Retail Analytics – E-Commerce

Group 9IIM LucknowAnju R Gothwal PGP28250Animesh PGP29181Malory Ravier IEP15003Mayank Khatri PGP29220Richa Narayan PGP29207Shashank 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 STUDYCustomer 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 ANALYTICSCustomer AcquisitionCustomer LoyaltyBehavioral SegmentationGeneral Merchandiser - TESCO

MARKETING ANALYTICSMarketing MixBrand HealthMultichannel Campaign OptimizationApparel Chain – SEARS CANADA

MERCHANDISING AND PLANNINGShelf space optimizationProduct PricingStore Location DecisionsFashion Retail – BELK

RISK ANALYTICSDetecting Fraudulent activityDetecting Process ErrorsDetecting Store TheftOnline Retailer - AMAZON

DEMAND AND SUPPLY CHAINInventory PlanningDemand ForecastingProduct Flow OptimizationDepartment Store – METRO GROUP

PREDICTIVE ANALYTICSDetermining Customer LTVRevenue forecastingProduct RecommendationsTrend 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 allServices 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

1) RETAIL ANALYTICS

2) ANALYTICS IN ECOMMERCE INDUSTRY

3) ANALYTICS IN ECOMMERCE COMPANIES

4) RESEARCH PAPERS STUDY

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 APPLICATIONSGoogle Analytics Monitors traffic from social media, emails Measures effectiveness of marketing

programAdobe Site Catalyst Real time segmentation Increase checkout conversion ratesIBM 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 DESCRIPTIONVisitors No of visitors tells how business is doingPage Views Maximum viewed Tells the popular contentReferring Sites Tells the interests of customerBounce Rates Tells why people leave the siteKeywords 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 searchPersonalization

• 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 inventoryManaging 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

2) ANALYTICS IN ECOMMERCE INDUSTRY

3) ANALYTICS IN ECOMMERCE COMPANIES

4) RESEARCH PAPERS STUDY

5) RECOMMENDATIONS

Analytics Practices – Amazon

ATTRIBUTES PRACTICESIn-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 Analytics2. Seller Analytics3. Trust Analytics4. 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 purchases2) Items customers have rated and liked3) Purchases compared to similar purchase by other competitors4) Items in virtual shopping carts

Generates approximately 29% sales from recommendationsCUSTOMER 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 terminalsINVENTORY 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 centerDEMAND 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 demandLOGISTICS Patented ‘Method and System for Anticipatory Package Shipping’ Anticipates customer needs before they express them Analyzes a) Customer Ordering History d) Feedbacksb) Wish-lists e) Searchesc) 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 address3) 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 – ScalabilityADVANTAGES 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 - FlipkartBIGFOOT - 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 analysisADVANTAGES 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 PRACTICESIn-house/ outsourced Most of the analytics done by the in- house analytics team

Few practices are outsourcedMajor Tools SAS

ExcelMajor Metrics Exit Rate, Transactional and operational metrics

Analytics major heads 1. Buyer Analytics2. Seller Analytics3. 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 - eBayEXIT 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 sessionsTRANSACTIONAL 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 productE.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 include1) 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

2) ANALYTICS IN ECOMMERCE INDUSTRY

3) ANALYTICS IN ECOMMERCE COMPANIES

4) RESEARCH PAPERS STUDY

5) RECOMMENDATIONS

Customer Segmentation and Promotional offersRFM 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 & Kitchenn_Bought_Item n_GMV n_months* score

0<=n<0.35 n<2.5 n<3 10.35<=n<0.5 2.5<=n<3 3<=n<5 20.5<=n<0.75 3<=n<3.75 5<=n<7 30.75<=n<1 3.75<=n<4.5 7<=n<10 4

1<=n 4.5<=n 10<=n 5

Appareln_Bought_Item n_GMV n_months* score

0<=n<0.35 n<2.5 n<3 10.35<=n<0.5 2.5<=n<3.25 3<=n<5 20.5<=n<0.75 3.25<=n<3.7

5 5<=n<7 30.75<=n<1 3.75<=n<4.5 7<=n<10 4

1<=n 4.5<=n 10<=n 5

Techn_Bought_Item n_GMV n_months* score

0<=n<0.35 n<2 n<2 10.35<=n<0.5 2<=n<2.5 2<=n<4 20.5<=n<0.75 2.5<=n<3.5 4<=n<6 30.75<=n<1 3.5<=n<4.2

5 6<=n<9 41<=n 4.5<=n 9<=n 5

Home & KitchenFactor Weight

Recency 1Frequency 2Monetary 3

ApparelFactor Weight

Recency 2Frequency 1Monetary 3

TechFactor Weight

Recency 2Frequency 1Monetary 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 RFMRecency Frequency Monetary Clusters

H H H BESTH H L VALUABLEH L H SHOPPERSH L L FIRST TIMESL H H CHURNL H L FREQUENTL L H SPENDERSL L L UNCERTAIN

Customer Segmentation and Promotional offersRFM Analysis : Suggested ImprovementsRate 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.

Customer Segmentation and Promotional offers- based on Customer Lifetime Value

THREE APPROACHES1) Segmentation by using Lifetime Value2) Segmentation by using Lifetime Value components3) 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)

Customer Segmentation and Promotional offers- based on Customer Lifetime Value

APPROACH II (LIFETIME VALUE COMPONENTS)Three components

1) Current Value2) Potential Value3) 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

DataExternal Data: Acquisition data; Co-operation data

Current Value; Potential Value; Customer Loyalty

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/productsProfitij : Profit that the company has when customer i uses product/service j

Calculation of Customer LoyaltyCustomer 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)

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

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 ValueChurn probability Low High

High II IVLow I III

1) RETAIL ANALYTICS

2) ANALYTICS IN ECOMMERCE INDUSTRY

3) ANALYTICS IN ECOMMERCE COMPANIES

4) RESEARCH PAPERS STUDY

5) RECOMMENDATIONS

Tactics for Building and Sustaining a Data Analytics TeamAs 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 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-custo

mer-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