Jessica Lachs: evaluation

Transcript context

…If there's no answer that comes to mind, that's totally cool. But is there an example of one of these insights that someone on the data team came up with that led to something big for DoorDash that you're able to share? So one interesting example was from a hackathon we did a couple of years ago where we were looking at referral as a channel for consumer acquisition. And when you compare that channel to others, it was below average in terms of the engagement you'd see from consumers who came through that channel and the payback period. And rather than just lowering spend on referrals and moving right along, we really wanted to understand what was happening. And so during the hackathon, we did a deep dive into referral. We actually tried referring each other. We tried committing referral fraud, creating new accounts to get around rules. And we uncovered a lot of fraudulent behavior through this deep dive. We ordered so many cupcakes to the office. I remember using referral credits because you had to place an order to be able to get the referral bonus. So we would create the account, place the orders, and we just kept ordering cupcakes. And what we noticed was that referral as a channel was a bit misleading when you would look at the average in terms of payback and that it was really a bimodal distribution and you had one group of really great consumers who were referring other really great consumers, and the payback on those consumers was really strong. In fact, if that's all you saw, you would spend a lot more on that channel. And then what was happening was you had this other group of consumers that were not as good people who were posting referral codes online and getting people who were just in it to get free discounts and credits. And we had at that point in time, pretty lax fraud rules. And we didn't have caps on these things. All of which came about from this deep dive where we found that this group of consumers was really a drag on the efficiency of this marketing channel. And so I think that's an example of a few things that we like to do at DoorDash. One being these deep dives and taking the time to really understand the problem and then ultimately make a bunch of recommendations for what we should do, including better fraud checks, caps on referrals, et cetera, et cetera. But also how the average can be incredibly misleading. And so looking at distributions and trying to break down what you're seeing to find ways that you can optimize in ways that you can gain in efficiencies. That's an awesome story, great memory to come up with that one. So this is a really good example of a way to carve out time for the data team to think long-term, think look for opportunities, find big ideas. So the hackathon is one idea. Imagine many data people are struggling often to push back on asks that are just like, "h, we need to know. We just need this one thing. Here's a question, just answer this one question part." Do you have any advice to data to get better at pushing back? Sounds like a bit of cultural like, "We have time, we need to work on these bigger things." But just any advice for data leaders or data ICs to find time for these sorts of things?…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.