insight

Organized retail in India is expected to grow at an astronomical pace over the next four years, making it critical for companies to differentiate themselves in a highly competitive environment. Retailers can use a data-driven approach to create a competitive advantage by better understanding customers and their needs. Retailers should overhaul/build their data analytics capabilities now if they hope to remain ahead of the curve.

Understanding the Indian Retail Customer—in Bits and Bytes!How India’s innovative organized retailers can leverage data-driven analytics to capture market share and build long-term customer relationships

By Gaurav Govil, Rajesh Balaraman, and Vinod Nair

Introduction . . . . . . . . . . . . .2

Retail in India . . . . . . . . . . . . . 3

Successful Retail Models: Low-Cost vs. Value-Centric . . . . . . . . 6

Data Analytics for a Value-Centric Model . . . 7

The Way Forward for Indian Retailers . . 12

Conclusion . . . . . . . . . . . . . 14

table of contents

Introduction Organized retail, one of the most notable emerging sectors of the Indian economy, continues to attract significant investments and interest from leading national and international retail players. It has also generated considerable opposition from small traders and shopkeepers who are worried about the impact of large-scale organized retail on their businesses. As a result, the government has been forced to carefully examine the long-term implications of organized retail in India.

According to independent estimates, the retail sector in India is poised to grow from US$270 billion in 2006 to $427 billion by 2010, at a 12-percent annual growth rate (Figure 1, page 3). Organized retailers in India today remain primarily focused on the essential building blocks of a successful retail model (e.g., acquiring prime retail space nationwide, developing optimal procurement models and supply chain infrastructure, designing appropriate store formats, and managing store operations). But often overlooked is the need to develop a continuous understanding of the Indian customer’s preferences and buying behavior—information that can be used to maximize customer lifetime value (CLTV).

Diamond believes a strong customer-data-driven analytics approach will become a key driver of success in the Indian retail market. Every aspect of a retailer’s P&L statement is impacted by retail analytics,

and retailers in India that define and implement a customer analytics approach now will maximize their opportunity to achieve significant increases in revenue and margins as other retailers play catch-up. By developing robust customer analytics capabilities, a retailer will build a solid platform for making and reviewing strategic decisions, lowering costs, and implementing operational improvement initiatives across the business.

A step-by-step approach may be required to build the skills and infrastructure necessary to achieve this customer understanding, an approach that includes the ability to:

• Identify the customer data that needs to be captured;

• Define and implement the appropriate retail technology and processes to capture this data; and

• Analyze the customer data to extract valuable insights that can drive store location, store formats, product selection, pricing, marketing, promotions, and other key elements of the overall strategy.

This paper captures Diamond’s research, experience, and capabilities in the use of data analytics to drive revenue growth and bottom-line improvements in the retail sector and proposes a clear and detailed approach that organized retailers in India must adopt to develop and implement this capability.

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For more information contact:

Vinod Nair, Partner, India Practice vinod.nair@diamondconsultants.com

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Growth of Organized RetailIndia’s retail sector generated total revenues of approximately $300 billion in 2007, of which the organized sector constituted only 6 percent. However, the sector is expected to grow at a CAGR of 12 percent to $427 billion by 2010 (Figure 1), with organized retail expected to contribute 15 percent of total retail revenues.

The Indian retail sector had been dominated so far by the 12 million1 small retail outlets (part of the unorganized sector) spread across India. A comparison among BRIC countries (Brazil, Russia, India, and China) indicates the expected steep growth in organized retail in India. China and Brazil took 10 to 15 years to raise the share of their organized retail sectors from 5 percent when they began, to 20 percent and 75 percent of the total

retail market in 2006, respectively. India’s organized retail sector is also moving at a fast pace, aided by improving infrastructure, a booming economy, and changing customer preferences. The most obvious indication is the rapid rate at which new retail space is being built—forecast to grow from 32 million square feet in 2005 to 400 million square feet in 2011, at an annualized growth rate of 52 percent.

Focus on Fundamental Building BlocksRetailers in India are presently focused on the essential building blocks of an organized retail model. The emphasis is on acquisition of retail space across the country in preferred locations. Following space acquisition, retailers are focused on developing regional

Retail in India

Conversion Rate Used: US$1 = INR 40 Source: Industry reports, Diamond analysis

Evolution of Retail in India and the Organized-Unorganized Divide

Organized Unorganized

2002 2004

98%96%

2062%

230

2006

Retail Market in India (US$ in billions)

Projected CAGR, 2006–2010: 12%

94%

6.3270

2008F

88%

12%

360

2010F

85%

15%

427

4%

6%

U.S. Brazil

15%

85%75%

25%

S. Africa

Comparison with Other Countries (2006)

India has tremendous potential for growth in organized retail.

65%

35%

Vietnam

78%

22%

China

80%

20%

India

94%

6%

Figure 1

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and national supply chain infrastructure. A closer look at the current scenario indicates the following observations:

• Retail space is one of the most critical building blocks for the majority of the organized retailers in India. Organized retail currently occupies about 90 million square feet of retail space and is projected to grow at a CAGR of 50 percent for the next five years (Figure 2, page 5). However, retail space growth has also resulted in steep increases in rental rates in key cities such as Mumbai and the National Capital Region (NCR).

• Supply chain management (SCM) is a pivotal area of focus for most retailers in India. Organized retailers have made significant investments in IT infrastructure,

posting annual IT expenditures of roughly $250 million in 2006. This figure is expected to grow at a CAGR of 44 percent, to $1.07 billion by 2010.2 ERP platforms from leading vendors such as SAP and Oracle are being widely deployed by the retailers. Many retailers are also evaluating leading technologies like Radio Frequency Identification (RFID) and Smart Cards for better supply chain and store management.

• Recruiting and training are also critical factors for retailers to consider. Recruiting needs span all levels—from top management to the storefront. The Indian organized retail sector currently employs more than 21 million people,3 which is expected to grow at a CAGR of 10 percent to 15 percent through 2010.

The preceding factors are vital for the success of any organized retailer in India. However, these three areas alone are unlikely to provide a retailer with a long-term, sustainable competitive advantage. Given the rapidly growing market, retailers appear to be concentrating on getting the basics in place with little or no emphasis on defining and describing the typical customer. As the market matures, long-term success will require deep insight into the customer’s psyche. Successful international retail models can also offer valuable lessons; it is critical to the long-term success of a retailer in India to build capabilities at the enterprise level that can help develop deep insights about the customer.

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Source: Industry reports, Diamond analysis

Growth of Retail Space in India

2005 2006E

3250

2007E

Growth in Retail Space (sq. ft. in millions)

CAGR 52%

6.3

90

2009F

159

2011F

400

NCR

12

36

Current and Projected Retail Space(sq. ft. in millions)

Figure 2

2006 2008

Mumbai

14

26

Bangalore

3

8

Channai

2

7

Hyderabad

3

10

Kolkata

5

12

NCR

22%

Mumbai

15%

Kolkata

8%

Hyderabad

7%

Bangalore

5%

Chennai

4%

Pune

4%

Others

35%

% Share of Retail Space in India, 2007

Delhi

345

Mumbai

280

Kolkata

205

Bangalore

140

Pune

140

Hyderabad

110

Chennai

60

Comparison of Real Estate Rentals, 2007 INR per sq. ft. per month

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Successful Retail Models: Low-Cost vs. Value-Centric

In Diamond’s experience, retailers (both inside and outside India) operate under two umbrella models: low-cost and value-centric. Retailers utilizing the low-cost model, with price as the key value proposition to the customer, typically use scale very effectively to lower their overall cost structure (e.g., Wal-mart, Big Bazaar). Wal-Mart’s competitive advantage resides primarily in the company’s “Every Day Low Pricing (EDLP)” strategy; the retail giant consistently offers its customers lower prices than its competitors. Lower pricing is possible as a result of Wal-Mart’s lower overall costs, which the company achieves through a highly efficient supply chain and by sourcing goods in large volumes.

The key proposition to customers in the value-centric model focuses on providing “value for money” by addressing a non-price attribute that is important to the customer, such as product assortment, convenience, customer service, or customer experience. Retailers employing the value-centric model typically address one or more specific customer preferences. For example:

• Target is known for better assortment;

• 7-11 is known for greater convenience;

• Nordstrom is known for better customer service; and

• Macy’s is known for better customer experience.

Value-centric retailers also seek to address customer expectations across one or more of these dimensions in a consistent and sustainable manner. Tesco is one of the top three international retailers and has more than 2,000 stores, worldwide. A key contributor to Tesco’s astounding success, both in its U.K. home market and abroad, has been the company’s superior understanding of customer needs through its Clubcard Loyalty Program. The Clubcard program helps the retailer identify customers individually, providing sales and transaction data that give Tesco unique customer insights. Using customer profiles and transaction data, Tesco can design and execute targeted campaigns that are reinforced by personalised communications to each Clubcard holder.

Diamond’s experience with clients suggests that it typically takes retailers two to three years to develop a comprehensive loyalty program that captures superior customer data and develops the requisite analytics skills to extract value from the data. However, retailers can begin reaping the benefits of data analytics from an early stage by capturing and analyzing basic customer information.

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Indian retailers that want to remain ahead of the curve should consider three distinct data capabilities when designing and implementing a customer analytics engine: collection, storage, and analysis (Figure 3).

Data CollectionThere are three main components of data collection: type of data, method of collection, and mechanism of collection. In addition, customer data can be divided into four different categories: demographic, store-specific, behavioral, and psychographic (Figure 4, page 8). Each data category serves a different purpose. Collectively, however, they generate detailed insights into each retail customer.

Demographic data can be obtained using loyalty card registration forms, Web sites,

contests, and external databases. Basic behavioral information can be recorded using RFID tags, field team observations, and transaction information. An important part of data collection is the feedback received from customers. Retailers need to seek feedback regularly through market research, interviews, call centers, surveys, focus groups, or direct mail. Diamond believes that store-level data can also offer several insights; electronic traffic counters, video tracking, and image studies reveal important trends in shopping behavior.

Once a retailer identifies its data requirements, it is important to understand the methods of collecting that data. Companies must ensure they have both in-store systems and external databases to guarantee the quality of the collected data.

Key Focus Areas in Customer Data Analytics

Source: Diamond Management & Technology Consultants

Figure 3

Data Warehouse

Types of Data• Demographic• Behavioral• Psychographic• Store-level

Methods of Collection• Loyalty program registrations• Web sites, contests• External databases• Consumer surveys, market research

Mechanisms of Collection• Magnetic cards• Smart cards• RFID• EPoS (Electronic Point-of-Sale)

Data Collection

Duration of Storage • Grocery transaction data is stored for 2–3 years• Specialty retail product transaction data is stored for 3–5 years• Inventory data is typically stored for a 12-month duration

Applications and Vendors • Microsoft and IBM are widely used databases• Oracle, Teradata, and Sun Solaris are platforms adopted by many retailers

Data Security and Back-up • Data back-up is a daily practice in most leading retail operations• Centralized databases are designed to withstand most manmade and natural disasters

To Increase Revenue• Increase ticket size and frequency• Optimize discounts• Minimize listed price

To Reduce Cost to Serve• Reduce back-end operational cost• Reduce in-store costs by optimizing resource allocation

To Reduce Investments• Reduce retention cost by optimizing redemption and reducing promotional cost• Reduce acquisition cost through targeted acquisition and branding

Data Storage

Data-marts

Data Analysis

Analysis

Data Analytics for a Value-Centric Model

8

The mechanisms for data collection are mainly comprised of Electronic Point of Sale (EPoS), RFID, Magnetic Cards, and Smart Cards. The biggest stumbling block Indian retailers are likely to face is the large initial investment required to set up robust data collection and analytics capabilities. Diamond estimates that these costs can reach up to $30 million in the first year and between $5 million and $10 million during subsequent years. However, Indian retailers may incur substantially lower costs due to a smaller scale of operations and a lower cost structure for analytics capabilities.

Data StorageData storage constitutes a major cost for companies, and it is essential that retailers understand at the outset how much data will be stored, and then plan accordingly. It is desirable to have sales, transaction, and margin data from at least the trailing season or year. In the grocery segment, where frequency of shopping is higher, one to two years of data history may be adequate, while speciality segments, which have lower transaction frequency and basket content count, require three to five years of data

history. Inventory data is more voluminous than sales data. Therefore, retailers should, at a minimum, store detailed inventory information for the preceding 12 months and sales information history for the prior 24- to 36-month period.

Retailers should also ensure that they have taken sufficient data security and back-up measures. Data should be backed up at least once a day, if not more, and companies should also ensure that their centralized databases are designed to withstand most manmade and natural disasters.

Data Collection: Types of Data

Source: Diamond Management & Technology Consultants

Figure 4

• Name• Age/Birthday• Gender• Marital Status• Employment Status• Household Income• Zip Code• Number of Children• Asset Ownership • Education• Media Preferences• Brand Awareness• Advertising Awareness

Demographic

• Frequency & Recency of Purchase• Average Ticket Size• Channel Preferences —Ticket Size & Frequency — Category Affinity • Promotion-Prone behavior —Responsiveness —Promotion code used• Transaction /PoS Data —Date & Time of Purchase —Price —SKUs/ Number of Items —Discounts —Payment Mode

Behavioral

• Attitudes• Aspirations• Spiritual & Ethical Values• Emotive Drivers• Opinions• Concerns

Psychographic

• Footfall• Number of Tickets• Returns• Consumer Footpath within Store • Dwell Times at Departments• Transaction Log —Waiting Time —Time to Scan an Item —Time Between Transactions —Payment Type —Transaction Time

Store Specific

Loyalty card registration form collects demographic data

Bill generated at PoSprovides transaction data

Surveys can capture psychographic data

CCTV to capture in-store movement

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Data AnalysisData can be analyzed effectively once it has been collected and stored appropriately. There are four distinct steps during the analysis:

• Developing initial hypotheses;

• Preparing data for analysis;

• Testing the hypotheses; and

• Drawing final conclusions.

Developing an initial hypothesis and testing the hypothesis require considerable planning and conceptual thinking, whereas preparing data for analysis and drawing conclusions involve executing these plans. Each step is linked to the other, making it vital that a retailer performs each step effectively (Figure 5).

Retail analytics can be utilized to identify specific, actionable areas of improvement in order to increase sales and reduce costs.

Increasing SalesA retailer can increase its customer base by tailoring goods and services to the relevant target segments. A way to further optimize this approach is by knowing the profitability of each customer segment, which allows retailers to selectively retain profitable customers and “weed out” unprofitable customers. External price elasticity data can be utilized for pricing decisions. Store-specific and PoS data can be utilized to improve current product offerings and services, while customer profiles of existing customers can be used to acquire new customers using referral promotions. Finally, relevant cross-selling and up-selling sales initiatives can enable a retailer to increase sales per customer. An effective data-driven approach can help retailers maximize sales throughout a customer’s lifecycle.

Reducing CostsExecuting effective promotions: Data analytics can help retailers manage promotions more effectively. Optimal discounts can be offered using external price elasticity data. Communication channel cost and usage can be monitored to optimize channel utilization once a retailer has store-specific and PoS data. The timing and placement of in-store promotions can be optimized based on customer responsiveness to previous promotions as well as their footfall and dwell-time data. Customer data can also be used to identify, track, and filter unprofitable customers.

Optimizing product and resource allocation: Buying behavior data from external sources can be used for initial allocation of products and resources. Footfall data across the day can be utilized to execute dynamic allocation of

Data Analysis: Overall Approach

Source: Diamond Management & Technology Consultants

Figure 5

• Define business hypothesis• Identify key data and analysis needed to prove/disprove this hypothesis

1. Develop initial hypothesis

2. Prepare data for analysis

3. Test the hypothesis 4. Draw final conclusions

X

X

A ?

X W

Y Z

X/ AA, but Z

Ana

lysi

s pr

oces

sD

escr

iptio

n

Thinking

• Import data• Group and cleanse data in preparation for analysis

Execution

• Analyze data—consider multiple scenarios• Detect errors• Confirm/disprove initial hypothesis• Generate new hypothesis

Thinking

• Draw conclusions• Present findings

Execution

sales people and cash registers. It can also be utilized to allocate products and resources across categories. Customer profile data can be analyzed to segment customers, which allows for differentiated services (e.g., in-store experts to help customers who are not tech-savvy purchase advanced technology gadgets).

Customer analytics is one of the specific domains under retail analytics and, as

the name suggests, it is driven primarily by customer data (e.g., demographic, psychographic, behavior, etc.).

The objective of customer analytics is three-fold: to acquire new customers, to retain existing customers, and to maximize customer value. The immediate retailer benefit from collected customer information is market basket analysis, which allows for targeted offers to customers

based on demonstrated purchase behavior within the segment (Figure 6). Based on the depth and quality of information, the retailer can begin with basic market basket analysis, progress toward advanced customer segmentation, and ultimately develop a sophisticated personalization strategy to acquire and retain customers.

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Enhancing Capabilities Using Customer Analytics

Source: Diamond Management & Technology Consultants

Figure 6

Gain knowledge of individual customers based on information provided and transaction behaviors. The result is an ability to deliver personalized marketing, products, and services that are most relevant to individuals. (Personalized)

Personalization

Collect personal information/preferences and assign customers to segments (with a tiered service level). Then market to and serve customers based on assigned segments. (Proactive)

CustomerSegmentation

Target and serve customers based on analysis of aggregated data from like purchase behaviors of other customers. (Reactive)

Market Basket Analysis

Capacityto Serve Customers

Time

Capabilities

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Diamond worked with a leading retailer in India to define specific actions that would stimulate sales at the company’s hypermarkets. The retailer was particularly concerned about stagnant sales over a six-month period at its hypermarkets while the overall organized retail market seemed to be growing at a significant rate.

The Diamond team looked at root causes for the stagnant sales from both a quantitative and qualitative perspective. A comprehensive framework was used to analyze key drivers of revenue such as footfall, conversion rate, basket size, and bill value. In addition, the analyses included specific aspects of the hypermarket’s operations including inventory management, fill rates, promotions, and pricing. The qualitative analyses included understanding customer feedback, analyzing competition, and evaluating recent changes in terms of access or demographics of the surrounding area for the hypermarket.

Analyses of footfall information (Figure 7) clearly indicated a significant divergence in footfall numbers between one of the hypermarkets and the mall where it was housed. Qualitative analyses revealed that a large part of this divergence could be

attributed to the difference in the target segments of the hypermarket and the mall. The mall had a number of high street/high-end brands catering primarily to the upper middle-class segment, while the hypermarket was positioned to serve the regular middle-class segment. Additional analyses indicated potential issues around physical access and ease of shopping for customers of the hypermarket.

Analyses of the conversion rate trends showed that the conversion rates dropped significantly over a three-month period. This coincided with a rebranding effort by the hypermarket and could be attributed to a potential mismatch of customer expectations and the hypermarket’s new value proposition.

In addition to identifying the root causes for the client’s stagnant sales, Diamond’s customer data analysis helped the client understand the value of building a robust Customer Relationship Management system. The client has begun defining a comprehensive CVM/CRM strategy and is also developing a customer loyalty program that will help collect, store, manage, and analyze detailed customer information.

Source: Diamond analysis

April May June July Aug Sept Oct Nov0

50,000

100,000

150,000

200,000

250,000

Footfall Trends – Mall vs. Hypermarket

Commencement of divergence between mall and hypermarket

Hypermarket Mall

April May June July Aug Sept Oct Nov40%

50%

60%

70%

80%

Conversion Rate – Hypermarket

Drop in conversion rate during rebranding

58%62%

66%

50%53% 53%

65%

70%

Figure 7

Customer Data Analysis

Case Study: Stimulating Sales at Hypermarkets

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The Way Forward for Indian Retailers

Diamond recommends that Indian retailers build their data analytics capabilities through a phased approach, noting that data availability will be a key driver for each phase (Figure 8, page 13). Retailers in India that apply this phased approach will move ahead of the pack as the market experiences radical growth in the coming years.

Phase I: During this initial phase, retailers should primarily rely on a combination of primary research conducted with the help of external agencies, and secondary research information from outside agencies and the government (e.g., geo-demographic data, seasonality data, shopping behavior data, customer feedback, etc.). Data from these sources would assist retailers in identifying their initial target segment and format. In addition, it will help retailers decide on initial layout, pricing, promotions, etc.

Phase II: Within six to nine months of commencing operations, retailers should start utilizing their in-store data to improve operations. Retailers can capture several types of data—such as sales, footfall, basket, margin, and time-of-purchase—which

together will help retailers refine their product assortment, inventory management, pricing, and promotions to further improve sales and maximize customer value. Retailers may also optimize the margins on various SKUs by conducting variance analysis using different parameters: value, volume, and shopping baskets. Finally, as time progresses, retailers will have more historical data to identify trends, which can then be used for demand forecasting and resource allocation.

Phase III: Within 12 to 24 months of operations, retailers should implement full- fledged customer programs (e.g., customer loyalty program) to capture information about their customers at an individual level. This data will serve retailers on three key levels:

• Retailers will be able to segment their customers and, thus, will be able to serve each customer segment accordingly.

• Targeted marketing and promotions can be done based on the requirement of the customer segment.

• Store layout and product placement can be optimized using customer information.

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Phased Approach to Retail Analytics

Source: Diamond analysis

Figure 8

Analysis & Insights

• Optimal product and resource allocation decisions based on buying behaviors of target consumers• Pricing decisions taken with external price-elasticity information

• Timing and placement of in-store promotions determined based on consumer respon- siveness to previous promotions, footpath, and dwell-time data• Dynamic allocation of resources based on store traffic mapped

• Optimize margin for different SKUs by performing variance analysis• Demand forecasting based on historical sales, discount, and traffic data• Store layout optimized for shopping based on product correlation inferred from market basket analysis

• Differentiated services to different segments of customers, post- cluster analysis• New customers of similar profile acquired through referral promotions• New products intro- duced by life-stage mapping and gap analysis

• Personalized promo- tions to cross-sell and up-sell, based on shopping basket and profile information• Low-frequency customers are tracked and targeted to encourage more visits• Unprofitable customers identified and filtered

DataRequirement

Phase I

• External Data: geodemographic, price elasticity, shopping seasonality, etc.

• Store-Specific Data: footfall, transaction data, returns, etc.

• Historical Store- Specific Data: historical data of transaction, margin, basket contents, and category-level data, etc.

• Demographic Data: profile information of customers

• Assumption: Business model and targeted customer base selected; strategic decisions such as branding and pricing made based on external data

• Within six months into operation, transaction data is utilized to improve operations and the effectiveness of promotions • Historical PoS data is used to identify trends in sale. These trends are then used to carry out demand forecasting, resource allocation, etc.• Margin information is used to conduct variance analysis with volume, value, and shopping baskets.

• Two years into operation, initiatives such as loyalty programs are launched to track individual consumers • Above information is then used to do targeted marketing, promotions, cross-selling and up-selling, etc.

• Behavioral Data: frequency and value of purchase, average ticket size, channel preferences, etc.

Phase II Phase III

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Endnotes

1 Investment Commission of India. <www.investmentcommission.in/retail.htm>2 Springboard Research company release, 2006.3 PropertyBytes, the Indian Real Estate Blog. <www.propertybytes.com>

Conclusion Organized retail in India is poised to grow at 400 percent over the next four years. In an environment growing at such a frenzied pace, it becomes crucial for retailers to identify and differentiate themselves on one or more value-centric attributes that will provide them with long-term competitive advantage. Diamond believes that Indian retailers must adopt a phased approach when developing their customer analytics capabilities. Implementing robust customer analytics capabilities will provide the retailer with a strong platform to make and review strategic decisions as well as launch operational improvement initiatives across various dimensions.

Retailers in India need to look beyond the accepted methods of data collection and analysis because the time for change is now, when the value of customer analytics can be maximized. Once retailers have identified the necessary customer data, defined and implemented appropriate retail technology and data collection processes, and analyzed the data for valuable insights, they will see their customers in a different light—and create a competitive advantage that will directly improve both sales and margin.

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About the Firm Diamond (NASDAQ: DTPI) is a premier global management consulting firm that helps leading organizations develop and implement growth strategies, improve operations, and capitalize on technology. Mobilizing multidisciplinary teams from our highly skilled strategy, technology, and operations professionals worldwide, Diamond works collaboratively with clients, unleashing the power within their own organizations to achieve sustainable business advantage. Diamond is headquartered in Chicago, with offices in Washington, D.C., New York, Hartford, London, and Mumbai. To learn more, visit www.diamondconsultants.com.

Diamond’s Retail practice has worked on a variety of issues including market sizing and growth assessment, sales and margin enhancement programs for hypermarkets, customer value management, and launch support for ongoing growth/expansion and various operational aspects of the value chain including procurement/sourcing, inventory management, product mix, marketing, order to cash, promotions, and category management. The Retail practice also works closely with the Diamond Information Analytics Center (DIAC) in Mumbai. DIAC uses data to provide clients with rapid insightful analysis of complex operational issues. DIAC helps clients use information and analytics as differentiators in a highly competitive environment.

About the Authors Gaurav Govil is a Senior Associate in Diamond’s India practice who has more than six years of experience working in the retail, telecommunications, healthcare, and media industries. Gaurav has experience working in the Indian retail sector on a wide range of issues including category management and product mix. Prior to joining Diamond, Gaurav worked with GE as a six-sigma Green Belt where, among other roles, he led a team that benchmarked and implemented the improvement plan for long-range financial forecasting.

Rajesh Balaraman is a Principal in Diamond’s India practice with over 13 years of global consulting and industry experience. He has led initiatives on a wide range of strategic and operational issues helping clients increase revenue, reduce costs, and enhance operational efficiency. He has significant experience in program management, managing new business launches, market entry strategy development, business process analysis, technology strategy, supply chain management, and customer relationship management. Rajesh has worked in a number of sectors including retail, financial services, telecom, media and entertainment, and manufacturing.

Vinod Nair is a Partner at Diamond and leads the firm’s India practice. He has worked with clients in India, Europe, the Middle East, the United States, and South Africa on a range of strategic and operational issues. Vinod has focused on issues such as successful market entry strategies, proposition development and marketing planning, analytical marketing techniques to improve customer lifetime values, and operational improvement efforts to reduce costs and streamline processes. His clients include leading corporations in the telecommunications, retail, financial services, media, automotive, and manufacturing sectors.

The authors would like to acknowledge the contributions of Aishwarya Jayakumar and Srimoyee Mitra, both Associates in Diamond’s India practice. Their research work on the Indian Retail Sector contributed key insights toward the development of this paper.

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