Product Insights: Beyond Stats

I just stumbled across an old project idea while clearing out my GitHub profile. If there is one thing that will make your business stronger, it’s understanding your users better. There are lots of analytics tools which help with this, but they suffer from two big problems. People hate stats.┬áPeople want stories, not stats. Stats…… Continue reading Product Insights: Beyond Stats

“Next article” micro-analytics with Redis and Ruby

@elgrom has an interesting idea for predicting which article users are most likely to read next:Every time a user navigates between articles, log it.For each article, calculate the most popular “next” articleHere is how to do it in 12 lines of code with Redis and Ruby/Sinatra. (Next i’ll try it with Node for better scalability).require…… Continue reading “Next article” micro-analytics with Redis and Ruby

Metrics: Time on site

Average time on site is flawed. TOTAL time on site (TTS) is more robust. It’s the┬átotal time spent on the site by all (target) users. With this metric: some users spending more time on the site is always good; more users is always good. Other issues are detailed here. For example, “bounces” count as zero…… Continue reading Metrics: Time on site