Sentiment Analysis, RFM Analysis, Customer Satisfaction Analysis

1. Marketing Analytics for E-
Commerce
By
Tuhin Chattopadhyay, Ph.D.

2. Marketing Analytics
6.Customer Satisfaction model
1. Sentiment Analysis
2.RFM Analysis
3.Repurchase
Likelihood model
5.Refund Analysis
4.Conversion
Analytics
Key Recommendations:
1.Improve product quality to increase the overall satisfaction of the customer (6th Model).
2.Target the complaint customers who have done only one/two transactions (3rd Model).
3.Take necessary measures to reduce the customer complaints by incorporating on time delivery
of orders,
lowering shipping costs and ensuring product quality (for e.g. dark/ light) before dispatch (1st &
5th Model).
4.Special focus on 1st time customers to avoid complaints for longer association with client (2nd &
3rd Model).
5. Mobile App demands improvisation and should be user friendly (4th Model).

3. 1.WordCloud&SentimentAnalysis-Prints
Positive Words Negative Words
Top 5 Positive words which
customers are talking about
1. Shipping
2. Time
3. Better (context in
comparison
with competition, customer
experience)
4. Quality
5. Editing
Recommendations for top 5
words with negative
sentiments:
• On Time: Timely delivery of
orders and reduce the time
to upload/download photos
on the portal.
• On Shipping: Focus on
reducing shipping costs.
• On Quality: Quality control
check before shipping
products.
• On offer: customize offers
with free shipping costs.
• On option: More editing,
cropping, layout options for
prints category.
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19
20
20
22
26
36
38
45
51
-2
-6
-4
-3
-10
-1
-10
-6
-22
-21
-30 -20 -10 0 10 20 30 40 50 60
free
offer
option
size
use
editing
quality
better
time
shipping
Negative
Positive

4. Research Objective:
To identify the existing customers who are most likely to respond to a new offer.
R, F, and M stand for
• Recency – How recently did the customer purchase?
• Frequency – How often do they purchase?
• Monetary Value – How much do they spend (each time on average)?
2. RFM Analysis

5. 2.1. RFMAnalysis
4914 4218
5293 4768
5577
4158
5284
44583883
2915 3603 32033068 2358 2647 2350
4170 3689
5844 5100
17-22 days ago 12-16 days ago 8-11 days ago 4-7 days ago last 3 days ago
Frequency - One Time Visitors
Monetary (in dollars)
NumberofCustomers
Recency
3368 3276
4349 34974054 3564 3463 32943132
2504
3283 31053127 2671
4300 3968
3425 3110
5090 4582
2212 2189
3497 33623730 3817 4110 40414124 4029
6379 6441
4258 4617
7877 7819
2616 2781
4797 4575
0.0-2.75 2.76-7.75 7.76-18.12 Above 18.12
Frequency - Two Time Visitors
Frequency - More than two Time Visitors

6. 2.2.REFUNDBASEDONRFM
4907
6521
4524
3191
4375
2664
3668
3509
3317
2643
1292
2908
3612
2648
1453
0
1000
2000
3000
4000
5000
6000
7000
17-22 days 12-16 days 8-11 days 4-7 days last 3 days
One Time Visitor Two Time Visitor More Than Two Time Visitor
Most Refunds are happening across one time visitors
NumberofCustomers
This is for 22 days data only

7. 5330
3.RepurchaseLikelihoodModelforComplaintCustomers
Research Objective: To determine the repurchase likelihood of a customer based on the factors which impact on
purchase decisions.
2
3 3
1
2
3
Not Likely Neutral Very Likely
Avearge No. of Transactions before the Complaint
Avearge No. of Transactions after the Complaint
5329
4188
616 526
0
1000
2000
3000
4000
5000
6000
Did not buy 1-5 6-10 >10
Number of Transactions After the Complaint
NumberofCustomers
50.0%
50.0%
Status After the complaint
Purchased
Did not purchase
50% of customers are not
purchasing after the complaint
Clearly there is a fall in no, of
transactions after the complaint
Transactional data from 01-11-2012 to 22-02-2015
Complaint/Feedback data from 01-03-2013 to 30-09-2014

8. 3.1.Repurchasingbehaviorofcomplaintcustomerswiththefrequencycount
0.4%
50%
70%
82% 85% 85%
93% 97% 92% 94% 96%
1 2 3 4 5 6 7 8 9 10 10+
Not Likely
0.7%
48%
74% 73%
89% 85%
95%
83%
91% 96% 96%
1 2 3 4 5 6 7 8 9 10 10+
Neutral
1%
54%
71%
80% 84%
92% 92% 93% 93% 92% 96%
1 2 3 4 5 6 7 8 9 10 10+
Very Likely
Frequency

9. 3.2.1.Forcustomerswhohavenotrepurchasedpostcomplaint
(days)
(Number of Transactions)
Customers who have done only one
transaction are not repurchasing after
the complaint

10. Customers who have
done at least two
transactions are
repurchasing again.
(days)
Customers in these Segments
are spending at least 29$ per
transaction with the Frequency of
>3 transaction and Recency of
<195 days
3.2.2. For customers who have repurchased post complaint
(Number of Transactions)

11. 3.3.MODELRESULTS
Statistical Model: Logistic Regression
Results: Key Indicators affecting Probability to Repurchase:-
1. Frequency - If a transaction of a customer increases by 1 unit then the likelihood of Repurchasing
probability increases by 2 times.
2. Willing to be Contacted - If a customer is 'already in touch with a service agent' then the probability of
repurchase increases by 75% compared to 'not in contact with a service agent’.
3. Product Received on Time - If a customer ‘ receives a product on time' then the probability of
repurchase increases by 12% as compared to 'Not received on time‘.
4. Discount till date - If a customer ‘ receives 40%+ discount' then the likelihood of Repurchasing
probability increases by 2 times compared ‘No discount’.
5. Repurchase Tag – Neutral Customers are less likely to repurchase compared to Not likely customers
(due to number of transactions are high for ‘Not likely’ customers)
Insight:
Customers who have said “Not Likely to buy” with the more than two transactions before the complaint
have re-purchased.
Recommendations:
• Give at least 60% of discount to the customers who have made only 1 transaction and complaint.
• For one two time purchase customers, offer customized promotional campaigns to increase the
transactions count. It will lead to increase the likelihood of repurchase.
Accuracy of the model: 80%

12. MobileVs.WebsitePurchases
98.7%
1.3%
Website
Mobile
Saved in cart and Purchased
89%
11%
Did not Purchase
Puchased
2013 Nov-Dec
1048574
2013 Dec
1048574
There are only 1.3% of customers
who have made purchases
through Mobile app.
There are only 11% of customers
who have converted their saved
orders in cart to purchases.
4. Conversion Analytics – Cart & Mobile App

13. 5.RefundAnalysis
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36
39
40
42
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43
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0 10 20 30 40 50
Promotion Adjustment
Adjustments
Shipping Delay
Missing Items
Cropping
Shipping
Defective
Customer Error/Order
Modification
Resolution
Color
Text
Dark/Light
Cancelled Order
Average Refund Amount($)
27%
2%
14%
2%
4%
1%
12%
12%
2%
3%
5%
5%
11%
0% 10% 20% 30%
Refund Reason
Note:%arebasedontotalrefundamount

14. 6.CustomerSatisfactionModel
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.35 0.06 5.41 0.00
Package quality 0.02 0.01 3.05 0.00 *
Product quality 0.59 0.01 78.58 0.00 *
Value of the packaging & shipping 0.06 0.01 9.78 0.00 *
Value for money of photo 0.14 0.01 16.00 0.00
User’s experience 0.10 0.01 14.46 0.00
Critical Insights:
 Product Quality leaves the highest impact on Customer Satisfaction.
 If a product quality scale increases by 1 unit, the level of overall satisfaction increases by an
average of 0.59
 Package quality and value of the packaging & shipping are not leaving much impact on the
overall satisfaction of the customers.

15.  Model Operationalization:
• Models will be developed on an on going basis with the continual inflow of data.
• Recommendations/Customer insights from each of the model will be shared on regular
basis.
 Future Proposals:
Model Operationalization & Future Proposals
 Sales Forecast: To forecast demand.
 Uplift Modeling: To determine campaign effectiveness and target the customers for next
campaigning.
 Market basket Analysis: To know which products are selling together.
 Market Mix Modeling: To determine marketing’s impact on sales based on economic data,
Industry data, Advertising data, product data etc.
 Target Marketing: Develop the list of target customers with probability of conversion.

16. Appendix

17. 1.2WordCloud&SentimentAnalysis-WallArt Top 5 Positive words which
customers are talking about
1. Product
2. Better(context in comparison
with
competition, customer
experience)
3. Quality
4. Collage
5. Shipping
Recommendations for top 5 words
with negative sentiments:
• On Time: Timely delivery of
orders.
• On Quality: Improvise printing
quality for collage, canvas orders.
• On Poster: Customize poster
sizes,
flexibility in designing the
layout.
• On Shipping: Focus on reducing
shipping costs.
• On Collage: Customize
placement of photos in collage
as per different layout size.
Positive Words Negative Words
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31
37
41
41
41
45
54
57
-5
-2
-7
-5
-23
-6
-5
-10
-2
-3
-40 -20 0 20 40 60 80
service
customer
poster
use
time
shipping
collage
quality
better
product
Negative
Positive

18. 1.3WordCloud&SentimentAnalysis-Cards&Gifts
Top 5 Positive words which customers
are talking about
1. Shipping
2. Product
3. Better(context in comparison with
competition, customer experience)
4. Time
5. Quality
Recommendations for top 5 words
with negative sentiments:
• On Shipping: Focus on reducing
shipping costs.
• On Time: Timely delivery of orders.
• On Product: Improve delivery time,
online tracking system.
• On Quality: focus on quality of
printing on mugs, improve graphics.
• On Website: ease of use, design &
display of products.
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79
-9
-6
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-10
-3
-11
-20
-7
-14
-22
-40 -20 0 20 40 60 80 100
website
options
offer
use
easier
quality
time
better
product
shipping
Negative
Positive
Positive Words Negative Words

19. 1.4WordCloud&SentimentAnalysis-Stationery
Positive Words Negative Words
Top 5 Positive words which
customers are talking about
1. Time
2. Shipping
3. Options
4. Better(context in comparison
with
competition, customer
experience)
5. Product
Recommendations for top 5 words
with negative sentiments:
• On Time: Timely delivery of
orders.
• On Shipping: Focus on reducing
shipping costs.
• On Quality: Quality control check
before shipping products.
• On Customer: For loyal
customers, special promotions.
• On Options: More design options
like editing, text, templates, color.
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28
29
32
35
40
45
45
-3
-8
-3
-10
-9
-5
-4
-6
-17
-24
-30 -20 -10 0 10 20 30 40 50
allow
customer
offer
use
quality
product
better
options
shipping
time
Negative
Positive

20. 1.5WordCloud&SentimentAnalysis-Book
Positive Words Negative Words
Top 5 Positive words which
customers are talking about
1. Page
2. Better(context in comparison
with competition, customer
experience)
3. Shipping
4. Quality
5. Options
Recommendations for top 5
words with negative sentiments:
• On Page: More page layout
options like full page
view for booklets.
• On Better: image quality,
shipping tracking system.
• On Shipping: Reducing shipping
costs.
• On Quality: Improvement in
quality of book product like
thicker cover, laminated. Check
before the product is shipped.
• On Options: More design
options like editing, text,
templates, color.
47
53
60
60
74
78
80
82
99
139
-22
-23
-24
-24
-25
-30
-31
-32
-46
-50
-100 -50 0 50 100 150
add
use
easier
product
time
options
quality
shipping
better
page
Negative
Positive