Analytics frameworks and startup stages. Farley Millano -

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1 Analytics frameworks and startup stages Farley Millano - farleymillano@gmail.com 1

2 Agenda [1/2] Recap Analytics frameworks Startup stages Empathy Stickiness Virality Revenue Scale 2

3 Agenda [2/2] Start the development of proposed work Analysis of a commercial game regarding: Balancing + Monitoring Balanceamento_Jogos_15_16/ Balanceamento_Jogos_15_16.htm 3

4 Recap Fundamentals: Lean Startups (Strategic) + Agile (Operational) Business Model: Intro + Canvas Intro to Digital Product Metrics Types (Quali vs. Quanti, Vanity vs. Actionable, etc.) Analysis techniques (A/B Testing, Segmentation, etc.) 4

5 Analytics frameworks 5

6 Intro There s a myriad of factors that define which metrics to watch, but we mostly it relies on: Business model type Startup stage Some authors defines a set of categories to organize this task Frameworks 6

7 Analytics frameworks Help understand startups functioning in terms of: Lifecycle Grow Find market Costumer acquisition Revenue streams 7

8 Pirate Metrics - AARRR Created by Dave McClure, startup investor and founder of business accelerator 500 startups Its name comes from a acronym for Acquisition, Activation, Retention, Revenue and Referral (AARRR) A user generates value not only from revenue, but also from referral and content creation 8

9 Pirate Metrics - AARRR 9 Lean Analytics (Croll & Yoskovitz, 2013)

10 Dave McClure - Pirate Metrics 10

11 Engines of growth Created by Eric Ries, author of Lean Startup Startup growth comes from the works of 3 engines: Sticky Virality Paid Each is associated with specific KPIs (key process indicator) 11

12 Engines of growth Sticky engine: focused in user engagement Returning active users Key metrics: costumer retention, churn rates and usage frequency Secondary metrics: time since last visit, clickthrough rate ( , notifications) Examples: Game notifications, reminder, Ads, etc. 12

13 Engines of growth Virality engine: focused in user virality Users bringing other users Key metrics: viral coefficient Secondary metrics: Viral cycle time, number of connected accounts, number of invitations, number of answered invitation Examples: Facebook Connect, invite users to speed progression (Farm Ville), etc. 13

14 Engines of growth Paid engine: focused in user monetization Key metrics: Costumer lifetime value / costumer acquisition cost Caution: focusing on receiving money before sustainability (stickiness and virality) is dangerous Examples: number of subscriptions, inventory availability, etc. 14

15 Lean Canvas 15

16 Lean Canvas Lean Canvas was created by Ash Maurya (detailed last class) It can be interpreted as a current snapshot of your game / product Hypothesis and Results Its lightness should be used to provide dynamism to strategic decisions 16

17 Lean Canvas Video: 17 Lean Analytics (Croll & Yoskovitz, 2013)

18 Startup stages 18

19 Startup stages It is usually hard to have a specific "hard number metric telling which stage a startup is at or should be do next Lean Analytics authors created a five stage gate model to help that out Empathy; Stickiness; Virality; Revenue and Scale Draws some aspects of models presented previously and puts emphasis on metrics for transitioning 19

20 20

21 Empathy It is the time to get into your user s head Discover and validate a problem/idea/need Qualitative data is key in this stage Observe patterns that emerge Methodology is extremely important Tools, methods => Collect, analyse and report data Avoid bias and unwanted effects 21

22 Empathy Decouple problem from solution: Painful enough? Enough people care? Are they trying to solve it themselves? What it takes to make them aware of the problem? 22

23 Empathy Prepare for interviews Face to face Neutral location Have a script (Ash Maurya s Running Lean) Set the stage: put the interviewee in the right frame of mind Segment: collect demographics Problem context: tell the story Test the problem: rank problems and others that might come up with Test the solution: how do they solve it today Ask for something: schedule a solution interview and refer other people 23

24 Empathy Choose an approach Convergent: some specific problems to be ranked Divergent: diving into a more broad problem space 24

25 Empathy How do I know if I have found the right problem? The metric here is Pain (or Fun) Ideally, results from your interview should be scored 25

26 Empathy Did the interviewee ranked the problems presented? 26

27 Empathy Is the interviewee actively trying to solve the problems (or already 27did so)?

28 Empathy Was the interviewee engaged and focused throughout the interview? 28

29 Empathy Did she agree on a follow with you (possibly with a solution)? 29

30 Empathy Did she refer other people for you to interview? 30

31 Empathy Did she offer to pay you for the solution? 31

32 Empathy At TechStars, LikeBright founders were asked to talk to 100 women to understand better their problem" Frustrations on dating Report Used Mechanical Turk from Amazon and Google Voice $2 per interview and got 100 responses in 4 hours This gave a better understanding and insights of their problem and had the company accepted 32

33 Empathy - Summary Goal: identify a need that people want to pay at scale. Conduct qualitative, exploratory discussions early on Finding the right questions, scale to more quantitative surveys, reach more people Use existing tools to collect data and simulate your solution Balsamiq, Google Docs, Twitter, Linkedin, etc. 33

34 Empathy - Moving to next stage 34

35 Stickiness Once the problem is known and realistic, start build something Iterate your MVP: methodical work Improve core metrics Avoid premature scaling The goal at this stage is retention. 35

36 Stickiness Prioritise features according to the following: Develop estimates on why things will be better with that feature Measure the impacts (metrics), avoid scope creep Estimate time to develop vs. expected results Usability: avoid complexity Anticipate risks: technical and user response Level of innovation: it is allowed to bake big bets on this stage User voice: listen (carefully), take deeper look to their actions. 36

37 Stickiness Prioritise features according to the following: Develop estimates on why things will be better with that feature Measure the impacts (metrics), avoid scope creep Estimate time to develop vs. expected results Usability: avoid complexity Anticipate risks: technical and user response Level of innovation: it is allowed to bake big bets on this stage User voice: listen (carefully), take deeper look to their actions. Problem-solution canvas: weekly reality-check tool 37

38 Stickiness Problem-solution canvas: weekly reality-check tool 38

39 Stickiness Simple tool for conducting focused surveys for a small group First iteration of the product required that respondents first signed-up and then answered the questions Problem: low response rate: 10-25% Test: What if the user is already considered signed up if they answer a question? They prioritised this feature (based on ) instead of building a mobile version

40 Stickiness 10-25% 70-90%

41 Stickiness - Moving to next stage Are people using the product as expected? What is an active user? What is current percentage? Can this be higher? Evaluate product roadmap against presented criteria Evaluate user complaints 41

42 Virality Stands for the phase that users shares your product with other users There are three types Inherent: function of use GDocs: share documents Artificial: stimulated through reward Dropbox: Space Race Word-of-mouth: natural, happens from users exchange of experience Blog post, Influentials, Spontaneous media, etc. 42

43 Virality Most important metric: Viral coefficient It is obtained by combining (multiplying): Invitation rate: number of invites sent / number of users Acceptance rate: number of signups / number of invites 43

44 Virality Second most important metric: Viral cycle time Time taken for a user to invite another one It can make a huge difference when combined with viral coefficient 44

45 Virality Leading indicator Social engagement (links to friends) Content creation (post, share, likes) Return frequency (days since last visit, time on site, etc.) Tied to part of a business model (revenue, daily traffic, etc.) Come early on user lifecycle: increase data points Early extrapolation = Sooner prediction 45

46 Virality Circle of Moms "Being a mom" was a leading indicator of engagement. Facebook / Linkedin Friends suggestion / invitation early in user lifecycle leads to higher spreadability 46

47 Virality Timehop is a startup focused on recalling/reposting previous posts from social networks. They already had an extremely engaged audience: 40-50% s open rate It was needed to go viral Through pixel tracking on s: 50% of s were on ios As is no inherently sharable, led them to develop an ios version of their app

48 Virality - Moving to next stage Which types of virality are you employing? What is your viral coefficient? Is it enough to sustain business growth? What is viral cycle time? Can it be speed up? 48

49 Revenue This stage is focused in making money After showing you are solving a real problem and the solution is sticky and viral Charging up-front Focusing on revenue and margins Most startups spend sometime until being selfsustainable 49

50 Revenue Revenue per costumer is a better metric than raw revenue Clearer perspective and actionable Other important metrics: Ad revenue Conversion rate Shopping cart size Subscription Costumer lifetime value Costumer acquisition cost 50

51 Revenue Goal: more stuff to more people for more money more often more efficiently Focus on your strength: Selling physical, per-transaction cost: focus on more efficiently High viral: more people for every dollar Loyal costumer: buy more often One-time big ticket: more money for each purchase Recurring: more stuff through higher-capacity packages 51

52 Revenue Costumer lifetime value > Costumer acquisition cost Oversimplified math: delay between acquiring and paying costumers Balance between: Investment Amount spent on costumer acquisition each month Revenue brought from each user per month Churn rate 52

53 Revenue Pivoting Changing part of a product and its business model in order to fit a new market. If business model is not working: avoid the urge to build more features It might be easier to change a market (target users) than a product But it is difficult anyway 53

54 Revenue parse.ly is focused on giving some tools for publishers in order to track their metrics Their first product was a reader for end user An extremely engaging product and earned really good media attention All the metrics were fine, except one, revenue. "They love, but they don t pay. They built an entirely new product, using part of the knowledge and architecture from the past This new offering uses a trial mode (one month)

55 Revenue - Moving to next stage Breakeven on variable costs: Costumer revenue > costumer acquisition + delivering service Time to costumer breakeven Investment made by the company until a user pays itself EBITDA Breakeven Ignores large investments and previous debts Hibernation Breakeven No new marketing is spent, only minimum Lights on, servicing existing costumers, word of mouth, no new features 55

56 Scale Represents a stage for wider audience, new market entrance A product/service can stand on its own High order metrics come into play Channels, regions and marketing campaigns 56

57 Scale Represents a stage for wider audience, new market entrance A product/service can stand on its own High order metrics come into play Channels, regions and marketing campaigns Compensation, API traffic, channel relationship and competitors Efficiency vs. Differentiation: reduce cost vs. increase margins 57

58 Scale Strategy, tactics and implementation must be aligned regarding their goals It might be harder to innovate at this stage Big company feelings 58