Business Model and Introduction to Digital...
Transcript of Business Model and Introduction to Digital...
Agenda [1/2]Recap
Business model
Business model canvas
Lean Canvas
Introduction to digital product metrics
Types
Analysis techniques
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Agenda [2/2]
Exercise
Sprint planning (Scrum) - Planning Poker
Business model canvas
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RecapLean Startup (Strategic level)
MVP
Build Measure Learn cycle
Agile (Operational level)
Scrum
Iterative product development
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Business model canvas
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Business Model - IntroConsistent set of informations regarding business objectives
Represents a rationale of how a company creates, delivers and captures value from various contexts
They are classically represented in a many large / heavy document(s)
Static
Difficult to adjust
Doesn’t offer an overall / visual picture
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Business Model CanvasA visual chart created for business models created by Alex Osterwalder from his PhD research.
It is composed by nine complementary building blocks
It has been extensively adopted by several startups
Dynamic, flexible, visual
Some tools and adaptations has been developed on top of its core concepts
Ash Maurya: Lean Canvas
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Business Model Canvas
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Business Model CanvasKey Activities: The most important activities in executing a company's value proposition.
Key Resources: The resources that are necessary to create value for the customer.
Human, financial, physical and intellectual.
Partner Network: Complementary business alliances to avoid risk and optimize costs.
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Value Propositions:
The collection of products and services a business offers to meet the needs of its customers.
Distinguishes a company from its competitors.
The value propositions may be:
Quantitative- price and efficiency
Qualitative- overall customer experience and outcome
Business Model Canvas
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Customer Segments: Different needs and attributes to ensure appropriate implementation.
Mass Market: No specific segmentation.
Niche Market: Customer segmentation based on specialized needs and characteristics of its clients. e.g. Rolex
Segmented: Distinguish its clients based on gender, age, and/or income, etc.
Business Model Canvas
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Channels: Way that a company deliver its value proposition.
Physical store, online, door to door sales, etc.
Customer Relationships: Map of planned interactions with costumers.
Personal Assistance
Automated Services: (e.g. recommendation systems from Amazon)
Communities
Co-creation
Business Model Canvas
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Cost Structure: This describes the most important monetary consequences while operating under different business models. A company's DOC.
Characteristics of Cost Structures:
Fixed Costs - Costs are unchanged across different applications. e.g. salary, rent
Variable Costs - These costs vary depending on the amount of production of goods or services. e.g. music festivals
Business Model Canvas
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Revenue Streams: How to make money from costumers.
Subscription Fees - Revenue generated by selling a continuous service. e.g. Netflix
Lending/Leasing/Renting - Giving exclusive right to an asset for a particular period of time. e.g. Leasing a Car
Licensing - Revenue generated from charging for
Business Model Canvas
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Lean CanvasDeveloped by Ash Maurya in the book Running Lean, deriving from Osterwalder’s original model.
Provide a straight-forward / “under construction” approach to business modelling.
Prospective instead of Retrospective
Added blocks: Problem, Solution, Key Metrics and Unfair advantage.
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Lean Canvas
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Introduction do Digital Product Metrics
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Why analytics matter?It’s a fact check of a product/game reality
It tells us:
Where money comes from
How much anything costs
How many users you have
If our strategies are working
Measuring something makes you accountable
Translates strategy into action
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What makes a good metrics?
It is comparative
Ex: time periods or competitors
It is understandable
Discussable and memorable => Data culture
It is usually a ratio
Easier to act on
Inherently comparative, especially conflicting factors
Changes the way you behave
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Types of metrics
Qualitative vs. Quantitative
Vanity vs. Actionable
Exploratory vs. Reporting
Leading vs. Lagging
Correlated vs. Causal
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Qualitative vs. Quantitative metrics
Quali (WHY): unstructured, revealing, insightful hard to summarise
Quanti (WHAT): statistical, aggregates large sets of data, less insight
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Qualitative vs. Quantitative metrics
Rule of thumb: qualitative metrics are more important in the beginning.
Am I handling a real problem? What does my costumer want?
Quantitative data collection requires a lot of preparation and knowing what to ask
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Vanity vs. Actionable metrics
Vanity: makes you feel good (but tells only part of the story)
Actionable: enables behavioural changes
Ex:
% of active users
# of acquired users over specific time
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Vanity vs. Actionable metrics
Vanity metrics examples to watch out:
Total signups: It can only increase over time.
Number of hits: number of clicks in a web site.
Number of page views / visits / unique visitors: number of pages loaded, visits or people who visited a website. Tells very little what they did, what stuck or left.
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Vanity vs. Actionable metrics
Vanity metrics examples to watch out:
Number of followers, fans, friends: static number, unless they are doing something for you.
Time on site / number of pages: unqualified substitute for engaged or active users.
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Vanity vs. Actionable metrics
Vanity metrics examples to watch out:
Emails collected: again it is just a database, unless they act on your intentions.
Number of downloads: may affect app stores ranking, but tells only part of the story (e.g. activations, conversion, etc.).
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Exploratory vs. Reporting metrics
Exploratory: Speculative, looking for insights
Reporting: Keep day-to-day operations
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Known unknowns: accounting metrics we need (e.g. #sells).
Automate these
Unknown unknowns: helps disrupt a market
Searching for "scalable, repeatable business model"
image: Lean Analytics (Croll & Yoskovitz, 2013)
Exploratory vs. Reporting metrics
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Circle of friends' case:
Organize friends in groups for targeted content sharing, launched in 2008
Facebook API
Mid-2008: 10 million users, only 20% had any activity.
Problem: Would not scale to monetise
Exploratory vs. Reporting metrics
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Exploratory vs. Reporting metrics
CoF’s founders went digging to search for patterns of most active user group
50% longer messages
115% more likely to attach pictures on posts
50% more likely to invite engaged friends
Moms!
Pivoted to Circles of Moms in October 2008.
Numbers dropped, but they engaged a 4.5 million users active community
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Leading vs. Lagging metrics
Leading: predictive understanding
Lagging: explains the past
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Leading vs. Lagging metrics
Leading: predictive understanding
Ex: Number of prospects => Number new costumers
Lagging: explains the past
Useful in the beginning, provides baseline
Ex: Churn (# costumers who stop using a service)
Both can be actionable33
Correlated vs. Causal metrics
Correlated: two metrics that changes together
Causal: one causes the changes on the other
Correlations are good; Causations are great.
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Correlated vs. Causal metrics
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Correlated vs. Causal metrics
Correlations help you predict
Many factors may lead to a causation
Several metrics explain the behaviour of a dependent metric
Finding causation is a complex process
Scientific methodology and statistics
Find a correlation and then run a experiment controlling other variables
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Analysis techniques
Segmentation
Cohorts
A/B Testing
Multivariate analysis
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SegmentationSegment: group that shares similarities
Demographics
Technology
Enable comparison
Firefox vs. IE users
Replicate success from one group to another
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Cohort AnalysisCompares similar groups over time
Revenue, churn, virality, usage, etc.
Agile/Lean paradigms enables constant changes in product/game.
Ex: João registers in week 1; Maria registers in week 20
Do they have the same average usage? Why? Why not?
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Cohort Analysis
Pretty much static, does not inform much.
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Cohort Analysis
Costumers that arrived on Month 5 are spending nearly a double than initial ones.
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Cohort Analysis
Clearer perspective of what matters; a company that seemed stalled is actually flourishing.
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A/B TestingLongitudinal vs. Cross-sectional studies
Cohort: longitudinal (along lifespan)
A/B Testing: cross-sectional (different experiences at the same time)
Rule: all else held equal, except the feature you are testing.
Ex: label of call to action button
"Sign up for free" vs. "Try it for free”
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A/B Testing
Goal: Increase number of pre-orders
A B
A/B Testing
Version B: drove 43% more purchases, people just wanted to buy the game.
A B
http://blog.hubspot.com/marketing/a-b-testing-experiments-examples
A/B Testing
Goal: Get more people to register
A B
A/B TestingA B
Version B: drove 128% more registrations.
https://blog.kissmetrics.com/100-conversion-optimization-case-studies/
Multivariate analysis
A/B Testing limitation: demands a high traffic to test a single attribute and get a quick answer
Slow down product lifecycle updates
Multivariate analysis: test multiple attributes at once
Statistical: which factor correlates more with an improvement in a key metric
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Analysis techniques - Summary
Lean Analytics Cycle
Model that integrates much of Lean Startup thinking and data-driven strategies
Developed by Alistair Croll and Benjamin Yoskovitz
It serves as a way to summarise the Build, Measure and Learn cycle in more practical terms
Lean
Ana
lytic
s C
ycle
Exercise - Poker planning e Business Model Canvas
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Exercise - BMCDivide yourself in 2 groups
Choose one these games:
Fifa 16
Angry Birds
Clash of Clans
Metal Gear Solid 5
Research about its current state and strategy - 20 min
Create a canvas that represents its business model - 20 min
Present the results - 10 min
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