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

Transcription

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

2 1. Sentiment Analysis 2.RFM Analysis Marketing Analytics 3.Repurchase Likelihood model 4.Conversion Analytics 5.Refund Analysis 6.Customer Satisfaction model Key Recommendations: 1.Improve product quality to increase the overall satisfaction of the customer (6 th Model). 2.Target the complaint customers who have done only one/two transactions (3 rd 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 (1 st & 5 th Model). 4.Special focus on 1 st time customers to avoid complaints for longer association with client (2 nd & 3 rd Model). 5. Mobile App demands improvisation and should be user friendly (4 th Model).

3 1. Word Cloud & Sentiment Analysis - Prints -21 shipping time 45-6 better quality 36-1 editing use Negative 22-3 size Positive 20-4 option 20-6 offer 19-2 free 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.

4 2. RFM Analysis 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)?

5 Number of Customers 2.1. RFM Analysis Recency days ago days ago 8-11 days ago 4-7 days ago last 3 days ago Frequency - One Time Visitors Frequency - Two Time Visitors Frequency - More than two Time Visitors Above Monetary (in dollars)

6 2.2. REFUND BASED ON RFM Number of Customers This is for 22 days data only One Time Visitor Two Time Visitor More Than Two Time Visitor days days 8-11 days 4-7 days last 3 days Most Refunds are happening across one time visitors

7 3. Repurchase Likelihood Model for Complaint Customers Number of Customers Research Objective: To determine the repurchase likelihood of a customer based on the factors which impact on purchase decisions. Status After the complaint Purchased Did not purchase Avearge No. of Transactions before the Complaint Avearge No. of Transactions after the Complaint 50.0% 50.0% % of customers are not purchasing after the complaint 1 Number of Transactions After the Complaint Not Likely Neutral Very Likely Did not buy > Clearly there is a fall in no, of transactions after the complaint Transactional data from to Complaint/Feedback data from to

8 3.1. Repurchasing behavior of complaint customers with the frequency count Not Likely 82% 85% 85% 70% 50% 93% 97% 92% 94% 96% 0.4% % 73% Neutral 89% 85% 95% 83% 91% 96% 96% 48% 0.7% % 71% 80% 84% Very Likely 92% 92% 93% 93% 92% 96% 1% Frequency

9 For customers who have not repurchased post complaint (days) Customers who have done only one transaction are not repurchasing after the complaint (Number of Transactions)

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

11 3.3. MODEL RESULTS Statistical Model: Logistic Regression Results: Key Indicators affecting Probability to Repurchase:- Accuracy of the model: 80% 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.

12 4. Conversion Analytics Cart & Mobile App Mobile Vs. Website Purchases Saved in cart and Purchased 1.3% 11% Website Mobile Did not Purchase Puchased 98.7% 89% 2013 Nov-Dec Dec 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.

13 Note: % are based on total refund amount 5. Refund Analysis Average Refund Amount($) Refund Reason Cancelled Order 46 11% Dark/Light 44 5% Text 43 5% Color 42 3% Resolution Customer Error/Order Modification Defective % 12% 12% Shipping 36 1% Cropping 32 4% Missing Items 31 2% Shipping Delay 29 14% Adjustments 28 2% Promotion Adjustment % 0% 10% 20% 30%

14 6. Customer Satisfaction Model Estimate Std. Error t value Pr(> t ) (Intercept) Package quality * Product quality * Value of the packaging & shipping * Value for money of photo User s experience 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 & Future Proposals 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: Target Marketing: Develop the list of target customers with probability of conversion. 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.

16 Appendix

17 1.2 Word Cloud & Sentiment Analysis - Wall Art -3 product 57-2 better quality 45-5 collage 41-6 shipping time 41-5 use 37-7 poster 31-2 customer 27-5 service Negative Positive 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

18 1.3 Word Cloud & Sentiment Analysis - Cards & Gifts -22 shipping product 69-7 better time quality 52-3 easier use 41-1 offer 40-6 options 35-9 website Negative Positive 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. Positive Words Negative Words

19 1.4 Word Cloud & Sentiment Analysis - Stationery time shipping options better product quality use offer customer allow Negative Positive 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. Positive Words Negative Words

20 1.5 Word Cloud & Sentiment Analysis - Book page better shipping quality options -25 time product easier -23 use -22 add Negative Positive 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. Positive Words Negative Words