Friday Updates: Preceden Pricing, Policy Page Design, Time Series Prediction, and Embracing Minimalism

What I’m working on at Preceden

New Pricing



Big changes are:

  • Renaming the higher end plans to better match the type of people who those plans are targeting. For example, if you’re a project manager coming to Preceden to create a roadmap, these new plans should make it clearer that the Business plan is for you, whereas before the Basic and Pro plan names were nebulous.
  • Nixed the mid-range $69/year price point and introduced the higher $199/year one both as a way to make the $29/year and $99/year seem like better deals, and also to try to make as much money from business and high-usage (6+ timeline) users.

In the past I would have A/B tested these plans together for a month to compare their performance side by side, but Preceden really does not have enough conversions to draw conclusions from these tests. I normally would justify that by saying that some A/B test data is better than no data, but I think I’ve made a lot of wrong decisions in the past based on inconclusive A/B test data so am going to try to do better and just not going to run pricing tests going forward.

Policy Page Redesign



Hat-tip to Milan, the front-end developer I’m working with, for these lovely improvements.

These policy pages (Terms of Service, Privacy Policy, Cookie Policy) don’t get much traffic, but I think it’s a nice touch putting in the extra effort to make them look really polished.


  • Chatted with a lawyer about revising the Terms of Service and Privacy documents.
  • Researched different types of insurance for SaaS businesses.
  • Moved Drip tracking from front-end to back-end tracking to both fix some bugs (the Sign Up event wasn’t getting fired for all new sign ups, for example, probably due to ad blockers) and also to have one less cookie on the site (back-end tracking doesn’t require a cookie whereas front-end does).
  • More Drip improvements: if a user updates their email, make sure to update it in Drip. If a user deletes their account, make sure to delete it from Drip.
  • Making sure if a user deletes their account, there’s no trace of their email in the database after that (with some exceptions like for people who pay).
  • Adding admin functionality so that if we refund a user, it automatically cancels their subscription by default as well, so they don’t accidentally get charged again down the road.

I do miss product work, but it feels good putting the time into these professionalization, maintenance, and bug fix tasks right now.

What I’m working on at Help Scout

Mostly the same things I’ve mentioned recently: PM’ing a project to evaluate Heap, getting the ball rolling on hiring a data team this year, answering data requests, etc.

One new thing though: We send out an automated email to a lot of people at the company each day reporting on various KPIs. I gave a talk about this Daily Metrics Email at Looker’s JOIN conference a few years ago. One of the things we report on is what we’re projecting each KPI to be by the end of the ongoing month. For example, how many new trials are we forecasting for January?

Historically, most of these projections are simply extrapolations based on the MTD performance. If 5 days of a 30 day month have passed and we’ve had 1,000 new trials, that’s 200 trials/day so we project 6,000 new trials for the month.

This works alright, but has some issues, especially at the beginning of the month when we’re extrapolating based on only a few days of data. This is made worse when the month starts on a weekend which brings the daily average (and therefore the projection for the month down).

I’ve been playing around with alternative approaches to doing the projections, all in a Jupyter Notebook for now. Some time series machine learning solution is an obvious thing to try, but it’s unclear to me whether I could take a scikit-learn model and use it in the Ruby script that generates the email. Probably. But I think there’s also a strong argument for keeping the projection method simple which will make it easier to explain and debug, even if it winds up not being as accurate as a fancy blackbox ML model. We will see.

What I’m watching

Most things I watch don’t lead to any actual changes in my life, but I recently watched this documentary on minimalism on Netflix and it has inspired some change:

I started going through our house and figuring out whether we really need to keep each thing or not. For example, cleaning out junk drawers and going through boxes in our attic that we haven’t opened since we moved into this house in 2019. I got the kids involved too and they have had them going through their old toys and picking out things they want to donate.

I started going through Preceden’s code and cleaning up obsolete methods and rake tasks that aren’t needed anymore. I also bought a new Macbook (which I’ve been meaning to do for a while anyway) and have been setting it up from scratch so it doesn’t have all the files and whatnot that have accumulated over the past 5 years on my old Macbook. It’s been very freeing getting rid of stuff that no longer serves a purpose.

If this resonates with you, I’d encourage you to check out that documentary.

Be well my friends 👋

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