Friday Updates: Preceden’s Data Retention Dashboard, Hiring a Data Lead at Help Scout, Hanna

What I’m working on at Preceden

This week I created a data retention dashboard (using Mode) to help me monitor how everything is progressing.

There are 225k users scheduled for deletion in the next few months and 46k have already been deleted (these are folks who hadn’t been active in years and had little to no content in Preceden). It has sent out 8k “Your account will be cancelled in 60 days” notices which has led to 63 users logging back into Preceden, and a whopping $58 in reactivation revenue. These emails have gone almost entirely to people who never paid in the past, so it’s not surprising not many have paid.

This week I got former customers added to the schedule and they should reactivate at a much higher rate. Not expecting a ton of reactivation revenue from this project, but if it winds up bringing in a few thousand extra dollars in revenue this year I’ll be happy with it.

Here’s the number of users deleted each day:

We can see some initial experiments in the beginning there, followed by a few days of bug fixes before it ramped up again.

It’s sending about 1,500 notifications per day now informing folks their accounts will be deleted in 60 days (and eventually 7-day reminders too):

And here’s the 225k scheduled for deletion:

These are spread out just to reduce the risk if something goes awry.

The only segment of inactive users remaining to handle are those with public timelines. Some of these timelines have hundreds of thousands of page views so I don’t want to delete them even if the user who created it hasn’t been active in years (since people are still getting value from them, plus some people go on to sign up and pay for Preceden). This week I spent a lot of time building out a system for quickly measuring how many total views a timeline has had. Preceden does have a views table that tracks every view a timeline has gotten, but with tens of millions of records it was impossible to query efficiently to say like “give me a list of timelines with more than 100 views”. Took creating a daily timeline views table to make it possible to query quickly.

It’s all coming along. Should wrap up this project in the next 2 weeks or so and move onto something new.

What I’m working on at Help Scout

This week we published a Data Lead job opening that I’ve been working on recently. I’ve been the Data Lead for the past 4 years, but with me being a part-time contractor now and preferring an IC role anyway, it makes sense to bring on a full time person who can take Help Scout’s data initiatives to the next level.

If this role sounds interesting to you, I’d encourage you to apply (and bonus points for being a reader of this blog!). Some highlights:

As our Data Lead, you will

We’d look to you to lead, develop, mentor, and manage our data team, currently consisting of an analytics engineer, and oversee the hiring efforts to grow the team to 5 people in the next 12-18 months.
Collaborate with leadership from across the company including marketing, sales, finance, support, and product to gain a deep understanding how data is used at Help Scout today and how we can make it even more useful in years to come.
Take ownership of our data stack which currently includes Looker, dbt, BigQuery and Fivetran. You’ll maintain the long-term roadmap for our data infrastructure and make sure it can grow alongside the company.
Evaluate and implement new tools and processes to expand Help Scout’s data capabilities.
Prioritize and assist with data initiatives and data requests and communicate analyses to stakeholders.
Initially spend roughly 50% of your time on individual contributor/technical/analysis work and 50% on management/strategy work, but gradually spend a larger % of your time on the latter as you grow the team.

In your first 90 days at Help Scout, you will

Learn the ins and outs of the business, our data stack, our processes, our key metrics, etc.
Gain experience by applying what you learn to solving real-world data requests.
Meet with other team leads to understand how they use data currently and brainstorm opportunities for improvement that the data team can tackle in the future.
After you get spun up and better understand the state of data and the needs of the business, you’ll decide on what role we need to hire for next and lead the effort to hire that person in Q3.