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

Marketing Analytics for ecommerce

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Page 1: Marketing Analytics for ecommerce

Marketing Analytics for E-Commerce

By

Tuhin Chattopadhyay, Ph.D.

Page 2: Marketing Analytics for ecommerce

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).

Page 3: Marketing Analytics for ecommerce

1. Word Cloud & Sentiment Analysis - 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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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

Page 4: Marketing Analytics for ecommerce

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

Page 5: Marketing Analytics for ecommerce

2.1. RFM Analysis

4914 4218 5293 4768

5577 4158

5284 4458 3883

2915 3603 3203 3068 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)

Num

be

r o

f C

usto

me

rs

Recency

3368 3276 4349 3497 4054 3564 3463 3294 3132

2504 3283 3105 3127 2671

4300 3968 3425 3110

5090 4582

2212 2189

3497 3362 3730 3817 4110 4041 4124 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

Page 6: Marketing Analytics for ecommerce

2.2. REFUND BASED ON RFM

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

Nu

mb

er

of

Cu

sto

mers

This is for 22 days data only

Page 7: Marketing Analytics for ecommerce

5330

3. Repurchase Likelihood Model for Complaint Customers

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

Nu

mb

er

of

Cu

sto

mers

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

Page 8: Marketing Analytics for ecommerce

3.1. Repurchasing behavior of complaint customers with the frequency count

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

Page 9: Marketing Analytics for ecommerce

3.2.1. For customers who have not repurchased post complaint (d

ays)

(Number of Transactions)

Customers who have done only one transaction are not repurchasing after the complaint

Page 10: Marketing Analytics for ecommerce

Customers who have done at least two transactions are repurchasing again.

(day

s)

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)

Page 11: Marketing Analytics for ecommerce

3.3. MODEL RESULTS

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%

Page 12: Marketing Analytics for ecommerce

Mobile Vs. Website Purchases

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

Page 13: Marketing Analytics for ecommerce

5. Refund Analysis

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0 10 20 30 40 50

Promotion Adjustment

Adjustments

Shipping Delay

Missing Items

Cropping

Shipping

Defective

Customer Error/OrderModification

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

No

te:

% a

re b

ased

on

to

tal r

efu

nd

am

ou

nt

Page 14: Marketing Analytics for ecommerce

6. Customer Satisfaction Model

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.

Page 15: Marketing Analytics for ecommerce

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.

Page 16: Marketing Analytics for ecommerce

Appendix

Page 17: Marketing Analytics for ecommerce

1.2 Word Cloud & Sentiment Analysis - Wall Art 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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-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

Page 18: Marketing Analytics for ecommerce

1.3 Word Cloud & Sentiment Analysis - 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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-9

-6

-1

-10

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-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

Page 19: Marketing Analytics for ecommerce

1.4 Word Cloud & Sentiment Analysis - 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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-3

-8 -3

-10

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-5

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-6

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-30 -20 -10 0 10 20 30 40 50

allow

customer

offer

use

quality

product

better

options

shipping

time

Negative

Positive

Page 20: Marketing Analytics for ecommerce

1.5 Word Cloud & Sentiment Analysis - 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.

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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