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— the pitfalls of benchmarking and how you should think about retention

Please let me present Action Metric’s first Guest Article. Thank you, Lauri, from Creandum to share your knowledge on retention! The article was originally published here.

Ability to retain customers over time is a good indicator of product-market fit and general business health and viability when looked at in cohorts. It’s a vital metric that every startup should track, but can be difficult to interpret off the bat.







So naturally, many entrepreneurs we meet ask us questions along the lines of:

What’s a good 1 week retention for a gaming company?

What’s a good 3 month retention for a marketplace?

What’s a good 6 month retention for a consumer app?

What’s a good 1 year retention for enterprise SaaS?

Given the number of companies we meet and our portfolio companies that we have the fortune to work with, expectations to what makes a high potential venture are natural to form (investors often refer to it as pattern matching). But we try to constantly challenge our thinking, and try avoid just that when we look at startup metrics. Here’s why.

People like to find yardsticks to calibrate their feelings around seemingly abstract concepts that various non-revenue metrics and ratios tend to be. The pitfall of benchmarking your thinking around retention is that it’s rarely ever bound to be an 🍏s to 🍏s comparison, even with seemingly similar products and services.

Peeking under the hood of retention

Retention is a result of various factors. Factors, which sometimes render a 10% retention rate perfectly fine — something that could in most other cases be a sign of a terminal illness on your business. Counterintuitively enough, in some cases, a high retention rate can also be symptomatic. Thus, it’s often good to take a step back and instead look under the proverbial hood of a retention metric. Something we as investors need to constantly remind ourselves about, and suggest entrepreneurs follow suit.


Peeking under the hood

Broadly speaking, retention is a function of commitment (i.e. 💰, ⏰etc.), product utility, frequency of use, competition and switching costs.


If onboarding your platform is made very easy (kudos to your product team 👊), then you may, on one hand, increase the number of new users you onboard, but on the other hand, it can have a negative impact on your retention rate. That is because you also convert users that may not have an immediate need for your product, but decide to test it out anyhow — simply because it is very easy to do so. New technologies and press coverage can have a somewhat comparable effect — create hype and attract a broad range of users that may not have an explicit need for your product, resulting in a poor retention rate.

Conversely, if users go through the trouble of filling out lengthy forms or pay to use your service in the first place — they make a commitment and go through a process of self-selection that indicates a stronger need for your product. Thus, you can expect a significantly higher retention with likely more people dropping off earlier in your onboarding funnel. So, your particular business model also affects your expected retention rate. If you sport a free(mium) model, you would likely do injustice to yourself by benchmarking your retention metrics against competitors with different business models, but otherwise similar products.

To illustrate this, think Spotify vs. Soundcloud or vs. Tinder. In the case of you need to both pay to play and invest time in filling out lengthy forms, or so I’ve been told 😏. As a result, ends up with a selection of far more committed users in comparison to Tinder whose potential users are just a click of a button away from starting their search for ❤️.

Another good example is neobanks vs. high street banks. I often see neobanks boasting about their much lower customer acquisition costs (CAC) in comparison to traditional banks. This, however, is a source of false confidence, and should not be treated as a benchmark. Traditional banks pay to acquire customers who have bothered to physically show up at a branch and most likely endure lengthy queues and form filling to open up an account. Whereas neobanks pay to acquire customers who can complete their swift onboarding process wherever and whenever they feel comfortable. Due to this key difference, neobanks need to have a much lower CAC, because they acquire less committed users and can thus expect a much lower customer lifetime value (LTV).

Switching costs can further muddy the water when it comes to retention. In some cases, high retention can be misinterpreted as a good product-market fit, whereas it can actually be the result of high switching costs that may help you to get away with a sub-par product. Think LMSs for schools, CRMs, customer support tools, or other products that tightly integrate into your workflow and/or in which you accumulate data that is not easily transferable. In such cases, complementary metrics such as net promoter score (NPS) can come in handy to get a clearer picture.

A holistic approach to looking at retention

To avoid any pitfalls discussed, retention should never be looked at in isolation. You should always look at the complete funnel — from a potential customer becoming aware (e.g. visiting your website or showing an intent to onboard) to actively using or paying for your service. A high rate of retention can indicate a high drop-off rate in the top-end of your funnel, and conversely, a high conversion rate in the top-end can result in a low retention rate.

At the end of the day, the crude retention rate in itself is somewhat irrelevant — it’s the underlying drivers and the holistic picture you need to focus on. Figure out what makes up retention in your specific setting, and find levers to influence that. Benchmark against your historic cohorts, but be vary of benchmarking against seemingly similar products. Ideally, you would expect (or hope) retention to flatten out at some level, but even then there are certain products and services for which this is an unreasonable expectation. Take weight loss apps for example — the irony being that the better you are at helping your customers, the more you reduce the need of your product for your customers over time, and thus the lower your long-term retention.

The important bit to focus on: does your (expected) LTV/CAC ratio enable you to build a sustainable business (at scale), what are the implications of your retention on your total addressable market (TAM) and the size of the business you can expect to be able to build?

Want to understand your specific case? Let’s take a look together!

Scale away! As long and as far as the foundation supports it.

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