understanding customer perceptions and trends from unstructured data for better business decisions

Understanding Customer Perceptions and Trends from Unstructured Data for Better Business decisions

Presented by: Madhur Bhatia, Data Scientist

Swaroop Johnson, Consultant (Analytics)

March 17, 2016

Enabling incremental insight generation through comprehensive coverage of data sources

© 2016 Blueocean Market Intelligence

New Product Development

• Key opinion leader blogs

• Feedback/comments

• Product reviews

Customer Interaction Analysis

• Call centre data

• Feedback/comments

• Call logs

Digital Research

• Technology blogs and forums

• Technical papers

• Journals and magazines

Customer Experience Management

• Microblogging sites/social media data

• Feedback/user comments

• Product reviews

Brand Monitoring

• Customer feedback

• Survey Data

• Social media/Twitter data

Fraud Detection

• Emails

• Historical claims documents

• Financial statements

of data generated is unstructured in nature, and growing exponentially

of business executives complain that they have too much unstructured text data and are unable to interpret them

There are incremental insights to be generated from the text data given willingly by your customers

Our text analytics services cover all phases of the product life cycle, and the customer journey

Imagine being able to predict future trends before it actually happens

And then design a product based on customer feedback, and product reviews

And track each customer touchpoint to identify customer queries and evaluate your customer experience program

To evaluate net sentiment of your customer base with deeper analysis and insights than ever before

Discover a new side of story telling by

discovering hidden insights from unused

sources of data

80%

40%


Brand Monitoring

Text Analytics

Sentiment Analytics

Semantic Advertising

Digital Research

Text Classification

Survey Analytics

Understanding Sentiments and Buzz

Identifying the positive/negative impact of content publishing and online marketing strategy

Brand Monitoring

Keeping a tab of the health of brand image by analyzing trends over a period of time and identifying key areas for concern

Some practical applications of text analytics

Customer Experience Management

Reducing time and costs involved in the identification of key business problem (customer and employee issues)

Digital Research

Reducing the time related to topics and document searches by grouping documents


Examples

• Extract interesting and non-trivial patterns or knowledge from unstructured text documents

• Identify different aspects /components of a single problem

• Overall sentiments for a specific aspect under the business scenario can be extracted

• Determine the underlying conditions that give rise to the reasons for the problem/phenomenon

“the restaurant is situated at an excellent location and the food is very delicious. there are fresh barbeques served over the table as starters including vegetables mutton chicken fish and prawns. all too good to enjoy. the main course and desserts are available over the buffet table and the food variety is quite a lot to choose from both between veg and non-veg. well maintained and good seating arrangement ideal for business parties or with friends. the only drawback was that the seats are limited and during rush hours the guests need to wait until they get their turn. however it was a very good eating experience along with work mates.”

Information extraction for deeper analysis of the business problem

Net Sentiment

0.489 (indicates positive sentiment)

Different aspects of the user review

Location Excellent

Food Delicious

Reservation Bad

Experience Good

Service Good

Entities

Variety Excellent

Non veg Good

Main course Bad

Barbeque Fresh*

Desserts Good


• The algorithm used for the analysis identified words in each tweet that were also present in the dictionary used

• Then a score for each tweet on the basis of the polarity of words matched was calculated

• A word cloud for the locations from which positive, negative and neutral tweets were made

Approach

Banking, Financial Services, and Insurance

Industry:

Business Challenge

• The client wanted to analyze the sentiment of its customer base across locations in UK for Q3 2014-2015

• Additionally, automate the classification of tweets to positive, negative, and neural sentiments to generate further insights on locations, and use the exercise as a platform to aid Net Promoter Score calculation using sentiment analysis

Country: United States

• The dataset consisted of 13581 documents, of which 7499 were classified as positive, 2392 as negative and 3740 as neutral

• The scores obtained suggested an accuracy of over 80%

Results

• Identified the locations that needed immediate attention as well as compared staff and services across geographies

• Created a roadmap to calculate NPS and understand the common pain points of customers by grouping tweets on basis of polarity

Business Impact:

Sentiment analysis to calculate net sentiments for an insurance service provider based on tweets


54.85%

17.61%

27.54% Positive

Negative

Neutral

Net Sentiment for Q3 2014-2015

Results

Locations with most number of positive tweets

Locations with most number of negative tweets Locations with

most number of neutral tweets

Sentiment analysis to calculate net sentiments for an insurance service provider based on tweets


Introducing

• User friendly, flexible user interface: One touch data pre-processing (removal of junk/stop words/URLs, lemmatization etc...)

• Automated text classification: Query classification for deeper and better understanding of customer queries

• Makes text classification 60% faster than traditional methods

• Preview screens for data extraction, and data cleansing

• Sliders and input boxes for easy definition of parameters and data split

• Pre-defined classification and sampling performed with the click of a button

• Get Accuracy Score, Confusion Matrix Score and Classification Report to be downloaded in .pdf/.doc format

Unstructured Text Analytics Platform (UTAP)


Platform Demo

Low cost per comment, making it economic for large data volumes

Economic

Ability to handles millions of rows of textual data per day

Scalability

Each step of the unstructured text classification process is pre-built

Automated

Comprehensive coverage of data sources (structured and unstructured)

Comprehensive

Value add through innovative platforms


Hierarchical classification – enabling a granular understanding of customer issues

• Color-coded categories with a drill down to examine each aspect of the business offering

• Predefined metric and formulas to aid understanding of categories as well as sub-issues over time

• Identifying problem areas and those requiring improvement by understanding specific issues

• Estimating and forecasting incident count based on previous data


Topic Modelling to Understand Customer Perception

Based on the article that we identified we were able to extract insights relevant to the marketing, and competition for XXX’s new YYY range of processors

Topic modelling and article extraction solutions were deployed to create a story that divided the entire 384 comments into 20 topics

These insights can be used to understand what customers are speaking and how they perceive Intel’s new range of processors, as well as evaluate the marketing and branding strategy

Customers have expressed that YYY processors enable power savings

Marketing Insights

Customers feel YYY X86 provides high performance with low power consumption

End users feel YYY processors are worth upgrading to as it a good improvement over previous generations

Oracle’s AAA solves the heartbleed issue

End users consider AAA to be a bit expensive

Customers have rated XXX’s X86 processors as the most efficient out there beating AAA

Competitive Insights


Summary

80% of data generated today is unstructured in nature – CANNOT be ignored any more 1

2

3

5

4

Call center data, social media comments/posts, open ended survey data etc. are some of the sources of unstructured data

Incremental insights related to customer perception/trends/sentiments can be extracted by mining unstructured data

Determine the underlying conditions that give rise to the reasons for the sentiment/perception/customer issue

Unstructured Text Analytics Platform (UTAP) make the text classification exercise easier, faster, and efficient

Hierarchical classification enables a granular understanding of issues 6


Thank You

For more information:

Madhur Bhatia (Data Scientist)

Swaroop Johnson Consultant (Analytics)

Blueocean Market Intelligence

Email: [email protected]

Website: www.blueoceanmi.com

United States| United Kingdom | Dubai | India | Singapore

understanding customer perceptions and trends from unstructured data for better business decisions

Data & Analytics