Campaignify

Story

Over the summer, I was working as an intern at an e-commerce startup. As a product manager, I was exposed to the many different aspects of conceptualizing and putting forth a new product. While this was a valuable experience, what really stuck out to me was their marketing strategy. For every new product they made, they poured hundreds of thousands of dollars into testing out different global markets. However, when they found a couple demographics that responded well to the product, they abandoned the rest, rendering all the effort and resources wasted.

This didn’t really make sense to me. With the recent advancements in artificial intelligence and the massive amounts of consumer data available, I didn’t understand why thousands of companies around the world were still wasting millions or billions of dollars on market testing, when instead it could be simulated. If we can use AI to predict the results of the Jays’ game or drive cars for us, why not apply it to digital marketing and cut advertising costs down to a fraction of what they were before? It just makes sense.

Functionality

Because of the complexity of this solution, I spent my time learning everything I could about marketing, artificial intelligence, and the different APIs and datasets that would make this project possible. I also talked to a number of professionals in software engineering, business, and marketing, and got extremely positive feedback on the topic and its viability.

In my research, I learned about regression analysis in marketing. By definition…

“…regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').”

When I told economists and market strategists about my idea to combine AI with regression analysis, they told me “this will be the biggest revolution in marketing since Microsoft Excel.” Because this process is simply an old method of finding correlations between datasets, applying a neural network to the same data will allow for the most accurate prediction models we’ve ever been capable of.

With this accuracy, market testing will become obsolete and a simple simulation will be all that’s needed to know exactly which demographics will respond the most positively to a particular campaign.

What's next for Campaignify

With validation and product research done, my next step is to develop the software. I’m excited to start working on this project with help from my network of engineers, market strategists, and business analysts. If you are interested and think you would be able to contribute, please reach out to me!

Slides

Please feel free to check out my pitch deck here! http://tommymoffat.ca/portfolio/next36/pitchdeck.pdf