UNDERSTANDING OUR CUSTOMERS' CUSTOMERS' CUSTOMERS' NEEDS TEXT ANALYTICS FOR B-TO-B BUSINESSES
Michael Dessauer and Justin Kauhl Dow Advanced Analytics June 21st 2016 1:30-2:15 PM
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WHO WE ARE - DOW’S ADVANCED ANALYTICS GROUP
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Transla'ng Data into Profit
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Chemical Industry Value Chain
MARKET LISTENING DOWN THE VALUE CHAIN
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Chemical feedstocks derived from raw materials • Petrochemical • Brine • Pulp
Polymers Specialty Brand Owner
Consumer Basic
Chemicals
Upstream Downstream
Tradi=onal communica=on chain
Dow’s own market listening creates a downstream feedback-‐loop Market Listening
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HOW CAN DOW UTILIZE SOCIAL MEDIA?
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• Strengthen our value proposi=ons • New opportuni=es – new markets and new improvements
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STRENGTHEN VALUE PROPOSITION
Dow can better assign value to our product’s improvement to an end product’s aspect
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How high does the market’s customers value the product aspect(s) that Dow’s technologies enhance?
Noisy Data –
Unrelated to topics of interest
Aspect C 1%
Aspect B 1%
Aspect A 1%
Iden'fied Product Aspect Categories
Quan=fying volume and sen=ment by product aspect
Brand-‐specific data
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NEW OPPORTUNITIES
Can Dow identify an opportunities within a new market?
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Market-‐specific data
General and market-‐specific sen=ment scoring
Filter on nega=ve posts
Iden=fy topic clusters Can Dow develop
solu=ons for these nega=ve topics?
Can we use market-‐specific social media sen=ment to iden=fy consumer (and upstream) needs?
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Targeted Text Collec=on
En=ty, Topic, & Sen=ment Extrac=on
Aggregate & model data for insights
Insight Delivery
TEXT ANALYTICS FOR MARKET LISTENING
General phases of market listening analysis
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Internal & Vendor tools to search and collect text
Create domain-‐specific topics and sen'ments
Use the derived structure to model trends / insighHul characteris'cs
Deliver search interfaces and interac've dashboard
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ARE WE SUCCESSFUL IN DELIVERING VALUABLE MARKET LISTENING INSIGHTS?
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1. Defining a key ques=on…”go look out in the internet” 2. Data availability 3. How to make sense of it…Categoriza=on 4. Do we have the right tools? Do we have the right technical
ability? 5. Communica=ng results – visualiza=on, level of interac=vity 6. Client, SME engagement
Our experience – It depends – We have successes and failures so we’ll try to model the characteristics of successful projects and unsuccessful projects
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§ Brand-specific social media data sources
§ Leveraged several tools for Sentence parsing Part-of-speech tagging Topic identification
§ Aspect-specific sentiment weighting
§ Simple delivery
CASE STUDY #1 Strengthening our Value Proposition
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Data Repository
§ Domain Expert targeted sources on Twitter and Facebook
§ Data extraction using Python through public APIs
§ Download to harmonized central repository
CASE STUDY #1 – TEXT COLLECTION
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§ Natural Language ToolKit (NLTK) leveraged for sentence level parsing
§ SAS Text Miner used to develop sentiment classes
§ Sentences weighted on sentiment content
CASE STUDY #1 – SENTIMENT LABELING
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Seed Terms
Posi=ve Class
Neutral Class
Nega=ve Class
Training Corpus
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§ Simple, Ad Hoc, delivery method
§ Each sentence in each post had an individual sentiment score for each of the aspects
§ Sentiment was rolled up to the Brand level and reported based on relevance to the post
CASE STUDY #1 – RESULTS DELIVERY
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Brand 1 Brand 2 Brand 3
Aspect
post sentence Topic 1 Score Topic 1 Sentiment Topic 2 Score Topci 2 Sentimentpost 1 sentence 1 0.78 -‐2.33 0.05 1.34post 1 sentence 2 0.03 1.22 0.88 -‐1.33post 1 sentence 3 0.34 0.2 0.33 0
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§ Much more ambitious than Case Study #1 § Loosely defined product aspects § Loosely defined product lines § Target Brand dictionaries § Grammar-based sentiment § All done in a Non-English language (Chinese) § Required interactive visual delivery
CASE STUDY #2
Novel Opportunity Identification
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§ Blog posts on regional Chinese sites
§ Many comments present in addition to main post data
§ Large quantity of associated files and images
§ Text extracted, cleansed, and stored in structured intermediary files
CASE STUDY #2 – TEXT COLLECTION
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§ Domain Experts leveraged to build and refine brand and aspect terms list
§ Native Chinese speakers aided in translation of terms lists and initial grammar build
§ Stanford Chinese tokenizer used to split out tokens
§ Co-occurrence strength between brands and aspects measured primarily using distance.
§ Iterative process to identify new brands/aspects when they appeared
§ Overall ~75% accuracy
CASE STUDY #2 – ENTITY EXTRACTION
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Product A applies very easily. Product B is not really very good. Product A, as opposed to Product B, is much beber as performing Func=on X.
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§ Brand-Aspect relations assigned sentiment
§ Scores for each brand by aspect category aggregated to create a competitive scorecard across the domain
§ Rough comparisons and generalizations can then be made at a glance
CASE STUDY #2 – DATA AGGREGATION
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0
2000
4000
6000
8000
Aspect 1 Aspect 2 Aspect 3 Aspect 1 Aspect 2 Aspect 3 Aspect 1 Aspect 2 Aspect 3
Product A
Product A
Product A
Product B
Product B
Product B
Product C
Product C
Product C
Aggregate Sen=ment
Posi=ve Nega=ve
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§ Created an interactive dashboard
§ User can drill down into the aspect/brand relationships at will
§ Easy sanity checks - All supporting data is provided in a linked view
CASE STUDY #2 – VISUALIZATION
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Tableau Dashboard
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EACH STEP IN THE PROCESS CALLS FOR VARYING DEGREES OF COMPLEXITY AND TOOLS
Targeted Text Collec=on
En=ty, Topic, & Sen=ment Extrac=on
Aggregate data for insights
Project Delivery
Market Listening projects must begin with some initial set of requirements to give the analyst guidance on: • Where to look • What we’re looking for • What questions we’re trying to answer
The answers will determine the appropriate process and tools to use for the project (or if we should even be doing the project)
Targeted Text Collec=on
En=ty & Topic
Extrac=on
Visualize data for insights
Deliver User Interface
Internal developed & Vendor tools to search and collect text
Crawl Extract from data aggrega'on:
Tabular file URL extract Vendor data portal
Walk Social Media / Web API Twiber, Facebook returns as JSON files Leveraging exis=ng pipelines
Run
Web crawlers: Directed search within domains of interest, or using a specific “depth” of links harvested Sophis=ca=on can vary greatly Custom-‐built data extrac=on
Fly Big Data Collec'on Data integra=on which pulls in mul=ple collec=on methodologies at once Indexing / staging data for fast search Can handle near-‐real =me inges=on and Big Data volumes POC in progress
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Maturity
Com
plex
ity
Valu
e
Ini=al discovery phase
Cases where specific authors / organiza=ons are known
Data spread across non-‐standard aggregators or source not known
When employed in our project
Large-‐scale collec=on requires mul=ple extrac=on methods + big data storage
Crawl Term coun=ng “bag of words” with no parsing
Walk Parsing documents or terms Data dic=onary for categoriza=on Sentence-‐level analysis REGEX logic
Run
All Walk Components + rule-‐based methods for custom categoriza=on En=ty extrac=on: -‐ organiza=ons, people, geography -‐ noun-‐phrase analysis
Fly All Run components + machine learning for categoriza=on and sen=ment analysis: Deep learning algorithms: -‐ IBM Watson -‐ Tensor Flow using grammars
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Maturity
Com
plex
ity
Valu
e
Ini=al topic detec=on & explora=on Simple analysis
Sentence categoriza=on Term-‐topic taxonomy
Simple term-‐topic categoriza=on not specific enough Find “unknowns” in text
When employed in our project
Annotated / labelled data available features do not need to be explicit Data volume is high
En=ty, Topic, & Sen=ment Extrac=on
Create domain-‐specific topics and sen'ments
Aggregate & model data for insights
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Maturity
Com
plex
ity
Valu
e
Ini=al results shared with clients
Voice of the customer New opportuni=es
Trend forecas=ng Aspect-‐Brand analysis Topic iden=fica=on
employed in our project
Crawl Word Cloud Histogram Time Series Pie charts
Walk Trend growth (volume and velocity) Co-‐occurrences
Run
Time series forecast Custom Sen=ment Analysis Sta=s=cal topics Machine learning models Linkages (subject-‐ac=on-‐objects)
Fly Dynamic Topic Models Discourse Analysis Deep Learning models
Use the derived structure to visualize trends / insighHul characteris'cs
Not current employed in models. Plans for future projects
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Maturity
Com
plex
ity
Valu
e
• Influencer networks
• Data delivery • Social Media
Landscape
• Priori=zed raw data by sen=ment/topic
• Create pivots on text data
• Sen=ment analysis • Influencer analysis • Hierarchical
analysis • Trend analysis
employed in our project
Crawl One-‐off deliverable Power point Raw data
Walk Structured Excel File Topic scoring sen=ment scoring
Run
Interac've Dashboard: Tableau dashboard allows user to use interac=ve plots to analyze brand-‐aspect level sen=ment Limited in text volume
Fly Search & Analysis Portal Perform text, en=ty, and plot searches Large volumes of analyzed text User-‐driven analysis (machine learning feedback loop)
Analy=cs Delivery
Not current employed in models. Plans for future projects
Deliver search interfaces and interac've dashboard
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§ There is proven value and a growing appetite! § Businesses need assistance translating their needs into actionable
projects § Dow’s Advanced Analytics team must continue to expand our capabilities
and knowledge (hopefully through follow-up discussions with each of you) § Choosing the right analysis path is 80% of the battle § Business must be engaged throughout
KEY LEARNINGS FOR B-TO-B MARKET LISTENING
What we’ve learned over the past three years…
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§ Dow’s Social Listening team assists businesses with identifying appropriate resources
DOW’S ROADMAP TO MARKET LISTENING “Triage” approach to identify the appropriate resources
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Enable User Consulting Assisted Custom Build
Triage – Determine Best Fit
2 days after discovery workshop
Self Service Use appropriate Dow-‐licensed tool
Dow Internal Service Use Dow internal consul=ng team’s exper=se
External Consulting Use when Dow does not have internal exper=se
External Service Custom-‐built applica=ons combining internal and external resources
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THANK YOU! QUESTIONS?
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ADVANCED ANALYTICS TRANSLATING DATA INTO PROFIT
THANK YOU
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