A Success/Failure method for Analytics

When identifying the Key Performance Indicators (KPI) of your business, it makes sense to choose the proper measures of success. I have written about choosing the proper measures of success in the past. Since most of the work that I do is in the realm of the web, the principles via which we operate and do reports are more or less the same.

The only thing that changes is the conversion … or the success metric. In other words, the reason for which the website is built, the purpose of that site. Hence, the measure of success approach works.

Designing for new paradigms

However, what would happen if the product being built is not meant for the web, or was not based on the same principles? How would we go about identifying metrics and actionable reports.

For that we would have to go to the very reason why we need analytics.

The Purpose of Analytics

If I were to define the reason why we use analytics in any product, it would be to –

  1. Identify the wins, celebrate them and try to find the rules which get us more wins
  2. Identify the failures, and figure out ways to fix those failures so that we can improve

This view helps us do two things primarily, one to find out and scale the good things, and the other to find out and weed out the bad things in our product.

To do this, we would need metrics (or KPIs) that would indicate a success or a failure.

Measures of Success

The measure of success metric help in identifying the clear wins and celebrating them within the team. These also help in figuring out what worked for you in the past and on how to re-create those wins. One definitive thing that needs to be done (and I have learnt this the hard way), is that wins or measures of success metrics need to shared in a broader audience to give a sense of purpose to the entire team on what they are working on.

A good measure of success is task completion rate, or conversion rate, or profitability.

Measures of Failure

The measure of failure metric help in identifying failures within a certain activity. These are also metrics which help in identifying opportunities of improvement. Measure of Failure metrics should help us root out problems within our current design/product. I say root out, because once you identify the failure, you have to act and ensure that the failure does not happen again.

An example of measure of failure could be bounce rate.

Unlike measures of success, measures of failure may not be shared with large teams. Rather I feel (and I am want your opinion on this), that they are much more effective when communicated to the right localized teams.

Game Theory in Dating, more towards understanding Nash’s Equilibrium

Game Theory is a fascinating subject. Especially when you take it out of theoretical economics and start applying it to human collectives.

I had written about applying Game Theory to SEO, a competitive field, where the more important point was to have a strategy and keep evolving instead of having a static winning strategy.

Game Theory in Dating

Do we really need to do this?

Yes, because applying the concepts helps us to understand some of the product features (Superlike for instance).

More importantly, in a space where the one currency both the members of the dating app has, is attention. That’s the time spent with your significant other. In an ideal scenario this would be equal. This is the Nash Equilibrium state.

Nash Equilibrium in Dating

However, the more men you have in a dating app (Tinder has 60% men global, in India this is all the more skewed), the more dynamic would be the state of the Nash equilibrium. Data from Tinder has shown that men are twice as more active on such apps.

The reason behind this is simple, men are spending more currency (attention and time) to find that ideal person on Tinder. Unfortunately, because the number of men is more on the app, the amount of attention an average man would have to spend will keep going up (since the Equilibrium is unbalanced).

Nash’s equilibrium is a simple concept that helps economists predict how competing companies will set prices, how much to pay a much-in-demand employee and even how to design auctions so as to squeeze the most out of bidders. It was developed by John Nash, the Nobel Prize-winning economist and mathematician, whose life story was told…

via Why we need a dating app that understands Nash’s equilibrium — Quartz

So what?

The next time you are on such an app and if you are a woman, don’t be surprised if you are hounded by men. The equilibrium will never be reached unless you have the same amount of men and women on the app.

Take this concept and apply in real life.

In a country such as India, where sons are preferred (there I said it, and it’s not politically correct), the gender ratio in population is skewed. The Nash Equilibrium is also getting badly skewed.

You have to woo and court your significant other, not just because it’s romantic, but because it is required!