Lean startup concepts

WHAT A STARTUP IS“Existing companies execute a business model; startups search for one.”

Why not a business plan?

• Business plans are full of untested assumptions and rarely survive first contact with customers

• Nobody, aside from venture capitalists and the former Soviet Union, requires five-year plans to forecast a series of unknowns.

• Startups are not smaller versions of large companies. Successful startups go quickly from failure to failure while adapting, testing new iterations, and improving ideas with continual feedback from customers

The lean launch pad approach

Key principles

1. Instead of engaging in months of research, entrepreneurs recognize that all they have is a series of untested hypotheses – good guesses

2. Lean startups “get out of the building” to test their hypotheses and collect evidence about whether they are true or false – this is customer development

3. Lean startups practice agile development by working iteratively and incrementally with the customer – this process leads to the minimum viable product

(a) Favorite quote

"My goal in advocating a scientific approach to the creation of startups is to channel human creativity into its most productive forms, and there is no bigger destroyer of creative potential than the misguided decision to persevere.“- Eric Ries, The Lean Startup (2011: p 149)

Motivation and intent (long version)

• Startups can shorten their product development cycles by adopting a combination of business-hypothesis-driven experimentation, iterative product releases, and "validated learning".

• If startups invest their time into iteratively building products or services to meet the needs of early customers, they can reduce the market risks and sidestep the need for large amounts of initial project funding and expensive product launches and failures.

Motivation and intent (shorter version)

• Build – Test – Learn • Build products (services) iteratively to meet

the needs of early customers – reduce market risks and funding needed to find successes and failures.

• Test hypotheses about customer behavior• Learn from their reactions to your product

(service)

Terms from Lean Startup

• Innovation accounting• Minimum viable product• Continuous deployment• Cohort analysis• Split testing• Actionable metrics• Pivot

Minimum viable product

• "version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort."

• Goal of an MVP is to test fundamental business hypotheses (or leap-of-faith assumptions) and to jump start learning process as quickly as possible.

Continuous deployment

• A process “whereby all code that is written for an application is immediately deployed into production,” which results in a reduction of cycle times.

• Ries states that some of the companies he’s worked with deploy new code into production as often as 50 times a day.

Cohort analysis

• A split or A/B test is an experiment in which "different versions of a product are offered to customers at the same time.“

• Goal is to observe differences in behavior between the two groups and to measure the impact of each version on an actionable metric.

• A/B can also be performed in serial fashion where a group of users one week may see one version of the product while the next week users see another

Actionable metrics• Measures that can lead to informed business decisions and subsequent

action. • IMVU used “funnel metrics” that were critical to company growth:

– Customer registration– Download of application– Trial, repeat usage, purchase

• Contrast to 'vanity metrics' - measurements that give “the rosiest picture possible” but do not accurately reflect the key drivers of a business.

• For example, a company specializing in creating web based dashboards for financial markets might view the number of web page views per person as a vanity metric as their revenue is not based on number of page views.

Actionable metrics

Actionable metrics

Pivot

• a “structured course correction designed to test a new fundamental hypothesis about the product, strategy, and engine of growth.”

• E.g., When Groupon first started, it was an online activism platform called The Point.

• After receiving almost no traction, the founders opened a WordPress blog and launched their first coupon promotion for a pizzeria located in their building lobby

Lean Analytics

• Use data to build abetter business faster.

Some background on Lean, analytics, metrics, and segmentation.

Hotmailwas a

database company

Flickrwas going to be an

MMOTwitterwas a

podcasting company

Autodeskmade

desktop automation

Paypalfirst built for Palmpilots

Freshbooks

was invoicing for

a web design firm

Wikipediawas to be written by

experts only

Mitelwas a

lawnmower company

Most startups don’t know what they’ll be when they grow up.

Possible problem space

Product/market

hypothesisTrial

startup

Product/market

hypothesisTrial

startup

Product/market

hypothesis

Trial startup

Product/market

hypothesis

Trial startup

You are

herePIVOT

Kevin Costner is a lousy entrepreneur.

• Don’t sell what you can make.Make what you can sell.

5 things you need to know about metrics

Qualitative or QuantitativeExploratory or ReportingVanity or ActionableCorrelated or CausalLeading or Lagging

Qualitative Quantitative

Unstructured, anecdotal, revealing, hard to aggregate.

Numbers and stats; hard facts but less insight.

http://www.flickr.com/photos/zooboing/8388257248/ http://www.flickr.com/photos/x1brett/4665645157/

Warm and fuzzy. Cold and hard.

Exploratory ReportingSpeculative, trying to find unexpected or interesting insights.

Predictable, keeping you abreast of normal, managerial operations.http://www.flickr.com/photos/elwillo/4737933662/http://www.flickr.com/photos/50755773@N06/541529

5449/

Donald Rumsfeld on analytics

(Or rather, Avinash Kaushik channeling Rumsfeld)

Things we

know

don’tknow

we know

we don’tknow

we know

we don’tknow

Are facts which may be wrong and should be checked against data.

Are questions we can answer by reporting, which we should baseline & automate.

Are intuition which we should quantify and teach to improve effectiveness, efficiency.

Are exploration which is where unfair advantage and interesting epiphanies live.

Vanity

Makes you feel good, but doesn’t change how you’ll act.

Picks a direction.

Actionable

http://www.flickr.com/photos/lostseouls/807253220/ http://www.flickr.com/photos/aussiegall/6382775153/

Hits A metric from the early, foolish days of the Web. Count people instead.

Page views Marginally better than hits. Unless you’re displaying ad inventory, count people.

Visits Is this one person visiting a hundred times, or are a hundred people visiting once? Fail.

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

Followers/friends/likes

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

Time on site, or pages/visit

Poor version of engagement. Lots of time spent on support pages is actually a bad sign.

Emails collected

How many recipients will act on what’s in them?

Number of downloads

Outside app stores, downloads alone don’t lead to lifetime value. Measure activations/active accounts.

Correlated CausalTwo variables that change in similar ways , perhaps because they’re linked to something else.

An independent factor that directly impacts a dependent one.

Summer

Ice creamconsumption

DrowningCorrelatedCau

salCausal

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 on for 17 minutes I will increase sales in 90 days.”

Find correlation

Test causality

Optimize the causal

factor

Leading LaggingNumber today that shows metric tomorrow—makes the news.

Historical metric that shows how you’re doing—reports the news.

A leading indicator for e-commerce

Acquisition

Hybrid

Loyalty

70%of retailers

20%of retailers

10%of retailers

You are just like

How many of your customers buy a second

time in 90 days?

Once

2-2.5per year

>2.5per year

Your customers will buy from

youThen you are in

this mode

1-15%

15-30%

>30%

Low CAC, high

checkout

Increasing returns

Loyalty, inventory expansion

Focus on

(Thanks to Kevin Hilstrom for this.)

Segments, cohorts, A/B, and multivariates

Segment:Cross-sectional comparison of

all people divided by some

attribute (age, gender, etc.)

☀

☁

Cohort:Comparison of similar groups along a timeline.

A/B test:Changing one thing (i.e. color) and measuring the result (i.e. revenue.)

MultivariateanalysisChanging several things at once to see which correlates with a result.

☀☁☀☁

Why use cohorts? Here’s an example.

  January February March April May

Rev/customer $5 $5 $4 $4 $5

Is this company growing

or stagnatin

g?

Cohort 1 2 3 4 5

January $5 $3 $2 $1 $1

February $6 $4 $2 $1

March $7 $6 $5

April   $8 $7

May       $9

How about now?

Why use cohorts? Here’s an example.

Cohort 1 2 3 4 5

January $5 $3 $2 $1 $1

February $6 $4 $2 $1  

March $7 $6 $5    

April $8 $7      

May $9        

Averages $7 $5 $3 $1 $1

Look at the same data in cohorts