By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.

A 5 Minute Cohort Analysis in 3 Steps (Part 2)

Álvaro González San Pedro (Associate)
October 18, 2016

Run a full stack cohort analysis from scratch in just 5 mins and leave your tech team to focus on the hard stuff.

By Álvaro González San Pedro (Associate)

If you’re reading this post it means you already know what cohorts are and what they mean, you know how to calculate your client’s lifetime value (LTV) and you’ve seen and understand cohort analyses. Perhaps what you don’t know yet is how to run the cohort analysis by yourself, without having to rely on your tech team. Have 5 minutes? Yes, it’s that easy, and that quick. If you’re intrigued keep on reading.

At Samaipata we want to share our internal cohort study with you, an easy 3-step Excel template that will allow you to run a quick cohort analysis on your own. The template is not just extremely easy to use (you just need to insert 3 rows of your business’ historical data); it also goes a bit further than traditional cohort studies you might have worked with before.

But first, why is it important to go deeper into cohorts? The answer is simple: average values from a typical cohort analysis can hide many things and make the story seem simpler than it actually is. Think about it, if you have 10 clients and 100 orders for that group of clients, the traditional cohort analysis will tell you that, in average, a customer makes 10 orders. But behind this conclusion there are hundreds of possible scenarios that are unfortunately left behind, but are nonetheless essential to understand in detail how your clients are behaving. Maybe just a few users stay, but become very loyal customers. Perhaps, many stay but are not too active. As you see, both scenarios are radically different.

From the Samaipata Ventures cohort analysis

That’s why at Samaipata we like to separate the recurrence analysis in its two main variables. On one hand, we analyse user retention (that is, how many FTB remain as users past the first month); on the other, the quantity ordered by the actual buyers (how many orders an actual buyer makes in the subsequent months). In the previous example, we could have two extreme cases before us. On one side, 1 single client could have made all of the 100 orders; on the other, each client could have made 10 orders. In both cases, the result is the same: in average, each client makes 10 orders. But as we can now see, behind this “10 average orders per new client” there are two very different possible scenarios. And –important to point out– two scenarios that, in many cases, will require unique and diverging business and marketing strategies.

In the first case, you could be having a problem retaining users. The retention rate is only 10%; perhaps you’ve acquired the wrong kind of customers. However, the FTBs that have stayed seem to be hooked to what you offer (because only 1 user (10%) has stayed, the 100 orders belong him). In the second scenario, you’re great at retaining customers (you manage to retain 100% of them, thanks to a very targeted and intelligent acquisition strategy), but the “quantity” bought per each actual buyer indicates they are not that “hooked” (each returning buyer only makes 10 orders vs. 100 orders of the first example).

There is no better or worse scenario, especially given that some users are more costly to acquire than others. But of course, the decisions you will have to take in each are completely different, no doubt about that. The situations are very different but at a first glance you could, mistakenly, think that both scenarios are the same.

From the Samaipata Ventures cohort analysis

At Samaipata we like to work in the following way. We first run the traditional cohort analysis, to obtain the average orders by first time buyers (FTB). On top of this, we look for average retention and average quantity bought by actual customers. In the end it’s just disaggregating the analysis into two parts. If the average orders by FTB can be achieved by multiplying R (avg retention rate of FTB) x Q (avg orders per actual buyers), why not obtain each separately and analyze each of the variables in more depth. Are you succesful at targeting and retaining the right customers? Or at hooking them to your product, to a point where they can’t live without it? Or even better, are you succeeding at both?

In any case, in order to optimize your strategy, the more you know about how your customer behaves, clearly the better. That is why we are sharing our internal cohort analysis template with you, so you can modify it with your business’ numbers and really find out what is going on. Don’t worry, the template is as intuitive as possible and you’ll just need 5 minutes to complete the 3 steps by yourself. Stop bothering your tech team with things you can do on your own and let them focus on the hard stuff!

What are you waiting for to try it out? If you have any doubts with interpreting or updating the data, we’ll be delighted to help out. Email us!

Click here to download the template. If you have any problems with the download please contact us.

For more quality analysis follow us on Medium or Twitter.

**

At Samaipata, we are always looking for ways to improve. Do not hesitate to send us your thoughts. We strive to partner with early-stage founders and to support them in taking their business to the next level. Check out more ways in which we can help here or for all our other content here

And as always, if you’re a European digital business founder looking for Seed funding, please send us your deck here or subscribe to our Quarterly updates here.

Latest News

See also

More insights to better the world through technology

Unlocking Business Expansion with This Strategic Framework

Unlocking Business Expansion with This Strategic Framework

There comes a time within your business where you realise that your team needs additional skill set and expansion. Typically, product-market fit has been established and the opportunity to grow revenue and profit is there.
Read more
Scaling your customer service team: in-house or outsource?

Scaling your customer service team: in-house or outsource?

As an early-stage startup, making the decision to manage your customer service team in-house or to outsource really depends on a variety of factors including where you are as a company in terms of your lifecycle, size and complexity, what your strategic customer service vision & goals are, and finally, what your financial resources and priorities are.
Read more
5 tips to retain top talent in a startup

5 tips to retain top talent in a startup

In a rapid-growth startup, demands can be high, budgets can be low, and processes can be lacking. There’s also strong competition for top talent in the startup environment and it can be challenging to keep talent as other companies also look to acquire people with specific skills, many of which can afford higher salaries.
Read more
Restructuring data teams that are ready to scale: 5 learnings from BlablaCar

Restructuring data teams that are ready to scale: 5 learnings from BlablaCar

In today's interconnected world, data has become a powerful driving force behind innovation and growth. Companies that harness the potential of data hold a competitive edge, and one such company at the forefront is BlaBlaCar. As a pioneer in the carpooling industry, BlaBlaCar has revolutionized the way people travel and the way it leverages data plays a critical role in its business strategy. In this article, we delve into the fascinating world of BlaBlaCar's data team strategy, exploring how we restructured our teams in order to scale.
Read more

What role software can really play in helping us reach net zero goals?

While considerable venture capital investments have already flowed into the software sector, the intensifying climate crisis is pushing the need for radical action to the forefront. Software is currently receiving negative publicity as seen as involving too “shy” of an effort. On the other hand, hardware and infrastructure investments, which were traditionally overlooked by asset-light venture capitalists, are gaining momentum.
Read more

The data challenge in Climate Tech

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros.
Read more
arrow icon
arrow icon