meaningful metrics for startups
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Meaningful metrics for startups.
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Why should you care?
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CULTURE EATS STRATEGY FOR BREAKFAST
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Peter Drucker
Peter Drucker, regarded as the founding father of modern business management, commented that it’s extremely hard to implement a new business strategy within a company if the existing culture doesn’t agree with it.
Company culture isn’t about ping-pong and free burritos, it’s how your team tackles problems. Do they make decisions on gut reaction or take a scientific approach?
As a startup, you’re at stage where you can shape the culture. If you want to create a culture that makes data-driven decisions then now is the time to do so!
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When was the last time you changed your mind based on data?
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The Lean Startup methodology by Eric Ries is one of the influential movements in how startups tackle problems. It relies on creating feedback loops so that quick and meaningful iterations can be made.
Measuring, Data and Learning are a critical part of the loop.
Meaningful metrics are metrics that enable you to make decisions.
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Types of metrics
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Vanity metrics
Hits
FavouritesFollows
Big Data
Subscribers
Klout score
LikesViews
Views
Klout score
ConnectionsStars
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Actionable metrics
Growth in LTV
DAU / WAU
Daily activations
Carts abandoned
MRR per AccountSharing invites sent per user
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VanityDon’t help you make decisions.
E.g. total signups. The number will always grow. You had 5,000 signups last month and now you have 6,000 this month. It looks good but isn’t actionable.
ActionableSpecifically ties your actions to an observed result.
E.g. the ratio of signups that activated this week.
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Lagging vs leading
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Happen after the user activity.Often easier to measure.
E.g. Revenue is a lagging indicator of future growth.
Lagging LeadingActivities that will lead to future outcomes.Often harder to measure.Hard to tie to any outcome.
E.g. Net Promoter Score (more on NPS later) – high NPS is often a leading indicator of revenue growth.
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Granularity vs volumeHow long is your sales cycle? If it takes 3 months to close a sale and there are only 10 opportunities in the pipeline then daily metrics on sales are going to erratic and misleading.
It’s important that the timeframe and volume of data in which metrics are reported make sense.
If the metric is erratically changing day to day from 0% growth to 100% growth you probably need to look at the metric on a longer timeframe. If it’s too flat then perhaps you need to look at the metric on shorter timeframe.
Ecommerce analytics
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Metrics you need to trackCAC - Customer Acquisition CostCLTV / LTV - Customer Lifetime Value
CAC > CLTV = 👎CAC < CLTV = 👍
The problem is that as a startup you don’t have a clear understanding of CLTV (you just don’t have the data) but you also don’t have a clear idea of CAC too. The important thing is to track these costs. If CLTV is not significantly higher that CAC (i.e. 4x) you might be in a bad spot.
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AOV - Average Order ValueCR - Conversion RateRPC - Repeat Customer RateShopping Cart Abandonment Rate
Remember to segment these metrics by product, channel and cohort. The act of segmenting each metric will help you spot strong and weak areas of your funnel.For example, breaking down CR by channel might reveal that Pinterest performs 10x better than Twitter.
Frameworks
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Pirate metrics
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By Dave McClure of 500 Startups focuses on the 5 steps of the customer lifecycle:• Acquisition• Activation • Retention• Referral• Revenue
Or “AARRR” hence why it’s called Pirate Metrics.
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TOTAL SIGNUPS
CAC
LEADING INDICATORS:SOCIAL METRICS (FACEBOOK, SNAPCHAT ETC)
NEW UNIQUE VISITORS TO THE SITE
SUBSCRIBERS TO THE MAILING LIST@HUGHHOPKINS
Activation
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FOLLOWS MORE THAN 15 PEOPLE
INSTALLS TRACKING CODE
INSTALLS NATIVE APP
FINDS WHAT THEY SEARCHED FOR IN LESS THAN 10 SECONDS
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Retention
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CHURN - % OF USERS WHO CANCEL IN A GIVEN
PERIODDAU / WAU / MAU
RETURNED ITEMS
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Revenue
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REVENUE GROWTH FROM NEW CUSTOMERS
AVERAGE MRR / USER
AOV
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Referral
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NET PROMOTER SCORE (NPS)
NO. OF SIGNUPS VIA REFERRAL
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Lean analytics
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By Alistair Croll and Ben Yoskovitz they advise focusing on the One Metric That Matters (OMTM) right now to your business. You should move methodically through the 5 stages of growth:
1.Empathy - Identified a problem that user will pay for.2.Stickiness - The product has the right features/value retaining
users.3.Virality - Uses and features fuel growth.4.Revenue - Finding a sustainable and scalable business.5.Scale - Scale mass market or successful exit.
Tools for the job
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Analytics stack
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Collection
Foundation
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Real-time dashboards
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A/B testing
User behaviour
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Optimisation
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Data visualisation
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Beyond analysis
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Cohort analysis
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Cohort analysis allows you to compare how different groups perform. The most common is to compare different groups of users based on when they signed up.
This is particular valuable to startups. Again, everything is in flux, the onboarding flow that user experience in January will be different to the one users experienced in July. You need to use cohort analysis to understand which one performed better.
NPS
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Net Promoter Score™
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You’ve taken an NPS survey before. You just don’t know it.
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Net Promoter Score™NPS aims to measure customer sentiment towards a company and can be used as a leading indicator of growth.
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The definitive guide to measuring NPS
Read the guide
Lessons learned
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Naming conventions
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Was the metric name “account created”, “Created-Account” or event “accountCreated”?
An inconsistent naming structure will lead to confusion in the team, sometimes referencing the wrong metrics.
There are many naming conventions but the important thing is to pick one and ensure the whole team follows it. i.e. “object - action” all in lower case.
Transparency
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The opposite is a culture of secrecy and how can you fix problems that aren’t visible. Use that old laptop to power a large display showing your key metrics. You’ll be amazed by how much team members will pick up from the metrics, spot anomalies and be quicker to act in a change of numbers.
Being transparent and clear also prevents confusion. Ensure you have a clear definition what each metric is. A common are of confusion are Daily/Weekly/Monthly Active Metrics (WAU), for example is an active user defined as just logging in or using feature x, sending more than 10 messages.
Track from day one
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Too often analytics is an afterthought when building a product.
Tracking and analysing the data is an essential part of the feedback loop. When building products think how user behaviour can be tracked, what metrics can we track that will enable us to make decisions.
When planning to build a product or feature plan how data will be tracked and how that will help form decisions.
Automate
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No one likes manual data collection. A manually updated spreadsheet will work for the first few weeks but eventually it will go out of sync as colleagues will forget to update it.
Automate the data collection. If you’re focusing on a few key metrics make sure the collection is automated.
Bugs 🐛
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If a metric looks off double check it’s not a bug!
In a fast moving startup changes are being made daily to the codebase, how customers are being acquired and onboarded are also changing. As a result bugs naturally creep in and metrics will be skewed.
Double check metrics from time to time, sign up to your own service, purchase a plan and check the numbers add up.
User testing
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Startups, being young, have limited data sets in which to make decisions.
User testing is one of the most effective forms of testing you can do for your startup.
It will help you spot bottlenecks and areas of confusion for the user in minutes.
A good tip to bake user testing into your workflow is to commit to doing a test on the last Friday of each month. Add it to your calendar and stick to it!
Go forth. Take action.
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Track metrics that are Actionable.
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Leading indicators enable you to iterate faster.
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Focus on the One Metric That Matters (OMTM) to your business right now.
Methodically move from one metric in the funnel to the next.
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Use a framework such as Pirate Metrics to structure your metrics.
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Measure metrics that encourage you to make a decision and iterate.
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