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

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

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

1) RETAIL ANALYTICS
2) ANALYTICS IN ECOMMERCE INDUSTRY
3) ANALYTICS IN ECOMMERCE COMPANIES
4) RESEARCH PAPERS STUDY
5) RECOMMENDATIONS

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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
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
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

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
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.

- 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)

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

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)

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

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

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

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

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/

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