Metrics and KPIs Reference

AARRR. AARRR aka “Pirate Metrics” were introduced in 2007 by Dave McClure. The acronym stands for Acquisition, Activation, Retention, Revenue, Referral – a high level funnel that could be applied to pretty much any product.

Activation rate. Activation rate is a percentage of users who actually started using the product. Sometimes, you might hear about the soft activation rate – percentage of users who completed the onboarding, for example.

Average Revenue per Paying User (ARPPU). ARPPU gives you an idea of the value your customers are generating. It is a very common metric in subscription-based products. To get the most out of ARPPU analysis, as usual, we need to take into account time frame and user cohorts. The metric is an absolute must for testing pricing strategies.

Average Revenue Per User (ARPU). ARPU is a foundation of Unit Economics. If you know ARPU and how much money you pay for a signup (CPS) then you have a good idea of whether your marketing efforts are profitable or not.

Average cart value (ACV). An average amount of money we earn from a purchase (all items in a cart) in an E-commerce store.

Customer Acquisition Cost (CAC). If we combining ARPU and a purchase rate, we’ll arrive at CAC – an ultimate Unit Economics metric. CAC shows us whether our business is sustainable or not.

Churn rate. Let’s start with a Leaky Bucket theory. You’ve got 100 customers; 60 of them are still paying and 40 stopped paying or :drums: churned. In that case, the churn rate is 40%. Most often the term churn rate is used when we’re talking about customers. We can also calculate churn for features, app trials, anything where users drop off and we want to measure how bad this drop is.

Cost per Action (CPA). When running performance marketing, we usually optimize campaigns towards a specific action, for example signup. Based on our Unit Economics, we know a profitable range for CPA and it’s used to scale campaigns up or down.

Cost per Click (CPC). There’re many types of marketing campaigns, CPC is one of them. You set a maximum CPC for a campaign (how much you’re willing to bid) and a daily budget; then a marketing platform (like Facebook or Google) figures out how to drive you these click without exceeding your CPC threshold. That’s why it’s important to monitor CPC for campaigns and know how to calculate it.

Cost per Acquisition (CPA). CPA is how much money did we pay for a signup, this metric also goes by the name Cost Per Signup (CPS). Unfortunately, CPA acronim collides with CPA-marketing, where CPA stands for Cost-per-Action (where we pay for action, like a trial subscription instead of ad views).

Cost per Signup (CPS). CPS is a price we pay for a new user, when someone created an account in your product. Regardless of a campaign type (CPC, CPM or affiliate campaign), you can always calculate how much you pay for a signup (given that your attribution is set up correctly). CPS is a very important, especially for discovering new channels – a high CPS is an indicator that a campaign or a channel can’t be scaled further.

Click Through Rate (CTR). CTR is a percentage of people on an app screen or web page who clicked on a certain link/button/element. It’s a classic metric that’s used in marketing (better ads have higher CTR-s) or AB-testing (often, winning variations have higher CTR-s).

Country distribution. It’s not really a metric, but a way to look at any metric – we can count users or purchases by country, look at conversion rates, etc. Country distribution helps to understand your audience, guide marketing efforts and pricing strategies, etc.

Cumulative Sum (Running Total). Cumulative Sum is another way of looking at timelines. For example, we often look at a number of signups per day. Cumulative Sum for every day shows how many signups in total we had by that day (a sum of all previous values from a timeline chart).

Cohort Analysis. Cohort Analysis is not a metric itself, but it’s one of the most powerful tools in Data Analysis. Let’s take a Signup Rate, for example. A signup rate of 9.3% doesn’t tell us much. Let’s apply Cohort Analysis and calculate signup rate for a set of countries (geo cohorts), for the past six month (time cohorts), for different age groups (age cohorts), etc. Difference in cohort metrics will spark a lot of great ideas for improving product and marketing.

Click to Install rate (CTI). CTI is one of the marketing funnel steps for mobile app marketing: someone sees the ad, clicks on it, goes to the AppStore, installs the app, goes through the onboarding, creates an account, starts a trial, etc. CTI is important especially when launching new marketing campaigns – if campaign’s CTI is very low it might be unwise to run the campaign further since there’s no way we’ll have enough revenue down the funnel to generate profits.

Feature adoption. Feature adoption is a percentage of your userbase that has engaged with a certain feature. This metric is often used when a new product feature is shipped and we want to know how many people have used it.

Gross Revenue. Gross revenue is the amount of money we get before paying fees or external costs (for example, 15% or 30% AppStore comission).

Month-over-month growth. We can focus on many metrics here – user base growth or revenue. Month-over-month scale is often used in strategy meetings, quarterly reports, etc.

Marketing attribution. Another non-metric entry, but it’s so vital to marketing Data Analysis that it’s included in this list. Marketing attribution is an underlying model that allows all marketing calculations. Attribution means figuring out the source of a website visit or a signup. Often it boils down to finding an ad click that a user came from, but it could be anything. There’re multiple attribution models – first click, last click and everything in between (multitouch attribution).

Purchase rate. Simply a percentage of users who made a purchase. Is 5.34% a good purchase rate? It’s impossible to tell, because we need to know the context – what was the purchase rate last month? What’s our product price? How much money do we pay to attract a customer? In other words, we need to understand our Unit Economics to correctly evalualte purchase rate.

Referral rate. Referral rate is a measured word of mouth – percentage of users who invited at least one user. Usually, it’s tracked via a sharing link that contains information about who shared it (simply a user ID).

Retention rate. There are a couple of retention rates: user retention rate – percentage of users who still use the product after a week, month, etc); customer retention rate – percentage of customers who didn’t cancelled their subscriptions after a period of time. When talking about retention rate, we need to specify a time frame. For example, D8 retention. The opposite of retention rate is churn rate.

Return on Investment (ROI). Probably, the single most important metric in running marketing. Mathematics of ROI is simple – we divide net profit by net marketing spend. If ROI is positive – our marketing is profitable, otherwise we’re burning money. Calculating ROI is a whole science in itself, there’s no such thing as just ROI, we need to reliably calculate profit, we need to take into account time frame (otherwise we can’t plan and make quick decision in marketing), cohorts, etc.

Signup rate. A percentage of people who signed up on our website after visiting a page or in the mobile app. We can measure signup rate for different pages, flows, countries, etc. Everyone wants to improve their signup rate and the key is, as usual, to look at different cohorts and learn from their differences.

Traffic. How many people visit our website. We can measure traffic for a whole website, for a specific page or for a section of a website. We can further split traffic into paid (coming from ads) or organic (Google searches or direct visits).

Trial retention rate. It’s a percentage of users who started a subscription trial and didn’t canceled, in other words a percentage of trials that became subscriptions. Often, trial retention rates differ between marketplaces (AppStore vs Google Play), payment methods (PayPal vs Credit Cards), etc.

Time Between Purchases (TBP). An imporant E-commerce metric that helps forecast sales. It’s also important to track TBP trends and see if our E-commerce does’t lose it’s relevance (ideally, customers buy regularly or even more and more often).

Trend (uplift). Uplift is a percentage difference between metric values in time. We can calculate an uplift for any metric – number of users, purchase rate, etc. Uplift is not a metric itself, but it’s a very useful technique. In my experience, you can get 80% of insights by doing 20% of Data Analysis efforts, which is often a combination of cohort and trend analysis.

Time to purchase. A metric that could be applied to any type of product – time for a user to make the first purchase. It’s an important metric for revenue forecasting and running marketing campaigns.

Unit Economics. Unit Economics is a framework of metrics that allows us to measure our business profitability. In any framework we want to answer the question “How profitable are we?”; Unit Economics’s way is to calculate unit numbers first – how much do we pay for a user, for a customer (Customer Acquisition Cost), how much value each unit brings (Life Time Value) and then we make a final comparison – LTV / CAC.