Understanding Customer Perceptions and Trends from Unstructured Data for Better Business decisions
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Transcript of 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%
© 2016 Blueocean Market Intelligence
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
© 2016 Blueocean Market Intelligence
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
© 2016 Blueocean Market Intelligence
• 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
© 2016 Blueocean Market Intelligence
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
© 2016 Blueocean Market Intelligence
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)
© 2016 Blueocean Market Intelligence
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
© 2016 Blueocean Market Intelligence
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
© 2016 Blueocean Market Intelligence
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
© 2016 Blueocean Market Intelligence
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
© 2016 Blueocean Market Intelligence
Thank You
For more information:
Madhur Bhatia (Data Scientist)
Swaroop Johnson Consultant (Analytics)
Blueocean Market Intelligence
Email: [email protected]
Website: www.blueoceanmi.com
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