End to end system for 1. Product Review Extraction 2. Topic Mining 3. Sentiment Analysis 4. Topic Prediction - Classification Algorithms 5. Insights & Actions

1. Product Management Strategy
Suite
Connecting the Customers to Product Managers
Group 10 | Advanced Program in Data Science | IIM - Calcutta
Team
Ashwini B C
C Suresh Kumar
Durga Prasad Kasturi
Manisha Ilke
Mohan Rao B C
Sreekanth Padmanabhan

2. Content
2
• The Project Background
• The Objective
• Data & Scope
• The Solution Methodology
• Literary Research
• Model Results & Validation
• The Analysis & Insights
• The Brand – Bajaj Electrical
• The Topic Categories
• Bajaj Mixer Grinder – Overall Sentiment
• Competition Benchmarking – by Topic
• Insights & Recommendations
• Scope for Further Research

3. The Project Background
What is the objective & How we did it?
The Cooking Starts!!

4. What to Cook - Project Objective
Objective
The background of the project is the challenge and the dilemma that every product manager
and the product development teams face in terms of understanding the attitude of the
customers/end users towards product features, product experience and the feedback on
product performance and also to understand the strengths and weaknesses of the competitors’
product in the same category
The connect between a product manager and customers is even more distant in product
categories like consumer durables and kitchen appliances as the business runs on sales channel
partners, distributors and dealers
To provide a product management suite with most influential product features for the product,
competition benchmarking on the product features, Benchmarking of company’s product and
competition product in same category based on review volume and customer sentiment
polarity
The Solution
Extract Reviews from e-
Commerce sites
Topic modeling &
Sentiment Analysis
Benchmarking and insights
on Customer Attitude
4

5. Ingredients – Data & Scope
Scope:
• The project scope is limited to customer ratings and reviews extracted from
Amazon.in and Flipkart.com
• Product Category : Mixer Grinders
• The Brand – Bajaj
• The Competitors –
• Philips
• Morphy Richards
• Preethi
• Maharaja
• Prestige
• Pigeon
• Butterfly
• The time scope is to collect ratings and reviews from 2011 to 2017
5

6. Recipe – The Solution Methodology
Selection of
Product
Category and
Competing
Brands
Listing of URLs
for Scraping
Product Reviews
Code
Development
for Web
scraping -
Flipkart
Code
Development
for Web
scraping -
Amazon
Code
Development
for Topic
Modeling
(Amazon)
Manual review
of topic clusters
& label
assignment
(Amazon)
Sentiment
Scoring & Topic
Prediction
Model on
reviews
(Amazon)
Apply model –
Topic Prediction
& Sentiment
Scoring (Flipkart)
Integration of
Amazon &
Flipkart reviews
Visualization –
Analysis & Insights
6

7. Utensils & Tools – Literary Research
•Beautiful Soup (Python Package)
•Selenium Web driver (Python Package)
Web scraping
•Latent Dirichlet allocation (LDA)
•Latent semantic analysis (LSA)
•Non-negative matrix factorization (NMF)
•K-means Clustering
Topic Modeling
•Naïve Bayes
•Naïve Bayes – Multinomial
•Random Forest
•Support Vector Machines – Linear
•Support Vector Machines – Non Linear
Classification
Algorithms
•TextBlob (Python Package)Sentiment Scoring
7

8. Taste Test – Model Results & Validation
Prediction Accuracy – Across Classification Models
Metric Naïve Bayes
Naïve Bayes -
Multinomial
Random Forest
Support Vector
Machines -
Linear
Support Vector
Machine – Non
Linear
Right Predictions 5,541 5,513 6,801 6,576 6,576
Total
Observations
7,603 7,603 7,603 7,603 7,603
Overall Accuracy 72.88% 72.51% 89.45% 86.49% 86.49%
• Of the 5 classification algorithms used for topic class prediction, Random Forest
and Support Vector Machine – Linear and Non-Linear are showing best results
• Naïve Bayes and Naïve Bayes – Multinomial lack overall accuracy
Ecommerce Platform No of Reviews No of Sentences in
Reviews
Amazon 10,094 19,006
Flipkart 6,424 10,631
Input Data Volume
Split Percentage Observations
Training 60% 11,403
Test 40% 7,603
Training Model on Amazon Data
8

9. Taste Test – Model Results & Validation
Prediction Accuracy – Across Classification Models – Class Level Accuracy
Final_Topic
Naïve
Bayes
Naïve Bayes -
Multinomial
Random
Forest
Support Vector Machines -
Linear
Support Vector Machine – Non
Linear
Ecommerce Site
Experience
37.60% 36.00% 90.40% 80.00% 80.00%
Blade Quality 1.40% 0.00% 70.80% 83.30% 83.30%
Burning Smell 14.30% 22.40% 77.60% 81.60% 81.60%
Heating Issues 0.00% 0.00% 36.40% 57.60% 57.60%
Jar & Lids Quality 89.50% 87.90% 93.50% 95.70% 95.70%
Motor Performance 40.00% 23.60% 93.60% 84.50% 84.50%
Motor Power 4.20% 15.30% 72.20% 79.20% 79.20%
Operational Performance 70.70% 76.20% 85.10% 67.90% 67.90%
Opinions - Generic 48.40% 36.80% 88.90% 83.40% 83.40%
Product Delivery 73.70% 62.50% 83.90% 85.30% 85.30%
Product Design 0.00% 0.00% 61.50% 50.00% 50.00%
Product Durability 70.00% 58.10% 88.50% 87.10% 87.10%
Product Expectations 7.80% 31.10% 77.80% 84.40% 84.40%
Product Packing 0.00% 12.70% 68.30% 63.50% 63.50%
Product Quality - Generic 90.50% 92.70% 93.20% 91.20% 91.20%
Product Returns 16.70% 52.60% 93.60% 98.70% 98.70%
Product Satisfaction 1.70% 45.00% 95.00% 96.70% 96.70%
Product Utility 0.00% 0.00% 40.90% 27.30% 27.30%
Purchase
Recommendation
73.10% 68.30% 91.40% 92.50% 92.50%
Running Noise 86.30% 84.20% 89.70% 90.40% 90.40%
Service & Warranty 47.20% 34.90% 88.70% 76.40% 76.40%
Value for Money 83.40% 80.90% 90.50% 91.80% 91.80%
Between Random Forest and Support Vector Machines (SVM), SVM seems to be a better model 9

10. The Analysis & Insights
What are customers talking about?
The Food is Served!!

11. The Brand – Bajaj Electrical
Bajaj Electricals
An Indian consumer electrical equipment manufacturing company based in Mumbai,
Maharashtra. It is a part of the Rs. 380 billion (US$5.9 billion) Bajaj Group. It has diversified
with interests in lighting, luminaries, appliances, fans, LPG based Generators, engineering and
projects. Its main domains are lighting, consumer durables, engineering and projects.
Bajaj
Electricals
Consumer
Products
Luminaires EPC
Exports
Business Areas
BajajConsumerProducts
Breakfast & Snacks
Preparation Essentials
Cooking Essentials
Food Preparation Mixer Grinder
Home Comforts
Home Essentials
Business Units
11

12. Topics - What People are talking about?
Product Quality - Generic
•Reviews where people are talking about overall quality of the product
• Ex: “Quality is below average”, “Good product”, “Osum product”
Opinions - Generic
• Generic opinions without specific feature
•Ex: “pigeon is good company”, “I like this product”, “Amazing”
Operational Performance
• Talking about general performance of the product
•Ex: “Not working”, “Working good”, “Performance not good”
Jar & Lids Quality
• Reviews specifically about Jars and Lid Quality
•Ex: “Jar was damaged.”, “Jars are not good, cap is very bad.”, “jars are of
good quality steel”
Value for Money
•Reviews about Price and worthiness of the product
•Ex: “Not worth.”, “Lowest price ever”, “Good purchase, worth buying”
Running Noise
• Reviews about the noise while running the mixer
•Ex: “noise is less and gets all jobs done”, “problem is little noisy”, “Makes
lots of sound”
Purchase Recommendation
•Reviews guiding purchase decision
•Ex: “one should not buy”, “Highly recommended”, “Extremely bad buy”
Product Delivery
• Reviews about delivery of the product
• “To day late delivery”, “delivered on time as promised”
Product Durability
• Reviews about the longevity of the product or duration of usage
•Ex: “using for the he last two months”, “It seems to be a durable product”
Motor Performance
• Reviews about performance of the motor
•Ex: “motor is good”, “Motor is not efficient at all”, “Motor is vry fast”
Service & Warranty
•Reviews on Service and warranty on the product
• Ex: “Ur warranty is of no use”, “Excellent service”, “bajaj is not providing
service centers”
Ecommerce Platform Experience
• Reviews about generic experiences with the ecommerce site (Amazon or
Flipkart)
•Ex: “Really appreciate flipkart for this deal”, “Amazon plz contact me”12

13. Topics - What People are talking about?
Product Returns
• Reviews related to product returns and return experience
• Ex: “No return policy”, “Thanks to fp for accepting the return”
Product Expectations
• Reviews about expectation customers had and reality about product
• Ex: “it meets My expectation”, “Disappointed”, “meeting expectation”
Motor Power
• Reviews commenting on power for the motor
• Ex: “powerful and efficient”, “Very powerfull”, “not a powerful”
Product Satisfaction
• Reviews discussing the satisfaction of the products
• Ex: “one hundred % Satisfied”, “Not satisfied”
Blade Quality
• Reviews commenting on quality of the blades
• Ex: “Blade quality is very poor”, “It has got good blades in it”
Product Packing
• Reviews discussing the product packaging
• Ex: “neatly packed”, “more over packing was pathetic”
Heating Issues
• Reviews explaining heating issues in mixer
• Ex: “But soon it gets hot”, “does not produce too much heat”
Burning Smell
• Reviews commenting on burning smell while using mixer
• Ex: “Burning smell after one month use”, “Food smells burnt plastic
and rubber”
Product Design
• Reviews discussing the design of the product
• Ex: “compact and small and cute”, “Also it is so light & small”
Product Utility
• Reviews about the utility of the product
• Ex: “Nice grinder for small family”, “Small and helpful”
13

14. Bajaj Mixer Grinder
• On an average, 15% of the
reviews are negative for Bajaj
mixer grinder
• The number of reviews are
growing exponentially year
over year, showing the
importance of ecommerce
platform for sales
• At an overall level the
sentiment for Bajaj is positive.
This is also reflected in the
increasing “4” and “5” ratings
• Bajaj is ahead of competitors,
considering the average
sentiment score and average
ratings as measurement
criteria
Bajaj Mixer Grinder – How is the Overall Picture?
14

15. Competition Benchmarking – Topics and Brands
Topic: Operational Performance Topic: Jars & Lid Quality
• Bajaj mixers are doing good when it comes to
“Operational Performance”
• Bajaj has higher ratings and sentiment scores
from customers and is ahead od most of the
competitors
• “Preethi” is the only competitor ahead of Bajaj
• Bajaj is below average in “Jars & Lid Quality”
• “Maharaja” is leading the pack and again
“Preethi” is ahead of Bajaj
• There is a need to investigate this category
further
15

16. Competition Benchmarking – Topics and Brands
Topic: Value for Money Topic: Running Noise
• Bajaj mixers are perceived to be “Value for
Money” with higher positive sentiment
• Bajaj has higher ratings and sentiment scores
from customers and is ahead od most of the
competitors
• “Preethi” is lagging given its price is higher
that Bajaj, Maharaja and Pigeon
• Bajaj mixers are barely above the average
mark when it comes to “Noise” while the
mixer is on
• But only “Butterfly” has better sentiment
scores and most of the other brands are below
Bajaj
16

17. Competition Benchmarking – Topics and Brands
Topic: Purchase Recommendation Topic: Product Delivery
• People are recommending to purchase Bajaj
and the brand has above average scores
• “Preethi” and “Butterfly” are the only brands
faring better than Bajaj
• People are satisfied with the product delivery
aspect of the Bajaj – Which means lesser
delivery issues from the ecommerce platform
• “Pigeon”, “Maharaja” and “Butterly” seem t
have more delivery issues
17

18. Competition Benchmarking – Topics and Brands
Topic: Product Durability Topic: Motor Performance
• The perception of people about the durability
of the Bajaj mixers is above average reflected
both by average sentiment score and also the
ratings
• “Morphy Richards” and “Butterfly” are the
only brands scoring higher than Bajaj
• Motor performance of Baja mixers seems to
be perceived negatively, confirmed by below
average scores in sentiment and ratings
• “Philips” has the highest score followed by
“Pigeon” and “Maharaja” in this category
18

19. Competition Benchmarking – Topics and Brands
Topic: Service & Warranty Topic: Ecommerce Platform Experience
• People are happy about the Service and
Warranty aspects of Bajaj, Only “Pigeon” and
“Morphy Richards” have better scores
• “Butterfly” has the worst score and “Preethi”
also has issues in this category
• The overall experience on the ecommerce
platform (Amazon & Flipkart) for Baja product
customers is above average
19

20. Competition Benchmarking – Topics and Brands
Topic: Product Returns Topic: Product Expectations
• People are happy with the ease and
experience with the return policy and process
with Bajaj mixers on the ecommerce platforms
• Both average ratings and sentiment score is
above average for Bajaj, but most of the
competitors are ahead of Bajaj
• Bajaj has a challenge in terms of meeting
overall expectations of the customers
• The sentiment is negative and there is a need
to understand if it is just a perception and how
to change it. The sentiment score is negative
20

21. Competition Benchmarking – Topics and Brands
Topic: Motor Power Topic: Product Satisfaction
• People’s perception and opinion about the
motor power of Bajaj mixers is just average
• “Preethi” is far ahead of Bajaj in scores while
“Philips” is at par
• Bajaj is doing good in terms of overall
satisfaction for the mixers and ahead of most
of the competitor brands
• “Preethi” again leads the pack and “Morphy
Richards” is also ahead of Bajaj
21

22. Competition Benchmarking – Topics and Brands
Topic: Blade Quality Topic: Heating Issues
• Bajaj’s score when it comes to Blade Quality is
just above average on both average sentiment
and average ratings
• “Preethi” leads the pack again, followed by
Philips
• Although average rating is higher, the below
average sentiment score shows that Baja
mixers have problem of overheating
• This might be an input to the product
manufacturing and design team with in Bajaj
• Most of the competitors are ahead of Bajaj
22

23. Insights & Recommendation
Operational
Performance
Jars & Lid Quality Value for Money Motor Power
Running Noise
Purchase
Recommendation
Product Delivery Product Satisfaction
Product Durability Motor Performance Service & Warranty Blade Quality
Ecommerce Platform
Experience
Product Returns Product Expectations Heating Issues
Bajaj Mixer Grinder – Issues Heat Map
• Sentiment is negative for Jar and Lid Quality – The product production team and design team needs to investigate
• Sentiment is negative for Motor Performance – The product production team and design team needs to investigate
• Sentiment is fairly negative for Product Returns – The return policy (of Bajaj, vendor & platform) needs review
• Sentiment is negative for Product Expectations – The marketing team needs to investigate and align messaging to set right expectations
• Sentiment is negative for Motor Power – The product production team and design team needs to investigate
• Sentiment is fairly negative for Blade Quality – The product production team and design team needs to investigate
• Sentiment is negative for Heating Issues – The product production team and design team needs to investigate
23

24. The Road Ahead - Scope for Further Research
 The current project is using “Bag of Words” approach and it limits the
semantic intelligence of the solution
 The current sentiment analysis is not able to handle sarcasm and attaches a
positive sentiment to sarcastic comments
 The prediction accuracy for the topic classification is at 90% - there is further
scope to improve this using advanced NLP concepts
 Currently, the topic identification is manual, going through results of each of
the category classified by 4 algorithms. There is scope of getting this
automated with ensemble of the results from all models and identifying the
right topic
 Multilingual reviews – People writing hindi reviews using English words. This
needs to explored more for solution
 Implementation of the methodology on reviews in other languages 24

25. Thank You!!
We are open to questions!!!
How was it??
The Chefs
Ashwini B C
C Suresh Kumar
Durga Prasad Kasturi
Manisha Ilke
Mohan Rao B C
Sreekanth Padmanabhan