In their early years companies are reflections of their founders and it should come as no surprise that at Splento we are obsessed with measuring everything. Apart from the usual suspects (such as LTV, CAC and everything you get in your management accounts), I can tell you everything about Splento. Average, mode and median for:
- First basket
- Number of orders per customer
- Order size per type of photoshoot
- Client retention for different cohorts
- Lead and traffic generation by channel
- Length of each photoshoot
- Most frequent days for when orders are made
- Most frequent days when photoshoots actually take place
- Difference between when an orders are placed and payments are made and photoshoot takes place
- and much, much more…
When our board members saw our comprehensive stats, they noted that even much larger companies that they advise don’t have these sophisticated models. To some of you it may appear that we are spending valuable resources on gathering pointless data. You may even quote Jonh Doerr and tell me that I should Measure What Matters (I strongly believe that his brilliant book by the same name should be a must-read not just for businesses, but for any organisation – large or small).
Of course, we set our strategy, objectives and key results, based on a handful of really important measurements and we always have our North Star Metric in sight, but we gather as much data as we can.
To understand why – we’ll have to get personal. You need to understand me better.
I love running marathons. This year I’ve completed my 8th consecutive London Marathon. Of course, to run a good marathon, just like in business, you need a plan and you need to stick to your plan… and change it accordingly when things go inevitably wrong.
You have to measure your pace, you heart rate, keep an eye on your hydration, sleep, rest. But I go above and beyond that – I painstakingly record every run, every core and strength workout, every cross training session, every race. You may wonder – what’s the point of keeping all this data?
Here is an example.
This year my result was my third worst marathon at 3:09:35. I knew I hadn’t trained enough, but I couldn’t quantify it. So I’ve pulled all my records and analysed the data. A very clear pattern emerged.
Whenever I ran fewer than 20 miles per week in the 16 weeks leading into the race – I ran slower than 3 hours. Whenever I ran 21+ miles per week – I came out of 3 hours. Without all this data – I might have blamed my sub-par result on other convenient factors (age, weather conditions, nutrition, rest) all of which are of course massively important, but the data revealed that for me to get out of 3 hours, I just need to run at least 3 hours/21+ miles per week (a mix of tempo and long runs, hills & sprints) all other factors being equal in the preceding 16 weeks.
Paraphrasing Dr Deming (since we are in London): “In Her Majesty we trust. All others must bring data”.
One thing matters today, something else may matter tomorrow. You never know what piece of data might suddenly come handy, so the sooner you start gathering as much data as you can – the better.