Business Model and Introduction to Digital

Metrics

Farley Millano farleymillano@gmail.com

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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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