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IBS Bangalore MANAGEMENT RESEARCH PROJECT A REPORT ON MARKETING ANALYTICS USING SAS By Anil KURHEKAR 08BS0000317 IBS Bangalore March 2010

Marketing Analytics using SAS

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A Research Project on Marketing Analytics / Retail Analytics using SAS 9.1 --- By Anil S. Kurhekar

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Page 1: Marketing Analytics using SAS

IBS BangaloreMANAGEMENT RESEARCH

PROJECT

A REPORT

ON

MARKETING ANALYTICS USING SAS

By

Anil KURHEKAR

08BS0000317

IBS Bangalore

March 2010

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A REPORT

ON

Marketing Analytics using SAS

-:By:-

Anil Kurhekar

08BS0000317

Submitted To:

Prof. Shailendra Dasari,

IBS Bangalore

A report submitted in partial fulfilment of the requirements of

MBA Program of

ICFAI Business School, BANGALORE

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Marketing Analytics using SAS

ACKNOWLEDGEMENT

I take this opportunity to express my profound gratitude and deep regard to my guide Prof. Shailendra Dasari, for their guidance, monitoring and constant encouragement throughout the course of this project work.

I am grateful to Dr. Latha Chakravarthy who gave me the opportunity to do this project.

I would also like to thank Mr. Mahesh of Fresh greens Pvt. Ltd. who assisted me in best possible manner. I would also like to thank Mr. Anil John of Genpact and Mr. Niranjan Prabhu of Dell who helped me during the course of my project.

Date: 10th March 2010 Anil Kurhekar

IBS, Bangalore

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Marketing Analytics using SAS

ABSTRACT

Marketing Analytics is the modern approach to analyze the customer and sales data. The analytics approach to marketing function helps in identifying key market as well as customer segments, assessing business needs and coming up with effective business strategies. Marketing analytics harnesses the capabilities of advance techniques in analysing customer-centric data that helps organizations to identify avenues to create business value, thereby making the business profitable.

The project titled “Marketing Analytics using SAS” is a research project undertaken to understand growing practices of Analytics in various Industry domains and to learn the role of analytics for giving competitive advantage to the company. For the practical understanding, the project aims to apply analytics for a company and provide recommendations that are profitable for the company.

The project broadly covers following points:

1. Base line study of Marketing Analytics in the Industry for various domains.

2. Application of Marketing Analytics at Fresh Greens Pvt. Ltd.

Base line study shows that the Analytics is the Next Big Thing for the business for various Industries like Retail, Telecom, Banking and finance. Primary and Secondary data analysis shows leading practices in Analytics field.

For Practical understanding, the sales data of “Fresh Greens Pvt. Ltd, Bangalore” is analysed for Market Basket Analysis and Demand Forecasting. The analytics software SAS is used for analyzing and understanding sales trends for each items. The analysis shows some of the magnificent results about future product demands, frequency of product purchases and the combination of product purchases. With this knowledge, company can plan and mange the operations very effectively and efficiently.

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Table of Contents

1 Project Brief...................................................................................................................................1

1.1. Background............................................................................................................................1

1.2. Objective of the Project:........................................................................................................1

1.3. Approach and Methodology:.................................................................................................1

2 Introduction to Marketing Analytics..............................................................................................1

2.1 Marketing challenges.............................................................................................................1

2.2. Marketing Analytics...............................................................................................................1

2.2 Business Benefits of Marketing Analytics..............................................................................1

2.3 Automation of Marketing Activities.......................................................................................1

3 Primary Research: Case Studies.....................................................................................................1

3.1 Analytics at Dell Computers:..................................................................................................1

3.2 GENPACT Analytics Services...................................................................................................1

4 Analytics in Retail sector................................................................................................................1

5 Analytics in Telecom Sector...........................................................................................................1

6 Analytics in Finance and Banking...................................................................................................1

7 Analytics Tools...............................................................................................................................1

8 Business Analytics Companies in India...........................................................................................1

9 Application of Marketing Analytics for Fresh Greens Pvt. Ltd........................................................1

9.1 Company Profile: Fresh Greens Pvt. Ltd. Bangalore...............................................................1

9.2 Problem Definition:................................................................................................................1

10 .Market Basket Analysis:-...........................................................................................................1

10.1 Analysis using SAS Enterprise Miner:.....................................................................................1

11 .Demand Forecasting.................................................................................................................1

11.1 Analysis using SAS Time Series Forecasting System:..............................................................1

12 Conclusion and Recommendations:...........................................................................................1

13 References.................................................................................................................................1

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1 Project Brief

1.1.BackgroundMarketing Analytics is the science of analysing customer and/or market data. The analytics approach to marketing function helps in identifying key market as well as customer segments, assessing business needs and coming up with effective business strategies. Marketing analytics harnesses the capabilities of advance techniques in analysing customer-centric data that helps organizations to identify avenues to create business value, thereby making the business profitable.

The project aims to understand growing practices of Analytics in various Industry domains and to learn the role of analytics for giving competitive advantage to the company. For the practical understanding, the project aims to apply analytics for a company and provide recommendations that are profitable for the company.

1.2. Objective of the Project:1.. Base line study of Marketing Analytics in the Industry (various domains).

2. Applying Marketing Analytics and giving recommendations to the chosen company i.e. Fresh Greens Pvt. Ltd.

1.1 Approach and Methodology: The project is focused on understanding this concept and its applications in industry. This was carried out with the help of discussion with industry experts. Primary Research conducted to understand how Analytics can be used to make marketing activities more effective and efficient. Primary Research comprise of Direct Interviews with Analytics Experts. Interviews may be through meeting personally, Tele-communication or e-mail communication. This exercise helped to give insights on the use of Analytics in company for various functions.In the second part, project carried out to analyze the data of a company using analytics software SAS 9.1. Exercise started from identifying the company to apply Analytics. Second step is defining the problem by discussing with company executives which is followed by important step of relevant data collection from company. Analysis of the data covered concepts like Demand Forecasting and Market Basket Analysis depending on company’s requirements. The project concluded with interpreting the results and recommending solutions to the company.

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2 Introduction to Marketing Analytics

2.1 Marketing challengesLet us understand the challenges for marketing in the current scenario:

1. Increase of customer touch points:

In the past marketers interacted with customers primarily through three channels: call centres, direct mail and face-to-face. Today, even small to mid-sized retailers reach customers through dozens of channels: email, fax, pagers, Internet, trade shows, value-added resellers, distributors and more.

How can marketers gather a consistent view of the customer that crosses all those diverse touch points, while still personalizing the view of each individual customer?

2. Heightened expectations for marketing campaigns:

It is very easy for Fortune 500 companies to plan as many as 3000 campaigns in a single year but still the effort are insignificant if they are not reaching prospects likely to buy. They can’t afford to send direct mails to huge, undifferentiated databases. The frequency and turnaround of campaigns is higher than ever, and so is the expectation for return on investment.

How can marketers be sure they’re accurately targeting the right audience with the right offer at the right time?

3. Lack of cross-functional cooperation:

The marketing process is shaped by different groups of users with widely differing requirements. Narrow technology that focuses on only a few small pieces of campaign implementation makes it extremely difficult for key players on the marketing team — including business analysts, database marketers, quantitative analysts and IT— to effectively leverage each other’s contributions and collaborate on a comprehensive, repeatable marketing process.

How can you implement a technology framework that supports the entire marketing team and the entire process, from setting strategy, to targeting opportunities, implementing customer communication initiatives and measuring results?

4. Rapid growth in organizational data:

Discrete enterprise systems churn out gigabytes of data about customers and campaigns both online and offline, yet few enterprises are in a position to assemble that information into a logical picture that can support informed, intelligent decision making.

How can marketers access, consolidate and clean all available customer data to create a comprehensive foundation for deriving the best customer intelligence?

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5. New regulatory challenges:

Anti spam legislation, the Do-Not-Call Registry and other regulatory initiatives are forcing marketing departments to rethink their communication strategies. Blind delivery of unsolicited offers is now illegal in many cases, making it more important than ever to implement a reliable method for controlling customer contacts.

How can you consistently enforce a customer contact policy and ensure that different business units aren’t sending multiple or conflicting offers to the same customers?

6. The need to respond more quickly and effectively to customer behaviour:

The interaction between business and customer is best understood as a two-way communication. Customers often don’t communicate with vendors directly, however. Instead they respond to offers through various behaviours: purchasing a new product immediately or failing to purchase anything for a period of time. Even when a customer purchases a different type of product than usual, that behaviour can be a significant input to use when evaluating future interactions with that customer.

How can companies most effectively keep up with the listening (event-driven) end of the customer dialogue and translate that information into more profitable, timely customer interactions?

7. Resource constraints that limit possibilities:

Even with the volume of campaigns that large companies run in a given year, the reality is that marketing resources are not unlimited. Every marketer knows the pressure of budget constraints, but how do channel constraints, such as call centre capacity or revenue goals affect the offers that a company presents to its customers?

How can a marketing organization determine the best possible set of offers to present, to which customers, within the bounds of resource constraints, available offers and marketing goals?

With increased customer expectations and demand for an exact fit to requirements, it is increasingly important to not only provide accurate insights about the customer, but to put that information within reach of all contributors to the marketing process.

2.2 Marketing Analytics

Marketing is the backbone of any profitable business. This function contributes in understanding the customers and devising strategies beneficial for the business. This customer-facing entity in any business has the responsibility of managing competitors, stakeholders’ satisfaction and demands and the ROI of the business. This entity interacts with all other operational and strategic entities of a business to gather inputs and come up with a business strategy in handling the key responsibilities.

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The analytics approach helps the marketing function in identifying the key market as well as customer segments, assessing the business needs and coming up with effective business strategies. Marketing analytics harnesses the capabilities of advanced techniques in analyzing customer-centric data that helps organizations identify avenues to create business value, thereby making the business profitable.

Marketing analytics and campaign analytics are considered synonymous by service provider organizations. Marketing is the key entity in a service provider organization acting as an interface between customers and an organization’s other internal entities.

The business value of any service provider organization is highly dependent on its valuable customer base. With growing competition and customer demand, globalization and mergers and acquisitions, the service provider needs a high-value persistent customer base for survival. Customer relationship management is an integral part of a marketing portfolio in any business. The major strategic decisions influenced by marketing are:

Valuable and reliable customer acquisition – creating value;

Cross-/up-sell of products and services – enhancing value; and

High-value customer retention – sustaining value.

Campaign management encompasses analysis of data for planning campaigns, campaign execution, monitoring campaign performance and incorporating lessons learned in enhancing business value from campaigns.

Marketing analytics is highly customer-centric and primarily utilizes statistical and advanced mathematical techniques in predicting future behavioural characteristics and product/service preferences of customers based on the historical information. Figure 2 provides an overview of components of marketing analytics. Since “marketing” is the key customer-facing entity in any business, marketing analytics analyzes the data in the following areas:

Customer interaction data – demographic, behavioural, attitudinal.

Customer tenure in the system.

Customer net present value.

Customer product and service preferences.

Marketing analytics aids in structuring campaigns with higher success rates and also fine-tunes the campaign management strategy based on insights gathered by monitoring campaign execution and aftermath.

The three major components of marketing analytics are:

Associate – Finding the potentially strong association(s) amongst products and services preferred by customers – based on the recency, frequency and monetary analysis of the historical associations.

Profile – Profiling customers based on their product and service preferences and based on the strength of associations identified as part of previous component.

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Predict – Predicting customers’ propensity to respond/buy - Their future response behavioural trend prediction for targeted marketing.

These three components help in understanding prospects and customers across all stages of a customer lifecycle.  Interestingly, this approach is cyclical, with the learning in each phase carried over to the next phase by a feedback loop, reflecting “continuum learning,” fine-tuning every successive phase.

One of the key derivatives of this analysis is gaining insights on customer-centric product and service affinities. One of the business objectives of campaigns is to understand the customers’ perception of products and services offered by the service provider. This approach helps in gaining deeper insights into customers’ preferences of products and services, product/service-based customer profiling, propensity to respond in future campaigns and also their perception of offered products and services. This additional information is beneficial for internal entities working on product development and service-providing strategies.

2.3 Business Benefits of Marketing Analytics Create more effective marketing strategies. Targeting potential customers with

higher propensity to respond makes the campaigns more effective.

Develop bundled promotions and product offerings. Preferred associations of products and services as prioritized based on customer buying behaviours aids in designing profitable product-service mix.

Reduce marketing efforts to unlikely buyers. Bring down the campaign cost by avoiding mass campaigning and targeting the customers with higher propensity to buy. Receive up-to-date information on product performance across the organization. Get the customer-centric product and service affinities as a derivative of campaign analytics approach.

Gain insight into customers and their purchasing behaviour. Customer profiling will identify common characteristics of customers with specific product and service preferences.

Increase promotional profitability and campaign effectiveness. This increases the ability to measure, manage and improve overall service provider performance, enhancing the business value of service provider.

2.4 Automation of Marketing ActivitiesSome of the common analytics practices in Marketing Activities are listed below:

1. Market basket analysis — Analyze the mix of products that a given customer purchases, with a view to understanding what other products to sell them.

2. Segmentation analysis — Identify the most valuable and profitable customers to helpDefine appropriate target marketing programs

3. Cross-selling predictions — Identify the right time to make an offer to an existingCustomer, and determine the optimal content and contact channel.

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4. Customer channel analysis — Analyze and predict the most suitable and efficientChannels for initial contact, up-selling and cross-selling activities.

5. What-if analysis — Change key campaign variables and determine how they affect the outcome.

6. Customer value modelling — Calculate the total value of keeping customers throughout the lifetime of the relationship.

7. Customer risk analysis — Calculate the risks associated with a given customer, including credit risk, likelihood of defection to a competitor and so on

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3 Primary Research: Case Studies

3.1 Analytics at Dell Computers:

Dell Inc. is a multinational technology corporation that develops, manufactures, sells, and supports personal computers and other computer-related products. The company also uses Analytics for their marketing activities.

Following is a project at Dell that describes the use of Analytics in Marketing:

1. Project Title: To understand the ROI of Marketing Communication (MARCOM) investments made through various MARCOM vehicles like newspapers, magazines, flyers, etc

2. Project Area: Computer hardware sector3. Objectives of the project :

To understand the ROI across various advertising vehicles and help in reassign/re-plan MARCOM spend to maximize ROI.

4. Project Description in brief : The various aspects of the business starting from advertisements being placed in various advertising vehicles, prospective callers calling in, making purchases if convinced by the sales agent at dell, are all captured in various systems in Dell. The objective was to create a marketing database that would contain all this information in one single data mart which could then be used to pull out actionable information that would be used in maximization of ROI

5. Analytical Tools used in the project: SAS Enterprise Guide, SQL Server 2000, MS Analysis Services, Excel

6. Analytical Techniques: The data mart itself can be used for exercises such as market mix modelling, though the same is pending initiative. Currently time series analysis is being conducted to check out the ROI month on month on various advertising vehicles, the same is studied, and shifts are executed in MARCOM spend with the objective of improving ROI.

7. Benefits for Dell Computers: Helped in giving direction for analysis and spend of MARCOM budgets

8. Project Duration : One year

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3.2 GENPACT Analytics Services

GENPACT is leading Analytics provider in India providing analytics services to almost all industry domains.

1. Business Problem: A credit card company which is a client of GENPACT, wants to promote new offers to their existing customers. This they want to promote through direct mails but they are not clear about whom to send the mails out of their huge customer database. They can’t send mails to each and every customer that costs them huge amount.

2. Objectives of the project: To find out target customers who are likely to response for the new offer. To segment the customers by analysing their past data to reduce the cost direct mailings.

3. Project Area: Banking Sector4. Project in brief:

Database of existing credit card customers are used for analysis and their possibility to respond to that offer is predicted. This can be done using predictive modelling. The objective was to create model that would segment the customers who are likely to response.

5. Analytical tools used in the project: Base SAS, SAS Enterprise Guide

6. Analytical Technique: Logistic Regression.SAS programming can be done in SAS enterprise Guide and the predictive model was prepared using following variables. Dependant Variable(Y): Response or No Response- whether customer will respond to that offer or not.Independent Variables (X): Variables that describes demographic characteristic and performance of Account X1: Credit card amount,X2: card holder income,X3: card validity period, X4: Balance amount in the accountX5: Different kinds/types of cards that customer has.X6: Loan amount for that account.

7. Benefits to the client:1. Being the knowledge of target customers, company can send lesser mails that save lot

of money (thousands of dollars).2. Increase in response rate: Generally response rate through this channel is hardly 1% to

2%. Using analytics there is increase in response rate up to 30%.

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4 Analytics in Retail sector

Retail is an industry that is in a constant state of change. Increasing competition, a wide array of product offerings, increasing customer sophistication, multiple touch points to the customer, and consumer complexity are just a few of the many examples that constitute a dynamic and constant state of change in the area of retail sales. As the population continues to increase and consumers are presented with more choices, the numerous challenges that a retailer faces become more pervasive. To stay competitive, retailers must take an analytical, guided, and prescriptive approach to better understand their business and anticipate customer behavior.

Marketing Campaign Effectiveness : As the channels by which consumers shop evolve, retailers are faced with multiple dilemmas on how to target customers . The analytics can assist in targeting the appropriate customer, increase response rates for the campaign, reduce associated costs, and increase customer satisfaction. With analytics, an organization can wisely target the correct customers with the correct offering.

Market Segmentation : Segmentation can be used to help retailers better understand the spending patterns, communication preferences, and merchandising preferences of their customers, and to group customers uniquely by these characteristics. These same principles can be used to segment stores, partners, and vendors. This knowledge helps you drive promotions, pricing strategies, and marketing campaigns, and achieve a better relationship with customers, vendors, and partners.

Market Basket Analysis: The Market Basket Analysis used to understand the probability of product purchased together by customer. The information gathered from a Market basket analysis can be used to cross-sell, bundle products that are frequently purchased together to increase customer satisfaction as well as up-sell, and offer products with higher margins alongside products with high demand to increase revenues. Businesses can leverage the insights gained from a Market basket analysis to optimize assortment planning and validate promotions. The results of a Market basket analysis can also be incorporated into a real-time marketing offer environment at check out. When a customer makes their purchase at check out, the company can offer the appropriate marketing ad or product recommendation for future purchases on the customer receipt, which can potentially affect sales and customer satisfaction.

Demand planning and Sales forecasting: Accurate demand planning has a tremendous impact on customer satisfaction, store operating costs, relationships with suppliers, and much more. A stock out occurs when a store is out of stock on a particular item. This causes not only a loss in sales, but potentially a decline in customer satisfaction. On the other hand, over stocking a store with too many of a particular item suggests that the retail store does not understand their customer base. Hence, accurate demand planning and sales forecasting is very important and can be done through various advanced analytical tools like SAS and SPSS.

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5 Analytics in Telecom Sector

The telecom Industry is growing substantially with significant innovation and expansion. To keep up with this changing trends and highly competitive environment, telecom companies are evolving into full service communication providers delivering high speed connectivity and bunch of value added services. The key focus is on retaining existing customer besides adding new customer and servicing them, at reduced cost.

To sustain in this highly competitive industry and to meet rising challenges, telecom operators world over have begun using data as differentiating tool to enable timely and effective business decisions. Many companies are investing in complex data analytical solutions to maximize their profits and winning best returns on the invest made.

Analytics is used for following activities:

1. Churn Management: By identifying the major contributing factors to the generated churn score, the reasons for customer’s probable churn can be detected. By addressing those issues successfully, the customer can be made more loyal to the company and can be prevented from churning.

2. Customer Segmentation : Analytics can be used to analyze various parameters like Incoming & outgoing voice usage, Recharge, Revenue, Usage of value added services, Usage of data services etc. the entire customer base can be segmented on various groups whose behaviour and needs will have significant difference which can be identified and addressed to enhance their share of wallet.

3. Campaign Management: The business analytics solutions can provide an integrated platform for campaign design and execution. Based on the customer segmentation, churn score, usage pattern, recharge history, campaigns can be designed for retention, revenue enhancement (increasing customer wallet share) and cross-sell and up sell.For example: A retention campaign can be designed for the customers who have high usage (monthly usage more than ARPU) and have high churn score. These are valued customers and the company will not like to lose them out.

4. Cross sell and Up sell : Analyzing the data extracted regarding the customer usage, their balance availability and their subscription pattern, the telecom service providers can build up data models to identify the product bundling and hence can detect the opportunities for cross sell and up sell.For example: By analyzing the historic data (say for 6 months) and building a data model on the basis of various parameters of a voice usage customer, the potential customers for data usage and other value added services provided by the telco can be identified, who can be targeted by product bundled promotions.

5. Profitability Analysis: With the help of the available data, the telcos can monitor the performance of the individual portfolios. They can measure the return on investments for individual product/services offered, price plans. Based on the performance

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analysis, the service providers can decide on which plan/products/services to go ahead with in future. They can perform a profitability analysis on the new products.

6. Credit Risk Management: Business analytics provides views regarding credit payment risk. It can rate the customers based on their historical behaviour and credit dues. Therefore, the services need not have to be disconnected for those customers having low credit risk scores, whereas the customers with high credit risk score need to be dealt with lesser tolerance and can be decided for a termination or shifted to pre-paid scheme depending on further data analysis about those customers.

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6 Analytics in Finance and Banking

Banks have shown highest adoption of analytics. Analytics in the banking industry help banks to reduce their exposure, cut down on customer acquisition costs and extract better profitability from existing customers.

Banks use analytics for various reasons such as retention of credit card customer, to understand risk of loan defaulters etc.

Following are the common practices in Banking Analytics:

Pricing Analytics: Selection of optimum pricing strategy for different products and identify critical price points relevant to market

Customer Segmentation Analysis: Segmentation of the customers and design appropriate offerings based on their profile

Fraud Modelling: Assignment of risk scores to customers and predictive modelling for early detection of fraud

Customer Lifetime Value Modelling: Estimation of the revenue stream from a customer over time

Scoring Analytics: Prediction of whether/ how a customer will respond to any campaign or product offering

Churn Analytics: Identification of the reasons for customers to discontinue usage of a product/service, determine the time of likely attrition of a customer and select the appropriate retention strategies (Loyalty Programs, Customer Service, etc)

Campaign Design Services:  Selection of targeting methods (mailers, telemarketing, etc), marketing channels (agents, brokers, in-house telemarketers), and to structure campaign features

Constraint Optimization: Determination of product-brand mixes and optimal ROI subject to budget constraints

Cross Sell / Up Sell Analytics: Identification of selling opportunities in complementary products, and to sell higher end products

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7 Analytics Tools

1. SAS

SAS is a widely used Business intelligence software developed by SAS Institute, USA. SAS is an integrated system of software products provided by SAS Institute that enables the programmer to perform:

data entry, retrieval, management, and mining report writing and graphics statistical analysis business planning, forecasting, and decision support operations research and project management quality improvement applications development data warehousing (extract, transform, load) platform independent and remote computing

SAS Enterprise Miner is mainly used for Data mining and Business Intelligence. Association Rules and Decision Trees are used for Market Basket Analysis and Segmentation Analysis respectively.

2. SPSSPASW (formerly SPSS) is a computer program used for statistical analysis. Before 2009 it was called SPSS, but in 2009 it was re-branded as PASW (Predictive Analytics SoftWare) .The Company announced July 28, 2009 that it was being acquired by IBM for US$1.2 billion.

Statistics included in the base software:

Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore, Descriptive Ratio Statistics

Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial, distances), Nonparametric tests

Prediction for numerical outcomes: Linear regression Prediction for identifying groups: Factor analysis, cluster analysis (two-step,

K-means, hierarchical), Discriminant

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8 Business Analytics Companies in India

Following is the list of analytics companies in India (using SAS/SPSS)

Branded Analytics providers:

Sr.No.

Company Location in India

Services Provided Industries Website

1. Fractal Analytics Gurgaon CRM Analytics, Market mix modelling, Consumer Analytics, Risk Analytics

Financial Services, Retail, CPG, Insurance, Telecom

www.fractalanalytics.com

2. EXL Services Gurgaon Marketing and customer analytics

Insurance, Banking and Finance, Utilities, media, Health care.

www.exlservice.com

3. Mu-Sigma Bangalore Marketing Analytics, Risk Analytics, Supply Chain Analytics

Financial, Retails, Healthcare, Pharmaceutical

www.mu-sigma.com

4. Marketics or WPP Bangalore Consumer and MR Analytics, CRM Analytics, Hybrid Analytics.

CPG, Beverages, Travel, Automotive, Pharma, Apparels,

www.marketics.com

5. Meritus -Mindtree Bangalore Market Mix Models, Market Forecasting, Market Budget, Allocation and Optimization, KPI Assessment and Monitoring

Luxury Goods, Insurance, Fuel Retail, Consumer Retail, garment Retail, Impulse Food

www.meritusglobal.com

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KPI, Customer Analytics and Predictive Analytics

6. Denuosource Hyderabad MarkSeg, AdSpace, LoyalyFactor

Consumer Goods, product Marketing, Publishing Retail and Private Equity

www.denuosource.com

7. Modelytics Bangalore Marketing and Consumer analytics

Mortgage and Home Equity, Retail banking, Consumer lending portfolio management

www.modelytics.com

8. Dexterity Chennai Marketing Analytics, MR Analytics, Customer Analytics

Food, Beverages, Automotive, Cosmetics, Retail

www.dexterity.in

9. PharmArc Bangalore Marketing and Sales Analytics Health and Pharmaceutical www.pharmarc.com 10. GENPACT Bangalore Predictive Scoring, Business

Research, Market Research, MIS & Reporting, Forecasting, Credit Decisioning, Financial Modelling, Simulation, Clinical Data Intelligence, Customer Loyalty, Direct Marketing, Sales Force Effectiveness, Inventory Optimization, Logistics Analytics, Pricing Analytics, and Strategic Sourcing.

Automotive, banking and Finance, CPG, Healthcare, Manufacturing, oil & gas, Insurance, Logistics, Pharmaceuticals

www.genpact.com

11. Symphony Marketing solutions

Bangalore Marketing Analytics, Customer Analytics

airlines, consumer goods, pharmaceuticals, financial services, telecommunications

www.symphonyms.com

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IT companies with a strong analytics practice:

1. Infosys, Bangalore2. HCL -Chennai and Bangalore3. Cognizant (Market Rx)- Chennai, Pune, Gurgaon4. TCS, Chennai and Mumbai5. Wipro, Kolkata, Bangalore6. Hexaware,Chennai

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MNC's Units:

ZS Associates, Pune JP MORGAN, Mumbai Acnielsen, Mumbai

Netapps, Bangalore Amex, Gurgaon Milwardbrown, Chennai (Maps)

Cisco, BangaloreStandard Chartered Bank, Chennai Novartis, Hyderabad

Google, Hyderabad IBM, Bangalore Deloitee, Hyderabad Chainalytics, Bangalore

Accenture, Gurgaon, Bangalore HLL, Bangalore

Amazon, Bangalore

UBS, Hyderabad (Acquired by Cognizant) Mckinsey, Gurgaon

eBay -Chennai Microsoft, Bangalore Boston consulting, Mumbai

Citibank, Chennai Fair Isaac IndiaRedpill solutions, Chennai (Acquired by IBM)

Dell Analytics, Bangalore

Dun & Bradstreet, Chennai Target, Bangalore

Fidelity, BangaloreGlobal Analytics Inc., Chennai Supervalu, Bangalore

HP Analytics, Bangalore Dunhummby, Gurgaon Tesco, Bangalore HSBC Analytics, Bangalore

General mills, Mumbai UST Global, Chennai

Citianalytics, Bangalore, Mumbai (Acquired by TCS)

Indian Banks with Analytics operations 1. ICICI, Mumbai 2. HDFC, Mumbai 3. Standard Chartered Bank, Chennai

Mobile Service provider with Analytics Operations 1. Vodafone Telecommunications, Chennai, Mumbai

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2. Nokia Networks, Gurgaon 3. Airtel, Gurgaon

Indian companies with Advanced web analytics (Behavioural Targeting using SPSS/Unica Affinium/R) 1. Rediff.com 2.Bharatmatrimony.com (Consim info pvt ltd.), Chennai 3. Naukri.com (Info edge group), Chennai 4. Timesofindia (Online Newspaper), Mumbai

Firms involved with Quantitative research Techniques 1. Irevena, Chennai 2. Amba Research, Bangalore

Indian Retail stores within house Analytics operations 1. Shopperstop 2. Reliance retail

Indian Manufacturers having in house Analytics operations 1. Caterpillar,Chennai 2.Johndeere,Pune

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9 Application of Marketing Analytics for Fresh Greens Pvt. Ltd.

9.1 Company Profile: Fresh Greens Pvt. Ltd. Bangalore.

Fresh Greens is a well organised vegetable retail store located at ISRO Layout, Bangalore. It was started an young entrepreneur in April 2009 with target to open the chain of stores in Bangalore. In the next four months they are planning to open another stores at two locations.

9.2 Problem Definition:

1. Company has a billing and data storage system which can store the complete sales data bill wise as well as item wise. Company can get sales figures item wise but system can’t able to forecast the demand for particular item. Company wants a solution which can able to forecast the demand of the product by analysing previous trends of sales.

2. Company also wants to analyze the customer’s basket i.e. frequency of items and combinations with other items. It wants to know which are most frequently sold items so that they can plan their inventory and money.

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10 .Market Basket Analysis:-

Market Basket Analysis has emerged as the next step in the evolution of retail merchandizing and promotion. Market Basket Analysis allows leading retailers to quickly and easily look at the size, contents and values of their customers’ market basket to understand the patterns in how the products are purchased together. Advanced implementations of market basket analysis leverage near instant results to encourage train of thoughts or interactive analysis, enabling retailers drill down into customer buying pattern over time to precisely target and understand the specific combinations of the products, departments, brands, categories and even time of day.

With Market Basket Analysis leading retailers can drive more profitable advertising and promotions, attract more customers and increase values of the basket. Buyers, planners, merchandisers and store managers are beginning to understand how this new generation of easy to use market basket analysis tools helps them to work smarter and compete them more successfully.

Leading Retailers are leveraging Market Basket Analysis to:

1. Develop more profitable and advertising promotions2. Target offers more precisely3. Improve loyalty card promotions with longitudinal analysis4. Attract more traffic into the stores5. Increase the size and value of the basket purchases6. Test and learn by using marketplace as laboratory7. Empower planners and merchants to make smarter decisions8. Determine magic price point for the individual stores9. Match inventory to needs by customizing layouts, assortment and prising to the local

demographic.

Market Basket Analysis is a data mining technique to derive association between two data sets. We have categorical data of transaction records as input to the analysis and output of the analysis is association rules as new knowledge directly from the data.

Fresh greens wants to analyze the sales data for following reasons:

1. To understand the associations between purchased items2. To understand the most frequently purchased items

10.1 Analysis using SAS Enterprise Miner:

SAS 9.1 is one of the best analytics tool for analyzing the sales data. SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models based on analysis of vast amounts of data from across the enterprise. Forward-thinking organizations today are using SAS data mining software to detect fraud, minimize

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credit risk, anticipate resource demands, increase response rates for marketing campaigns and curb customer attrition

Let us consider any two random days for sales data analysis. There are 527 bill transactions or say 527 customers visited the store in two days.

Now using SAS Enterprise Miner, we can derive following output of association rules:

Above screenshot is the Results Window for Associations Rules.

(Appendix 1 shows Association Rules for 2, 3 and 4 item combinations)

Let us take an example:

TOMATO HYBRID ONION ECONOMY

Support = 6.44% ; Confidence = 38.20% ; Lift= 1.75

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Result: - When a customer buys TOMATO HYBRID, in 38.20% of the cases he/she will buy ONION ECONOMY. We find this happens in 6.44% of all purchases.

Support:-Support is the frequency of transaction to have all the items on both sets TOMATO HYBRID and ONION ECONOMY bought together. In above case, a support of 6.44% shows that 6.44% of all transactions indicate that items both sets purchased together. In formula, support can be computed as Probability of union of set TOMATO HYBRID and set ONION ECONOMY. P (TOMATO HYBRID U ONION ECONOMY)

Confidence: Confidence of 38.20% shows that 38.20% of the customers who bought TOMATO HYBRID also bought ONION ECONOMY. In formula, confidence is computed as conditional probability to obtain set ‘ONION ECONOMY’ given set ‘TOMATO HYBRID’. Confidence is a measure of accuracy or reliability about the inference made by the rule that the number of instances that association rules will predict correctly among all instances it applies to. P (ONION ECONOMY/ TOMATO HYBRID)

Lift: Lift is a good measure of how much better the rule is doing. It is the ratio of density of

target (using the left hand side of the rule) to density of target overall. When lift > 1 then the

rule is better at predicting the result than guessing and when lift < 1, the rule is doing worse than informed guessing and using the Negative Rule produces a better rule than guessing.

In above case Lift is 1.75 that shows positive correlation.

We have to go through different steps in Enterprise miner of SAS to obtain above results.

Following figure shows the window of enterprise miner that shows two diagrams, one for input data source and other for association.

Following window shows Variables in Input Data Source

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Model Role: The variable model role. Examples of model roles include cost, freq, id, input, predict, rejected, sequence, target, and trial. The role of a variable is automatically assigned in the Input Data Source node based on the information in the metadata sample. e.g. id for BILL_NO.

Measurement: The measurement type of the variable. Examples of measurement type include binary, interval, ordinal, and nominal. The measurement type of variables is automatically assigned in the Input Data Source node based on the information in the metadata sample. e.g. interval scale for nominal.

Type: Either character (char) or numeric (num). The variable type is displayed in the Input Data Source node, but it cannot be changed (the values in this column are protected). e.g. char for BILL_NO.

Format: The format of the variable. Examples of formats include $12. (12 characters) and BEST12. (12-digit numeric). The variable formats are assigned in the Input Data Source node.

Variable Label: The descriptive variable label, which is assigned in the Input Data Source node.

Following figure shows the Association properties under General tab.

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Here we have to set minimum support which is set to 5% and minimum confidence which is set to 10%.

Also we have to specify maximum no. Items in association (here it is 4).

Following table shows some of the important results of the analysis:

Relations

Confidence(%)

Support(%)

Lift Transaction Count

RULE

2 38.20 6.44 1.75 34 TOMATO HYBRID ==> ONION ECONOMY

2 61.11 2.08 2.81 11 BEANS ROUND WHITE ==> ONION ECONOMY

3 72.22 2.46 3.32 13 TOMATO HYBRID & Ladies Finger ==> ONION ECONOMY

3 75.00 2.27 3.44 12 POTATO ECONOMY & Ladies Finger ==> ONION ECONOMY

4 77.78 1.33 3.57 7 TOMATO HYBRID & Ladies Finger & Cabbage sandoz ==> ONION ECONOMY

4 100.00 1.33 9.26 7 ONION ECONOMY & Chilli green & Cabbage sandoz ==> Ladies Finger

Frequency ITEM   Frequency Item

115 ONION ECONOMY   16 Beans Chikdi89 TOMATO HYBRID   16 Apple

Washington

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74 BANANA ROBUSTA   15 LV DIL73 POTATO ECONOMY   15 LV

AMARANTHUS

70 Lemon   15 Coccinea57 Ladies Finger   15 Ash Gourd53 Cabbage sandoz   14 BRINJAL LONG

GREEN

50 Cucumber Hybrid   14 BANANA RAW

49 Chilli green   13 SAPOTA49 CARROT OOTY   13 Pumkin48 BANANA YELLAKI   13 ORANGE

AUSTRALIA

46 LV CURRY   13 Musambi42 TOMATO LOCAL   13 LV Mint42 LV CORRIANDER   13 Chow Chow39 LV CORRIANDER

BIG  12 PEAR

SHANDONG

36 Beans Round   12 LV Palak35 GINGER DRY   11 Drumstick34 CHILLI GREEN

SMALL  11 Cucumber

Mangalore

30 ORANGE COORG   11 BEANS INDIAN

30 Capsicum Green   10 Brinjal Small Green

28 CAULIFLOWER BIG   9 Beetroot24 Apple Shimla   9 BEANS

COWPEA

22 RADISH WHITE PREMIUM

  8 ONION SAMBAR

22 Bitter Gourd   8 Chilli Bajji21 BRINJAL

VARIKATRI PREMIUM

  8 BEANS CLUSTER

20 RIDGE GOURD   7 SWEET POTATO

20 POMO KESAR   6 GREEN PEAS OOTY

20 COCONUT SMALL   6 BRINJAL SUPHAL

19 APPLE CHINA FUJI   6 BEANS RED GRAM

19 AMLA   5 SNAKE GOURD

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18 Papaya   5 PUMPKIN DISCO

18 Musk Melon   5 PINEAPPLE LARGE

18 Garlic   5 LV CORRIANDER SMALL

18 Bottle Gourd   5 Beans Double18 Beans French   5 BRINJAL LONG

PURPLE

18 BEANS ROUND WHITE

     

17 LV Methi      17 KNOL KHOL      

Above two tables shows some of important conclusions as below:

1. ONION ECONOMY and TOMATO HYBRID is the most frequently purchased items by the customers or we can say these two items are found in most of the customer’s basket. Same thing applies for POTATO ECONOMY and TOMATO HYBRID

2. Combinations of TOMATO HYBRID, Ladies finger and ONION ECONOMY or TOMATO HYBRID, Ladies finger and POTATO ECONOMY are also frequently purchased items if we consider association rule of three items.

3. When we consider association rule between four items, we conclude that TOMATO HYBRID & Ladies Finger & Cabbage sandoz ONION ECONOMY or ONION ECONOMY & Chilli green & Cabbage sandoz & Ladies Finger are the items found in most of the customer’s basket.

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11 .Demand ForecastingDemand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.

For Retailers, it is very important to understand the demand for each product. Retailers face several challenges when it comes to forecasting:

Scale of the problem (large number of stores and items to forecast).

Intermittent demand (slow and erratic sales for many items at the store level).

Assortment instability (frequent new-item introductions and seasonal assortment changes).

Pricing and promotional activity.

Fresh Greens wants to understand the demand for their items by analyzing previous sales trends. Accurate demand forecast for the item will give the company exact planning for inventory and money control.

11.1 Analysis using SAS Time Series Forecasting System:

The Time Series Forecasting System is a point-and-click system that provides automatic model fitting and forecasting as well as interactive model development. The system provides a completely automatic forecasting model selection feature that selects the best-fitting model for each time series. Or, you can use system features to identify series behaviour, fit candidate forecasting models, and perform diagnostic checks on the fitted models.

The Time Series Forecasting System includes a wide range of forecasting models. It provides tools to do the following:

fit forecasting models, select from a list of models, or build your own forecasting models and add them to the list of available models

use the automatic time series diagnostic facility to subset the available models list according to series properties of trend, seasonality, and need for log transformation, thus directing attention to the most promising models

decompose raw and transformed series variables and display the seasonally adjusted series, the trend-cycle component, the seasonal component, or the irregular component.

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For Fresh Greens, the sales data for two weeks were analysed for forecasting the demand for the next 30 days. There are overall 87 vegetable items including Fruits, Vegetables and Leafy Vegetables. Here sales data is the quantity sold for each item.

First, the raw sales data is first segregated according to the input data format for SAS Time Series Forecasting system. Following screenshot shows the initial screen for the same:

Above screenshot shows various options, let us see one by one:

1. Project: It is the destination folder where the complete project of forecasting to be saved.

2. Data Set: It is the input data to be calculated for forecasting. Here “ANIL.FEBQTY” signifies the input data file name is FEBQTY in the Library named “Anil”.

3. Time ID: Perhaps the most important parameter for the analysis. The software automatically identifies the Time ID variable. Here it is DATE.

4. Interval: It is the interval between dates i.e. interval between observations is a day, week, month or year. Here it is one day.

Now here for the forecasting, software can fit models automatically or we can select from the choice of models available.

Following screenshot the “Automatic Model Fitting” window

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Here we need to specify the series the process the forecasting. Here are 86 items series is to process.

Also we need to give a selection criterion to chose a model. Here we have given “Root Mean square Error” as the selection criteria selecting the mode.

Then click on Run tab to start the processing. After processing it will show following Results Window:

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The above results window shows which model is fitted for which series. E.g. for Banana_Raw, the model is ‘Linear trend’ model for predicting the future values. Other statistics is also calculated for the series like Root Mean Square error, Mean square error etc.

Now let us understand the forecasts Results for an item. Take the example of Chilli Green.

Following screen shot shows the forecasts graph for the item Chilli Green.

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The graph shows Log linear trend for the item ‘Chilli Green’. The vertical dotted line in graph divides the actual values and predicted values. The graph also shows the 95% upper and lower confidence limits.

Following figure shows the Forecast Dataset for the same item:

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The table clearly shows the Actual and Predicted values. There are also other values shown in the table.

Every forecast data set contains the following variables:

ACTUAL Actual data values.

PREDICT Predictions based on previous actual data.

U95 Upper confidence limit.

L95 Lower confidence limit.

STD Prediction standard errors.

ERROR Prediction errors.

NERROR Normalized prediction errors.

Forecast data sets for models with transformations contain the following variables:

RESIDUAL Model residuals.

NRESID Normalized model residuals.

RESSTD Residual standard errors.

.

From the analysis we found out that Onion economy is the highest revenue generating item. Some other high revenue generating items are Banana Robusta, Banana Yalaki, Tomato Hybrid and Potato Economy.

For Onion Economy, the trend model is “Damped Trend Exponential Smoothing” as the following graph shows

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The Forecast dataset for the same is also given below:

DATE ACTUAL PREDICT UPPER LOWER ERROR _TREND_5/1/2009 900 900.906053 1328.101 473.7113 -0.90605 116.63436/1/2009 1084 991.474904 1418.67 564.2802 92.5251 136.10947/1/2009 1232.07 1157.23285 1584.428 730.0381 74.83715 142.73458/1/2009 1497.12 1316.81529 1744.01 889.6206 180.3047 199.15999/1/2009 1570 1588.18938 2015.384 1160.995 -18.1894 146.806310/1/2009 1500 1691.24446 2118.439 1264.05 -191.244 21.8022111/1/2009 1201.76 1585.53191 2012.727 1158.337 -383.772 -169.4412/1/2009 1417.09 1206.78745 1633.982 779.5927 210.3026 -30.23041/1/2010 1218.37 1318.14581 1745.341 890.9511 -99.7758 -72.10742/1/2010   1197.75187 1624.947 770.5572   -56.35243/1/2010   1153.71212 1764.415 543.0089   -44.03974/1/2010   1119.2948 1950.049 288.5406   -34.41735/1/2010   1092.39745 2154.831 29.96384   -26.89746/1/2010   1071.377 2365.956 -223.202   -21.02047/1/2010   1054.9494 2576.885 -466.986   -16.42768/1/2010   1042.11113 2784.112 -699.889   -12.83839/1/2010   1032.07794 2985.755 -921.599   -10.033210/1/2010   1024.23694 3180.87 -1132.4   -7.84111/1/2010   1018.10915 3369.071 -1332.85   -6.1277912/1/2010   1013.32025 3550.309 -1523.67   -4.78891/1/2011   1009.57769 3724.735 -1705.58   -3.74256

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ACTUAL Actual data values.

PREDICT Predictions based on previous actual data.

U95 Upper confidence limit.

L95 Lower confidence limit.

ERROR Prediction errors.

TREND Smoothed Trend States

The table compares the actual and predicted values for the same period and forecast for next specified period of time.

The accuracy of the predicted values can be checked out from the ERROR column where it shows the difference between predicted and actual values.

Graph and table shows the trend of the item is exponential and that’s why there is no wide difference in predicted values for every month.

Last year there was also variability is food prices. Price for onion rose from Rs.12 per Kg in May-09 to Rs. 31 per Kg in Dec-09. But the sales data analysis shows that the sales are not at all dependent on the price. This is mainly because the item come under essential commodities and sales is not affected.

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In this way, we can analyze each item very effectively and accurately. With the knowledge of predicted values for each item, retailers can plan their budget and manage the operations very efficiently

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12 Conclusion and Recommendations:

1. With the use of analytics software like SAS, retailers can see the future trends for their

individual products.

2. The complete sales data analysis of one year shows trends for individual item and forecast

the future trends due to which Fresh Greens can understand exact demand for the

products.

3. It is found out that Onion Economy is the highest revenue generating item for the

company which is followed by Banana Robusta, Banana Yalaki, Tomato Hybrid and

Potato Economy.

4. The sales trends for these products are steady over period of time (exponential

smoothing) because of generic and basic nature of the items.

5. Also during food inflation, the sales of these basic products were not affected but the food

inflation affected the sales of Fruits and some vegetables like capsicum, Mushrooms etc.

6. The Fruits like Apple and vegetables like Mushrooms show linear decreasing trends.

7. However, it should be noted that the software does not consider the price changes, it only

consider the sales trend in the past.

8. Market Basket Analysis is used for optimised store layout. It is use to improve space

planning and visual merchandizing for improved cross sell and up sell. It gives clear idea

about the items to be placed together.

9. Market Basket Analysis shows that customer always buys TOMATO HYBRID and

ONION ECONOMY together. So it is recommended to place them adjacent to each other

so that customer can pick up the items easily, ultimately increasing the sales of both

items.

10. The analysis shows that Tomato Hybrid, Potato Economy, Onion Economy, Ladies

Finger, Cabbage Sandoz and Chilli green, are the items found in most of the customer’s

basket.

11. The results of Market Basket Analysis shows the frequency of product purchased but at

the same time it does not show the quantity of items purchased. So there is further scope

to modify the software so that there can be an accurate solution that shows both frequency

and quantity of the items and rank them accordingly.

12. Having the knowledge of the items having high frequency of purchase, retailer can plan

their inventory and manage the suppliers accordingly.

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13. Price bundling is also possible for the items purchased in combination. For example, one

Kg. of Tomato and one Kg. of onion priced together with a discounted rate. This

ultimately increases the sales of both items.

14. Fresh Greens can plan their pricing strategy according to the sales of each item. For

example, if an item with low purchase rate, price can be discounted and item having high

purchase rate, price can be increased.

15. The store is open for whole day but usually rush hours are 8 to 12 in the morning and 5

to 9 in the evening. Between 12 to 5 in the afternoon there is hardly any customer comes

in the store. Within this time frame, company can think about “Home Delivery” of Fruits

and vegetables. Orders can be taken through phone that can store the phone number of the

customer with his/her name. Having this customer’s knowledge, Fresh Greens can

segment the customer based on the items purchased by them. This will help to easily

target the customer and retain the customer for longer period.

16. Packaging of the items is also recommended for standard items. For example, most of the

customers purchase Potato within one Kg or two Kg. ranges. If there are packed bags of 1

kg or 2 kg then it saves time of both customer and retailer.

17. Retailer can better understand which product and offers will get customers into the store

by correlating Market Basket Analysis with foot traffic counts, then attachment rates, to

understand what they purchased once you got them into the store. With this knowledge,

retailer can attract more traffic into the store.

18. Retailer can easily find out shelf life period for an item. For example, in this analysis,

Shelf life for Onion is very low i.e. hardly four hours but shelf life period for fruits like

Apple China Fuji may long up to two days.

19. Fresh Greens can think about Marketing or Advertising of their products. Advertising

may be with a small leaflet through daily newspaper highlighting about their items and

special offers for particular products like Fruits.

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______________________________________________________________Appendix 1Market Basket Analysis: For relation between 2 items

Relations

Confidence(%)

Support(%)

Lift Transaction Count

RULE

2 38.20 6.44 1.75 34 TOMATO HYBRID ==> ONION ECONOMY2 29.57 6.44 1.75 34 ONION ECONOMY ==> TOMATO HYBRID2 43.84 6.06 2.01 32 POTATOECONOMY ==> ONION ECONOMY2 27.83 6.06 2.01 32 ONIONECONOMY ==> POTATO ECONOMY2 21.74 4.73 2.01 25 ONION ECONOMY ==> Ladies Finger2 43.86 4.73 2.01 25 Ladies Finger ==> ONION ECONOMY2 19.13 4.17 1.91 22 ONION ECONOMY ==> Cabbage sandoz2 41.51 4.17 1.91 22 Cabbage sandoz ==> ONION ECONOMY2 23.60 3.98 1.71 21 TOMATO HYBRID ==> POTATO ECONOMY2 28.77 3.98 1.71 21 POTATO ECONOMY ==> TOMATO HYBRID2 18.26 3.98 1.97 21 ONION ECONOMY ==> Chilli green2 42.86 3.98 1.97 21 Chilli green ==> ONION ECONOMY2 18.26 3.98 1.97 21 ONION ECONOMY ==> CARROT OOTY2 42.86 3.98 1.97 21 CARROT OOTY ==> ONION ECONOMY2 20.22 3.41 1.87 18 TOMATO HYBRID ==> Ladies Finger2 31.58 3.41 1.87 18 Ladies Finger ==> TOMATO HYBRID2 15.65 3.41 1.18 18 ONION ECONOMY ==> Lemon2 25.71 3.41 1.18 18 Lemon ==> ONION ECONOMY2 15.65 3.41 1.80 18 ONION ECONOMY ==> LV CURRY2 39.13 3.41 1.80 18 LV CURRY ==> ONION ECONOMY2 19.10 3.22 1.44 17 TOMATO HYBRID ==> Lemon2 24.29 3.22 1.44 17 Lemon ==> TOMATO HYBRID2 19.10 3.22 2.06 17 TOMATO HYBRID ==> Chilli green2 34.69 3.22 2.06 17 Chilli green ==> TOMATO HYBRID2 14.78 3.22 2.23 17 ONION ECONOMY ==> GINGER DRY2 48.57 3.22 2.23 17 GINGER DRY ==> ONION ECONOMY2 17.98 3.03 1.79 16 TOMATO HYBRID ==> Cabbage sandoz2 30.19 3.03 1.79 16 Cabbage sandoz ==> TOMATO HYBRID2 17.98 3.03 1.94 16 TOMATO HYBRID ==> CARROT OOTY2 32.65 3.03 1.94 16 CARROT OOTY ==> TOMATO HYBRID2 21.92 3.03 2.03 16 POTATO ECONOMY ==> Ladies Finger2 28.07 3.03 2.03 16 Ladies Finger ==> POTATO ECONOMY2 28.07 3.03 2.80 16 Ladies Finger ==> Cabbage sandoz2 30.19 3.03 2.80 16 Cabbage sandoz ==> Ladies Finger2 35.71 2.84 1.64 15 TOMATO LOCAL ==> ONION ECONOMY2 13.04 2.84 1.64 15 ONION ECONOMY ==> TOMATO LOCAL2 32.61 2.84 3.51 15 LV CURRY ==> Chilli green

Market Basket Analysis: For relation between 3 items

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Relations

Confidence(%)

Support(%)

Lift Transaction Count

RULE

3 14.61 2.46 3.08 13 TOMATO HYBRID ==> ONION ECONOMY & Ladies Finger

3 11.30 2.46 3.32 13 ONION ECONOMY ==> TOMATO HYBRID & Ladies Finger

3 22.81 2.46 3.54 13 Ladies Finger ==> TOMATO HYBRID & ONION ECONOMY

3 38.24 2.46 3.54 13 TOMATO HYBRID & ONION ECONOMY ==> Ladies Finger

3 72.22 2.46 3.32 13 TOMATO HYBRID & Ladies Finger ==> ONION ECONOMY

3 52.00 2.46 3.08 13 ONION ECONOMY & Ladies Finger ==> TOMATO HYBRID

3 16.44 2.27 3.47 12 POTATO ECONOMY ==> ONION ECONOMY & Ladies Finger

3 10.43 2.27 3.44 12 ONION ECONOMY ==> POTATO ECONOMY & Ladies Finger

3 21.05 2.27 3.47 12 Ladies Finger ==> POTATO ECONOMY & ONION ECONOMY

3 37.50 2.27 3.47 12 POTATO ECONOMY & ONION ECONOMY ==> Ladies Finger

3 75.00 2.27 3.44 12 POTATO ECONOMY & Ladies Finger ==> ONION ECONOMY

3 48.00 2.27 3.47 12 ONION ECONOMY & Ladies Finger ==> POTATO ECONOMY

3 11.24 1.89 1.85 10 TOMATO HYBRID ==> POTATO ECONOMY & ONION ECONOMY

3 13.70 1.89 2.13 10 POTATO ECONOMY ==> TOMATO HYBRID & ONION ECONOMY

3 47.62 1.89 2.19 10 TOMATO HYBRID & POTATO ECONOMY ==> ONION ECONOMY

3 29.41 1.89 2.13 10 TOMATO HYBRID & ONION ECONOMY ==> POTATO ECONOMY

3 31.25 1.89 1.85 10 POTATO ECONOMY & ONION ECONOMY ==> TOMATO HYBRID

3 11.24 1.89 2.70 10 TOMATO HYBRID ==> ONION ECONOMY & Cabbage sandoz

3 18.87 1.89 2.93 10 Cabbage sandoz ==> TOMATO HYBRID & ONION ECONOMY

Market Basket Analysis: For relation between 4 items

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Relations

Confidence(%)

Support(%)

Lift Transaction Count

RULE

4 12.28 1.33 6.48 7 Ladies Finger ==> TOMATO HYBRID & ONION ECONOMY & Cabbage sandoz

4 13.21 1.33 5.36 7 Cabbage sandoz ==> TOMATO HYBRID & ONION ECONOMY & Ladies Finger

4 20.59 1.33 6.79 7 TOMATO HYBRID & ONION ECONOMY ==> Ladies Finger & Cabbage sandoz

4 38.89 1.33 9.33 7 TOMATO HYBRID & Ladies Finger ==> ONION ECONOMY & Cabbage sandoz

4 43.75 1.33 9.24 7 TOMATO HYBRID & Cabbage sandoz ==> ONION ECONOMY & Ladies Finger

4 28.00 1.33 9.24 7 ONION ECONOMY & Ladies Finger ==> TOMATO HYBRID & Cabbage sandoz

4 31.82 1.33 9.33 7 ONION ECONOMY & Cabbage sandoz ==> TOMATO HYBRID & Ladies Finger

4 43.75 1.33 6.79 7 Ladies Finger & Cabbage sandoz ==> TOMATO HYBRID & ONION ECONOMY

4 53.85 1.33 5.36 7 TOMATO HYBRID & ONION ECONOMY & Ladies Finger ==> Cabbage sandoz

4 70.00 1.33 6.48 7 TOMATO HYBRID & ONION ECONOMY & Cabbage sandoz ==> Ladies Finger

4 77.78 1.33 3.57 7 TOMATO HYBRID & Ladies Finger & Cabbage sandoz ==> ONION ECONOMY

4 70.00 1.33 4.15 7 ONION ECONOMY & Ladies Finger & Cabbage sandoz ==> TOMATO HYBRID

4 12.28 1.33 9.26 7 Ladies Finger ==> ONION ECONOMY & Chilli green & Cabbage sandoz

4 14.29 1.33 7.54 7 Chilli green ==> ONION ECONOMY & Ladies Finger & Cabbage sandoz

Note: Above tables shows some of the selected observation from a long list of observations. However, the complete lists of observations are provided to the company.

Demand Forecasting______________________________________________ Appendix 2

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DATE

APPLE_CHINA__FUJI_QTY

BANANA_ROBUSTA

BANANA_YELLAKI

LADIES_FINGER

ONION_ECONOMY

POTATO_ECONOMY

TOMATO_HYBRID

2/1/2010 6.735 41.29 0 9.13 31.33 21.285 20.452/2/2010 8.7 26.505 23.38 8.85 52.46 32.385 28.362/3/2010 2.305 40.68 24.35 10.98 59.57 22.77 38.482/4/2010 2.98 31.73 19.76 8.85 42.5 26.545 40.9752/5/2010 2.695 33.02 16.395 9.01 59.87 27.32 21.442/6/2010 3.275 59.95 32.405 11.12 50.675 39.54 302/7/2010 2.22 9.88 24.925 11.17 49.97 27.215 34.132/8/2010 4.29 51.125 14.125 7.9 62.37 29.76 31.622/9/2010 4.6 21.94 15.28 9.515 62.27 38.125 32.762/10/2010 3.49 14.15 21.655 7.045 49.94 28.825 23.232/11/2010 4.3 83.65 48.73 12.645 40.4 21.37 34.2652/12/2010 6.095 50.155 29.88 9.14 50.365 35.37 32.9952/13/2010 7.045 1.03 17.385 11.37 45.965 30.63 35.0252/14/2010 1.45 22.665 33.94 14.34 45.805 41.615 38.132/15/2010 3.84834051 32.598622 24.9801913 11.3898094 49.4993323 31.7706855 32.66471312/16/2010 3.78129358 32.598622 24.9801913 11.5772924 49.5018296 31.7865677 32.67290192/17/2010 3.71541476 32.598622 24.9801913 11.7678615 49.5043271 31.8024578 32.68109292/18/2010 3.65068371 32.598622 24.9801913 11.9615675 49.5068246 31.8183558 32.68928582/19/2010 3.58708042 32.598622 24.9801913 12.158462 49.5093223 31.8342618 32.69748092/20/2010 3.52458524 32.598622 24.9801913 12.3585975 49.5118201 31.8501757 32.7056782/21/2010 3.46317888 32.598622 24.9801913 12.5620274 49.5143181 31.8660976 32.71387712/22/2010 3.40284235 32.598622 24.9801913 12.7688058 49.5168162 31.8820274 32.72207832/23/2010 3.34355703 32.598622 24.9801913 12.9789879 49.5193143 31.8979652 32.73028152/24/2010 3.28530459 32.598622 24.9801913 13.1926298 49.5218127 31.913911 32.73848682/25/2010 3.22806704 32.598622 24.9801913 13.4097883 49.5243111 31.9298647 32.74669422/26/2010 3.1718267 32.598622 24.9801913 13.6305213 49.5268097 31.9458264 32.75490362/27/2010 3.1165662 32.598622 24.9801913 13.8548878 49.5293084 31.9617961 32.76311512/28/2010 3.06226846 32.598622 24.9801913 14.0829475 49.5318072 31.9777738 32.77132873/1/2010 3.00891671 32.598622 24.9801913 14.3147612 49.5343061 31.9937595 32.77954433/2/2010 2.95649448 32.598622 24.9801913 14.5503906 49.5368052 32.0097531 32.78776193/3/2010 2.90498556 32.598622 24.9801913 14.7898987 49.5393044 32.0257548 32.79598163/4/2010 2.85437404 32.598622 24.9801913 15.0333492 49.5418037 32.0417644 32.80420343/5/2010 2.80464429 32.598622 24.9801913 15.280807 49.5443032 32.0577821 32.81242723/6/2010 2.75578095 32.598622 24.9801913 15.5323382 49.5468028 32.0738078 32.82065313/7/2010 2.70776892 32.598622 24.9801913 15.7880097 49.5493025 32.0898414 32.82888113/8/2010 2.66059337 32.598622 24.9801913 16.0478897 49.5518023 32.1058831 32.83711113/9/2010 2.61423972 32.598622 24.9801913 16.3120475 49.5543023 32.1219328 32.84534323/10/2010 2.56869366 32.598622 24.9801913 16.5805535 49.5568023 32.1379906 32.85357743/11/2010 2.52394112 32.598622 24.9801913 16.8534792 49.5593026 32.1540563 32.86181363/12/2010 2.47996828 32.598622 24.9801913 17.1308975 49.5618029 32.1701301 32.87005193/13/2010 2.43676153 32.598622 24.9801913 17.4128822 49.5643034 32.1862119 32.8782922

Note: Above tables shows some of the selected observation from a long list of observations. However, the complete lists of observations are provided to the company.

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Page 48: Marketing Analytics using SAS

Marketing Analytics using SAS

13 References

Direct Interviews

1. Mr. Niranjan Prabhu, Senior Business Analyst, Dell Computers, BangaloreEmail: [email protected]

2. Mr. Anil John , Business Analyst, GENPACT Analytics, BangaloreEmail: [email protected]

Websites :

1. “Marketing Challenges”, “Advanced Marketing Automation”, whitepaper, http://www.sas.com/whitepapers/index.html

2. “Marketing Analytics”, http://www.information-management.com/specialreports/3. “Trends in Marketing Analytics”, http://www.clickz.com/3635458 4. “Marketing Analytics to the rescue: Next Big Thing”, http://www.information-

management.com/specialreports/20030211/6346-1.html5. “Analytics in Retail”, http://www.bhups.net/2007/12/analytics-in-retail-cpg-and.html 6. “Size optimization for retailers”, SAS-Whitepaper,

http://www.sas.com/whitepapers/index.html 7. “Business Intelligence for Telecom Industry”,

http://www.iec.org/online/tutorials/acrobat/bus_int.pdf 8. Business Intelligence for Telecom,

http://www.elegantjbi.com/Solutions/industry_BI_for_Telecommunication.htm 9. “Harnessing data for Marketing effectiveness”, SAS-Whitepaper,

http://www.sas.com/whitepapers/index.html10. “Competing on Customer Intelligence SAS-Whitepaper”,

http://www.sas.com/whitepapers/index.html11. “Bringing Scorecard development in house with SAS credit scoring for Banking”,

SAS-Whitepaper, http://www.sas.com/whitepapers/index.html12. “Using Behaviour maps to understand customers”, SAS-Whitepaper,

http://www.sas.com/whitepapers/index.html13. List of Analytics Companies in India,

http://discussionalytics.blogspot.com/2007/02/list-of-analytics-companies-in-india.html

Books

1. Michael J. A. Berry, Data Mining Techniques,2nd Edition, New Delhi, Wiley India (P) Ltd.,2004, chapter 4,6 and 9.

2. Naresh K. Malhotra, Marketing Research - An Applied Orientation,4th Edition, Delhi, Pearson Education Pvt. Ltd., 2005, chapter 4,6 and 11.

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