This headline reminds me of the old principle that I emphasise:
Money is the final metric
If our metrics don’t directly correlate to, or convert into money1 in the near term, then they are not the correct metrics.
Too many metrics, in my experience, are designed for being:
- easy to measure (or easily available),
- easy to improve, and
- comfortable to explain
What they are not designed for: being strongly correlated with current or future supply of money.
For growth, revenue (total, unit, net unit) is the best metric.
Views of our videos2 can be good metrics if they convert directly into money:
– product sales show a direct correlation, or
– advertisers accept them as proxy for ad views, or
– investors require them as a valuation input for the next raise
Views of our videos are a bad metric when they aren’t directly impacting revenue. If sales aren’t growing in proportion with the views, then counting views is of no relevance to the health of the business.
The availability bias trip-wire
The availability heuristic operates on the notion that if something can be recalled, it must be important, or at least more important than alternative solutions which are not as readily recalled.
Most online advertising platforms understand this well, and use it to hook their customers (advertisers). If they show us good3, clean and easily accessible metrics of their choice, we will give those metrics more weight than they deserve.
The views count is right there – in the analytics dashboard. While finding the correct metric that actually correlates with sales, and then tracking it, can be hard.
This also ties in the second part of the hook. The view count number is also easily movable. Spend some money on advertisements, the view count will go up. Voila! View counts – a metric that is easy to measure, and easy to improve!
Those money-correlated metrics, they are even harder to improve than they are to discover and track. Oh look, the views went up again!
We all have heard of ‘Our viewer/user/visitor numbers were amazing, but the money ran out before…’
This is why the money ran out. Because we chose the easy metric, over the metric that really matters – money.
Headcount is another widely used, and well misplaced metric. The costs don’t accrue by headcount, nor does revenue, so why do we use it as the metric of control?
Lets use the real metric – total cost of human talent. Assert that as a metric and then let the metric owner decide how to allocate it – over 10 temps with no benefits, or one senior exec with a golden handshake, or three mid level execs with standard benefits.
Headcount is a metric designed to address other non-money things
– size of the leader’s empire,
– something that ‘HR’ can own.
Neither of them directly impact the money being made. Yet, this metric drives the money being spent. So, tie it in with that money. Use that real number – total cost to company4.
If vs when
I have found that if and when are an indicator of honesty of a metric’s correlation with the money.
A metric that is explained using if is usually a bad indicator: ‘If we reach 10,000 DAU, and if 1% of them convert, ….’.
A metric based on when is usually better: ‘When we increase our DAU by 10%, it increases revenues by …’.
The answer is because ‘when’ comes from pre-existing, proven correlation. So the metric tends to be grounded in reality. On the other hand, ‘if’ tends to have assumptions involved. It is easily blinded by hopes, dreams, and fancy dashboards.
View count seems to be a great metric for Facebook. Its customers – the advertisers – accepted it as an indicator of the value of their ad spend. So, it could drive a metric that it controlled, and see a direct impact on its revenues. Perfect metric.
I track user counts of my apps and extensions. They are a terrible metric. I don’t advertise those apps, but these user counts had a significant impact on my decision about which app gets how much of my dev time.
And this is metric-blindness by someone who understands (and advises) choice of metrics.
I opened my own eyes after a recent analysis of in-app donations. Turns out that I have recieved similar totals from two apps – one with 7x as many users as the other. And there’s other apps in between those two that have seen hardly any donations. This has clarified, more than user numbers ever could, where to focus my development efforts.
This is another example of the hook. I get user numbers in my email inbox every morning, so I was lazily following them. Accumulating donation amounts takes more effort, so I hadn’t bothered. To my apparent detriment.
- If not for its criminal investigation connotations, ‘follow the money’ would have been a way catchier phrase. ↩
- Yes, Facebook lied. That’s what it does. But it was the advertisers that were foolish to believe it in the first place. They were accepting Facebook’s metrics to measure their own ad spends. If the ad spending was not reflecting in their own money metrics, and they still spent because Facebook’s metrics were looking good… ↩
- Always up and to the right. So, please keep spending those ad dollars. ↩
- Total cost to company: Great metric to measure. Absolutely horrendous thing to shove in face of the recruits. They don’t, and shouldn’t, care what their total cost is to you. They do care what they will take home. And what they are expected to deliver in return. Tell them those instead. ↩