different marketing channels and spot possibilities to improve conversion paths further. If you want to start with channel-based attribution, use Google Analytics. Next, use pageview-based attribution to delve deeper into a customer’s conversion process.

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Conclusion

Multi-touch attribution is a powerful tool for marketers seeking to understand the effectiveness of their marketing efforts. However, it requires careful consideration of various factors such as data quality, model selection, and team collaboration. Brands should also ensure they have a clear understanding of their business goals and customer journeys to make the most of multi-touch attribution.

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MTS Staff Writer

MarTech Series (MTS) is a business publication dedicated to helping marketers get more from marketing technology through in-depth journalism, expert author blogs and research reports.

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