The ad spend and its performance mismatch for a long time.

“Half the money I spend on advertising is wasted; the trouble is I don't know which half.” Quote from John Wanamaker

Attribuly offers a mathematical method to evaluate the matches. The formula is:

BMI=(Event performance/total performance)/(campaign cost/total cost)-1

BMI value range is ( -100%, +∞). -100% means no conversion( any conversion defined such as purchase, check out, add to cart, or page view) generated by ads. so the budget should be decreased as much as 100% to match its performance. On the other hand, if the index value is >0, which means you may increase the budget.

e.g.: Campaign #1 costs 100USD, total cost 300USD; Revenue generated by this campaign is 400USD, and total revenue across all campaigns is 1,000USD.

BMI is (400/1000)/(100/300)-1=21%,

Meaning the revenue contribution is higher than its cost, so you may increase the budget as much as 21% to match its performance proportion.

💡By default, the Ads & Influencer attribution table is sorted by BMI.

Due to IOS 14 and third party cookie issue, it's getting difficult to track accurate conversion data.

With Attribuly pixel 1st party data and tons of other technologies, we make the conversion tracking accurate and traceable. That's why marketers optimize their campaigns by relying more and more on our data.

So you will see conversion data both from ads platform ( Google, Facebook, etc) and Attribuly.

Based on Attribuly purchase conversions, add Facebook's view-through purchase conversions(1-day view).

Based on Attribuly purchase conversion value, add Facebook's view-through purchase conversion value(1-day view).

Purchase conversion value (Incl. view-through)/Spend

Example: If the Attribuly Pixel ROAS was 3, and Facebook's view-through attribution claimed an additional 0.5 ROAS, then toggling to the Attribuly Attribution + FB Views model will plus that additional 0.5 ROAS, which is 3.5.

New customer purchase value: Attribuly will check this customer's historic orders, and only customer has no any order in history will be defined as new customer. This metric will help merchant to understand how many new customers are converted by specific campaign.