Consumer Subscription KPI Benchmarks: Retention, Engagement, and Conversion Rates
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In 2019, the average person in the US spent over $640 on digital subscription apps. The proliferation of the subscription model comes from its key benefits, which include a predictable, recurring revenue stream and stronger relationships with users. A few years ago, I wrote a TechCrunch post highlighting the key metrics to measure the health of a consumer subscription business. In that post, I linked to a subscription lifetime value (LTV) model, which I’ve included once again here. Note that the model works best in Microsoft Excel. I use this model often myself and hope you find it useful!
Subscription LTV Model vF.xlsx
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While tracking the key metrics is important, it’s more helpful when combined with benchmarks. The problem? It is hard to find benchmarks online… so I set out to do the dirty work. A quick overview of the methodology:
- Compiled anonymized data from digital consumer digital subscription businesses across several verticals (note: excludes e-commerce)
- Calculated key metrics including 12-month retention, DAU / MAU, conversion rate, monthly revenue per user, and marketing spend
Note that this is not an exhaustive list of companies and that benchmarks can vary depending on the data gathered. The data here was gathered from various sources including App Annie, SecondMeasure, SEC filings, investor presentations, etc. I’ll aim to update this report once a year if there are meaningful changes.
12-Month Cohort Retention
There is perhaps no more important metric for any business than retention. It is difficult and expensive to build a large subscriber base if you have a leaky bucket problem. To put this in perspective, a company with a 1% monthly churn rate retains 90% of its userbase after 12 months whereas a company with an 8% monthly churn retains less than 40%, After one year, the company with the 1% churn rate will have 50% more customers. You don’t want to see the chart after two or three years… (hint: 1% churn has 3x+ more customers while 8% churn flatlines).
Lenny Rachitsky wrote a great blog post on retention and I wanted to complement that with some hard data. In this analysis, I benchmark month 12 (i.e. one year) retention. This is calculated by looking at a cohort of users, calculating total spend in month 12, and dividing it by the total spend in month 0. To make sure you understand, here’s a quick example:
- Let’s say you acquire 100 customers in a month and they pay you an average of $10 each for $1,000. One year later, you retained 54 of those customers and generated $540. That would be 54% retention.
Retention benchmarks by percentile
The best subscription businesses retain 65%+ of their revenue after one year. Retention above 50% is considered in the top quartile and 42% as the median. Many companies hover between 40–45%.
Blended retention includes a mix of all pricing models (monthly, quarterly, annually) and is the most commonly-reported retention number as companies don’t always break out retention by pricing plan. In general, annual pricing plans tend to have higher month-12 retention than quarterly or monthly plans and it is becoming more common for companies to push users towards annual plans (if they offer several options) because of this factor as well as its favorable cash dynamics. Not shown above, but the median annual retention was 51%.
Retention is a function of many factors, including price
The benchmarks above bundle in retention without factoring in price-point. This can be a bit misleading as retention is inversely correlated to price. In our data set the average retention for a product with a $5 monthly price was ~80% whereas a product with a $20 monthly price-point was around ~20%. Most consumer digital products hover between $7–12 per month. Keep this in mind when evaluating where you stand (and where you want to price your product).
Daily Engagement Rates
Engagement can be expressed in a variety of ways. The most commonly reported metric is DAU / MAU ratio, which is the ratio of daily active users over monthly active users. This is a solid proxy for how dedicated users are to your product. To meet the 90th percentile, strive to get this percentage close to or above~50%. Anything above 35% is considered in the top quartile.
Once again, lumping in all apps together can be misleading as different verticals present different user behaviors. It is not surprising that Online Dating and Games have strong average daily usage. On the flip side, EdTech and Finance apps benchmark much lower. An EdTech product with 15% DAU / MAU would benchmark in the 30th percentile of all apps but well above the average among its peers. It’s not easy to get people to build a habit around studying (and few want to study on a weekend)!
Companies that operate under a freemium business model offer basic features for free, then charge for “upgrades” to the basic package. One of the most difficult metrics to benchmark is the free-to-paid conversion rate (paid users / monthly active users). Try a quick Google search… most websites will tell you that freemium companies have conversion rates of between 1 to 10%. This is a wide range and not really that helpful…
Conversion rate is strongly correlated with daily engagement
Across the dataset, I grouped the companies into six verticals and graphed the averages of engagement (DAU / MAU) vs conversion rate. As you can see, the strength of the relationship between these two metrics is very strong! It makes intuitive sense as more daily engaged users should translate to more paying users. Every 5% change in engagement correlates to a ~90 bps change in conversion rate.
Free features serve as a potent marketing tool and allow freemium businesses to cost-effectively build a channel for paid subscriber acquisition. Freemium businesses spend significantly less on marketing than their paid-only peers, driving higher long-term operating margins. Across the data set, when normalizing for gross margins, the top quartile freemium company spent 17% of gross profit on S&M vs 33% for the equivalent paid subscription business.
My colleague at IVP, Jules Maltz, wrote a great post on freemium several years ago and the principles hold true today. It is definitely worth a read!
- 1) Start with the product
- 2) Know your customer: Is freemium right for them?
- 3) Understand the value of free users: Is freemium right for you?
- 4) Make sure the math works
- 5) Measure: Love your cohorts
- 6) Beyond Freemium
These figures should be used only as guidelines to help you benchmark if you’re in-line with your peers. Where you fall on the charts will depend a lot on your product and your specific value proposition to your end customers. It’s also not realistic to assume you’ll hit these benchmarks on day one. It takes years of product development, customer interviews, and iteration to improve. Just make sure you’re seeing improvements over time.
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