Metrics for early stage startups

Metrics forEarly-Stage Startups #scb13 – @andreasklinger

@andreasklinger“Startup Founder”“Product Guy” #scb13 – @andreasklinger

@andreasklinger“Startup Founder”“Product Guy”What we will cover- Why early stage metrics are different.- Applicable methods & Lessons Learned. (this is an excerpt of 2h workshop - but with prettier slides ;) ) #scb13 – @andreasklinger

The Main Problem with Metrics in Early Stage:- Product not ready or even wrong.- Little to no useable data.- Data points contradict each other.- External Trafﬁc can easily mess up our insights.- What is actionable?- Are we on the “right” track? #scb13 – @andreasklinger

Startup Founders. #scb13 – @andreasklinger

Some startups haveideas for a new product.Looking for customersto buy (or at least use) it.Customers don’t buy.“early stage” #scb13 – @andreasklinger

Product/Market Fittraction time With early stage I do not mean “X Years” I mean before product/market ﬁt. #scb13 – @andreasklinger

Product/market ﬁtBeing in a good marketwith a product that can satisfythat market.~ Marc Andreessen #scb13 – @andreasklinger

Product/market ﬁtBeing in a good marketwith a product that can satisfythat market.~ Marc Andreessen= People want your stuff. #scb13 – @andreasklinger

Product/Market Fittraction time #scb13 – @andreasklinger

Product/Market Fittraction time Discovery Validation Efﬁciency Scale Steve Blank - Customer Development #scb13 – @andreasklinger

Product/Market Fittraction time Discovery Validation Efﬁciency Scale Find a product the market wants. #scb13 – @andreasklinger

Product/Market Fittraction time Discovery Validation Efﬁciency Scale Find a product Optimise the product the market wants. for the market. #scb13 – @andreasklinger

Product/Market Fit traction time Discovery Validation Efﬁciency Scale Find a product Optimise the product the market wants. for the market.People in search Most clonesfor new product start here. start here. #scb13 – @andreasklinger

Product/Market Fittraction time Discovery Validation Efﬁciency Scale Product & Customer Scale Marketing Development & Operations #scb13 – @andreasklinger

Product/Market Fittraction time Discovery Validation Efﬁciency Scale Startups have phases but they overlap. #scb13 – @andreasklinger

Product/Market Fittraction time Discovery Validation Efﬁciency Scale 83% of all startups are in here. #scb13 – @andreasklinger

Product/Market Fittraction time Discovery Validation Efﬁciency Scale 83% of all startups are in here. Most stuff we learn about web analytics is meant for this part #scb13 – @andreasklinger

Startups drown innon actionable datapoints.

What does this mean for my product?Are we on the right track?Meant for channel (referral)optimization.

Use of Metrics in Early Stage #scb13 – @andreasklinger

Use of Metrics in Early StageFocus on People- Not Hits, Pageviews, Visits, EventsValidation of customer feedback- saying vs doing- eg. did they really use the app?- does the app do what they need it to?Validation of internal opinions- believing vs knowing- eg. “Our users need/are/do/try…”Doublecheck + Falsify #scb13 – @andreasklinger

Segment Users into CohortsCohorts = Groups of people that share attributes. #scb13 – @andreasklinger

Segment Users into Cohorts #scb13 – @andreasklinger

Apply a framework: AARRR #scb13 – @andreasklinger

AcquisitionVisit / Signup / etc ActivationUse of core feature RetentionCome + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure

Example: PhotoappCohorts based on registration week WK acquisition activation retention referral revenue twice aPhotoapp registration ﬁrst photo share … month 1 400 62,5% 25% 10% 2 875 65% 23% 9% 3 350 64% 26% 4% … … … … …

Acquisition Visit / Signup / etc Activation Use of core featureWhich Metrics to focus on? Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure

Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: RetentionFocus on Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure

Acquisition Visit / Signup / etc Activation Use of core featureLong answer - It depends on two things: Retention Come + use againPhase of company Referral Invite + SignupType of Product (esp. Engine of Growth) Revenue $$$ Earned (c) Dave McClure

Long answer - It depends on two things: Acquisition Visit / Signup / etc Activation Use of core feature Retention Come + use again Referral Invite + Signup Revenue $$$ Earned Source: Lean Analytics Book - highly recommend

Acquisition Visit / Signup / etc Activation Use of core feature Short Answer: RetentionFocus on Retention Come + use again Referral Invite + Signup Revenue $$$ Earned (c) Dave McClure

BecauseRetention = f(user_happiness)

Because Retention = f(user_happiness)Crashpadder’s Happiness Indexe.g. Weighted sum over core activities by hosts.Cohorts by cities and time.= Health/Happiness Dashboard

AARRR misses something AcquisitionAnd Happiness is not everything Activation Retention Referral Revenue (c) Dave McClure

CUSTOMER INTENT Acquisition Activation Retention Referral RevenueFULFILMENT OF CUSTOMER INTENT (c) Dave McClure

Metrics are horrible way to understand customer intent (c) Dave McClure

Metrics are horrible way to understand customer intent Customer Intent = His “Job to be done” Products are bought because they solve a “job to be done”. Learn about Jobs to be done Framework Watch: http://bit.ly/cc-jtbd (c) Dave McClure

Startups are obsessed by their solutionAnd ignore the customers job/problem Market Job/ Problem Our Solution #scb13 – @andreasklinger

Metrics are horrible way to understand customer intent (c) Dave McClure

Metrics are horrible way to understand customer intent Great Way: Customer Interviews But: We bias our people, when we ask them. Even if we try not to. Reason: we believe our own bullshit. Watch: www.hackertalks.io (c) Dave McClure

Metrics are horrible way to understand customer intent OK Way: Smoke Tests If interviews suggest a new feature but you are Download Mobile Client unsure about critical mass (e.g. due to sample bias). Create Smoke Tests measure Click Conversion/ Signups Not for veriﬁcation but falsiﬁcation (c) Dave McClure

CUSTOMER INTENT (JOB) AcquisitionCustomerInterviews Activation Retention ReferralCustomerInterviews& Metrics Revenue FULFILMENT OF CUSTOMER INTENT (c) Dave McClure

Dig deeper - Good product centric KPIs:Framework: AARRR #scb13 – @andreasklinger

Dig deeper - Good product centric KPIs:Linked to assumptions of your product (validation/falsify)Rate or Ratio (0.X or %)Framework: AARRRComparable (To your history (or a/b). Forget the market)Explainable (If you don’t get it it means nothing) #scb13 – @andreasklinger

“Industry Standards”Framework: AARRR Use industry averages as reality check. Not as benchmark. - Usually very hard to get. - Everyone deﬁnes stuff different. - You might end up with another business model anyway. - Compare yourself vs your history data. #scb13 – @andreasklinger

Example Mobile App: Pusher2000Trainer2peer pressure sport app (prelaunch “beta”).Rev channel: Trainers pay monthly fee.Two sided => Segment AARRR for both sides (trainer/user)Marketplace => Value = Transactions / SupplierSocial Software => DAU/MAU to see if activated users stay activeChicken/Egg => You need a few very happy chickens for loads of eggs.Week/Week retention to see if public launch makes senseFramework: AARRROptimize retention: Interviews with Users that leftMeasure Trainer Happiness ScoreActivated User: More than two training sessionsPushups / User / Week to see if the core assumption (People will domore pushups) is valid #scb13 – @andreasklinger

Dig Deeper - Dataschmutz A layer of dirt obfuscating your useable data. Usually “wrong intent”. Usually our fault. (~ sample noise we created ourselves) #scb13 – @andreasklinger

Dataschmutz A layer of dirt obfuscating your useable data.e.g. Trafﬁc Spikes of wrongcustomer segment.(have wrong intent) #scb13 – @andreasklinger

Dataschmutz Exam MySugr is praised as “beautiful app” example.… => Downloads => Problem: Not all are diabetic They focus on people who activated.

How to minimize the impact of Dataschmutz Base your KPIs on wavebreakers. WK visitors acquisition activation retention referral revenue twice aBirchbox visit registration ﬁrst photo share … month 1 6000 66% / 4000 62,5% 25% 10% 2 25000 35% / 8750 65% 23% 9% 3 5000 70% / 3500 64% 26% 4%

DataschmutzCompetitions create artiﬁcial incentive Competition Created “Dataschmutz” Competitions (before P/M Fit) “Would you use my app and might are nothing but Teﬂon Marketing win 1.000.000 USD?” * Users had huge extra incentive. People come.can hurt your numbers. * Marketing People leave. * While we decided on how to relaunch we had dirty numbers. #scb13 – @andreasklinger

Dig Deeper - Metrics need to hurt #scb13 – @andreasklinger

Dig Deeper - Metrics need to hurtIf you are not ashamed about the KPIs inyour dashboard than something is wrong.Either you do not drill deep enough.Or you focus on the wrong KPIs. #scb13 – @andreasklinger

Dig Deeper - Metrics need to hurtExample: Garmz/LOOKKGreat Numbers:90% activation (activation = vote)But they only voted for friendsinstead of actually using the platform.We drilled (not far) deeper:Activation = Vote for 2 different designers. Boom. Pain. #scb13 – @andreasklinger

User activation.Some users are happy (power users)Some come never again.What differs them? It’s their activities in their ﬁrst 30 days.How we think about Churn is wrong. #scb13 – @andreasklinger

Example TwitterHow often did activated usersuse twitter in the ﬁrst month:7 timesWhat did they do?Follow 20 people, followedback by 10Churn:If they don’t keep them 7 timesin the ﬁrst 30 days.They will lose them forever.It doesn’t matter when a userremembers to unsubscribe #scb13 – @andreasklinger

Example TwitterExample Twitter:How did they get more peopleto follow 30people within7visits in the ﬁrst 30 days?Ran assumptions, createdfeatures and ran experiments!Watch: http://www.youtube.com/watch?v=L2snRPbhsF0 #scb13 – @andreasklinger

Checkout Intercom.ioCustomer segmenting and messaging done right. #scb13 – @andreasklinger

Summary #scb13 – @andreasklinger

Summary- Use Metrics for Product and Customer Development.- Use Cohorts.- Use AARRR.- Figure Customer Intent through non-biasing interviews.- Understand your type of product and it’s core drivers- Find KPIs that mean something to your speciﬁc product.- Avoid Telfonmarketing (eg Campaigns pre-product).- Filter Dataschmutz- Metrics need to hurt- Focus on the ﬁrst 30 days of customer activation.TL;DR: Use metrics to validate/doublecheck.Use those insights when designing for/speaking to your customers. #scb13 – @andreasklinger

Read onStartup metrics for Pirates by Dave McClurehttp://www.slideshare.net/dmc500hats/startup-metrics-for-pirates-long-versionActionable Metrics by Ash Mauyrahttp://www.ashmaurya.com/2010/07/3-rules-to-actionable-metrics/Data Science Secrets by DJ Patil - LeWeb London 2012http://www.youtube.com/watch?v=L2snRPbhsF0Twitter sign up processhttp://www.lukew.com/ff/entry.asp?1128Lean startup metrics - @stueccleshttp://www.slideshare.net/stueccles/lean-startup-metricsCohorts in Google Analytics - @serenestudioshttp://danhilltech.tumblr.com/post/12509218078/startups-hacking-a-cohort-analysis-with-googleRob Fitzpatrick’s Collection of best Custdev Videos - @robfitzhttp://www.hackertalks.ioLean Analytics Bookhttp://leananalyticsbook.com/introducing-lean-analytics/Actionable Metrics - @lfittlhttp://www.slideshare.net/lfittl/actionable-metrics-lean-startup-meetup-berlinApp Engagement Matrix - Flurryhttp://blog.flurry.com/bid/90743/App-Engagement-The-Matrix-ReloadedMy Bloghttp://www.klinger.io #scb13 – @andreasklinger

Thank you@andreasklinger #SCB13Slides: http://slideshare.net/andreasklingerAll pictures: http://ﬂickr.com/commons #scb13 – @andreasklinger

Metrics for early stage startups

Andreas Klinger

Metrics for early stage startups