Measuring campaign success across multiple channels is essential to gaining insight to invest on each channel. Nowadays, common buzz word is multi-channel attribution, even though marketers don’t practice it. Multi-channel attribution discusses profitability of each channel while considering the channel position in the customer’s journey. Channel positions vary from the first click, last click through to view through. Finding the right attribution model is quite challenging but once you have found it, doors will be opened to maximize the revenue while at the same time reducing the cost per acquisition. Apart from the multi-channel attribution, cross device attribution provides insight to allocate budgets across devices and to optimize campaigns to generate better returns.

Now it is time to introduce a new concept to measure the profitability across websites/properties. Modern customers go through multiple properties before they eventually make a purchase. Right now it is not possible to use multi-channel attribution modeling to solve this problem, as customers don’t click on a link to move from website to website. Let’s see an example in the travel vertical below.

The customer starts the journey with TripAdvisor and reads a review then goes to the hotel website to get the information. Finally, they close the deal with agoda.com as he is a loyal customer. The conversion will be attributed to the last engaged website which is agoda.com and you can’t see the impact of various websites beyond agoda.com with the existing analytics tools. In this case, the hotel website supports in creating the awareness (assisting) while Agoda closes the sale (last click). Multi-property optimization allows users to identify the real contribution of each websites.

It is important to quantify the impact of each customer engagement so that marketers get the opportunity to allocate a budget to each campaign. Let’s consider the same customer journey with hypothetical numbers. Let’s assume a hotel room is priced at $100 and the hotel owner pays 15% commission to Agoda.com.

Scenario 01: Without Driving Traffic to the hotel website

The Hotel Owner makes money entirely from agoda.com with no investment on the website.

Scenario 02: Customer Drive Traffic to the Hotel website

Hotel owners expect to keep the cost per acquisition below $30 with paid campaigns. However, hotel owners don’t have a way to figure the multi-property attribution with the existing tools. The owner might’ve spent $1,000 on paid campaigns and only received 10 conversions from the website. Based on the campaign statistics, the cost per acquisition (CPA) for the campaign is $100 which is not profitable, as he previously expected. A marketing consultant would recommend him to stop the campaign immediately.

Quantifying the Multi-Website Attribution

If we have a mechanism to quantify the multi-website attribution then it allows us to identify the true impact of the campaign. Let’s consider the impact of the above campaign. Agoda.com drove an additional 30 conversions. Now, we have 40 conversions as a result of the paid campaign and it reduces the CPA to just $25. Yes, now the hotel owner has a profitable campaign and the hotel owner can expand it to maximize his revenue. This is the insight that we can extract from a multi-property attribution model.

Conclusion

Multi-channel and cross device attribution can be analyzed through Google Analytics and other similar analytics tools in the market. However, it is challenging to build a tool to find the attribution across websites for which we don’t have access. Good tagging framework won’t help us to solve this problem unless we have a mechanism to share a unique identifier across the customer. As a solution, we can use mathematical models such as regressions analysis to figure the revenue relationship between the hotel website and the agoda.com (as in the example). Apart from that, there are websites that provide the complete user journey and they are able extract user data from their add-on users. Finally a multi-website attribution is a model that marketers can no longer ignore.

‍