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Integration of Is and Business

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IS5111:Integration of IS & Business Business Intelligence and Analytics

Abhishek Kumar Singh Anbarasan Thangapalam R Deepak Chattani

Naadiya Danapal

Ram Vibhakar S

[A0120022] [A0119959] [A0119975] [A0119961] [A0120054]

A Case study on Business Intelligent and Analytics Systems in NTUC FairPrice

Table of Contents Executive Summary ............................................................................................................................. 3 Acknowledgments ............................................................................................................................... 4 1. Introduction ...................................................................................................................................... 5 2. Business Intelligence in Retail Industry: ................................................................................. 6 3. Combining BI in CRM ...................................................................................................................... 7 3.1 Analytical CRM ............................................................................................................................... 7 4. Case Study .......................................................................................................................................... 9 4.1 Customer Relationship background of NTUC Fairprice ................................................... 9 4.1.1 Spring Customer Centric Initiative ............................................................................................................... 9 4.1.2 Key features of Customer Centric Initiative ........................................................................................... 10 4.1.3 CCI implementation in NTUC Fairprice ................................................................................................... 11 4.1.4 Impact of CCI on Fairprice ............................................................................................................................. 12 4.2 IDA’s Data Analytics Initiative ............................................................................................................... 12 4.3 Manthan System BI suites ...................................................................................................................... 14 4.4 Why Manthan System’s Arc Customer360? ........................................................................................ 16 4.4.1 Market Basket Analysis (MBA) .............................................................................................................. 16 4.4.2 Churn prediction ................................................................................................................................................ 17 4.4.3 Campaign Optimizer ........................................................................................................................................ 17 4.4.4 RFM Analysis ....................................................................................................................................................... 17 4.4.5 Segmentation ...................................................................................................................................................... 17 4.5 Advantages of Arc Customer 360 and Existing Programs ............................................................... 18 4.5.1 Customer Loyalty Program ........................................................................................................................... 20 4.6 Integration of Manthan System’s Arc Customer360 into Fairprice ........................... 21 4.6.1 Timeline of the project .................................................................................................................................... 21 4.6.2 Surveys before implementing Arc Customer 360 ............................................................................... 21 4.6.3 Impact of Business Intelligence and Customer Analytics on Fairprice ...................................... 21 7. Conclusion ........................................................................................................................................ 23 8. Glossary ............................................................................................................................................ 24 9. References ....................................................................................................................................... 25 Appendix ............................................................................................................................................... 26

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Executive Summary
Retail
business has become highly competitive these days. The challenges that they face include entry of new channels (eg. e-­‐commerce), serve changing consumer demands, globalization and competitive prices. They need information management solutions to make correct decisions. Also, retailers should know his customers well in order to serve them better and to survive in this competitive retail industry. The retailers have volumes of data from supply chain to stores. It is hard to find which information is important and how to extract information required for decision-­‐making process. This is where Business Intelligence and Analytics can play a crucial part. The services offered by Business Intelligence to retail are limitless. Some of the possibilities are ● Marketing: Using loyalty programs, promotions and customer analytics, retailers can execute more profits. ● Loss Prevention: By analyzing the data and taking effective decisions, managers can prevent losses. ● Pricing: Choosing a right price for a product and providing markdowns can be done effectively by analyzing the sales data ● Sales forecast: Estimating the sales of next quarters is possible through BI. This helps managers to make decisions in procurement ● Stock and inventory optimization: BI helps managers to manage stock and inventory of products across stores We conducted a case study on how Customer Relationship Management has been done at NTUC Fairprice to improve their process through many years. And how BI solutions were adopted recently for Customer Analytics to keep in pace with growing customer expectations. Manthan Systems provide various BI solutions to NTUC for this purpose. We studied how they help NTUC to improve customer satisfaction and retention.

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Acknowledgments
We
would like to express our gratitude to the following people for helping us in completing this project on Business Intelligence and Analysis in Business. Professor Alex Siow Yuen Khong, for providing us with deep insights on this subject matter in his lectures and setting the foundations for our progress in our project. Teaching Assistant Shen Yi, for her support and guidance for our project. Mr. C S Raman -­‐ Senior Datawarehouse Architect – Manthan Systems for helping us with required information to write our case study on Business Intelligence system developed by Manthan

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1. Introduction
“Data
is a precious thing and will last longer than the systems themselves” -­‐ Tim Berners Lee (Inventor of world wide web) The most challenging job in every business is to make it efficient to survive this volatile market place. To tackle this issue the business should know about its customer better. This can be achieved by analyzing the huge data that the business generates. Business Intelligence refers to skills, process, technologies, application and practices used to support decision-­‐making. Business Intelligence tools and software are now used in almost all-­‐major industries. BI tools like data warehousing, data mining, and OLAP, dashboard can help in keeping an eye on different organizational functions and hence can play a vital role in increasing the operational efficiency of the various departments in an organization. A huge amount of data is generated every day and has accumulated over the years through Information Technology. This makes it necessary for companies and organizations to use Business Analytics to stay in the game and maintain an edge over their competitors. Through data analytics, companies get key insights about their clients, and informed decisions to increase satisfaction and internal efficiency thus increasing profits. Demands for such services have also provided significant opportunities for ICT industry and partners in Singapore. The worldwide business analytics market for software, hardware and services is expected to reach US $123 billion by 2015, according to a recent study by International Data Corporation. BI through SaaS:

BI is gaining popularity as Software as a Service (SaaS) model. It is being widely used in healthcare sector, manufacturing industries; financial services sector, retail sector, and real estate businesses. The current trend in Business Intelligence is to utilize the company’s data to rejuvenate and constantly innovate the existing BI solutions to extract more business values. BI through SaaS can be categorized as, ● Generic BI -­‐ OLAP and reporting tools, data mining methodologies. ● Application Specific BI -­‐ Social media Analytics, web analytics, video analytics. Predictive Analytics:

Statistical and probabilistic techniques from modeling, game theory and data mining are used to derive predictions based in the analysis of the current and historical data. Predictive analytics is used areas of, ● Consumers -­‐ Customer behavior can be understood better and predictive analytics can be used for subsequent upselling and cross selling. This will help in customer retention and helps in target marketing. This is widely used in retail sector. ● Risk -­‐ In financial sector, predictive analysis is used for identifying the probability of a customer

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defaulting a payment and also for fraudulent detection. This will help in increasing the efficiency of the investigation resources in such sectors. Operations -­‐ Predictive analysis is used in inventory management in the operations area. This analysis can help in reducing the maintenance cost by predicting failures before hand.

Mobile BI: Mobile BI gives the privilege of accessing analytics on the go. They can give timely and accurate access to analytics via interactive dashboards. This ability to access data irrespective of geographic locations benefits business performance by creating more productive workforce. But scalability and security always needs focus when utilizing mobile devices. Other than these innovations social analytics has been evolving as a strong means of business intelligence and analytics. Customers’ feelings about a firm and its competitors on web can leverage the support of the fans and can help in winning over detractors and convert them into supporters. Our focus in this research will be the area of predictive analytics in retail sector particularly customer analytics. This report will explore the combination of business intelligence and analytics with customer relationship management.

2. Business Intelligence in Retail Industry:
Retail
Industry has seen immense growth in the last decade. The retail industry is diverse and hence faces challenges from various avenues. There are various competitors in this industry and to sustain the profit margins innovative business intelligence solutions are needed. NTUC Fairprice’s Group CIO Bernard Chew was quoted saying, “Big data is something that we have been grappling with for a long time. With some three to four gigabytes of data generated every day, and the streaming of point of sales data every five to ten minutes from the many supermarket branches, that's a lot of data. Our challenge is how to deal with it.”

Other than the challenges of meeting customer expectations, retail industries faces other challenges like choosing right suppliers, setting pricing strategies, manage demands and keep track of the inventory needs.

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3. Combining BI in CRM
Customer
relationship management goes hand in hand with business intelligence and analytics. CRM provides tools for sales, customer service and marketing.

Sales force Automation in CRM systems help in productivity by concentrating the efforts on the most profitable customers. Customer Service is concerned with improving the customer support. Dedicated teams work on giving personalized support to customers. Marketing CRM systems support target marketing and direct marketing where in target marketing is done based on the customer’s buying behavior. All these applications support either the analytical or operational aspects of CRM. Business intelligence comes under the analytical aspects of CRM. Analytical CRM consists of applications that use data from operational CRM to provide analysis for better business performance.

3.1 Analytical CRM
Analytical
CRM are based on data from operational CRM. These data are organized in data warehouses or analytic platforms for use in online analytical processing, data mining, and other data analysis techniques. The BI tools are used to analyze buying patterns, to create segments for targeted marketing and to find valuable customers. Another important factor that can be derived from analytical CRM is the Customer Lifetime Value (CLTV) in a firm. CLTV is a predicted value on the relationship between a customer and firm based on the returns from a customer and cost incurred in acquiring and servicing a customer. The predictive analysis also includes customer churn prediction. Churn is the rate at which a

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firm loses the customers. Churn indicates the growth and decline of a company’s customer base.

Benefits of Analytical CRM ●

Analytical CRM

Designing and implementing targeted marketing: Analytical CRM can be used to assess customers who are more profitable. This assessment helps in developing target marketing and optimizing the effectiveness of the marketing. Customer segmentation: Analytical CRM can be used to break down customer base into categories of most profitable customers who are bound to become repeat customers and customers can don’t tend to return. Predictive analysis: Analytical CRM uses historical data to analyze past trends so that future sales and marketing plans can be drawn accordingly. Analytical CRM recognizes the peak of demands so that stocking can be done accordingly.





Analytical CRM is gaining popularity and is being rapidly implemented in many industries.

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4. Case Study
In
our case study we will be analyzing how customer relationship management was done in NTUC Fairprice from 2005. NTUC Fairprice initially participated in Customer Centric Initiative in 2005. Many strategic changes were made in NTUC Fairprice in this phase. Later in 2008 new Customer Experience Management was implemented to improve the Customer Satisfaction Index. Further in 2012, IDA decided to use Business Intelligence and Analytics solutions in retail industries. In our case study we will explore the implementation of these initiatives in NTUC Fairprice.

4.1 Customer Relationship background of NTUC Fairprice
4.1.1 Spring Customer Centric Initiative
“Go
the extra mile for service” (GEMS) program was launched as collaborative effort by Singapore Workforce Development Agency (SWDA), Singapore Tourism Board and SPRING Singapore on 6th October 2006. This program was launched to encourage the businessmen, service men and customers to play their part in improving Singapore’s service levels and focused particularly in tourism, hospitality, food, beverage, retail and transport industries in its first phase. GEMS Up was the second phase of this program and aimed at achieving excellence using three main strategies, ● Promotion, Publicity & Recognition for Service – The idea behind this strategy is to promote the importance of service to all levels in an organization. This was planned to be done by setting good service providers as benchmarks. ● Service Capability Development – This strategy aimed at upgrading the skills of the workforce in various industries. This involved training the workers for making them capable service providers. ● Research & Thought Leadership for Service – The goal behind this strategy was to analyze and bring innovations through research for better service qualities. The Customer Satisfaction Index of Singapore was studied for this purpose.

The table below provides an overview of the respective initiatives that the GEMS Up agencies oversee.

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SPRING Singapore is a national standards and development agency that specializes in accreditation. SPRING endorses international level standards and best practices recognized worldwide to implement competitive quality assurance to facilitate trade. SPRING launched Customer Centric Initiative (CCI) in 2005 for the Singapore Retail Sector. The motivation for this program was, • To improve drastically the service levels across various industries in Singapore. • To increase satisfaction of the native people and tourists. • To encourage service leaders who can influence the service levels of the entire organization. • To establish service benchmarks that retail organizations can use to score their service levels. The industries that were targeted in this initiative are, • Enterprises • Industries • Private companies • Partnership ventures

4.1.2 Key features of Customer Centric Initiative
The
key features of CCI are, ● ● ● ● Service Leadership Service Agility Customer Delight Customer Experience

Key features for CCI are Service Leadership: In a firm adopting CCI, the senior executives are personally involved in setting the focus on customer needs. Additionally their customer-­‐focused leadership is evaluated regularly to implement any improvements if needed. The planning area of service leadership focuses on organization’s customer-­‐ focused strategies and how key performance indicators are integrated into organization’s plans.

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Service Agility: Service agility focuses on information management and benchmarking techniques to achieve service excellence. This information management is regularly reviewed to ensure accuracy, reliability, accessibility and comprehensiveness.

Customer Experience: Customer delight focuses on customer expectations. The goal is to build a long-­‐term relationship with customers and look into their needs. Customer Intelligence is analyzed to identify key areas of customer delight and the attributes of its offerings are reviewed taking into account the first mover advantage and competition scenario. Customer Delight: The results are obtained over time, and focus is on examining organizations performance and its improvements in areas of importance for service excellence; this performance is further compared relatively to competitors and industry benchmarks

4.1.3 CCI implementation in NTUC Fairprice
NTUC
Fairprice joined the SPRING Customer Centric Initiative in 2005.Various improvement projects were introduced as part of this initiative. These include, ● Service Audits: Mystery audits, customer satisfaction surveys are conducted to find areas in service where Fairprice was lacking. Fairprice introduced a monthly mystery-­‐shopper audit program as a part of this step. These surveys were reviewed on a weekly basis to analyze the challenges in order to improve the service standards. ● Service Strategies: New service strategies were developed and implemented. This included new standards for services. Under these strategies Lifestyle shopping was introduced and improving of the visual communication by improving store lighting, better signboards were incorporated. Fairprice became the first supermarket chain to be certified under Hazard Analysis and Critical Control Point; this assured the quality of the products sold to the customers hence increasing the customer satisfaction levels. ● Information Technology: IT services were incorporated to improve service levels. As part of this step, point of sale systems were introduced which considerably the check out time and hence helped in improving customer satisfaction. ● Training programs: The Fairprice employees were trained under Workforce Skills Qualification framework to meet with the service competencies. Fairprice sent all its employees on a five day course to become service professionals. Fairprice invested a total of $1.2 million for this training program.

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4.1.4 Impact of CCI on Fairprice
NTUC
Fairprice’s performance levels improved drastically on adopting the Customer Centric Initiative. ● On a 10-­‐month period Fairprice saw an average improvement of 8% in overall service and corresponding 9% improvement in sales. ● NTUC Fairprice received the National Service Excellence Award, and was recognized as the leader in the supermarket category of the Customer Satisfaction Index of Singapore. ● Fairprice saw a whooping 173% increase in the compliments it received. ● Also, the complaints rate came down to 11%, which translated to 209% in compliments to complaint ratio. This meant that in place where it was receiving 1 complaint for every 2 compliments it started getting 1 complaint for every 30 compliments. This was seen as huge success on adoption of CCI. ● Further the members in NTUC Fairprice’s various clubs associated with Lifestyle shopping also saw a tremendous growth. ● Mystery shopping scorecard revealed 85% even in peak hours of business. ● In addition to the advantages this program generated to NTUC Fairprice it also played a major role in increasing the revenue of the Singapore Retail sector by 26.4%. Other key financial improvements included rise in 25% in value added per worker, 13.6% increase in sales per worker, and 13.8% hike in remuneration per worker. With growing competition and areas of customer loyalty, which needed creative practices to keep up with the market, these initiatives were not enough. This initiative needed enhancements over time. In 2008 NTUC Fairprice adopted a new Customer Experience Management system.

With the CEM system, every customer feedback was recorded, monitored and responded to in a timely manner. The automated workflow employed by the CEM system greatly improved the productivity of customer feedback management, contributing to faster service recovery and higher customer satisfaction.

Although constant changes were being made throughout these years the Customer Satisfaction Index did not show a great improvement. Hence, innovative custom solutions were required to improve the CSISG and technology was needed to retain customers.

4.2 IDA’s Data Analytics Initiative
Infocomm
Development Authority took the big step towards Business Intelligence and Analytics to keep up with growing customer expectations. IDA identified Data and Analytics as a main focus area, and aimed to develop Singapore into an international Data and Analytics Hub. IDA also noted that business Analytics is becoming a key competitive differentiator for companies and organizations, as analysis of large amount of data would enable companies to discover greater insights about their customers and operations.

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IDA took an unified approach that involves catalyzing demand and broadcasting early acceptance of analytics in key industry divisions, developing infocomm industry and manpower capabilities, framing the appropriate data policies, starting scalable and secure data exchange platforms and mounting "data centers" that will deliver advanced data services and applications.

IDA focused on 3 areas under this initiative, 1. Developing Singapore's Data & Analytics abilities This is done with intending to place Singapore as an international Data & Analytics Center focusing on Industry development. Following steps were taken to achieve this, ● A cross agency initiative was undertaken in 2012 by A*STAR (Institute of Infocomm Research and Exploit Technologies Private Ltd) and IDA to set up Business Analytics Translational Center (BATC). The BATC strives to catalyze invention, perform technology transformation and drive implementation in the field of Business Analytics. ● Formerly known as Business Analytics Innovation Challenge, Data Innovation Challenge is organized, which serves as a medium where enterprise users influence offline and online interactive sessions for BA ideas. ● With the cooperation of Institutes of Higher Learning prominent analytics ecosystem players, promoting conversion opportunities to professionals through trainings and workshops thus achieving Manpower Development ● Providing training opportunities in the field of Business analytics and cloud computing. In collaboration with WDA and key ecosystem players, job roles and competency and competency standards for Business Analytics and cloud computing were set under the National Infocomm Competency Framework (NICF) 2. Using advanced applications of Data Analytics to drive sectoral economic competitiveness ● Business Analytics service for Retail and wholesale areas -­‐ to initiate and promote commercially available shared services and analytics for certain industries ● Social Analytics for Business Enterprises programme -­‐ promoting the benefits and value of Social Analytics among business; thus aiming to build Social Analytics services for accelerated and lower cost adoption by business enterprises ● Government Business Analytics Programme -­‐ enabling analytics with capabilities which would drive its adoption in government organizations and public sectors 3. Develop platforms and enablers for Data & Analytics With a goal to provide and deliver secure, efficient and accessible capabilities to business for storing managing, acquiring, processing, selling and analyzing data thus producing business value for themselves and their customers. ● Software-­‐as-­‐a-­‐Service (SaaS) Enablement Program -­‐ lowering the barriers of Saas enablement by providing financial support for projects in specific industry verticals in Singapore ● Development of Standards -­‐ In 2011, IDA and ITSC (Information Technology Standards Committee) formed a Cloud Computing Standards directing Task Force to address user’s concerns on cloud security and improvise appropriate standards and rules to support 13



development and cloud adoption, and setting helpful guidelines for cloud computing service providers Proposed New Regulatory framework -­‐ A law that delivers an outline for collection, use, transfer and security of personal data, thus strengthening Singapore’s position as a trusted global hub.

As focus on the second thrust area IDA and its industry partners invested a total of $5.3 million to develop a suite of Business Analytics shared services for the retail and wholesale sectors. Service providers were selected through a Call-­‐For-­‐Collaboration (CFC) to provide the analytics services. The CFC was awarded to Manthan Systems, CyanSYS and PracBiz in May 2012. The CFC aimed to achieve the following outcomes: ● To reduce the obstacles to adoption of Business Analytics for enterprises in the Retail and other Wholesale sectors ● Encourage better adoption of Business Analytics in the Retail and Wholesale sectors by establishing the business value of Business Analytics ● To enhance the abilities of ICT companies in the deployment of Business Analytics solutions. The effective implementation of the shared services leads to a more vibrant Business Analytics environment in Singapore and profit both user enterprises and ICT companies Customer behaviour Analytics will enable retailers to find their key customer segments, recognize purchase trends and activities, and enhance their marketing and promotion campaigns to better meet the needs of the customers. These understandings will help retailers to improve the customer experience by bringing more relevant products and services, and improve the effectiveness of their campaigns and promotions. NTUC Link, NTUC Fairprice, NTUC Unity Healthcare, C K Tang, Best Denki and YG Marketing were the first six organizations to adopt it. These enterprises worked with the selected service providers to create analytics applications for different retail segments spanning grocery, consumer electronics and departmental stores

4.3 Manthan System BI suites
Manthan
Systems is a world leader in Retail Business Intelligence, Advanced Analytics and Optimization solutions. Manthan System has a variety of Business Suites. These BI suites focus on, ● Customer Marketing ● Merchandise Management ● Multi-­‐Channel Management ● Supplier Collaboration ● Store Operations

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Manthan’s PDSA Cycle Our case study will analyze the Customer Marketing solutions of Manthan Systems. This segment of Manthan has 3 solutions, ● ARC TargetOne ● ARC Customer360 ● ARC Loyalty247 ARC TargetOne: This product helps in creating and managing micro-­‐segments, identifying preferred communication channels and executing personalized, targeted offers and promotions to customers. Also, in responding to each customer interaction uniquely and driving customer engagement through relevance, reaching and reinforcing the most appropriate marketing message. The key features are thus, ● Central personalization hub ● Trigger based communication ● Omni channel connect ARC Customer360: This product helps in getting a unified, actionable understanding of customer topography. Put product affinity, campaign propensity, churn prediction, segmentation and customer scoring to work to identify cross-­‐sell and up-­‐sell opportunities and create personalization strategies. All of this is done on-­‐demand and in real-­‐time. The key features are, ● Understand customer topography ● Predict behavior ● Drive marketing outcomes 15

ARC Loyalty 247: This product helps in delivering personalized communication to customer's smartphone. Creates a repository of marketing messages, offers, ads and coupons based on customer's preferences. Uses preferences and in-­‐app behavior to personalize the messages and improves customers’ experience. The key features are, ● Mobile app ● Preference-­‐based notification ● Social sharing Of these products ARC Customer360 was implemented in NTUC Fairprice.

4.4 Why Manthan System’s Arc Customer360?
Today,
retailers rely on technology to identify opportunities to make their business more profitable with increasing revenues which depends on one crucial aspect -­‐ the ability to get a 360° understanding of the customer. Conventionally, it was about tracking which customers buy what products and through which channel. The channel used to be the store – located in the customer’s neighborhood, or a shopping mall located nearby. By keeping the track of product purchase behavior, retailers can understand the customers better. As time passed and with introduction of other channels, complexity of understanding the customer increased along with the expectations of customer. Therefore scope of understanding the customer was not limited to store, it now involves monitoring the behavior of customer’s across social media platforms.

Arc Customer360 is a business intelligence tool architected for retail marketers for customer analytics solution, which provides a blend view of customer topography. It gives detailed analysis of what when and where they buy, what products they prefer along with the frequency of their purchase and what kind of offer’s will attract them to the same store back next time they purchase. Key Capabilities of Arc Customer 360 are highlighted as follows:

4.4.1 Market Basket Analysis (MBA)
This
analysis helps in accessing the basket characteristics, popularity of an item, marketing events tracking and analyzing the affinity for products. MBA is considered as an inexpensive technique to identify cross sell opportunities. It provides insight in to Customer’s profile about who they are and what are the major purchases they make whenever they visit the store. This helps in determining the products tend to be purchased together and products, which requires more marketing promotions or which needed to be go for sale or discount.

This analysis can be applied in various ways: • Providing offer on products that are purchased together. • Inventory control based on demand of products. • Placing associated products together inside the store.

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Market Basket Analysis is an excellent way to understand customer and their behaviors

4.4.2 Churn prediction
Churn
Rate is the number of customers who cut relations with the company (Store) during a given time period. Tracking of churn rate will be useful to the company in preventing to lose relations with an existing customer by taking appropriate measures. Churn Prediction emphasizes on predicting the probability that a customer will stop buying from the store and utilizing it in bringing those customers back by offering them attractive offer’s. It’s expensive to acquire new customers when compared to retaining old customers. The process begins with identification of pattern of customers in customer lifecycle in order to segment them according to their value and profile. This segmentation will be helpful in predicting the potential churners. Monitoring churn will help the store in considering offer’s to keep customers resulting in a high retention rate. The tool uses regression Model is to predict the churn behavior from predictive variables like redemption behavior, renewal behavior, purchase behavior, demographics, etc.

4.4.3 Campaign Optimizer
Campaign
optimization is not only important to advertising success but also provides several ways to approach campaign improvement.it improves budget and effectiveness of campaign .Arc Customer 360 enables functionality of campaign optimization for retailers, from collection of data and analytics through personalized execution, delivery and promotions.

4.4.4 RFM Analysis
Customer
segmentation model for improved loyalty marketing is a popular tool for retailers. It is a marketing process for loyal customers and utilizes the behavior of purchase by frequency and monetary to analyze what kind of offers should be there for each type of customers. Usually whenever an offer is made, it is seen that less number of customers respond to such offers. But with RFM analysis, it can be ensured that we are targeting the set of right customers who have more probability of responding to offers made. It is a powerful method for customer response prediction ensuring improvement in profits as well as response. Main focus areas include cross sell, targeted campaigning, and customer retention etc.

4.4.5 Segmentation
This
deals with classifying a customer into a group having similar customers based on their demographic and behavioral attributes followed by planning strategies for each group separately. It assists in altering and strengthening your retail structure. It is a Data mining tool which focuses on marketing and promotions, tracks changes in customer’s behavior, helps in maximizing return of investment.

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4.5 Advantages of Arc Customer 360 and Existing Programs

Overview of Arc Customer 360 The organization incorporating Arc Customer 360 as a customer analytic solution can gain benefits like 1. Understanding Customers ● Profiling of Customers

Populating data about customers purchase behavior, their current preferences, which can be useful in extrapolating the future outcomes with them Customers trend and reach ü Understanding the customer trends which can be supportive in expanding their spending. ·∙

Segment Migration and Profiling ü Segmentation of customers into groups having similar purchase characteristics which would be helpful in analyzing each of them with all customers of one type under one roof è Product Mix ·∙

Products which have maximum affinity and concentration. ü This denotes to determining the products which are most often bought by customers and increasing their stock in the store in order to increase the revenue generated. ·∙

Brand Migration Analysis-­‐ ü Analyzing the market impact due to migration of brands. ·∙

Seasonality Analysis. ü Seasonality Analysis is detected by measuring/analyzing the customer’s interest for a small time interval such as days, weeks or quarters.

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è Promotions ·∙

Effectiveness of the channel ü Promotions are a crucial element in any vendor’s magazine of tools to help drive customer traffic. Key feature of Arc Customer 360 provides several ways to approach promotion improvement. it improves budget and effectiveness of promotion campaign. It measures the effectiveness of promotion as part of the overall planning process. ·∙

Experimental Analysis Design ü It deals with planning, steering, examining and inferring tests to evaluate the promotion factors. ·∙

Analysis of Historical Promotion ü It utilizes historical data taking into account a past promotion and how it can help in future occurring promotional campaigns.

è Purchase Behavior ·∙

Purchase trends by individual customer or other segments ü It provides insight in to Customer’s profile about who they are and what are the major purchases they make whenever they visit the store . ·∙

Analysis of Sales-­‐ Channel ü Companies use a several channels to promote products to customers. Sales Channel analysis allows organizations to comprehend the efficacy of various sales channels. By reviewing channel assessments, managers in sales team can focus assets on building and maintaining the channels that best meet their customers’ need.

è Loyalty ·∙

Performance of Loyalty Program ü This basic principle of Loyalty program is increasing the spending of existing customers, acquiring new customers and improvisation of churn rate. Arc customer 360 enhances the performance of Loyalty program making it feasible to attain the objectives easily. ·∙

Member Activity Analysis ü Member-­‐activity ratings provides the visual measure of engagement levels of customers. ·∙

Customer Lifecycle Analysis ü Customer cycle is used to describe the progress of steps a customer goes through when , purchasing, consuming, and continuing loyalty to a product or service.

è Marketing Effectiveness ·∙

Measurement of Return of Index 19

ü It denotes the measures of affinity of products assuming that all cash distributions are reinvested ,in addition to observing the component’s proce movements. ·∙

Analysis of Performance Drivers ü Analyzing the drivers in regards with the business which either enhances or inhibits it. ·∙

Sales Trend ü Analyzing the trend in sales for effective marketing.

è Campaigns ·∙

Response of Campaigning, its frequency and reach ü It deals with analyzing the response obtained after campaigning a product along with its frequency and reach of customers. ·∙

Analysis of Return of Investment ü Analyzing the increase in revenue obtained as a result o successful campaigning.

4.5.1 Customer Loyalty Program
NTUC
had launched a loyalty program called plus in 2010, which offers rewards to unionists and regular customers of NTUC's 12 social enterprises including NTUC fair price. This was done in collaboration with Link Loyalty Services and Oversea-­‐Chinese Banking Corporation Limited (OCBC Bank). It targets to reach one million union members by 2015.

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It was an expansion and enhancement of existing loyalty programme and brought more benefits to its existing members and a wider group of workers, especially the customers. These include an exclusive 8 per cent discount on over 500 selected NTUC FairPrice house brand items, fuel discount and preferential rates for healthcare services when using the Plus! Payment card.

4.6 Integration of Manthan System’s Arc Customer360 into Fairprice
4.6.1 Timeline of the project
Manthan
systems were selected as the service provider for Customer and Marketing Analytics under IDA's Sectoral Business Analytics Call-­‐For-­‐Collaboration in June 2012. Manthan hosted a conclave titled “Return of The Customer: Driving Revenue Through Loyalty” on May 29, 2013 at Marina Bay Sands, Singapore. Manthan hosted this event to discuss customer loyalty and mark the completion of its implementation of ARC Customer360 at CK Tangs, NTUC Fairprice and NTUC Link.

Hence the project took 1 year to be implemented into the NTUC Fairprice existing systems.

4.6.2 Surveys before implementing Arc Customer 360
There
were various surveys and research initiatives conducted by Manthan to analyze an organization’s understanding of its customers. The survey-­‐included questions like, ● How well do you know the purchase history of your customers? ● ● ● ● ● What are your current analytics capabilities? What solution(s) does your company use to manage marketing functions and services? What are you doing with your customer and loyalty data? Where are planning to invest within loyalty marketing? What obstacles need to be overcome in your organization to effectively use loyalty data and loyalty solution? ● ● What is the status of measurements/KPIs that track loyalty efforts? From the shopper’s perspective, what are the drivers of customer loyalty?

The survey answers are included in the Appendix.

4.6.3 Impact of Business Intelligence and Customer Analytics on Fairprice
NTUC
can now effectively use analytics solutions in the following ways. ● Do an efficient assortment optimization and shelf space allocation of products 21

● ● ● ● ● ● ● ● ● ●

Leverage customer data to segment, target and personalize offerings Customize multiple aspects of retailing to local stores or similar clusters Determine which marketing investments work, and which are less effective Discover the optimal pricing of products and services through their lifecycles Optimize inventory, replenishment, and transportation costs Optimize staffing with regard to cost Study customer shopping patterns, and locations Structure collaborations with external partners Empower store managers and employees with the ability to analyze their businesses Predict financial performance from non-­‐financial and intangible performance factors.

NTUC can now do a better profiling of its customers, their characters, segments and history; Understand purchase behavior, sales analysis, purchase and spend trends, and store preferences. This will help for better targeting and cross sell opportunities; Improve product and inventory affinities and concentration by knowing the preferences, growth trends, season trends and brand preference; do a better promotion and campaign visibility with the knowledge of campaign ROI; Make effective loyalty programs with points, redemption and activity status. With integration of arc customer 360, loyalty is now much more than just a program; It makes a customer feel special and appreciated, and much beyond discounts. It helps to build a deeper engagement with customers. It caters to specific segments, not only based on the amount of money they spend, but their shopping behavior and preferences. Thus analytics integrated with existing loyalty program would help further segmentation of audience, and better devised promotional strategies. The results are evident in the growing CSISG of NTUC Fairprice.

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The growth in CSISG score has been the highest in the year 2013. We could infer that these results were achieved by implementing Customer Analytics solutions into the existing system of NTUC Fairprice.

A comparative statistics of NTUC Fairprice with other supermarket chains reveals that NTUC Fairprice is ahead of them as it has implemented custom solutions to meet the customer expectations and is striving to achieve maximum customer retention.

7. Conclusion
This
case study gave us insights about how integrating Business Intelligence and Analytics IT solutions can improve the business processes. This study also helped us in understanding how BI and CRM can be combined to reap profits for an organization, particularly the retail sector. The research showed how Business Intelligence systems provide better means by which NTUC Fairprice can connect with their customers. They can now explore key parameters such as customer loyalty, sales transaction and store data to gain knowledge about the market and opportunities. With the increased insight about the market and customers, NTUC Fairprice can improve their promotion effectiveness, create targeted promotions, optimize costs and increase loyalty base. Recent surveys have shown that NTUC Fairprice has increased retail customer response rates by 2 to 5% and reduced customer churn by 8 to 12%. A major contributor to this achievement is the knowledge they gain through Business Intelligence. With such insight about the market NTUC Fairprice can continue to be the pioneer in Singapore’s retail industry for several years to come.

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8. Glossary Term/Acronym BI CCI CRM CSISG ICT IDA IDC KPI NTUC OLAP Definition Business Intelligence Customer Centric Initiative Customer Relationship Management Customer Satisfaction Index of Singapore Information and Communication Technology Infocomm Development Authority of Singapore International Data Corporation Key Performance Indicators National Trades Union Congress Online Analytical Processing

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9. References
1. NCS Business Intelligence Brochure, Go Big, Volume 3 2. Management Information Systems, Kenneth C.Laudon, Jane Laudon, 13th Edition. 3. Manthan Systems Analyze, Decide, Do brochure 4. The Wall Street Journal, “Return of the Customer-­‐Driving Revenue Through Loyalty”, 22nd May, 2013 5. NTUC Media Release, Fresh Journey at Fairprice, 20th July 2006 6. Enterprise Innovation news article “Singapore to invest $5.3 million to develop business analytics shared services”,7th July, 2013 7. News Asia One, “Fairprice’s efforts to raise service standards pay off”,23rd Mar, 2010 8. CCI learning Journal, “Insights to Service Excellence”. 9. NTUC Fairprice Media Release, “NTUC FairPrice tops Customer Satisfaction Rating in latest Customer Satisfaction Index and achieves highest-­‐ever score”. 10. IDA Media Release, CFC awardees, Annex 1 11. IDA Media Release, IDA’s Data and Analytics Programme,Annex 4 12. RIS Custom Research -­‐ The power of Emotional Connections 13. EnterpriseOne News, Customer Service Initiative for Retail 14. EnterpriseOne News,”26.4% Increase In Retail Companies' Revenues With Customer-­‐Centric Initiatives”.

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Appendix
Snippets of the survey questions,

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