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

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

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.