One of the most important (and most underreported) product metrics is stickiness. Stickiness is a critical health metric that is often the only difference between a successful product and an unhealthy one. Top line metrics such as Registrations, Retention, Monthly Active Users (MAU) are one thing, but the ratios you build on top of these often reveal more than their component parts.

In this post I’ll run through how we think about stickiness and some similar ratios tech companies and VCs use to think about product health.

Data beats opinions

– Anonymous

So what on earth is stickiness anyway?

Stickiness is traditionally is calculated with the formula below

Stickiness formula

By taking your DAU over MAU, this tells us what % of your MAU are using your product on a single day. Typically it ranges from 80+% for the top messengers and social networks, right the way down to <1% for the most niche websites and apps.

So what is defined as a good score of stickiness? Well… it massively depends on the type of product. I think a nice way to look at it is on a axis with the value of an individual interaction. This lets us see patterns across popular apps, I’ve mapped roughly where the apps on my phone might lie

Stickiness/interaction value quadrant for Toby’s iPhone

Here you can see, that its quite easy to group types of products by stickiness Products with a low individual interaction value tend to require a high stickiness in order to be successful. On the flip side – products with very high transaction values, or that have niche use cases usually have far lower rates of stickiness – but make more from each individual visit.

So to determine whether a product is healthy, stickiness is part of the health equation along with other engagement and revenue metrics. As a general rule of thumb, I generally think if you’ve got a pure play social app with the typical low interaction value and <25% stickiness you cant shift – you might be in the shadow of the valley of death đŸ¤˜.

Some limitations – balance of use

Another factor not quite explained by this chart is the balance of usage over time. If you think about TripAdvisor, Runkeeper etc you may wonder how their business is successful with medium stickiness and low transaction value. Well – it’s because people tend to use them intensively for a short period then not use them for a long time – picking them up again intensively the next time. This is a factor not well picked up by stickiness if cycles are longer than a month.

Dating app usage actually often falls into this cyclical usage pattern too, though with shorter cycles. People feel lonely & motivated to find a partner, then lose interest before returning with vengeance.

MoM retained MAU

Another really important health formula is this one.

I like calling this MoM stickiness. This takes into account your current months MAU minus all the new registrations you got in the month. If you then divide this number by the MAU you had last month, you’ll get the fraction you’re retaining month on month.

This weeds out all the noise from fast growth that typically clouds MAU figures. Its a metric that takes no prisoners and can take the shine off even the best hockey stick MAU growth curve

So thats it. A couple more ratios to think about đŸ™‚