I recently spoke at Code Slinger on the subject of scaling engineering teams through periods of rapid growth.
Mrs Fragile recently bought a hand made lamp shade online and was disappointed with the results, as a keen crafter she wondered if she could do better, and perhaps even sell some of her own creations.
A key idea in lean startups is that metrics ought to be actionable. On his blog Ash Maurya defines explains Actionable Metrics
An actionable metric is one that ties specific and repeatable actions to observed results.
The opposite of actionable metrics are vanity metrics (like web hits or number of downloads) which only serve to document the current state of the product but offer no insight into how we got here or what to do next.
Tracking sales is of course an obvious thing to do but it is a very coarse measure. A more interesting metric is to look at how easy it is to convert a potential customer into a real customer. Over time we not only expect sales to increase but also expect to get better at selling such that our conversion rates also increase.
In an ideal world I would like to perform Cohort Analysis. This means tracking individual user behaviour and using it determine key actionable metrics. While more commonly applied in medical research in order to study the long term affects of drugs, common examples in the context of Lean Startups might be tracking user sign up and subsequent engagement over time. If it can be shown that 2 months after sign up users generally cease to engage in a service, it provides a pointer to what to work on next, as well as a clean means to determine if progress is being made.
The in-house analytics provided by Etsy do not provide the means to track the habits of specific users, but they do allow for aggregations of periods of time. This means that some level of analysis is still possible, though cannot be describes as true cohort analysis.
I’ve modelled my funnel like so:-
Of those that viewed the shop
- What percentage favourited the shop or a product. There is no reason to assume that someone buying the product will also favourite it, though at this point it is reasonable to assume some level of correlation.
- What percentage bought a product for the first time
- What percentage are returning paying customers buying a subsequent item.
As you can see from the graph, there is not a lot of data. Throughout the process our absolute views and favourites have increased, though it is interesting to see that our favourited percentage has improved. We put this down to improving the pictures and copy, though without more data it’s hard to make any firm statements.
What I’ve not done is break this down on a per product basis, right now we do not have enough products or traffic to justify it but we’re certainly noticing that some products are more popular.
In a few months times I’ll revisit this post and let you know how things are going. With a bit of luck there’ll be some yellow and green on there.