How This Growth PM Turned an 'Unachievable' 5% Conversion Goal into Reality

Got it, um, Give me a moment to think about that. Sure. OK, um, So the goal was to improve um a specific line of service conversion from uh let's say it was 3% um and we wanted to get it up to 5%. Um and the reason that was To me that seemed unachievable at the time was um when we looked at similar like lines of business, um. That had less complexity and the respective flow. Um, conversion was only 5% for those, uh, for those services. So what I was asked to do was to get conversion up to 5% for this specific service while keeping all like the constraints associated with this specific line of service, so like having a subscription um and being committed to that subscription plan. OK. Um, so one of the first levers I looked at, uh, in terms of improving conversion, um, that I had control of was around price. So I did, uh, a, a pricing comparison between the service as well as other competitors, and I saw that we were priced higher, um. Then Majority of competitors, but also within our own like. Uh, specific like product lines. Um, so I saw an opportunity to, uh, reduce price, um, which would also, uh, reduce margin, but we were, uh, had strong margins on the respective P&L, um. To first improve conversion, but when I did that, you know, over 7 days, over 14 days, I didn't see conversion rebound as all the way to 5%. So then I started looking at other levers I had control of and one of the ones that um Like I didn't have uh control of like those how a user eventually got to the page um so the, the prefrontal questions but I did have control over what what questions the user saw um so I started um. What's it called? I started working through, uh, those questions and seeing, OK, what are the minimum number of questions I can ask the user in order to eventually get that booking fulfilled as well as have that provider eventually claim it because there's a bit of a um conflicting energy in terms of like the provider needs a lot of information but you don't want to overburden the consumer uh when they're checking out, um, so after doing that still conversion didn't get up to 5% and I started digging into the data and figuring out OK well what. You know, is there a specific uh set of users that are converting better, uh, versus another? So I started looking at that on a regional analysis, so looking at uh users that come from a city versus uh a rural area, um. And by and large, it was the conversion trends were the same across all these uh Um, industries, or excuse me, across all these like geographies, um. And so the ultimate thing that I ended up doing was digging further into the data, um, and seeing that there were other services um that had lower conversion that were being mapped into um disrespective service. So what was uh being counted as uh. 3% which was like the original service uh that we wanted to optimize for we're also seeing like lower uh converting services also being in our data model being aggregated into this uh respective service. So when we're able to uh cleanse the data, um, we, we haven't hit 5% yet, but we're able to see that uh trend towards uh 5%. So instead of 3 got it to 4 through pricing changes uh and question changes and then when Uh, the conversion number wasn't moving any further, started looking into various dimensions and seeing if there was a specific type of user converting what I wasn't seeing any trend there. Um, I was trying to see, well, OK, what, how is the data actually being aggregated together and I saw that, uh, data anomaly.

Uh, I think the biggest challenge I had to overcome, uh, was understanding how pulling one lever would impact a variety of others. So for example, um, in the first one I mentioned, uh, reducing price. So understanding how that would impact margin was, uh, critically important for the P&L owner, but then as I started layering in additional ques uh changes, so for example, um, What's it called, uh, reducing the number of questions I needed to understand how that would impact the provider, the pros claim rate, um. And then the last thing I'll mention is Um, What's it called? Um, is, is understanding like. If other changes were being were impacting conversion, so for example, we have a phone sales team and while I was doing this analysis, uh they've ramped up the number of agents that were reaching out to leads so that could also impact uh positively impact uh conversion. Um, so I was trying to, um, handle the, you know, understand if the changes I were making was actually positively impacting conversion.