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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

DOW CONFIDENTIAL - Do not share without permission

WHO WE ARE - DOW’S ADVANCED ANALYTICS GROUP

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Transla'ng Data into Profit


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


HOW CAN DOW UTILIZE SOCIAL MEDIA?

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•  Strengthen our value proposi=ons •  New opportuni=es – new markets and new improvements


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


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?


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


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


§  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


§  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


§  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.


§  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


§  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


EACH STEP IN THE PROCESS CALLS FOR VARYING DEGREES OF COMPLEXITY AND TOOLS



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)


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


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


§  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


THANK YOU! QUESTIONS?

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ADVANCED ANALYTICS TRANSLATING DATA INTO PROFIT

THANK YOU

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