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

Retail Analytics – E-Commerce

Group 9IIM LucknowAnju R Gothwal PGP28250

Animesh PGP29181

Malory Ravier IEP15003

Mayank Khatri PGP29220

Richa Narayan PGP29207

Shashank Singh Chandel PGP29493

Tushar Gupta PGP29197

AGENDA1) 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



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 ANALYTICSMarket 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

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5) RECOMMENDATIONS

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 servicesPredictive 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

1) RETAIL ANALYTICS




5) RECOMMENDATIONS

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 - AmazonPRODUCT 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 include1) 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 -eBayRecency | 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

1) RETAIL ANALYTICS




5) RECOMMENDATIONS

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

1) RETAIL ANALYTICS




5) RECOMMENDATIONS

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 customerlifetime value: A case studySu-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 ProceduresRuiwen 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/

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/

Thank you

Data & Analytics

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