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Published · transcript-backed

Hila Qu: recommendation

2 Apr 2023 Lenny's Podcast The ultimate guide to adding a PLG motion | Hila Qu (Reforge, GitLab)

“It's like he doesn't know whether to trust the data, what to use. So a lot of company I work with, the first step is maybe not looking into tool, but do an audit of your data instrumentation situation, to understand how many of the key actions are intact, is the format correct, is the data, what are the gaps?”

— Hila Qu

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Speaker
Hila Qu
Attribution
Verified speaker
Claim type
recommendation
Recorded
2 Apr 2023
Publisher
Lenny's Podcast

Transcript context

…I'm trying to channel what listeners might be thinking right now, and I imagine some people might be like what if I pick the wrong tool? I'm kind of stressed, I have to do all this research. I'm kind of worried about starting, because it'll set me up for failure later. Which of these buckets do you think is most important to get right, right from the beginning, and any advice on how to just avoid messing that up? To get started, I would say probably a product analytics tool is the first step, and maybe the data hub, such as Segment. So if you have Segment and particle tools like that, it allows you to plug into so many different tools. You can basically try all the different tools, and if it doesn't work, you just flip a switch, you can try another tool. So there is a benefit there, but it is expensive, so I know companies may just go right into the product analytics tool. I would say it's hard to get it wrong completely, right? In order for a product analytics tool to be meaningful, the first step is you need to collect the data, you need to do some instrumentation, you need to have the foundation. And then, because it's garbage in, garbage out, if you send a bunch of garbage data into your product analytics tool, your analyst will be just even more confusing, right? It's like he doesn't know whether to trust the data, what to use. So a lot of company I work with, the first step is maybe not looking into tool, but do an audit of your data instrumentation situation, to understand how many of the key actions are intact, is the format correct, is the data, what are the gaps? And you may need to do some re-instrumenta ... Right, what are the gaps? And you may need to do some reinstrumentation, reformatting and things like that before you even plug into a product analytics to make it useful. For someone that may want to do that audit, is there a thing you would point them to, or, I don't know, a blog, a course, something to help them understand if they're doing it right? Or is it like, "Bring Hila on," and you need someone like you to kind of help them through it?…

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