Gaining Actionable Insights Out of Big Data: Couchbase Connect 2014

follow the Hippo trail

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Hippo CMS

Gaining Actionable Insights out of Big Data

TJ - Tjeerd Brenninkmeijer

Co-founder & CMO at Hippo

@tbrenninkmeijer


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Hippo CMS


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Hippo CMS


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Hippo CMS


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Hippo CMS


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Hippo CMS

Now

Vision

Future

Company

Visitors

Visitors

Company


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Dynamic Content Paradigm


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Hippo CMS


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Hippo in Brief

Open Source Java-based WCM platform

Personalized Experiences Across all Channels

Headquarters in Boston & Amsterdam


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A brief recap of Relevance

http://vimeo.com/64808537

http://vimeo.com/64808537


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Multi Channel Orchestration

Affiliates

and

PartnersSocial

Mobile

Web

Applications &

offline channels

Hippo

Repository


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Journey based Targeting

CUSTOMER JOURNEY

Situation

Anonymous

Persona

Engaged

Stages of Customer Intimacy

Accumulated Targeting Techniques

History

Known


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Holistic Relevance

Visitor Profile

HISTORY

Your existing customer data

Enrich with external data


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Registration

1) Visitor - entity making HTTP requests

2) Collector - records data about a visitor or his behaviour

Example: location collector (GeoIPCollector)

3) Targeting Data - all data about a specific visitor

Example: IP address is located in Amsterdam


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Matching

1) Characteristic - a type of fact about visitors

Example: "comes from a city", "experiences a type of weather"

2) Target Group - the specification of a Characteristic

Example: "comes from a European city", "comes from Amsterdam"

3) Persona - one or more target groups that describe a certain type of visitor

Example: "Jim, the European urban consumer", "Alice, the Pet owner"


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Real time analytics


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Architecture Hippo CMS


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Architecture delivery tier Hippo CMS


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Architecture delivery tier Hippo CMS & relevance


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Assessment criteria NoSQL

SCALABILITY

PERFORMANCE

MATURITY

REPLICATION

CACHING MODEL

CONSISTENCY MODEL

DATA MODEL

RELIABILITY

QUERY MODEL SUPPORT

MONITORING

SIMPLICITY


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Relevance Scoring

Scores of Personas &

Characteristics

X Y Z

1

2

Z

Scores of Personas &

Characteristics

X Y Z

1

2

?


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Solving the problem

Averages

1

2

2.1

1.7

1.2

Normalization

X Y Z

1

2

Z

2.1

1.7

1.2


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"We chose Hippo for one reason:Targeting content to consumers is paramount."

Laurent Vonach, Online Manager International Marketing, Weleda


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“We use relevance to get better candidates, by looking at the entry page, search terms, geo location and click behavior.”

Stephan Bon from Randstad


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“We created a digital experience that evolved around the citizen.”

Ruud Paulusse from the Dutch Police


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What’s next


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CMO <3 CIO

60%Big opportunity

52%We can’t do it


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OneFalse positive

TwoFalse negative


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“Patternicity: when the cost of making a Type 1 ErrorIs greater than making a Type 2 error”

Marketeerswired

Type one

errors


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Being Right isn’t always right


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Hippo is developing algorithms to find patterns in visitor

data that help marketeers understand how they can further

improve their visitor’s Web Experiences

Based on historical and real time data, the influence of

changes on visitor segments will be tracked and reported

back to the end user


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A/B Test This

No, push live right

away

X

GO


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25% sees the old version (B) 75% sees the new version (A)


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Persona: Impulse

Buyer


Persona: Loyal

Customer

Did you know..?

You changed the homepage last week; which was awesome

for our Impulse Buyers. Their conversion went up a massive 23% !

But unfortunately - our Loyal Customers are really unhappy with

the change. Their conversion went down with 15%.. :-(

Hey, Martin - did you know?

So - do you want me to keep the change for Impulse Buyers but stick with the

previous version for Loyal Customers?

Yes NoActionable

Insight


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Question & Answers

Thank you so much for your time.

@oneHippo

www.onehippo.com

Data & Analytics

Gaining Actionable Insights Out of Big Data: Couchbase Connect 2014