Etuma Customer Feedback Analysis - how to keep your customers loyal

Making sense of customer emotions

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Etuma Customer Feedback Analysis

Matti Airas, CEO and Co-Founder, Etuma

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• Brick vs. click • Competition crossing borders • Consumer trends are hard to

predict and short lived • Consumers tend to be less loyal

Companies Are Facing Ever Increasing Competition

There are things that your customers would like to tell

you: selection, emerging trends, operational

problems…

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If you only listened

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80% of all Data is Unstructured

“Vos emballages sont trop fragiles”

Please, send my purchase in a

stronger container next time.

My sunglasses were broken."

“Ensinnäkin toimitus kesti yli kolme viikkoa ja lisäksi tilaamani

aurinkolasit olivat vahingoittuneet."

“Your website does not have the

information to make a purchase

decision”

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Etuma turns open text into structured intelligence

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Etuma has solved the key problems in customer

feedback analysis

Real-time Reporting

Actionable Information

Ambiguous Language

Results can be used directly in decision making and are consistent over time for trend analysis.

Results are available immediately after the customer submits the

feedback.

Language is a living thing. Etuma uses a combination of machine

learning and manual optimization work by Etuma computational linguistics experts to keep the

system always up to date.

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Text to Statistics

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TOPIC-LEVEL SENTIMENT

SEMANTICS

PART OF SPEECH

MORPHOLOGY

LANGUAGE RECOGNITION

SYNTAX

Fast adaptation to any feedback channel and type!

Optimized for customer

experience!Relevant and consistent

analysis results!

Etuma understands language the same way a

human does

Optimized for customer experience Relevant and consistent analysis resultsFast adaptation to any feedback channel and type

LANGUAGE RECOGNITION

ENGLISH

please (ADVERB) send (VERB)

my (PRONOUN) purchase (NOUN) in (PREPOSITION)

a (ARTICLE) strong

(ADJECTIVE) container (NOUN)

send (VERB PHRASE)

my purchase (NOUN PHRASE)

stronger container (NOUN PHRASE)

“Please, send my purchase in a stronger container next time. My sunglasses were damaged.”

MORPHOLOGY& PART OF SPEECH SYNTAX

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Connect Feedback Channel(s)

Analyze and Enrich With Structured Information

Correlate, Model and Recognize

Patterns

Visualize, “Taskify”

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Easy to implement cloud service

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Optimize analysis based on historical data.

2.3.

Define roles and scenarios. Create users, design reports.

Connect feedback channels using web interfaces.

All you need to do is participate in one workshop

and implement the connectors.

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NEED • Lot of customer data but inability

to predict when customers are about to churn or reduce buying

SOLUTION • Predicting which customers are

about to leave the service or reduce buying and do personalized communication.

RESULTS • Reduce churn first year 1%, next

year 5%, 3rd year 10%

Lower Churn - Increased loyalty

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Thank You!

To continue discussion please contact matti.airas@etuma.com

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