Conversion 2015 - Jamie Brighton - Adobe

Emerce Conversion: The personalisation spectrum Jamie Brighton | Strategic Marketing, Adobe

Consumers expect a relevant, engaging experience – data driven personalisation from unknown visitor to authenticated customer is the key.

Key Takeaways

Personalisation should be approached as a

strategy.

1 Build a solid

optimisation platform by avoiding common

mistakes.

2 Use statistical methods

and automation to enhance your

personalisation.

Adobe’s definition of personalisation

Personalisation is the use of

data to deliver a relevant and

engaging experience to a

consumer across channels and devices and the ability to measure its impact

10% Conversion rates

14% Lift in RPV

Why this is important

19% Uplift in sales

14% Click-through rates

Advocacy Cross-sell/up-sell Awareness Consideration Self-identification

Authenticated Anonymous

Rules-based targeting of known customers

Campaign planning & orchestration

Cross-channel execution

Integrated known customer profile

Data – Content – APIs – Core Services

Algorithmic-based targeting & decisioning

A/B & multivariate testing

Conversion optimization

Product recommendations

Web/Mobile Personalization

Display Advertising Personalization

Direct Personalization

Advertising personalization

Audience Management

Anonymized data

Standard customer types

Anonymous visitor Visitor that engages with the brand through paid, owned or earned media without providing PII.

Authenticated customer Visitor that has provided PII to the brand (and consent to use that PII in communications) and is receiving a direct communication or is authenticated/logged in.

Standard data types

First-party Data collected directly by the organization.

Second-party Data shared by a trusted source.

Third-party Aggregated data from other sources.

•  Web behavior •  Survey responses •  PII examples:

−  Email address −  Postal address −  Telephone number −  Social Security number

•  Data shared between a credit card company and a co-brand partner such as an airline

•  The airline provides loyalty program data to the credit card company

•  The credit card company provides spend pattern data to the airline

•  May be PII

•  Data purchased from providers like Bizo, Exelate or Acxiom

•  Demographic data •  Spend-pattern data •  Geographic data

Key Takeaways

strategy.

1 Build a solid

mistakes.

personalisation.

Key Questions to Ask: •  Where are the 800lb gorillas? •  Which pages are cash cows? •  Do I have any low performers?

Potential testing pitfalls and how to avoid them

§  Choosing the wrong metric

§  Not designing your test

§  Stopping the test early

Click-through rate or time-on-site may not measure what you think

Time Spent on Site Click-Through-Rate

Free iPad!!

Choose the metric closest to profit that has enough traffic

Add to Cart

Revenue

Profit

Lifetime Profit

Higher traffic Less variance

Closer to business goals

Test design: you have five levers

§  Minimum detectable lift §  Statistical power §  Statistical confidence §  Number of experiences

§  Sample size (time)

Use a test duration calculator

adobe-target.com/testcalculator.html

After full test period B beats A

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Experience A

Experience B

Experience A

Experience B

Effect of ending tests at arbitrary stopping points

Stopping a test arbitrarily dramatically increases false positives

Each check of the test is another chance for a false positive

A/A Test Simulation

A A vs.

Continuous Monitoring Simulation of 100 tests at 95% Confidence Level

Continuous monitoring can greatly inflate your false positive rate

At 95% confidence level we expected 5% false positives

With continuous monitoring, we got 34% false positives

Checklist for calling a winner or loser

þ  Did you run the test for the time the test duration calculator said?

þ  Did you run the test over a representative time period?

þ  Were the results consistent over time (graphically)?

þ  Was the reported confidence above the level input in the calculator?

§  Choosing the wrong metric >

§  Not designing your test >

§  Stopping the test early >

Optimize to revenue

Use a calculator

Preset how long to run

Automated Personalisation Methods

Determines predictive attributes toward a specific

conversion event to understand high value

segments.

1 Identifies relationships

between groups of visitors to show strength between

segments.

3 Propensity Modeling:

Attributes Cluster

Methodology

Ranks your visitors from low to high based on

a specific conversion event.

2 Propensity Modeling:

Uses your existing audience profiles to serve relevant content to the individual

based on high value behavior.

4 Automated

Personalisation

Propensity Modeling – Predictive Attributes

Email Paid Search

Direct Load

Paid Search

Direct Load

Natural Search

Display

Social

Affiliates -2 1 2 Pick variables

that you can action off of

within testing.

Propensity Modeling – Predictive Attributes

Sample Attribute Data

Shirts

Pages Views

Return Visits

Category Affinity

Referrer Prominence of recommendations by category on product detail pages 1

2 Encourage email newsletter signup after 2-3 page views

3 Create an email campaign for cart abandoners

Propensity Modeling – Clothing Retail Site

Challenge §  Are there more actionable attributes

within search?

§  Ran a second propensity model.

Result

§  Correlation between visitors sorting by size and placing an order.

§  Tested the default sort setting to be by size.

§  5% lift in order conversion rate.

200,000,000

100,000,000

60% 70% 50%

300,000,000

400,000,000

90% 100% 80% 10% 20% 40% 30%

Propensity Modeling – Visitor Rank

Challenge §  The Jazz wanted to sell more season

passes which are very profitable.

Approach §  Ran a propensity model with CRM

data and Analytics. §  People more than 75% likely to

purchase received an invitation to a special event at the stadium.

Result §  60% of those who attended

purchased a season pass.

Cluster Methodology – Men’s Fashion/Lifestyle Publishing Example

Mostly Mobile Brand Casual One and Done Brand Addicts

% of Total Population: 44% 23% 27% 6%

% of Total Visits: 16% 12% 19% 53%

Visits per Month: 1.00 1.18 1.52 5.20

% of Total Page Views: 11% 8% 32% 49%

Page Views per Month: 10.93 13.47 46.61 155.81

Page Views per Visit: 10.69 10.38 25.91 14.17

Time Spent per Visit: 2.86 3.44 9.93 13.01

Time Spent per Month: 2.86 4.04 12.99 39.84

% Mobile: 0% 99% 7% 28%

% of Searches: 0% 0% 45% 55%

% of Logins: 1% 2% 3% 94%

Brand Casual

% of Total Population: 27%

% of Total Visits: 19%

Visits per Month: 1.52

% of Total Page Views: 32%

Page Views per Month: 46.61

Page Views per Visit: 25.91

Time Spent per Visit: 9.93

Time Spent per Month: 12.99

% Mobile: 7%

% of Searches: 45%

% of Logins: 3%

Brand Casuals: Encourage return visits to drive page views: §  Newsletter signup §  Email testing §  Display retargeting

HIGH PAGE VIEW CONSUMPTION PER

LOW RETURN VISITS

Home Page

Fashion Shows

How To

News/Politics

Sports Food/Travel

Style Women Entertainment SD_M Cars/ Gear

80% One and Done

Mostly Mobile

Brand Casual Brand Addicts

Percent of Total Page Views by Site Section

70% OF FASHION AND

60% OF STYLE PAGE VIEWS

6% VISITORS

Brand Addicts: Promote style content on top entry pages: §  Targeted navigation §  Category-based Recommendations

Algorithm – Dynamic Offer Population

General Score

Algorithm Targeted Offer

User Score

Automated Personalization – Testing Strategy

Choose an optimizing metric that can support

your traffic and your site goals.

Run on high traffic pages of the site in a high impact area where the visitor has

a high level decision to make.

Make your offers unique to allow the

algorithm to differentiate.

Enrich your visitor segmentation strategy

with Target profiles.

Key Takeaways

strategy.

1 Build a solid

mistakes.

personalisation.

VT1 – RBS Audio 01:43 Plays auto

Jamie Brighton jbrighto@adobe.com Twitter: @jamiebrighton LinkedIn: jamiebrighton

Conversion 2015 - Jamie Brighton - Adobe

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