Shift happens: Why Gilt selected mParticle and you should, too
It’s a new fact of life that martech change is not only constant, it's accelerating. That's why Gilt decided to future proof their stack with a customer data platform.
Two years ago, I was leading mobile user acquisition at Gilt—spending millions on acquisition media—and decided that my attribution vendor at the time wasn’t innovating quickly enough. After digging in, it became apparent that switching attribution vendors would require performing the equivalent of open-heart surgery on our app. If I was going to rip the patient open, I wanted to do it once, and do it right.
That's when I really started to worry… How could I be sure that the next vendor I selected wouldn’t also be leapfrogged in their capabilities? Or, what if they were innovating, but their methodology, for whatever reason, no longer worked with Facebook or Google? Or, what if my new vendor was acquired and subsequently removed features or diverted their roadmap in the process?
The problem is you can never be sure. In fact, the only thing you can be sure of is that the marketing tech vendor landscape is going to keep changing. And, with my luck, these changes would inevitably happen suddenly and at the most inopportune time, like Black Friday. Whenever it happened, there would be major chaos. With this cloud of instability looming overhead, I began to research. Eventually, I came across mParticle.
Using mParticle as the data foundation enabled our measurement and strategy to be future-proof. We could switch vendors on and off on a dime. We could also access mobile data as easily as web data, for both measurement and first-party audience targeting use cases.
After leaving Gilt, I took some time to think through my career goals and decided I wanted to help others future-proof their systems. For me, there was only one obvious choice: mParticle. If you want to learn more about how Gilt uses mParticle to power their advertising attribution and improve marketing efficiency, you can read the case study here.
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