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Retail Analytics – E-Commerce
Group 9
IIM Lucknow
Anju R Gothwal PGP28250
Animesh PGP29181
Malory Ravier IEP15003
Mayank K...
AGENDA
1) RETAIL ANALYTICS
 Industry Practice – Types of Analytics
 Information Providers
2) ANALYTICS IN ECOMMERCE INDU...
1) RETAIL ANALYTICS
2) ANALYTICS IN ECOMMERCE INDUSTRY
3) ANALYTICS IN ECOMMERCE COMPANIES
4) RESEARCH PAPERS STUDY
5) REC...
Industry Practices - Types of Analytics – RETAIL ANALYTICS
CUSTOMER ANALYTICS
Customer Acquisition
Customer Loyalty
Behavi...
Information Providers -RETAIL ANALYTICS
Market research companies providing retail intelligence
IRI: Information Resource ...
1) RETAIL ANALYTICS
2) ANALYTICS IN ECOMMERCE INDUSTRY
3) ANALYTICS IN ECOMMERCE COMPANIES
4) RESEARCH PAPERS STUDY
5) REC...
Web Analytics – E Commerce
 Web Analytics involves mainly studying consumer behavior and traffic online
 Ecommerce appli...
DATA HANDLING - Software in trend - HADOOP
 HADOOP: Open source software project
 Accomplishes two tasks: massive data s...
Major Analytics applications – E Commerce
• Personalization helps to increase conversion rates
• HBR say personalization i...
Platforms for Predictive Analytics
Platforms
Predictive Tools that integrate
with e-commerce platform
• Tools and Plugins
...
1) RETAIL ANALYTICS
2) ANALYTICS IN ECOMMERCE INDUSTRY
3) ANALYTICS IN ECOMMERCE COMPANIES
4) RESEARCH PAPERS STUDY
5) REC...
Analytics Practices – Amazon
ATTRIBUTES PRACTICES
In-house/ outsourced  All analytics done in- house
Major Tools  Open S...
Customer Analytics - Amazon
PRODUCT RECOMMENDATIONS
 Hybrid Recommender Systems – a mix of both content and collaborative...
Seller Analytics - Amazon
 Amazon treats its over 2 million sellers as its customers, provide all the technology and serv...
Supply Chain Analytics - Amazon
 Monitors, tracks and secures 1.5 Billion items laying around 200 fulfillment centers
 5...
Control and Trust - Amazon
CREDIT CARD FRAUD DETECTION
 Uses a scoring approach to identify the most likely fraud situati...
Software Used - Flipkart
QLIKVIEW – Parent Company: Qlik, based at Pennsylvania
Improved Inventory Management tool to opti...
Software Used - Flipkart
BIGFOOT - Computerized Maintenance Management Software (CMMS)
 1) Managing the maintenance opera...
Analytics Practices – eBay
ATTRIBUTES PRACTICES
In-house/ outsourced  Most of the analytics done by the in- house analyti...
Major Metrics - eBay
EXIT RATE
 Which is the page which marks the termination of user’s session
 Find the dissatisfying ...
Buyers Analytics- eBay
ANALYTICS FOR HOMEPAGE
 Arrange the homepage according to the purchase history, likes and comments...
Seller Analytics - eBay
ASSORTMENT ANALYTICS
 What are the suggested assortments for a seller
 Which sellers to be liste...
Trust Analytics - eBay
FRAUD ANALYTICS
 Which are the sellers or Buyers who are included in fraud
 For Example A Buyer m...
Notable Practices- AB TESTING -eBay
DIVISION OF CUSTOMERS INTO TWO SEGMENTS
 Control Group (30% customers)
 Test Group (...
Notable Practice - RFM Analysis -eBay
Recency | Frequency | Monetary
for Customer segmentation and Promotional Offers
Reco...
1) RETAIL ANALYTICS
2) ANALYTICS IN ECOMMERCE INDUSTRY
3) ANALYTICS IN ECOMMERCE COMPANIES
4) RESEARCH PAPERS STUDY
5) REC...
Customer Segmentation and Promotional offers
RFM Analysis : Suggested Improvements
 Instead of rating similarly for all t...
Ideal Clusters based on RFM
Recency Frequency Monetary Clusters
H H H BEST
H H L VALUABLE
H L H SHOPPERS
H L L FIRST TIMES...
Customer Segmentation and Promotional offers
- based on Customer Lifetime Value
THREE APPROACHES
1) Segmentation by using ...
Customer Segmentation and Promotional offers
- based on Customer Lifetime Value
APPROACH II (LIFETIME VALUE COMPONENTS)
Th...
Customer Segmentation and Promotional offers
- based on Customer Lifetime Value
APPROACH II (LIFETIME VALUE COMPONENTS)
 ...
Customer Segmentation and Promotional offers
- based on Customer Lifetime Value
APPROACH III (LIFETIME VALUE & OTHER COMPO...
Customer Segmentation and Promotional offers
- based on Customer Lifetime Value
CROSS SELLING AND UPSELLING
 Segmentation...
1) RETAIL ANALYTICS
2) ANALYTICS IN ECOMMERCE INDUSTRY
3) ANALYTICS IN ECOMMERCE COMPANIES
4) RESEARCH PAPERS STUDY
5) REC...
Tactics for Building and Sustaining a Data Analytics Team
As per our study we have found that the companies doing major an...
Building an Analytics Culture
 Make intellectual curiosity a priority
Technical skills alone are insufficient
 Find tech...
References
• Customer segmentation and strategy development based on customer
lifetime value: A case study
Su-Yeon Kim a, ...
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All you wanted to know about analytics in e commerce- amazon, ebay, flipkart

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One stop solution to your search for Data Analytics practices being followed by various E-commerce giants like Amazon, Ebay, Flipkart, Snapdeal and also some of the suggested approaches.

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All you wanted to know about analytics in e commerce- amazon, ebay, flipkart

  1. 1. Retail Analytics – E-Commerce Group 9 IIM Lucknow Anju R Gothwal PGP28250 Animesh PGP29181 Malory Ravier IEP15003 Mayank Khatri PGP29220 Richa Narayan PGP29207 Shashank Singh Chandel PGP29493 Tushar Gupta PGP29197
  2. 2. AGENDA 1) RETAIL ANALYTICS  Industry Practice – Types of Analytics  Information Providers 2) ANALYTICS IN ECOMMERCE INDUSTRY  Web analytics – basic metrics, top tools  Data Handling – Software in Trend- HADOOP  Major Analytics Applications in Ecommerce 3) ANALYTICS IN ECOMMERCE COMPANIES  Amazon  Flipkart  Ebay 4) RESEARCH PAPER STUDY Customer Segmentation and Promotional Offers  RFM  Lifetime Value 5) RECOMMENDATIONS
  3. 3. 1) RETAIL ANALYTICS 2) ANALYTICS IN ECOMMERCE INDUSTRY 3) ANALYTICS IN ECOMMERCE COMPANIES 4) RESEARCH PAPERS STUDY 5) RECOMMENDATIONS
  4. 4. Industry Practices - Types of Analytics – RETAIL ANALYTICS CUSTOMER ANALYTICS Customer Acquisition Customer Loyalty Behavioral Segmentation General Merchandiser - TESCO MARKETING ANALYTICS Marketing Mix Brand Health Multichannel Campaign Optimization Apparel Chain – SEARS CANADA MERCHANDISING AND PLANNING Shelf space optimization Product Pricing Store Location Decisions Fashion Retail – BELK RISK ANALYTICS Detecting Fraudulent activity Detecting Process Errors Detecting Store Theft Online Retailer - AMAZON DEMAND AND SUPPLY CHAIN Inventory Planning Demand Forecasting Product Flow Optimization Department Store – METRO GROUP PREDICTIVE ANALYTICS Determining Customer LTV Revenue forecasting Product Recommendations Trend Analysis
  5. 5. Information Providers -RETAIL ANALYTICS Market research companies providing retail intelligence IRI: Information Resource Inc.  Leader in delivering powerful market and shopper information, predictive analysis and the foresight  Keeps systems on big retailers, collect info, sell data and trends, simplifies and supports manufacturers and all Services Provided  Market, consumer and shopper intelligence  Retail tracking information  Online and offline marketing ROI strategy and effectiveness  Predictive analytics and modeling  Enterprise-class business intelligence software platforms and solutions  Pricing, trade promotion and brand portfolio maximization  Store level and merchandising insights  Strategic consulting and thought leadership AC Neislen: Another Player in the arena
  6. 6. 1) RETAIL ANALYTICS 2) ANALYTICS IN ECOMMERCE INDUSTRY 3) ANALYTICS IN ECOMMERCE COMPANIES 4) RESEARCH PAPERS STUDY 5) RECOMMENDATIONS
  7. 7. Web Analytics – E Commerce  Web Analytics involves mainly studying consumer behavior and traffic online  Ecommerce applications – study consumer purchase to boost sales, attract more customers, build brand BASIC METRICS TO TRACK TOP ANALYTICS TOOLS FOR ECOMMERCE: TOOL CAPABILITIES APPLICATIONS Google Analytics Monitors traffic from social media, emails Measures effectiveness of marketing program Adobe Site Catalyst Real time segmentation Increase checkout conversion rates IBM Corementrics Enterprise level Solution, provides actionable information Know how website affects visitors, advertisement ROI Webtrends Digital marketing intelligence Increase Conversions, Search and social advertising, visitors segmentation and scoring MEASURE DESCRIPTION Visitors No of visitors tells how business is doing Page Views Maximum viewed Tells the popular content Referring Sites Tells the interests of customer Bounce Rates Tells why people leave the site Keywords and Phrases Tells about customers requirements
  8. 8. DATA HANDLING - Software in trend - HADOOP  HADOOP: Open source software project  Accomplishes two tasks: massive data storage , faster processing  ADVANTAGES: • Handle huge amount of data - great volumes and varieties – esp. from social media and automated sensors • Low cost - the open-source framework is free and uses commodity hardware to store large quantities of data • Computing power - distributed computing model can quickly process very large volumes of data • Scalability - can easily grow your system simply by adding more nodes. Little administration is required. • Storage flexibility - can store as much data as you want and decide how to use it later. • Inherent data protection and self-healing capabilities - Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. And it automatically stores multiple copies of all data. Other S/W involved – Tableau, TeraData etc.
  9. 9. Major Analytics applications – E Commerce • Personalization helps to increase conversion rates • HBR say personalization increases ROI by 8 to 10 times • Ex: Gilt Group ecommerce company uses targeted emails to give offers matching customer search Personalization • Analyzing buying pattern to make online purchase seamless process • Optimizing services like customer call Improving Customer Experience • Develop models for real time pricing of millions of SKU’s • Parameters considers are competition, inventory, required margins etc.Pricing • Used to predict consumer behavior ex. Used by Amazon to predict customer purchase • Vendors like Atterix, SAS, Lattice provide such services Predictive Analysis • Supply chain intelligence for real time communication between different stakeholders like vendors, warehouses, customer etc. • Helps achieve faster delivery, higher fulfillment, low inventory Managing Supply Chain
  10. 10. Platforms for Predictive Analytics Platforms Predictive Tools that integrate with e-commerce platform • Tools and Plugins • No headache of integration • Springbot, Custora, Canopy Labs • $199-$300/month Open Source Product • Suitable for an analytics team • Hiring the right skilled resources a challenge • R, KNIME, PredicitionIO • Free Full Featured Site • Most functionality • Point solutions for various areas • Consulting options provided • SAS, SAP, Predixion • Approx. $10,000 for single user license
  11. 11. 1) RETAIL ANALYTICS 2) ANALYTICS IN ECOMMERCE INDUSTRY 3) ANALYTICS IN ECOMMERCE COMPANIES 4) RESEARCH PAPERS STUDY 5) RECOMMENDATIONS
  12. 12. Analytics Practices – Amazon ATTRIBUTES PRACTICES In-house/ outsourced  All analytics done in- house Major Tools  Open Source  Tweaked to Amazon’s needs  Amazon uses its native analytics platform – Hadoop with Elastic Map Reduce and S3 database  Amazon also uses Glacier for archiving data and Kinesis for stream processing of high volume real time data streams Major Metrics  One of the most Metrics driven company almost everything measured and evaluated Analytics major heads 1. Customer Analytics 2. Seller Analytics 3. Trust Analytics 4. Supply Chain Analytics Notable attributes  They also monetize the platform by offering it to other companies
  13. 13. Customer Analytics - Amazon PRODUCT RECOMMENDATIONS  Hybrid Recommender Systems – a mix of both content and collaborative filtering  Main metrics analyzed are – 1) Customer’s past purchases 2) Items customers have rated and liked 3) Purchases compared to similar purchase by other competitors 4) Items in virtual shopping carts  Generates approximately 29% sales from recommendations CUSTOMER SERVICE  No attempts to up sell over customer service calls  Data network allows Amazon to call the customer in under a minute after he places a service request  Reports and Views are extensively used to have selected customer information on screen  Customers are only last name and address to fetch all their data  Customer service reps are well informed due to big data analytics; leads to individualized and human
  14. 14. Seller Analytics - Amazon  Amazon treats its over 2 million sellers as its customers, provide all the technology and services sellers need to run their business  Personalization with sellers, proactive, data driven recommendations to each and every seller on the platform  Tens of millions of recommendations to entire seller base in a day through emails and the native platform ‘Seller Central’  Business reports are also available for purchase for in depth insights  Examples of some recommendations 1) Almost out of stock – Recommendation on how much to add to inventory based on forward looking demand for the product adjusted for seasonality and festivals 2) Search Results – When customer encounters no search results or results of low relevancy, the results are surfaced back to the seller and recommend to carry products customers are looking for 3) Fulfillment by Amazon – Recommendations based on the characteristics of how difficult the products are to fulfill 4) Performance Feedback – Metrics on satisfying customers, serving their needs and getting products to them fast and easily 5) Sharpness of Pricing – Surface up the sellers of all different products a seller is carrying on Amazon, determine whether it makes sense to lower prices for customers
  15. 15. Supply Chain Analytics - Amazon  Monitors, tracks and secures 1.5 Billion items laying around 200 fulfillment centers  50 million updates are made to the database per week  Entire data is crunched every 30 minutes and the results are transmitted to all the terminals INVENTORY CONTROL  Amazon uses ‘non-stationary stochastic model’ for optimizing inventory  Has developed algorithms for joint and coordinated replenishments  Algorithms also support fulfillment, sourcing and capacity decisions  Forecasting is done at an SKU level for each fulfillment center DEMAND  Analytics on customer wish lists, gift registries and pre-orders to anticipate demand apart from usual forecasting techniques  Wish lists are publicly visible, software crawls wish lists to aggregate data about customer demand LOGISTICS  Patented ‘Method and System for Anticipatory Package Shipping’  Anticipates customer needs before they express them  Analyzes a) Customer Ordering History d) Feedbacks b) Wish-lists e) Searches c) Average Shopping Cart Content f) How long a cursor hovers over a product page  Results in very fast delivery, sends off packages to a shipping hub or a truck near the customer’s address and waits to receive a go ahead to deliver
  16. 16. Control and Trust - Amazon CREDIT CARD FRAUD DETECTION  Uses a scoring approach to identify the most likely fraud situations  Some of the situations analyzed are 1) Purchase of easily resold goods on gray market such as electronics 2) Use of different billing and shipping address 3) Use of fastest shipping option WAREHOUSE THEFTS  Constantly Updates database of high ticket, most likely to be stolen items
  17. 17. Software Used - Flipkart QLIKVIEW – Parent Company: Qlik, based at Pennsylvania Improved Inventory Management tool to optimize Stock Levels CHALLENGES  Integrate Complex Data from disparate sources  Deliver Analytical data to staff in various departments  Improve inventory utilization Initial Usage: Open source Business Intelligence (BI) but the problem faced – Scalability ADVANTAGES  Provided transparent and up-to-date information for analysis  Embedded data-driven decision making at Flipkart  Improved Inventory Utilization Information gathered over telephonic conversation with IIM L alumnus working in Flipkart
  18. 18. Software Used - Flipkart BIGFOOT - Computerized Maintenance Management Software (CMMS)  1) Managing the maintenance operational needs of organizations  2) Bigfoot CMMS' full functionality paired with its intuitive design allows to implement the solution and get results quickly. KEY FUNCTIONS  preventive and predictive maintenance  inventory management, work order  asset, and equipment management  purchasing  built-in reporting and analysis ADVANTAGES  The system can support any number of facilities and multiple languages  Increases staff productivity and reduce maintenance costs today  Support integration with other systems like ERP, bar code, custom interfaces, advanced reporting solutions building Automation solutions, and Active Directory  Bigfoot CMMS can be configured for different user types, security settings, site and location details, and user access settings
  19. 19. Analytics Practices – eBay ATTRIBUTES PRACTICES In-house/ outsourced  Most of the analytics done by the in- house analytics team  Few practices are outsourced Major Tools  SAS  Excel Major Metrics  Exit Rate, Transactional and operational metrics Analytics major heads 1. Buyer Analytics 2. Seller Analytics 3. Trust Analytics Notable attributes  Analytics used by Marketing team for segmentation of customers or predicting churn rate for customers is handled differently  AB Testing for measuring efficiency of new feature Information gathered over telephonic conversation with IIM L alumnus working in eBay
  20. 20. Major Metrics - eBay EXIT RATE  Which is the page which marks the termination of user’s session  Find the dissatisfying elements of the page if the page is not meant for user to exit the session  Improve the elements from pages in order to increase the length of session and reduce chances for abrupt end of user sessions TRANSACTIONAL METRICS  Number of bought items  Revenue from bought items  Frequency of transaction OPERATIONAL METRICS  Conversion from home page or search results to cart due to some features  Easy payment options increasing number of sales  One click payment option or reach cart at least steps  Customer engagement and avoid exit rates
  21. 21. Buyers Analytics- eBay ANALYTICS FOR HOMEPAGE  Arrange the homepage according to the purchase history, likes and comments of customers  Analyze the increase in number of clicks on home screen and difference in navigation flow  Analyze the increase in number of visits on home page during one session  Analyze number of items listed on homepage to be selected for wishlist or cart ANALYTICS FOR SEARCH  Add a pop up/layer when clicked on an item from search result  Give multiple options on pop up: Checkout, check details, compare  Analyze increased or decreased number of clicks and conversions to cart in order to see efficiency of the new feature and hence decide on whether to continue with the feature or not. BUYERS ANALYTICS deals with the analytics used to design or experiment with the process flow related to purchase of a product E.g. Homepage, Search, View Item window, Checkout, Cart, Wish list etc.
  22. 22. Seller Analytics - eBay ASSORTMENT ANALYTICS  What are the suggested assortments for a seller  Which sellers to be listed so as to maintain the assortments  Major trends like most number of clicks for an item and most selling items  Analyze if the most clicked items is most selling or not? If No, why not? RATING OF SELLERS  Categorize sellers into groups and hence decide on what types of deals to be done with the sellers  Analytics used for recommendation of established and flourishing practices of high rated sellers to the less performing sellers  Categorize sellers as High and low trusted or performing enabling recommendation and listing of items from good sellers to enhance customer experience SELLER ANALYTICS include 1) Assortment Analytics 2)Ratings of Sellers
  23. 23. Trust Analytics - eBay FRAUD ANALYTICS  Which are the sellers or Buyers who are included in fraud  For Example A Buyer may buy a product but deny paying multiple times suggesting fraud  A seller may claim shipment but actually delay the shipment and increase customer waiying time reducing their customer experience  Such accounts for Buyers/ Sellers needs to be blocked for significant duration  Model allow to create a new account  Analyze the fraud accounts either new or old to unlist /block them CREDIT CARDS ANALYTICS  Analyze the credit rating history of customers  Identify the exposure of the card and decide on highest allowed purchase amount. The allowed exposed amount is at risk  Analyze the probability of loosing this money if the customer defaults PRODUCT HEALTH MANAGEMENT  Analytics on products categories to increase customer’s experience and hence loyalty by fostering trust for the product, seller or e-bay as whole TRUST ANALYTICS include 1) Fraud Analytics 2) Credit Cards Analytics 3) Product Health Management
  24. 24. Notable Practices- AB TESTING -eBay DIVISION OF CUSTOMERS INTO TWO SEGMENTS  Control Group (30% customers)  Test Group (30% customers) STEPS IN AB TESTING  Introduce a feature - Eg. Increase the size of a button  Enable the feature for Test Group and keep it disabled for the control Group  Notice the change in behavior - Had the number of clicks increased significantly to measure the positive response of the introduced feature. If yes continue with the feature to enhance customer experience  Decision Making - If the result in not significantly better then retract the introduced feature AB testing  is to check the efficiency of the introduced eBay product or feature  is widely used by Ebay and probably the only major player using it
  25. 25. Notable Practice - RFM Analysis -eBay Recency | Frequency | Monetary for Customer segmentation and Promotional Offers Recorded data in form: Customer ID | Category of purchase | Date of purchase | Quantity of purchase | Amount of purchase Recency Frequency Monetary Get Recency, Frequency & Monetary score out of 5 Calculate the combined score Decide number of clusters & segment customers according to score. Apply promotional schemes. Influence of category is not considered Frequency outweighs other two factors Ideal number of segments- Managerial Decision Which parameters should be focused for the target customer segments Current Scenario Recommendations Analytics used to segment customers and then direct suitable promotional in order to increase the overall revenue generated by each customer Recency – last visit to site Frequency – how frequent is purchase and in what quantity Monetary – amount of money spend
  26. 26. 1) RETAIL ANALYTICS 2) ANALYTICS IN ECOMMERCE INDUSTRY 3) ANALYTICS IN ECOMMERCE COMPANIES 4) RESEARCH PAPERS STUDY 5) RECOMMENDATIONS
  27. 27. Customer Segmentation and Promotional offers RFM Analysis : Suggested Improvements  Instead of rating similarly for all the product for Recency, Frequency and Monetary. Ratings can be done differently for different category. For E.g.  This is so because a customer buying apparel 3 month back may not be term as recent but buying cell phone 5 month back may be termed as recent because of difference in life cycle of the product or category of product  Assign weights to Recency Frequency and Monetary instead of equal weights Home & Kitchen n_Bought_Item n_GMV n_months* score 0<=n<0.35 n<2.5 n<3 1 0.35<=n<0.5 2.5<=n<3 3<=n<5 2 0.5<=n<0.75 3<=n<3.75 5<=n<7 3 0.75<=n<1 3.75<=n<4.5 7<=n<10 4 1<=n 4.5<=n 10<=n 5 Apparel n_Bought_Item n_GMV n_months* score 0<=n<0.35 n<2.5 n<3 1 0.35<=n<0.5 2.5<=n<3.25 3<=n<5 2 0.5<=n<0.75 3.25<=n<3.75 5<=n<7 3 0.75<=n<1 3.75<=n<4.5 7<=n<10 4 1<=n 4.5<=n 10<=n 5 Tech n_Bought_Item n_GMV n_months* score 0<=n<0.35 n<2 n<2 1 0.35<=n<0.5 2<=n<2.5 2<=n<4 2 0.5<=n<0.75 2.5<=n<3.5 4<=n<6 3 0.75<=n<1 3.5<=n<4.25 6<=n<9 4 1<=n 4.5<=n 9<=n 5 Home & Kitchen Factor Weight Recency 1 Frequency 2 Monetary 3 Apparel Factor Weight Recency 2 Frequency 1 Monetary 3 Tech Factor Weight Recency 2 Frequency 1 Monetary 3 Depending on the category one may want customer to be more recent, or more frequent or more revenue generator per purchase
  28. 28. Ideal Clusters based on RFM Recency Frequency Monetary Clusters H H H BEST H H L VALUABLE H L H SHOPPERS H L L FIRST TIMES L H H CHURN L H L FREQUENT L L H SPENDERS L L L UNCERTAIN Customer Segmentation and Promotional offers RFM Analysis : Suggested Improvements Rate the Recency, Frequency and Monetary as High or Low for each customers and then define the segments based on the combination of these values Divide your customers into these 8 segments Now if one wants to convert his valuable customers into best customers he knows that he can target the Monetary value of the customers and direct promotional which would increase the per purchase spending of the customers.
  29. 29. Customer Segmentation and Promotional offers - based on Customer Lifetime Value THREE APPROACHES 1) Segmentation by using Lifetime Value 2) Segmentation by using Lifetime Value components 3) Segmentation by using Lifetime Value & other information Eg: socio-demographic factors or transaction analysis APPROACH I (LIFETIME VALUE)  Customers are sorted in descending order of LTV  Percentile score is generated  Target customers (constraints usually financial budgeting determines how many customers to be targeted)
  30. 30. Customer Segmentation and Promotional offers - based on Customer Lifetime Value APPROACH II (LIFETIME VALUE COMPONENTS) Three components 1) Current Value 2) Potential Value 3) Customer Loyalty  Three axis is derived  Scoring of each customer for each component on a scale of 0 to 1  Segments based on scoring  Eg: A customer with High Current value, Potential Value & Customer loyalty must be retained Internal Data: Customer Profile; Behavior Data; Survey Data External Data: Acquisition data; Co-operation data Current Value; Potential Value; Customer Loyalty
  31. 31. Customer Segmentation and Promotional offers - based on Customer Lifetime Value APPROACH II (LIFETIME VALUE COMPONENTS)  Calculation of Present value Present Value= Amount paid by customer – cost  Calculation of Potential value  Probij : Probability that the customer i uses service/product j out of n services/products Profitij : Profit that the company has when customer i uses product/service j  Calculation of Customer Loyalty Customer Loyalty = 1- Churn rate  Probij and Customer loyalty can be calculated through models like decision tree, neural networks and logistic regression (Training data set : Validation data set :: 30 : 70)
  32. 32. Customer Segmentation and Promotional offers - based on Customer Lifetime Value APPROACH III (LIFETIME VALUE & OTHER COMPONENTS)  Behavioral segmentation in terms of usage volume  Heavy users  Medium users  Light users  Brand buying behavior  Brand loyal  Brand switchers  Customer profitability  Marketing Strategy based on the segments
  33. 33. Customer Segmentation and Promotional offers - based on Customer Lifetime Value CROSS SELLING AND UPSELLING  Segmentation based on current value and Customer Loyalty  SEGMENT I (Loyal but less profitable)  Companies may have large opportunity for upselling  SEGMENT II (Unattractive)  SEGMENT III (Loyal and profitable)  Best for Cross selling of products  SEGMENT IV (profitable but likely to Churn)  Unfit for cross selling but company would like to retain them Current Value Churn probability Low High High II IV Low I III
  34. 34. 1) RETAIL ANALYTICS 2) ANALYTICS IN ECOMMERCE INDUSTRY 3) ANALYTICS IN ECOMMERCE COMPANIES 4) RESEARCH PAPERS STUDY 5) RECOMMENDATIONS
  35. 35. Tactics for Building and Sustaining a Data Analytics Team As per our study we have found that the companies doing major analytics work have in house teams hence we suggest in- house centralized analytics team One core analytics team located at one spot in the organizational chart Ability to allocate resources as needed Team gets exposure and experience on multiple parts of the company Jack of all Trades, Master of None Expertise can be built once the analytics practices have been set In the long run, the company should move to decentralized analytics team to leverage expertise in each of the domains
  36. 36. Building an Analytics Culture  Make intellectual curiosity a priority Technical skills alone are insufficient  Find techies who also can communicate visually Express ideas about how a business use can best consume the output of data analysis  Business Savvy Analytics Focus on important and the right level of granularity  Ensure Cross-Training Expert doing a lunch and learn with the team or writing documents with tips and tricks  Look for domain expertise in your industry They add the perspective of reality  Keep top talent in steady rotation Domain experts gain a stronger understanding of the impact of actionable insights on a company’s day-to-day decision-making  Cultivate a touch of conflict Biggest breakthroughs come from disagreement
  37. 37. References • Customer segmentation and strategy development based on customer lifetime value: A case study Su-Yeon Kim a, Tae-Soo Jung b, Eui-Ho Suh c, Hyun-Seok Hwang d,* • Realizing the Potential of Retail Analytics Plenty of Food for Those with the Appetite – Thomas H Davenport • Explore RFM Analysis using SAS® Data Mining Procedures Ruiwen Zhang, Cary, NC; Feng Liu, University of North Carolina at Chapel Hill, NC • How Predictive Analytics Is Transforming eCommerce & Conversion Rate Optimization (http://conversionxl.com/predictive-analytics-changing-world-retail/?hvid=352IDw) • http://techcrunch.com/2013/08/31/how-amazon-is-tackling-personalization-and-curation-for-sellers- on-its-marketplace/ • http://www.ecommercebytes.com/pr/?id=794560 • http://www.infoworld.com/article/2619375/big-data/amazon-cto--big-data-not-just-about-the- analytics.html • http://blog.sqreamtech.com/2013/12/how-retailers-are-using-big-data-to-improve-sales-and- customer-service/ • http://aws.amazon.com/elasticmapreduce/ • https://gigaom.com/2011/10/18/amazon-aws-elastic-map-reduce-hadoop/ • https://datafloq.com/read/amazon-leveraging-big-data/517 • http://www.predictiveanalyticsworld.com/patimes/amazon-knows-what-you-want-before-you-buy- it/
  38. 38. Thank you

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