Lean Analytics for Startups and Enterprises

Download Lean Analytics for Startups and Enterprises

Post on 21-Apr-2017

3.874 views

Category:

Business

1 download

Embed Size (px)

TRANSCRIPT

  • Using Lean Analytics for Startups and

    Enterprises

    Ben Yoskovitz | @byosko

  • Introduction

    @byosko

    I am a

    product guy entrepreneur author angel investor

  • Find me online

    Blog: http://instigatorblog.com

    Slideshare: http://slideshare.net/LeanAnalytics

    Book: http://leananalyticsbook.com

    Email: byosko@gmail.com

    @byosko

    http://instigatorblog.comhttp://slideshare.net/LeanAnalyticshttp://leananalyticsbook.commailto:byosko@gmail.com

  • CORPORATE PARTNERS VENTURE-BACKABLE FOUNDERS PRE-SEED FUNDING

    BETTER STARTUPS

    + +=

    Highline BETA is a startup co-creation company that launches new ventures with leading corporations.

    http://highlinebeta.com @byosko

    http://highlinebeta.com

  • Metrics:The Fundamentals

  • Metrics: The fundamentals

    How data fits in

    What makes a good metric

    Types of metrics

    Analytical superpowers

    @byosko

  • How to get things built properly (in theory)

  • Everyone has great ideas, right?

    People love this part (but thats not always a good thing!)

    This is where things start to fall apart.

    No data, no learning.

    Build Measure Learn seems so easy!

  • INTELLECTUALLY HONESTY

    Follow the Lean model and it becomes

    increasingly hard to lie, especially to yourself.

  • FOCUS

    Dont chase shiny objects. You might

    succeed without focus, but itll be by accident.

  • BETTER DECISION MAKING

    Everyone has data. The key is figuring

    out what pieces will improve your learning

    and decision making.

  • USE YOUR GUT PROPERLY

    Instincts are experiments.

    Data is proof.

  • So what makes a good metric?

  • Question: What are the metrics youre tracking?

    Take 2 minutes to write down the key metrics youre tracking (or your business is tracking) right now.

    These could be at a business level or project level.

    At the end of this section we can re-evaluate if the metrics youre tracking are still the right ones.

    @byosko

  • WHAT IS ANALYTICS?

    Analytics is the measurement of movementtowards business goals.

  • A good metric is:

    Understandable

    If youre busy explaining the data, you wont be busy acting on it.

    Comparative

    Active Users vs. Active Users/month

    Ratio / Rate

    % Monthly Active Users

    Behavior Changing

    Youll know how youll change your business based on what the metric tells you.

    @byosko

  • If a metric wont change how you behave, its a

    bad metric.

    THE GOLDEN RULE OF METRICS

    http://www.flickr.com/photos/circasassy/7858155676/

    http://www.flickr.com/photos/circasassy/7858155676/

  • Acquisition1-15% Low cost of acquisition, high checkout

    Customers that buy >1x in 90d

    Then you are in this mode

    Your customers will buy from you

    You are just like Focus on

    15-30%

    >30%

    Hybrid

    Loyalty

    Once

    2-2.5

    >2.5

    per year

    per year

    70%

    20%

    10%

    of retailers

    of retailers

    of retailers

    Increasing return rate, market share

    Loyalty, selection, inventory size

    (Thanks to Kevin Hillstrom for this.)

    Metrics help you know yourself:

  • Types of Metrics

  • Vanity vs. Actionable metrics

    Vanity ActionableMakes you feel good but doesnt change how youll act.

    Helps you pick a direction and change your behavior.

    Up and to the right. These are good.

    @byosko

  • Beware of vanity metrics:

    Users

    Follows / friends / likes

    Logins

    This tells you nothing about what they did, why they stuck around, or why they left.

    Count actions instead. Count how many followers will do your bidding.

    What are they actually doing when they login? Logins dont tell you about actions and value.

    DownloadsSure, people need to download your app in order to use it, but so what?

    @byosko

  • The best (worst!) vanity metric of all time

    # of Features

    @byosko

    https://www.flickr.com/photos/pinoyed/5009440499

  • Qualitative vs. Quantitative metrics

    Qualitative QuantitativeUnstructured, anecdotal, revealing, hard to aggregate.

    Numbers and stats; hard facts, but less insights.

    Warm and fuzzy. Cold and hard.

    @byosko

  • Discover qualitatively.

    Prove quantitatively.

  • Do Airbnb hosts get more business if their property is professionally photographed?

  • Gut instinct (hypothesis)Professional photography helps Airbnbs business

    Concierge MVPSent 20 photographers out into the field

    Measure the resultsCompared photographed listings to control group

    Make a decisionLaunched photography as a new feature to all hosts

    CASE STUDY

    Do professional photos make a difference?

  • Exploratory vs. Reporting metrics

    Exploratory ReportingSpeculative. Tries to find unexpected or interesting insights. Source of unfair advantages.

    Predictable. Keeps you abreast of normal, day-to-day operations. Can be managed by exception.

    Cool. Necessary.

    @byosko

  • Started as Circle of Friends Leveraged Facebook early Grew to 10M users fast

    ENGAGEMENT SUCKED!

    CASE STUDY

    Finding insights in the data

  • ENGAGEMENT SOLVED.

    CASE STUDY

    Moms are crazy! (in a good way) Messages to one another were ~50% longer

    115% more likely to attach a picture to a post

    110% more likely to engage in a threaded conversation

    Invited friends were 50% more likely to become engaged users

    60% more likely to accept invitations to the app

  • Lagging vs. Leading metrics

    Lagging LeadingHistorical metric that shows you how youre doing: reports the news.

    Number today that shows a metric tomorrow: makes the news.

    Start here. Try and get here.

    @byosko

  • Examples of leading metrics

    A Facebook user reaching 7 friends within 10 days of signing up. (Chamath Palihapitiya)

    A Dropbox user who puts at least 1 file in 1 folder on 1 device. (ChenLi Wang)

    A Twitter user who follows a certain number of people, and a certain percentage of those people follow the user back. (Josh Elman)

    A LinkedIn user getting to X connections in Y days. (Elliot Schmukler)

    @byosko

  • 1. People who install the Chrome extension 2. People who connect more than 1 social account 3. People who share 15 pieces of content in 7 days

    CASE STUDY

    Buffer discovered 3 leading metrics

  • Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

    Correlation vs. causation

  • Correlated vs. Causal metrics

    Correlated CausalTwo variables that are related (but may be dependent on something else.)

    An independent variable that directly impacts a dependent one.

    Ice cream and drowning.

    Summertime and drowning / Summertime and eating ice cream

    @byosko

  • A leading, causal metricis a superpower.

  • Causality is a superpower because it lets you change the future.

    Correlation lets you predict the future

    Causality lets you change the future

    I will have 420 engaged users and 75 paying customers next month.

    If I can make more first time visitors stay for 17 minutes I will increase sales in 90 days.

    Pick a metric to change

    Find correlation

    Test for causality

    Optimize the causal factor

    @byosko

  • Cohort analysis

    https://blog.kissmetrics.com/cohort-and-multi-touch-attribution/

    @byosko

    https://blog.kissmetrics.com/cohort-and-multi-touch-attribution/https://blog.kissmetrics.com/cohort-and-multi-touch-attribution/

  • Ricky (product manager) has some ideas for improving the Proposal Send Screen (based on qualitative feedback & his gut), but before prioritizing this work, he digs into the data.

    http://proposify.biz

    Putting basic data to use

    http://proposify.biz

  • 50% of people send proposals through Proposify (50% dont) (quantitative)

    Is this good or bad?

    Putting basic data to usehttp://proposify.biz

    http://proposify.biz

  • Ricky isnt sure. So hes going to need to look at additional data (exploratory):

    Churn Proposal won rate Any correlations here?

    Putting basic data to usehttp://proposify.biz

    http://proposify.biz

  • @byosko

    Also needs to do more direct customer development to learn more (qualitative)

    All of this work might lead to additional, meaningful product dev (actionable)

    Putting basic data to usehttp://proposify.biz

    http://proposify.biz

  • Look back at the metrics youre tracking

    Remember the metrics you wrote down earlier? How do they stack up now? Are they good metrics?

    What might you change about the metrics youre tracking as a business and/or on a project/feature level?

    @byosko

  • Quick summary on the basics of analytics

    Analytics is about measuring movement towards business goals

    Analytics is about simplifying not complicating

    Analytics is about helping you focus on what really matters

    Remember the Golden Rule: A good metric has to change your behaviour

    @byosko

  • Measuring Success:An introduction to

    Lean Analytics

  • Lean Analytics Framework

    The five stages of business & product development

    Mapping business models

    The One Metric That Matter