It’s said that you should measure what you value, and for founders, nothing is more valuable than growth. Growth provides revenues, venture capital, prestige and scale — ultimately driving the success of every business. Yet, measuring growth is complex and challenging — and it’s only getting tougher. Changes to attribution in iOS 14 and further refinements in iOS 15, plus other privacy-preserving initiatives in the industry, have forced growth marketers to rethink how they define their growth analytics engines.
On the Extra Crunch stage at TechCrunch Disrupt 2021, we convened a distinguished panel of growth experts: Jenifer Ho, vice president of marketing at Elation Health; Shoji Ueki, head of marketing and analytics at Point; and Nik Sharma, the owner of Sharma Brands, a notable DTC growth marketing consultancy and investment shop.
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We talked about the crisis around attribution and how startups should define their models, how to acquire the first customers in a business and when growth marketing should really start, and finally, how companies should select the right metrics for their business.
The fundamentals of growth marketing: Building the attribution model
Our conversation started with a deep dive into the world of attribution, which is a hot topic for growth marketers these days. Attribution is all about connecting marketing spend on channels like Facebook and Google to actual purchase actions made by a customer. If you display an Instagram ad for shovels, and that user then buys a shovel two days later, attribution models are designed to capture that activity.
Purchasing decisions can be quite involved for businesses and consumers, which is why defining attribution is so important for a startup’s success. When it comes to how to model it, Ueki emphasized one word to describe all aspects of attribution: Simplicity.
I think one of the main things I’ve learned from experiences with attribution is that it’s really important to start basic and simple and then try to evolve from there. I’ve definitely been in companies where we’ve made that mistake before.
The example I like to give is, if you think about attribution, it’s a vehicle. It’s really enticing to try to build that Ferrari of attribution systems, but a lot of times when you’re just starting out, all you really need is a skateboard, right? It’s something that’s simple, and it gets the job done.
One example from a few years ago at SeatGeek: We tried to build a media-mix model before we had something much more basic in place. We invested a lot of time and effort into that with our data science team, but after all of that, it was actually never used and never adopted.
At the end of the day, the ultimate goal of attribution is to measure incrementality, or how much incremental revenue is being generated by your marketing efforts. A complex media-mix model or multitouch attribution model is intended to do that, but it’s hard to do it right. For us, we had built one that actually didn’t serve that need of measuring your incrementality as much as possible. (Timestamp: 2:48)
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