Revolutionizing ecommerce and media buying with predictive revenue and clean data

– The company is developing a new technology that will revolutionize ecommerce and media buying.
– They believe in the power of clean data and the ability to predict revenue based on data point combinations.
– The technology aims to provide more accurate insights into audience quality and make better decisions in media buying.Implement a multiple clean data point approach to media buying in order to accurately predict ROAs and make better decisions throughout the customer journey.We’re building something that will forever change e-commerce and media buying. It’s going to become the new standard, it’s a paradigm shift. It’s the missing link to automated media buying because the platforms are still only playing with the basic data inputs; we go beyond those to be more accurate at the leading point of intent. Since we started Formtoro, we believed that predictive revenue was possible with enough clean data and the right answers to the right questions. The idea that data point combinations can tell you the likelihood that someone will convert within a confidence interval and allow you to predict ROAs across an ad, ad set, or campaign over a longer time period more in line with the customer buying journey. These are outside of the 3-7 day tests you’re running on your campaigns where brands already aren’t collecting multiple data points during signup and have no idea what the data point breakdown is relevant to the ads they are running. We just completed step 1. ROAs measured on a 1:1 time period are outdated. Instead, it should always be based on the potential of a campaign with an understanding of the quality of the audience relevant to their customer journey. We added reporting to look at % of signups, cost per signups, and other leading indicators that an ad was working, but we hadn’t spent time to finish the equation. We needed to understand the breakdown of data collected during those signups in relation to the specific campaigns, ad sets, and ads. We just completed step 1. Step 2 is running some analysis over a long enough portion of time and building in confidence intervals on a per company basis to forward predict the “predictive ROAs” of a campaign over a longer time frame than anyone reports on. For example, if you had two ad sets and they both spent the same amount of money and they both drove 100 subscriptions, but the ROAs are different on them, we’ll be able to tell you why based on the responses. But we’ll also be able to tell you based on those responses if the quality of the audiences is different or one is just going to be on a longer customer journey. The best we’ve got right now is a pixel and tagging to make sure that reporting is more accurate. That’s the easy stuff; we’ve had that since before the iOS changes. What happens when you adopt a multiple clean data point approach that allows you to qualitatively know the quality of your audiences and allows you to make better decisions with all your media buying and know via data which groups and ads are more likely to provide a return over the full customer journey? Sound complicated, it is and a lot of people aren’t going to be ready or understand this. We don’t build for what people know, we build for what people don’t know they need yet.You have to run to where the ball is going to be before it gets there.All of this is patent pending. #marketing #ecommerce #customerjourney

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top