San Francisco August, 2014

Turn Customer’s Complaints into Revenue

Cost  of  Bad  Service  in  the  US  

2  *  Forbes  –  2013/06/26  –  Sales  Force  Report  2014  

83Bn  

Why  is  it  so  difficult  to  improve  Customer  Service?  

1  /  3  

Reading  and  classifying  is  not  so  simple  

4  

SemanHcs  

Grammar  

PragmaHcs  

assigns  meaning  to  each  term  

helps  you  understand  how  terms  relate  to  each  other  (structure)  

incorporates  the  context  to  decide  the  real  meaning  of  term  

[  ]  

5  [  ]  

We invented Wholemeaning

A  complaint  is  a  giS  

6  [  ]  

Listen  1  

Understand  2  

Act  3  

 Improve  4  

Monitoring  Emerging  Issues  

Feedback  on  Product  &  Services  

Churn  &  Customer  Loyalty  

AnalyHcs  Stakeholders  Call-­‐to-­‐AcHon  

Customers  communicate  and  want  answers  

7  [  ]  

Reduce  Complaints  50%  in  4  months  

We  provide  the  right  answer  for  you  

8  [  ]  

! Attributes!/!Players Whole meaning

Bag-of-Words

Semantics Packages

Call Centers

Capabilities

Specific Domain KnowledgeUnderstanding of complexitySentiment & Emotion Analysis

Business Applications

Churn DetectionRoutingRoot Cause AnalysisMonitoring

We  address  real  customer  issues  

9  [  ]  

SaaS  Service  and  ready  to  go  

10  [  ]  

2015à  

POC 2013

2014

Leading  Team  &  Advisors    

11  ADVISORS  

Team  Leader   Consultant   BeauHful  Mind   Biz  Development  

VP  Sales   MarkeHng  Chairman  

InnovaHon  in  Complaint  Management  

12  [  ]  

Text Classification Complexity!

Volume of Messages!

2  level      

Contact  Center  

       

1  level   Semantic Tools!

docs!

interviews!

surveys!

news!

chats!

blogs!trancripts!

web search!

complaints!

reviews!

Low  

High  

Low   High  

1. 1111. POS TAGGING & SPELLING CORRECTOR  

>>  Hidden  Markov  Models  (HMM)  

2. DEPENDENCY PARSING  >>  Support  Vector  Machines  

5. LINGUISTIC CONTEXTUAL MODEL >>  FP-­‐Growth  &            Finite  State  Machines  

4. ANAPHORAS & COREFERENCES RESOLUTION >>Decision  Tree  &  Finite  State  Machines  

3. SEMANTIC ANALYSIS >>  Finite  State  Machines  

6. CONTEXTUAL SENTIMENT ANALYSIS >>  Contextual  weighHng  

Wholemeaning’s Architecture