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Startup Metrics

By Arthur Kosten

Introduction

Introduction to metrics for startups. How to choose the right metrics to track performance and optimize learning.

Arthur Kosten — Chief Marketing Officer at Booking.com from 2003-2012. Now tech investor and advisor.

Added by Arthur Kosten: “This is a nice primer on startup metrics. It explains a wide variety of metrics: financial, user, sales, marketing and acquisition metrics. Forget about the metrics for investors vs founders. Metrics are indicators for the health and growth of a company, and i'm used to investors and founders largely agreeing on what to measure and why. Apart from that, a good primer on some important metrics.

1 The Complete Quantitative Guide To Judging Your Startup

Raising capital from investors is often a frustrating experience. While part of that frustration will always be present when working on high-risk projects, a lot of the aggravation comes from the lack of clear signposts that allow founders to judge their company's performance. The reality is, most founders only ever hear a "yes" or a "no" from a venture capitalist,

Added by Arthur Kosten: “"Metrics for Pirates" is the classic presentation on startup metrics from Dave McClure of 500 Startups. Dave uses a 5 stage model with metrics for Acquisition, Activation, Retention, Referral and Revenue (AARRR). A lot of startups and many later stage companies use this model to define their key metrics.

2 Dave McClure - Startup Metrics for Pirates: AARRR!

Added by Arthur Kosten: “Amazing presentation of Andreas Klinger on startup metrics. He offers different sets of metrics for startups that are in the phase before "product/market fit" and after. Also, he shows how cohort analysis can be used to track behaviour of customers over time and compare recent customers with earlier groups of customers. There's a youtube video where Andreas discusses parts of this presentation https://www.youtube.com/watch?v=mRy-T6D8fLw

3 Startup Metrics, a love story. All slides of a Lean Analytics workshop.

Added by Arthur Kosten: “Cohort analysis is one of the most powerful tools to understand your business and customers over time. It is often used for analysing customer retention over time. A cohort is nothing more than a group of people (usually customers). By following a cohort over time and comparing different cohorts with each other you can see trends and if cohorts show similar behaviour over time, you may be able to predict future behaviour of a new cohort (customers who joined this week) based on the behaviour cohorts who joined earlier. Cohort analysis can be a bit complicated, but can generate extremely powerful metrics for startups. That's why it has gained a lot of popularity recently. This is the first out of a few chapters on cohort analysis

4 Introduction to Cohort Analysis for Startups 1

Sometimes, when you're buried in data, statistics, graphs and reports, analytics work can feel a tad dry. Personally, I tolerate creating reports (generally by automating them) but find analysis (identifying why the data is the way it is) rather compelling. In this first of several posts on cohort analysis I'm going to explore why dividing your visitors into cohorts is

Added by Arthur Kosten: “This is the 2nd part of Jonathon Balogh's introduction to cohort analysis for startups

5 Introduction to Cohort Analysis for Startups 2

Sometimes, when you're buried in data, statistics, graphs and reports, analytics work can feel a tad dry. Personally, I tolerate creating reports (generally by automating them) but find analysis (identifying why the data is the way it is) rather compelling. In this first of several posts on cohort analysis I'm going to explore why dividing your visitors into cohorts is

Added by Arthur Kosten: “One of the key acquisition metrics are Unique Visitors (UV) and sessions. After reading this chapter you will know exactly how visitors, unique visitors, sessions are measured and how they are defined in google analytics.

6 UV - Unique Visitors and Sessions.

We talk about data every day - sessions, visits, conversions, pages, hits, etc. etc. etc. But sometimes we fail to understand how all of these metrics fit together and where they come from. Let's take a look at how digital analytics tools organize data. All digital analytics data is organized into a general hierarchy of users, sessions and hits. It

Added by Arthur Kosten: “The next few articles will explain a few of the most popular metrics. The first one is Customer Acquisition Cost (CAC), the cost associated in convincing a customer to buy a product/service. This article explains how to measure Customer Acquisition Cost and why it is often a startup killer.

7 CAC - Startup Killer, the Cost of Customer Acquisition

In the many thousands of articles advising entrepreneurs on what they have to focus on to build successful startups, much has been written about three key factors: team, product and market, with particular focus on the importance of product/market fit. Failure to get product/market fit right is very likely the number 1 cause of startup failure. However in all these

Added by Arthur Kosten: “Average Revenue per User or ARPU is a very common and simple metric that shows how much revenue you make for each user. There are a lot of variants ARPPU (average revenue per PAYING users) which ignores the non paying users, ARPS (per subscriber), ARPAU (per active user), etc. This article highlights an important point: the average can be very deceptive, skewed by a lot of non paying users or a few outlying users with a very high revenue.

8 ARPU - Your average revenue per customer is meaningless | Heap Data Blog

Added by Arthur Kosten: “Customer Lifetime Value (CLV or LTV) tries to capture the profit of a customer over multiple transactions during the relationship between a company and the customer. It is a more complicated metric and often involves assumptions. In mature companies LTV is often derived from the behaviour of older cohorts of customers. In startups, LTV is often a predictive metric based on assumptions like the number expected future purchases and expected order size. This article by Google's analytics Guru Avinash Kaushik explains more about the Customer Lifetime Value and how to calculate this metric.

9 LTV - Customer Lifetime Value

Some Marketers / Analysts use Click-thru Rate (CTR) to measure success of their acquisition campaigns. Nothing much to write home about, but certainly better than executing faith based initiatives. A smaller percent of those Marketers / Web Analysts will move beyond clicks and measure Visits / Visitors and Bounce Rates to measure success. Lovely, warm hugs and smiles for them.

Added by Arthur Kosten: “Measuring Advertising ROI seems simple. Popular analytics tools include ROI metrics, but in reality it's not so easy. This article by Jeff Sauer shows some of the complexities and helps understanding and measuring what he calls "True ROI".

10 Measuring Advertising ROI

This past Saturday I gave a presentation at Measurecamp in London about the topic of ROI. This is something that has been at the top of my mind recently as I have been working on some projects to help paid search advertisers easily determine the return on their investment for their e-commerce websites. While you may not be a heavy

Added by Arthur Kosten: “Net Promoter Score (NPS) is a measure of customer satisfaction and loyalty. It is a relatively simple metric and can be used as a benchmark to compare with other businesses. NPS scores of many well known business are available in the literature. NPS metrics are used a lot for mature businesses, but can also be used by startups to measure progress in creating happy and loyal customers.

11 NPS - Net Promoter Score. Advantages and disadvantages

For some time now there has been much hype and hopeful news that Net Promoter Score (NPS) was perhaps after all the Holy Grail of customer satisfaction measurement: one single question with a link to business KPIs. Yet over time the number of sceptics has grown, and they have become more vocal. As professionals that have worked with clients using

Added by Arthur Kosten: “The Viral Coefficient, also known as K-factor measures the viral growth of a customer base. The concept is derived from epidemiology where it is used to measure how fast a disease spreads. The startup world has embraced some of the scientific learnings from epidemiology to help understand how usage of products spread among users, and how this viral growth can be engineered. This chapter explains the K-factor and a related but less known metric, the cycle time. It also shows how changes in these metrics can have a significant impact in growth of a company.

12 Viral Coefficient or K-Factor - Lessons Learned in Viral Marketing

Added by Arthur Kosten: “Conversion Rate is one of the most important statistics for almost any startup. It measures how well you are able to convert users into customers, and usually the key thing companies do is producing customers. Conversion Rate is such an important metric that most online companies have processes for Conversion Rate Optimization (CRO), and there's a whole industry creating products and services for Conversion Rate Optimization. But Conversion Rate is also a complicated metric, there are many ways to define conversion rate. It depends on what desired action is counted as a conversion, how you measure users and what timeframe you look at. This article helps to understand the various ways to measure conversion and how different choices impact the measured conversion rate.

13 CR - Conversion Rate: How to measure conversion

Summary: Increased conversion is one of the strongest ROI arguments for better user experience and more user research. Track over time, because it's a relative metric. Defining Conversion RatesDefinition: The conversion rate is the percentage of users who take a desired action. The archetypical example of conversion rate is the percentage of website visitors who buy something on the site.

Added by Arthur Kosten: “After explaining that conversion rate is one of the most important metrics, i understand that this is a rather provocative title. I included this article because Conversion Rate is one of the most abused metrics in the industry. Conversion Rate is one of the metrics that doesn't make sense as a benchmark at all. Conversion Rate fluctuates per time of day, per day of week and over time. It highly depends on changes in user base. Typically, for ecommerce sites existing customers have much higher conversion rates than new customers, so when your business matures and you get a higher share of loyal customers, your conversion rate should increase. That's not a function of you doing a better job at converting customers, but simply a mix effect of your audience. The opposite effect sometimes happens for subscription based businesses when they don't exclude their existing user base. Audience mix effects can have a dramatic impact on conversion. If you enter a new territory where your product is new and your brand is unknown, you may be doing something very sensible for your business, but conversion rate may plunge. Marketing campaigns can have a big impact on conversion rates, both up and down. Both can make sense. Marketing ROI is the metric to evaluate success of marketing, not conversion rate. Never use conversion rate as an absolute measure. It simply doesn't make sense. When you hear marketeers brag about their conversion rates "my CR is 5% how is yours?", you know they don't understand this important metric. Conversion rates are used primarily in A/B experimenting (also called split testing or multivariate testing). This article why you cannot use conversion rate as an absolute metric. If there's one single thing you should never forget from this Metrics playlist, it's probably this ...

14 Why Your Conversion Rate Doesn't Matter

In 1907, a brilliant physicist proposed that "truth" is defined by the perspective from which it is observed. That observation by Albert Einstein reverberates throughout all of our current knowledge, essentially throwing the traditional concepts of time and motion on their heads. His theory of relativity gave scientists a foundation to pioneer new discoveries. It seems to me that marketers

Added by Arthur Kosten: “Traditionally in e-commerce a conversion happens when a transaction is completed, and a user is converted into a customer. In this article Google's analytics guru Avinash Kaushik argues that we should not only look at these large conversion events (that he calls Macro Conversions), but also at the smaller (Micro Conversion) events that may ultimately lead to macro conversion. These micro conversion (often called soft conversions) help understand and optimize the often complicated path to hard conversions.

15 Conversion rate: Measure Macro AND Micro Conversions.

Added by Arthur Kosten: “Eric Ries defined progress of a startup as "validated learning" in the Lean startup. This short article defines the term "product metabolism" as the speed of iteration and learning. The article doesn't describe how to measure precisely, but in my work at booking.com i used both the number of a/b experiments released and the combined effect (measured success) of these experiments as measure of product metabolism. Using only the number of experiments means the metric goes up if you do more smaller stuff. If you skip the harder, more impactful experiments, the metabolism doesn't go up and your metric shouldn't go up either. That's why looking at the combined impact of those experiments on your key evaluation metrics (eg conversion, retention, customer happiness) is the other part of metabolism. High product metabolism should mean that you can make a lot of meaningful product improvements done in a short cycle.

16 Measuring Innovation Metabolism

I recently came across a concept, described by Dustin Dolginow of Atlas Ventures, called " Product Metabolism ". Dustin's insight was that the speed at which startups iterate on their product, their "product metabolism", should be a key performance indicator (KPI), and startups with a higher product metabolism should realize more success than those with a lower metabolism. However, he

Added by Arthur Kosten: “One of the most useful categories of metrics are captured in A/B or multivariate experiments. The basics of experimenting is simple: groups of users are treated differently and a set of metrics are used to determine which "treatment" has the best results. A/B testing has recently become very popular among tech companies to evaluate success of product development iterations. It has been used for years by companies like amazon, google and booking.com, but with recent development of easy testing tools (like optimize.ly and visual website optimizer) has become more popular among early stage companies as well. This article explains the basics of how experimenting works and some examples.

17 The Ultimate Guide To A/B Testing - Smashing Magazine

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