Content Science ReviewA Case Study in Engineering

Personalization

Presented By Darin Wonn01/01/15

Agenda• About Content Science and Me

• Why is Personalization Important?

• 3 Lessons Learned When Implementing

• Next Steps to Get Started

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Content Science has advised Fortune 50 companies, niche brands, learning institutions, nonprofits, and government agencies since 2010.

Our Focus Areas

Content Analysis + Evaluation

Understanding your content

situation

Content Strategy

Envisioning the future of your

content

Content Experience

Planning the right content for the

right users across touchpoints

Content Systems + Leadership

Aligning people, process, +

technology to sustain your

content

+ + +

Executive Director of Operations

Product Manager Role for Content Science Review

15+ Years in UX + Product Management

MS in Human-Computer Interaction from CMU ‘04

About Me

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Why is Personalization Important?

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What is Personalization?

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serving unique content to a user based on something we know about him or her

Example – Doggyloot

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serving unique content to a user based on something we know about him or her

Not just a priority for content teams• Personalization is a top 3 priority for 80% of companies

(Accenture Business Trends, 2015)• #1 priority for 40% of companies

And its paying off• 60% report positive results from personalization

investments (Accenture Business Trends, 2015)• Businesses that personalize web experiences see an

average 19% increase in sales.(Monetate, 2014)

Personalization is a Top Business Priority

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A new focus on customer experience• 90% companies believe customer experience will be

their primary basis for competition by 2016 (Gartner, 2014)

• Up from 40% of companies in 2012

IF YOU’RE NOT FOCUSED ON PERSONALIZATION TODAY YOU WILL NOT COMPETE TOMORROW

Why Now?

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Changing user expectations for personalization• User Expectations - 1 in 3 users express frustration

when retailers don’t factor in purchase behavior (MyBuys, 2015)

• Changing Attitudes to Privacy - 3 out of 4 users are willing to allow retailers to use store purchase data for personalization purposes

WHY NOW?

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CMS improvements make it reasonable – even if you’re not Amazon

WHY NOW?

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Pennsylvania Tourism Board – California User

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Pennsylvania Tourism Board – Pennsylvania User

Case Study: Content Science Review

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Content Science Review

Where insights about content + business intersect

9,000 page views per month and growing

3+ page views / session

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Goals for Personalization

Have users read and watch more content• Great user experience if they receive valuable content

• Convert to premium content paid subscribers

• Increase in ad-based revenue

• Measurement• Avg Page Views / Session

• Returning Visitors

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Reality: Personalization is Challenging

3 Big Lessons We’ve Learned 1. Build features that capture explicit customer data

2. Start with rules-based logic

3. Gather more customer data

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#1: Build Features to Capture Data

A personalized library allowed us to gather valuable first party data about users• Explicit preference settings

o Favorite topics

o Saved and shared articles

• Passive tracking of user behavioro Browsing activity and searches

• Great user experience

• Supports selling advertisements

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My LibrarySave articles, follow favorite topics and subscribe to newsletters.

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#1: Build Features to Capture Data

UI needs lots of areas for users to set preferences• NOT just part of initial account setup

• Opportunity to merchandise registration

• Complex to maintain all of the state logic and interactive pieces

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Examples of Embedded Preferences

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Recommendations at end of every article

Article Header

Examples of Embedded Preferences

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Recommendations at end of every article

Article Listing

#1: Build Features to Capture Data

Registration must be marketed• Registration is necessary for one-to-one

personalization

• Registration and conversion rates are an important metric

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Opt-In Window on Exit

#2: Start With Rules-Based Logic

Rules-based logic for recommendations are manage-able, understandable and testable• The Amazons and Netflixes of the world use an

algorithm-based engineo Data scientists required to understand and tweak algorithms

o Require large amounts of traffic

• Cost-effectiveo Third-parties charge for ‘secret sauce’ algorithms

• Accurate tagging of content is critical

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Recommended ArticlesRecommendations are based on previous activity and preferences.

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#2: Start With Rules-Based Logic

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• Followed topics, saved articles + other activity is considered• Rules incorporate a structured taxonomy of related topics• Account for missing user data• We can tweak and adjust based on usage

Recommendations are driven by rules-based logic

#3: Gather More Customer Data

Opportunities to gather more customer data for better recommendations• First party data

o Geo-targeting

o Referral source

• Third party datao Social authentication

o Data brokers

• More data means changing from rules-based approach to algorithm-based approach

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AirBnB Social LoginFilters listings that have been reviewed by Friends

or are mutual Friends with the host.29

Next Steps

Get Started NOW• Your competition is already working on it

• It’s complicated and will take time to mature

• Personalized library is a great place to start to capture customer data

• Crawl with rules-based logic

• A limited number of data sources will make rule-based personalization easier

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To Help You Get Started

1. Read Taking the First Steps on the Path to Personalization by Sue Klumpp on Content Science Review

2. Register for free to Content Science Review to check out the personalization

3. Subscribe to Content Science Review

25% Discount: CSFriends

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