Friday Updates: Boone, Putting ML on the Back Burner, Embulk, Mandalorian, and A Princess of Mars

Last week my family and I vacationed in a cabin near Boone, North Carolina for a few days. We hiked, visited Grandfather Mountain, made s’mores, enjoyed the hot tub, did gem mining, and generally just tried to unwind for a few days. It was nice.

What I’m working on at Preceden

A few months ago when I was deep into the DataCamp machine learning courses, I wasn’t sure whether after I finished I would keep focusing on machine learning or start spending more time on Preceden again.

It turns out that the break wound up invigorating me to work on Preceden again. For the past few weeks my mornings have been spent almost entirely on Preceden and not on machine learning at all. I don’t regret spending the time getting smart on machine learning – it’s a skillset I’m glad I now somewhat have – but right now I’m just more excited to work on Preceden, so that’s what I’m doing.

I have three big projects in various stages:

  1. Redesign: As I’ve written about in previous updates, I’m working with a designer and front-end developer to modernize the site. This involves migrating over to Tailwind and a lot of other changes to update the appearance of the site.
  2. Data Cleanup & Reactivation Project: there’s a lot of old data in Preceden that doesn’t need to stick around forever. Imagine someone creating a timeline about a divorce back in 2011. Unless the user deleted their timeline or account, it’s still in there. I’m going to build out a system for emailing long-inactive users to remind them it’s in there and say if they want to keep it they just need to log back in. If not, I’ll have Preceden purge their data after some period of time. This should let me both nix a lot of that old unneeded data and also bring back some users who haven’t thought about Preceden in a long time.
  3. Pricing: Preceden’s revenue is mostly from one-time payments. I’d love to figure out a way to get more recurring revenue. Much easier said than done though due to the nature of the tool (most people don’t need to use it very long). I’m playing around with spreadsheet models to figure out whether there’s a recurring revenue model that could work. We’ll see.

What I’m working on at Help Scout

We use a service called Fivetran to get data from various sources (like MySQL, Salesforce, HubSpot, and Mixpanel) over to our data warehouse (BigQuery) for analysis in Looker. Problem is, Fivetran’s pricing model changed this year and combined with heavier data volume it was going to cause our renewal cost to be much higher than in the past.

Most of our volume was from syncing MySQL data over to BigQuery. Our ops team was able to figure out a way to use an open source tool called Embulk to get our MySQL data over to BigQuery, eliminating our need to use Fivetran for it, greatly reducing our bill for 2021.

What I’m watching

Mandalorian season 2 on Disney+:

What I’m reading

A Princess of Mars by Edgar Rice Burroughs (same guy who wrote Tarzan). I read this when I was younger, but saw it sitting on my bookshelf the other day and started reading it again. It’s a fantastic series.

Stay sane my friends πŸ‘‹

Friday Updates: Election, Hiring a Tailwind Dev, NPS, and the Queen’s Gambit

Photo courtesy of Element5 Digital

As I write this, the election still hasn’t been called was just called for Biden. Hallelujah πŸŽ‰.

What I’m working on at Preceden

Asif is still busy putting together mockups for Preceden’s ongoing redesign work. Here’s the latest Case Studies page mockup:

Tom Davies (in my mastermind group) suggested checking out the #jobs channel in Tailwind’s Discord group to find a front-end developer. That proved to be a great resource, and I quickly found and hired a talented developer in Bosnia named Milan to work on implementing Asif’s designs and help move Preceden over to Tailwind. Work is ongoing there.

What I’m working on at Help Scout

Net Promoter Score (NPS) is a way to gauge how satisfied your users are. You ask them how likely they are to recommend your service to a friend, and they give you a 1 to 10 score. You then take the % of 9s and 10s (your promoters) and subtract the % of 0s through 6s (your detractors) to get your NPS. At Help Scout, surveyed our users to determine our NPS, and I’ve been helping analyze the results.

The trick is knowing how to interpret the score. Is 30 good? 40? 50? 60? In a perfect world you would know what NPS score your competitors have, but that’s often not public and even if it is, it’s not always clear how reliable it is. TurboTax has a NPS score of 58. But which users do they ask and when? Are they asking people right after they get notified they’ll receive a refund? If so, they wouldn’t be surveying people who get frustrated and give up earlier in the funnel, which may give them a higher score than they would receive otherwise. If you’re a TurboTax competitor and survey users earlier in the funnel and get a lower score, it would be a mistake to compare it to TurboTax’s number, assuming you knew when users were surveyed.

The ideal process is likely automatically surveying a fraction of your users every month and tracking the trends over time. This will give you a sense of whether the changes you’re making to the product are making users happier or not, and you can segment the results in a variety of ways to understand how different groups of customers score your product.

What I’m studying

Didn’t spend much time on machine learning this week due to the focus on Preceden and election distractions. I did start porting my local Jupyter Notebook for the Housing Pricing Competition over to a Kaggle Notebook, but it’s not ready to be shared quite yet. Probably next week.

What I’m watching

The Queen’s Gambit on Netflix:

Be well my friends πŸ‘‹