Speaking in the language of your customer Simran Bagga and Fiona McNeill, SAS
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1 Speaking in the language of your customer Simran Bagga and Fiona McNeill, SAS #analyticsx
2 Real World Example: Manufacturing
3 Real World Example: Transportation DAYS to manually categorize. Act after that. HOURS to automatically categorize and act on priorities
4 Real World Example: Banking Payments made and received by transfer by Customers Wedding Thousands of Customers got married in May - Special gift - Mortgage discount 3 million per month 40% of mortgage repayments by bank transfer. No BPM mortgage held. Mortgages - Remortgaging proposal
5 Proactive vs. Reactive Use Reactive Proactive Customer engagement Who s doing the talking? Agent vs Customer Customer experience What is the conversation trending towards? Identify topics, trends What was the conversation about? Direct the behavior What change do you want right now?
6 Challenges Data storage Text data noise Talent gap Proliferation of choices
7 Text Data Considerations Machine languages. Human languages. Interactive communication. Continuous streams. Every aspect of the business
8 Big Text Data Considerations Size is relative. Think cost. Data diversity. Identify relevance. Analyze all. Determine when.
9 Importance of native interpretation Native tongue identified from terms Swedish: Ingen ko på isen - no worries or no cow on the ice Sentence level phrasing differences between languages Treat as pipelines, consolidate results to common schema
10 Why linguistics matters NLP (Natural Language Processing). Requires science of linguistics: Detection & Extraction Categorization Co-reference resolution Multi-word identification Syntactic models & grammars Logical representation Fact extraction Lemmatization & stemming Reusable entities Sentiment determination Language identification Term relationships Value Reduce noise Amplify context and meaning Identify negations and subjectivity
11 Automating text processing Mine for text processing (natively) Active learning Machine Learning for text discovery Processing closer content source expedites insights System generated communication Coming: Search without pre-defined schemas
12 Bridging linguistics and machine learning Cognitive Computing Enable natural interactions with software Reason on input. Explain on output.
13 Add a dose of reality Augmented Reality Virtual Reality Seeing things in their natural state Deriving state with the input of things
14 Where does VoC matter? #analyticsx SAS Text Analytics Marketing, Sales & Service Operations Finance Security, Compliance & Legal R&D Target customers Reduce attrition Content relevance Boost engagement Empower field personnel Search efficiency Prioritize activity Identifying root cause Expedite triage Identify emerging trends Improve revenue allocation Contract consolidation Inform investments Economic forecasting Detecting fraud Substantiating governance Identifying cyberthreats Protecting patents Improve design New product ideas Exclusivity rights Retaining skilled knowledge Competitor insight
15 Text Analytics Application Considerations Getting started. How you want to end. Agility to enhance. Go deep. Grow broad. Expand into global VoC. Localization matters.
16 SAS s Immediate Text Analytics Future SAS Analytics as a Service & open APIs Natural Language Interaction, Narrative summaries, document summaries, Image processing, classification, face detection Embeddable Search Unified text analytics offering Enhanced model deployment.. And more
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