Would you bet your job on your A/B test results?

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Would you bet your job on your A/B test results? @QubitGroup #QubitABtest Friday, 28 February 14

description

It is possible that 72% of your successful A/B tests may not be driving any business benefit, or may actually be harming your bottom line. This is because bad methodology is preventing you from detecting the truly successful ideas. In this webinar, Qubit explains how you can avoid this problem. With special guests from Forrester who explain how A/B testing fits into the digital landscape, and Staples, who give practical advice for how to set up an A/B testing campaign, this webinar will change the way you think about A/B testing forever

Transcript of Would you bet your job on your A/B test results?

Page 1: Would you bet your job on your A/B test results?

Would you bet your job on your A/B test results?

@QubitGroup #QubitABtest

Friday, 28 February 14

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Agenda

@QubitGroup

• 5 digital experience technology trends that will guide your technology investment — Anjali Yakkundi, Forrester Research, Inc.

• 5 fundamental A/B testing mistakes and how to avoid them—Will Browne, Qubit

• How to run a successful A/B testing program—Melanie Kyrklund, Staples

• Q&A

#QubitABtest

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5 Digital Experience Technology Trends To Guide Your Investment Anjali Yakkundi, AnalystFebruary 27, 2014

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© 2013 Forrester Research, Inc. Reproduction Prohibited4

Burberry brings digital into the store

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© 2013 Forrester Research, Inc. Reproduction Prohibited5

Room Number

Address & directions

Starwood’s Preferred Guest Streamlines The Check-In Process

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© 2013 Forrester Research, Inc. Reproduction Prohibited6

Agenda

›Why digital experience technology matters ›Key digital customer experience

technologies ›Five trends that will drive your digital

experience technology investment›Recommendations

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© 2013 Forrester Research, Inc. Reproduction Prohibited7

Agenda

›Why digital experience technology matters ›Key digital customer experience

technologies ›Five trends that will drive your digital

experience technology investment›Recommendations

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© 2013 Forrester Research, Inc. Reproduction Prohibited8

Source: October 10, 2013, “Competitive Strategy In The Age Of The Customer” Forrester report

Age of the customer

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© 2013 Forrester Research, Inc. Reproduction Prohibited9

Source: September 19, 2013, “Build Seamless Experiences Now” Forrester report

Your customers are always connected…

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…often on multiple devices

Source: Google - The New Multi-screen World: Understanding Cross-platform Consumer Behavior, August 2012

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You need to sustain your customer’s attention with engaging experiences.

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© 2013 Forrester Research, Inc. Reproduction Prohibited12

Engaging experiences requires strong technology management

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© 2013 Forrester Research, Inc. Reproduction Prohibited13

Agenda

›Why digital experience technology matters ›Key digital customer experience

technologies ›Five trends that will drive your digital

experience technology investment›Recommendations

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Digital experience reference architecture

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© 2013 Forrester Research, Inc. Reproduction Prohibited15

Source: February 12, 2013, “Choose Digital Experience Tools To Engage With Customers In A Cross-Channel World” Forrester report

Many solutions make up the digital experience ecosystem

TestingOptimization Analytics

(mobile, social, Web)

PortalsSite search

Commerce platform Web content management

Online video platform

Digital asset management

Product content management

Recommendation engine

Campaign management

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© 2013 Forrester Research, Inc. Reproduction Prohibited16

Source: February 12, 2013, “Choose Digital Experience Tools To Engage With Customers In A Cross-Channel World” Forrester report

Testing and optimization solutions are key to engagement and measurement

TestingOptimization Analytics

(mobile, social, Web)

PortalsSite search

Commerce platform Web content management

Online video platform

Digital asset management

Product content management

Recommendation engine

Campaign management

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© 2013 Forrester Research, Inc. Reproduction Prohibited17

Agenda

›Why digital experience technology matters ›Key digital customer experience

technologies ›Five trends that will drive your digital

experience technology investment›Recommendations

Friday, 28 February 14

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#1- This is the beginning of the end of the all-in-one-suite

Only 9% prefer a suite approach

Base: 233 Digital Experience Professionals Source: March 2013, “Digital Experience Delivery Online Survey”

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#2- Tools must create business value out of data

Solutions must effectively harness customer data, product data, social media data, and other

information to create and optimize experiences.

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© 2013 Forrester Research, Inc. Reproduction Prohibited20

#3- Tools must support personalized experiences

Situation Real-time optimization Prediction

• Device / Channel • Location• Time of day• Weather

• Historical past• Immediate past • Current session

• Likely next steps

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© 2013 Forrester Research, Inc. Reproduction Prohibited21

#4- Look for more than software: look for solutions ›Look for solutions that help marketers and business

users quickly adapt to changing customer needs. This can be achieved by:

•Keeping IT out of critical publishing paths •Prioritizing user interface needs

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© 2013 Forrester Research, Inc. Reproduction Prohibited22

#5- Solutions must prioritize multichannel

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© 2013 Forrester Research, Inc. Reproduction Prohibited23

Agenda

›Why digital experience technology matters ›Key digital customer experience

technologies ›Five trends that will drive your digital

experience technology investment›Recommendations

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© 2013 Forrester Research, Inc. Reproduction Prohibited24

Recommendations

›You can’t do it all at once — prioritize key customer touchpoints ›Assess current technology gaps– most

organizations tend to have gaps around:•Optimization•Personalization•Multichannel delivery

›When sourcing new solutions, look to vendors that can help you connect the dots.

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Thank youAnjali [email protected]@Ayakkundi

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5 fundamental A/B testing mistakes

and how to avoid them

Will Browne, [email protected]

@QubitGroup #QubitABtest

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Why do I have to test?

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A/B testing is an essential tool

@TheGrahamCooke

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A/B testing is an essential tool

@TheGrahamCooke

• It is the best, simplest way to prove the business value of your digital technology

@QubitGroup #QubitABtest

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A/B testing is an essential tool

@TheGrahamCooke

• It is the best, simplest way to prove the business value of your digital technology

• It is the only way to get close to proving cause and effect in what you do

@QubitGroup #QubitABtest

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But there are costs associated with A/B testing

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But there are costs associated with A/B testing

• The monthly cost of your testing platform

@QubitGroup #QubitABtest

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But there are costs associated with A/B testing

• The monthly cost of your testing platform

• The salaries and time of your developers and team

@QubitGroup #QubitABtest

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• The monthly cost of your testing platform

• The salaries and time of your developers and team

• The lost revenue from implementing changes that do nothing for your site (or actively harm it)

But there are costs associated with A/B testing

@QubitGroup #QubitABtest

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We’re here to show you how to reduce these costs, and increase ROI

@QubitGroup #QubitABtest

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We’re here to show you how to reduce these costs, and increase ROI

• Find the ideas that are likely to have a real positive effect on user behavior (true winners)

@QubitGroup #QubitABtest

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We’re here to show you how to reduce these costs, and increase ROI

• Find the ideas that are likely to have a real positive effect on user behavior (true winners)

• Detect as many true winners as possible

@QubitGroup #QubitABtest

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We’re here to show you how to reduce these costs, and increase ROI

• Find the ideas that are likely to have a real positive effect on user behavior (true winners)

• Detect as many true winners as possible

• Minimize the chance of finding tests that look like winners, but have no true effect

@QubitGroup #QubitABtest

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Find the ideas that are likely to have a real positive effect on user behavior

@QubitGroup #QubitABtest

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Size does matter

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Detect as many true winners as possible

We’ve found that tests based on data are

7.7x more effectivethan tests based on intuition alone

(Qubit, 2013)

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Imagine testing for weighted coins

We’ve found that tests based on data are

7.7x more effectivethan tests based on intuition alone

(Qubit, 2013)

• How many coin flips would be enough to prove that one coin was weighted?

@QubitGroup #QubitABtest

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Imagine testing for weighted coins

We’ve found that tests based on data are

7.7x more effectivethan tests based on intuition alone

(Qubit, 2013)

• How many coin flips would be enough to prove that one coin was weighted?

• It is impossible to be 100% certain, but the more data you have, the closer you can get

@QubitGroup #QubitABtest

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Imagine testing for weighted coins

We’ve found that tests based on data are

7.7x more effectivethan tests based on intuition alone

(Qubit, 2013)

• How many coin flips would be enough to prove that one coin was weighted?

• It is impossible to be 100% certain, but the more data you have, the closer you can get

• We follow scientific standards and wait until there is an 80% chance of detecting a difference

@QubitGroup #QubitABtest

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We’ve found that tests based on data are

7.7x more effectivethan tests based on intuition alone

(Qubit, 2013)

Minimize the chance of finding tests that look like winners,but have no true effect

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We’ve found that tests based on data are

7.7x more effectivethan tests based on intuition alone

(Qubit, 2013)

• We generally assume that with good testing there is a 5% false positive rate

Minimize the chance of finding tests that look like winners,but have no true effect

@QubitGroup #QubitABtest

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We’ve found that tests based on data are

7.7x more effectivethan tests based on intuition alone

(Qubit, 2013)

• We generally assume that with good testing there is a 5% false positive rate

• We can test this using A/A tests (showing both groups the exact thing)

Minimize the chance of finding tests that look like winners,but have no true effect

@QubitGroup #QubitABtest

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We’ve found that tests based on data are

7.7x more effectivethan tests based on intuition alone

(Qubit, 2013)

• We generally assume that with good testing there is a 5% false positive rate

• We can test this using A/A tests (showing both groups the exact thing)

• So out of 100 A/A tests, 5 would be winners, despite doing absolutely nothing

Minimize the chance of finding tests that look like winners,but have no true effect

@QubitGroup #QubitABtest

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Splitting tests too much can lead to false positives

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Get a second opinion

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Validate your results

We recommend a

95/5 splitto confirm your winning variation is still winning

“Most winning A/B test results are illusory”, Qubit 2014

@QubitGroup #QubitABtest

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A worked example: Imagine Mr Bean’s jelly beans shop

@QubitGroup“Most winning A/B test results are illusory”, Qubit 2014

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A worked example: Imagine Mr Bean’s jelly beans shop

@QubitGroup“Most winning A/B test results are illusory”, Qubit 2014

• Mr Bean has a 1% conversion rate

• He runs 20 different tests in one year

• There are 2 good tests with uplifts of 5%

• There are also 2 bad tests with uplifts of -5%

@QubitGroup #QubitABtest

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TruthCorrect method

Naive method

@QubitGroup #QubitABtest

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TruthCorrect method

Naive method

No. of tests 20 20 20

Reported winners 2 3 9

@QubitGroup #QubitABtest

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TruthCorrect method

Naive method

No. of tests 20 20 20

Reported winners 2 3 9

After validation 2 2 9

@QubitGroup #QubitABtest

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TruthCorrect method

Naive method

No. of tests 20 20 20

Reported winners 2 3 9

After validation 2 2 9

Reported uplift 2 x 5% 2 x ~5%

9 x (5-40)%

@QubitGroup #QubitABtest

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Correct method Naive method

Reported uplift 2 x 5% 9 x (5-40)%

Delivered uplift 2 x 5% 2 x 5% + (? x -5%)

@QubitGroup #QubitABtest

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Correct method Naive method

Reported uplift 2 x 5% 9 x (5-40)%

Delivered uplift 2 x 5% 2 x 5% + (? x -5%)

Wasted resource None7 unnecessary

tests

@QubitGroup #QubitABtest

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Correct method Naive method

Reported uplift 2 x 5% 9 x (5-40)%

Delivered uplift 2 x 5% 2 x 5% + (? x -5%)

Wasted resource None 7 unnecessary tests

Customer experience

Improved Degraded

Your understanding

Improved Confused

@QubitGroup #QubitABtest

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Correct method Naive method

Reported uplift 2 x 5% 9 x (5-40)%

Delivered uplift 2 x 5% 2 x 5% + (? x -5%)

Wasted resource None 7 unnecessary tests

Customer experience

Improved Degraded

Your understanding

Improved Confused

Your manager Happy Angry

@QubitGroup #QubitABtest

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So, to conclude, those 5 mistakes are:

• Mistake 1: Not A/B testing

• Mistake 2: Not prioritizing tests properly

• Mistake 3: Not waiting for significance and sample size

• Mistake 4: Testing too many things at once

• Mistake 5: Not validating the tests after implementation

@QubitGroup #QubitABtest

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Questions you should ask your testing provider

• How do you prioritize tests?

• How do you calculate the sample size required?

• How do you account for the multiple testing problem?

• How do you validate winning tests?

@QubitGroup #QubitABtest

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How to run a successful A/B testing program

Melanie KyrklundSenior Ecommerce Analyst

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Testing programs have the potential to drive the online customer experience

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Understand your customers and respond to their needs

Optimize the user journey

Create personalized experiences

Quickly respond to market pressures

Rapidly align marketing & onsite experience

Validate development items

Every visit represents an opportunity to establish a relationship with a customer

Customer experience

Personalization Persuasion

Targeted offers

User experience,

functionality & content

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The reality of running a testing program

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For many organizations, testing represents a cultural change with many political and operational barriers to be overcome:

Resource & operational factors

Buy-in / Multiple stakeholders

Alignment with other business priorities and development plans

Instilling a data-driven culture

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A lot of testing is required to deliver results

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1/5 tests or 1/10 tests will deliver statistically significant wins

A rapid pace of testing is required to build a program that is delivering consistent results month over month

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Two factors are critical in building a successful testing program

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Approach Speed

MethodologyIdea Generation

Operational ProcessPrioritization

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The right approach to optimization

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Be inspired – every visit is an opportunity to establish a relationship with a customer

Use data to understand how visitors are using your website, what is working and what isn’t

Customer feedback to understand why visitors are not purchasing on your website

If working over multiple websites & countries, look for insight at local level first and then identify commonalities at a later stage

Persuasion research, understand the psychology of your customers

Supplement with quick UX testing

Thoroughly research & quantify each opportunity (visitors reached, expected uplift and incremental revenue)

UX optimization Personalization Persuasion

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Idea Generation

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Idea Generation

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Idea Generation

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Idea Generation

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Speed – Operational Processes

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Testing Tool selection : Map out the most efficient implementation process

Operational efficiency Pick managed services (analysis, implementation) as required

Prioritization

Build a test pipeline logging:

Idea & hypothesisExpected uplift & incremental revenue Implementation estimatesTest duration Prioritize tests weekly, to keep up momentum of testing and

focus on biggest wins

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Conclusion: A rapid, data-driven approach is required

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Testing programs have the potential to drive the online customer experience, however a rapid data-driven approach is required to deliver results

Plan the required resources upfront

Consider ease of use, test time to market and managed services in tool selection

Map out the most efficient end to end process to get tests live

Process supported by data-driven idea generation & prioritization

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Thank you

Any questions?

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