1. 50 Years of Growth, Innovation and Leadership
Smarter Analytics for Retailers
Delivering Insight to Enable Business Success
A Frost & Sullivan
White Paper
Robert Worden
Brian Cotton
www.frost.com

2. Frost & Sullivan
Abstract................................................................................................................................... 3
Insight in the High-Velocity Retail Environment................................................................. 3
Smarter Analytics for Smarter Retail................................................................................... 5
Introducing Smarter Analytics.............................................................................................. 5
Performance and Organizational Benefits of Smarter Analytics...................................... 7
Retailers Using a Smarter Analytics Approach to Gain Competitive Advantage............ 8
GS Retail Propels Growth with Customer Insight............................................................... 8
Intersport is Future-Proofing with an Analytics Advantage................................................ 9
Migros.................................................................................................................................... 9
Deeper Insight, Better Responsiveness, and Business Success........................................... 10
Conclusion............................................................................................................................... 11
CONTENTS

3. Smarter Analytics for Retailers
ABSTRACT
The fundamental relationship between retailers and consumers has changed. Power has shifted
to consumers, enabled by Web and mobile technologies and the influence of social media.
Retailers are challenged to adapt to these changes and renew their relationships with their
customers to strengthen the brand experience and maintain customer loyalty. This requires
them to change their operational model to better understand their customers and to meet
increasingly more demanding expectations. Insight into their customers—their preferences,
needs, pricing, and buying behavior—is critical, as is insight into their own operations, from
marketing and merchandising to supply chain and order fulfillment.
Forward-thinking retailers are applying a Smarter Analytics approach to become more
customer aware, build customer loyalty and achieve higher levels of customer satisfaction. To
realize the benefits of this approach, retailers need to design their information technology (IT)
infrastructures to be able to support specific types of analytic domains, rather than rely on a
one-size-fits-all design. Retailers such as GS Retail Co. Ltd., INTERSPORT Group, and Migros,
are using the Smarter Analytics approach to build a business intelligence infrastructure that
enables them to deepen their insight, respond better and faster, and achieve business success
in a highly competitive market.
INSIGHT IN THE HIGH-VELOCITY RETAIL ENVIRONMENT
Retailers today are facing more technology-savvy, demanding customers and more sophisticated
competitors, which are forcing changes in retail business models. This transformation is being
influenced by three imperatives defining the retail landscape: 1) delivering a smarter shopping
experience, 2) developing smarter merchandising and supply networks, and 3) building smarter
retail operations. Retailers recognize that one of their primary opportunities in the fast-changing
retail environment is adapting to today’s empowered consumer. The power of the consumer
comes from their ability to leverage social and mobile technologies to gain access to competitive
product and price information and special offers, all to their advantage. By understanding their
customers better, retailers can more accurately predict their needs, preferences, and responses
to promotions, which can drive higher levels of customer satisfaction and, ultimately, higher
sales. In fact, a recent National Retail Federation (NRF) survey revealed that 82 percent of
retailers are making customer service strategies their primary strategic focus.1 The challenge
for retailers wanting to provide better service to these connected customers is to “know”
them better. With a deeper level of insight, retailers are better able to meet the customer
on the customer’s terms, with personalized service, offers, and promotions. But to be truly
successful, retailers need to be able to shape their future success by driving their organizations
on the basis of insight.
By applying business analytics, retailers can develop this insight. Retailers are exposed to a
wide variety of data. Structured, historic data, such as a customer purchase or billing records,
will only provide a partial picture of the customer. Increasingly, the connected customer is
generating data that is largely unstructured, such as customer service call recordings, chat
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sessions, product reviews, or social media postings. These can be rich sources of valuable
insight, as up to 85 percent2 of customer insight lies in unstructured data. Moreover, data needs
to be accurate to be useful, and an average of 23 percent of data in an organization’s database
is inaccurate, incomplete or out of date.3 Retailers are realizing that business analytics is an
important component to strategic success, and a recent study of CIOs found that 86 percent
of retail industry CIOs place business intelligence and analytics at the top of their list of
visionary plans.4
The types of insight that retailers can use to serve their customers better, and ultimately drive
business success, are illustrated in Figure 1 below. For instance, better insight into customers
and their price sensitivity needs to be complemented with operational insight (e.g., sales,
marketing, and merchandising insight) to give retailers the ability to link customers, suppliers,
and business partners together in a customer-oriented strategy. This enables retailers to move
away from reacting to customers based on history, to anticipating and planning for customers
based on insight. Deeper insight provides retailers with the ability to predict the likelihood
of a desired outcome and dynamically select the most acceptable offer to put forward to a
customer within seconds at the point of impact.
Figure 1: Information Drives Insight to Enable Retail Business Success
Pricing Insight Customer Insight
Know what price point will draw Sell what customers want at their
customers and increase profitability mix preferred point of purchase
Sales Insight Marketing Insight
Adapt new services and Deliver personalized
new ways to sell based on promotions and optimized
how customers are buying marketing spend
Merchandising Insight Business Success
Optimize inventory investment Drive maximum revenue and
with minimal stockouts profitability through all channels
Source: IBM and Frost & Sullivan Analysis
The types of analytics that produce insight range in complexity and answer different questions.
Simple, descriptive analytics include operational and ad-hoc reporting (“What happened?”,“How
many?”, “Where?”), and directed queries and drill-downs (“What exactly is the problem?”).
Retailers can apply these analytics to produce flash reports, marketing performance metrics,
and financial reports. More complex analytics include alerts (“What actions are needed if
something happens?”), simulations (“What could happen if…?”), forecasting (“What happens
if these trends continue?”), and predictive modeling (“What would be the best outcome
if…?”). Retailers could use these analytics to understand the impacts of cost changes, missing
sales targets, or the entry of new competitors into markets. Highly complex analytics include
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5. Smarter Analytics for Retailers
optimization (“How can we achieve the best outcome?”) and stochastic optimization (“How
can we achieve the best outcome given the effects of variability?”). These analytics can help
retailers create localized assortments, work with supply chain or production constraints, and
create personalized promotions.
Shifting from reacting to customers and market conditions, to anticipating future scenarios,
to actively using the variability in them can create complexity for retailers because they need
to make infrastructure design modifications to produce the kinds of insight their organization
needs. Although retailers are actively focusing on using IT to support a number of customer-
centric functions,5 they often need guidance to transform their current infrastructure to
become insight-oriented. Smarter Analytics is IBM’s approach to designing integrated systems
that harness all types of data to deliver focused, valuable insight and make it usable throughout
the organization for current and future action.
SMARTER ANALYTICS FOR SMARTER RETAIL
Introducing Smarter Analytics
To successfully apply insight, retailers have to make a commitment to embed the practice and
application of business analytics into the fabric of their organizations. Recent advances in the
way organizations are deploying business intelligence and analytics applications are driven by
the massive volumes of data, arriving at high velocity, in a variety of formats.This has important
implications to the computing infrastructure required to effectively run the applications in a
dynamic environment. Retail CIOs and IT managers have to consider the volume, velocity, and
variety of data available to their organizations to make informed infrastructure decisions, and
to think about this in the context of a structured approach.
The Smarter Analytics approach to building and deploying IT architectures is intended to
enable the application of analytics to all types of data. The goal is to provide retailers with the
systems and tools to adapt to the new imperatives of the retail industry by leveraging analytics
to become more relevant to customers, create competitive advantages, and drive profitable
growth. Retailers following the approach build their analytics infrastructures around three
central pillars:
• Align the organization and the IT infrastructure around information to effectively gather,
manage, and analyze the growing volume, variety and velocity of data, which drives the need
for a scalable integration platform to meet current and emerging data warehousing needs.
• Anticipate and accelerate actionable insights with systems and storage optimized for
analysis and information delivery to understand consumer behavior and build strategy,
shifting the analytics process from a purely passive, after-the-fact model, to an active,
during-the-fact model. As the number and diversity of stakeholders in a retail organization
requesting insight grows, the amount of processing resources in the analytics infrastructure
will grow, so an efficient and optimal harmony between analytics hardware and software is
necessary to ensure that the stakeholders are supported.
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• Act with confidence in real time with pervasive and embedded analytics supported by an
infrastructure foundation capable of swiftly handling critical actions to drive action. Integrating
analytics into time-sensitive point-of-sale or point-of-service retail applications means that
resiliency is critical, so the analytics infrastructure has to provide very high availability.
A central premise of the Smarter Analytics approach is that no one type of analytics
architecture will optimally meet all types of analytics needs. Instead, retail CIOs and line-of-
business managers have to jointly understand the organization’s insight needs to determine
a solution that is fit-for-purpose. Consistent with the Align principle, a retailer can start by
understanding the nature of the data it has as the raw material of insight, and must consider the
role of massive volumes of data, be it static, streaming, structured, unstructured, or any of the
above (e.g., Big Data) feeding into a retailer’s information supply chain. The questions concern
the volume of relevant data generated, such as millions of call records or transactions, which
can occur daily. Other criteria concern the velocity of data, which can be historic data collected
about a customer’s previous transactions, or be constantly streaming in from thousands or
millions of online shoppers accessing pages on a website. They can also revolve around the
variety of data, including static, structured data or rich, unstructured data including images,
social media, or audio recordings from a customer contact center. The key point from these
considerations is that different data types require different software and systems to analyze
them, and thereby deliver the various types of insight required by the organization.
Another set of considerations from the Smarter Analytics approach concerns the analytics
required to support the insight needed. Following the Anticipate principle of the Smarter
Analytics approach, retailers may need to employ descriptive analytics to support insight
into the current state of customers, operations, and the supply chain, as well as insight into
understanding historic trends. This could cover, for instance, what products different customer
segments have purchased, what promotions trigger them to respond, how often they contact
customer service, or how often they purchase through the same channel. Retailers may also
need predictive analytics to enable them to predict future states and develop contingency
plans to enact should any future states become realized. These analytics support insight
into potential customer responses to promotional offers, pricing changes, or new products
introduced. Prescriptive analytics can be used to direct business activity to shape the trajectory
of the business to attain strategic goals and business success. These analytics support insight-
informed decision-making around new services and new ways to sell to groups of customers,
develop personalized promotions, and optimize inventory investment.
Other considerations using the approach revolve around what the stakeholders in the
retail organization need from the analytics, and the level of resiliency built into the analytics
architecture. Building on the Act principle, some stakeholders may need on-demand, real-time
access to analytic tools in fail-safe environments. Others may be content with daily or less
frequently produced reports and are more tolerant of latency in getting results from the
system. Still others may only need analytic results that are infrequent or are not bound by
specific timeframes, and are tolerant of high levels of latency. The business insight tied to the
analytic results is enabled by embedding analytics throughout the retail organization. Figure 2
illustrates this principle, showing that some areas, such as point-of-sale or point-of-contact,
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need real-time insight. Other areas, such as just-in-time inventory insight on perishable or
volatile goods, are sufficient for daily insight, whereas inventory insight on non-perishable
goods can tolerate longer latency. It is not enough that the analytics infrastructure is always on;
it must also be always available, always secure, and always absolutely reliable.
Figure 2:Velocity of Retail Insight Embedded in Retail Organizations
Customer Perishable/ Non-perishable/ Month-to-Month Year over Year
Care Volatile Goods Non-volatile Sales Comparisons Comparison/
Inventory Goods Inventory Trends
Fraud Store Sales Advertising
Detection Figures Calendar
REAL TIME DAILY WEEKLY MONTHLY ANNUAL
Insight Velocity
(Frequency of Report Needed)
Source: Frost & Sullivan
Guided by these considerations, CIOs can design an analytic architecture with a mix of
operating points and the means to adjust systems to suit unplanned or emerging needs.With an
architecture inspired by the Smarter Analytics approach, CIOs can leverage the vast amounts of
data they have to better understand their customers and uncover patterns pointing to factors
that can positively impact sales.These could be insights such as time of day, geographic locations
that drive traffic or pages on the retailer’s website that are heavily browsed. Through analytics
and pattern recognition, retailers can better anticipate customer purchasing behavior and are
well prepared to act to minimize any disruption or event that could affect their reputation or
reduce customer confidence.
Performance and Organizational Benefits of Smarter Analytics
The performance benefits retailers can gain from using a Smarter Analytics approach flow from
hardware components that are carefully tuned to address specific analytic needs and work
in harmony with the analytics software to take full advantage of the capabilities of a fit-for-
purpose system. Increased performance is gained from better data management and storage,
data processing, and collaboration around the insight generated. It is also gained from more
efficient use of computing resources, and system management, energy, and data center space
savings. Performance benefits extend throughout the organization and can improve decision
support with systems that can reason and learn.
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In addition to the performance benefits generated, a Smarter Analytics approach to
implementing fit-for-purpose analytic infrastructures can also deliver real business benefits
to retailers by enabling retailers to better understand customer behaviors unique to their
businesses to build stronger strategies to meet buyer demand. For instance, retailers can use
descriptive analytics to sense what is happening with customers, suppliers, or the market, and
then respond to the trends. They can also uncover and infer buyer, supplier, and competitor
behavior, and then create and execute strategies to address potential outcomes. Web analytics
may help determine the optimal response times to encourage a sale, influence the best page
navigation or even the most attractive page design. Finally, retailers can create breakaway
competitive advantage by developing precise, targeted marketing campaigns, and highly
personalized shopping experiences.
Ultimately, retailers applying a Smarter Analytics approach enable themselves to harness the
full power of analytics on structured and unstructured data, with superior IT economics.
The approach allows the retailer to get a holistic view of what is happening with customers,
suppliers, partners, and the market, beyond the surface indications of purchasing activity. This
allows them to tune and optimize all the related systems in the organization to help them
efficiently meet the needs of the enterprise, and provide a personalized shopping experience
in a seamless manner across multiple touch points and channels.
RETAILERS USING A SMARTER ANALYTICS APPROACH TO GAIN
COMPETITIVE ADVANTAGE
Retailers are facing new business requirements to address newly-empowered and connected
customers, rising levels of competition, and increasing operational costs, and are using business
analytics to solve these challenges and gain competitive advantage. Retail CIOs are being
challenged to support these new business requirements and adopt new technologies and
access new data, while squeezing higher efficiencies out of their IT infrastructures. CMOs
and line-of-business managers are challenged to know which customers to target, how, when,
and with what. What follows are examples of how some forward-thinking retailers are solving
these challenges by following a Smarter Analytics approach and are recognizing the returns on
their investment.
GS Retail Propels Growth with Customer Insight
GS Retail Co., Ltd. (GS Retail) is a diversified enterprise consisting of four retail chains based
in Korea. GS Retail relies on analyzing customer data to maintain insight into customer
needs and has traditionally maintained separate data warehouses for its convenience store,
supermarket, and customer relationship management (CRM) systems. As the retailer’s business
grew, the performance of the systems decreased as the number of demands placed on them
increased. The time to generate reports and analytic results became a liability, and managers
could not perform complex customer, pricing, sales, or merchandising analyses they needed to
stay competitive. GS Retail decided to implement a new analytics system following a Smarter
Analytics approach.Working with IBM, the company updated the design of its infrastructure to
accommodate an appliance-like system, combining database, storage, and hardware elements
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to create a fast and easy-to-deploy, end-to-end business intelligence environment. The fit-for-
purpose nature of the solution meant that GS Retail achieved a faster time to value and reduced
the total cost of ownership (TCO) of the system by 30 percent. Other performance benefits
included a 60 percent reduction in storage space for their data, due to updated data management
and compression processes, and a reduction in time to analytic results, down to six hours, versus
nine to 15 hours with their previous system. Importantly, GS Retail laid the foundation for
employing sophisticated customer analysis tools, such as market basket analysis, enabling the
retailer to develop new cross-sell and up-sell opportunities to targeted customer segments.
Intersport is Future-Proofing with an Analytics Advantage
INTERSPORT International Corporation (IIC) is the purchasing and management company
for Austria-based INTERSPORT Group, a worldwide leader in sporting goods retail. IIC has
a tradition of using business intelligence to inform decisions throughout the many companies
within the group. With more than 4,900 associated retailers in 32 countries, business growth
began to overwhelm the IIC’s ability to use its analytics infrastructure effectively. As business
grew, the demands on the system to analyze transaction data increased, resulting in delayed
or unobtainable daily sales reports. This caused significant impacts on retail associates’ ability
to manage sales and pursue opportunities. Working with IBM, IIC designed and implemented
a new infrastructure optimized to support a highly resilient and available analytics system,
and also enhanced the company’s disaster recovery capabilities. The new Smarter Analytics-
inspired architecture consolidated and virtualized the IT infrastructure, enabling workloads
to be distributed between two powerful servers. Coupled with high-performance solid-state
storage, the new system greatly enhanced the performance of the analytics system and the
design of the architecture ensured a far greater level of resiliency than before. Because the new
system is optimized for transaction analysis workloads and leverages the servers’ virtualization
capabilities, peak time demands on the system can be easily accommodated, which solves
the problem of delayed or unobtainable daily sales reports. Moreover, the company is seeing
operational benefits from spending less on system management and by using 90 percent less
energy to power the new system.
Migros
Founded in Switzerland in 1941, the Migros Group comprises 10 supermarket and nonfood
retailers in a cooperative. Migros is the largest employer in the country, and to maintain its sales
momentum it is developing new channels (such as eCommerce) and new retail concepts that
blend commerce, food service, and entertainment. Migros Aare is the competence center for
the group and acts as a central hub that consolidates all of the IT solutions developed in-house
by the various cooperatives.The mainstay for Migros is fresh goods, which have a strictly limited
time frame, so sales data on them needs to be processed rapidly and on time. The company
needed to upgrade its IT infrastructure to enable it to keep up with its data processing needs,
and also to provide new sales and merchandising insight to support its new channels. Working
with IBM and its strategic partner SAP, the company built a new infrastructure based on two
powerful servers, leveraging the virtualization capabilities of the machines to accommodate
the multiple diverse workloads from the group’s many cooperative companies. By following
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a Smarter Analytics approach to designing this new infrastructure, Migros was able to have a
vastly improved sales and merchandise analysis capability, giving it far better visibility into buying
patterns. Improved insight also increased its responsiveness to changing patterns of demand,
and enabled better customer service and easier intelligence sharing among the cooperatives.
It also realized hard business benefits from a reduction of hardware acquisition, maintenance,
and software license costs, and a lower total cost of ownership for the new IT infrastructure.
DEEPER INSIGHT, BETTER RESPONSIVENESS, AND BUSINESS SUCCESS
There is no question that today’s retail environment is forcing retailers to change their business
models to become more responsive and competitive by understanding their customers better.
For retailers working to meet the imperatives guiding their transformation, the Smarter
Analytics approach can help.
• Retailers striving to deliver a smarter shopping experience want to engage their customers
on a personal basis, serving them whenever and wherever the customers want, and matching
inventory and brand experience across channels. Adopting a Smarter Analytics approach
enables retailers to harness the vast amounts of customer data at hand to develop single
views of their customers, find patterns in them, and make this insight available to the
marketing, finance, sales, and customer service personnel in the organization.
• Developing smarter merchandising and supply networks involves gathering customer
information continuously at every touch point to manage and deliver assortments based
on customer insight. Single-view perspectives of the customer, and of the retailer’s partner
ecosystem, can be used to anticipate customer needs and supply chain events, to enable
optimized supply chain management and product development. Fit-for-purpose and highly
resilient and available systems are able to support these demands.
• Building smarter retail operations involves inserting intelligence into customer data
management and processes to understand and predict sales trends, while improving
management across production, product development, and assets to drive operational
excellence and lower costs. Scalability and dynamic computing resource allocation are
critical to ensuring the availability and security necessary to realize this imperative,
particularly as the underlying analytics are embedded throughout the retail organization.
Following the approach means that retail CIOs need to carefully consider their organization’s
needs for insight to make the right infrastructure choices to support the analytics that will
produce the insight. An infrastructure design supported by Smarter Analytics can bring top-
line benefits and bottom-line savings to retailers. The return on investment from implementing
Smarter Analytics can translate to higher customer spend and growing revenue in new markets
with new customers.The approach can, at the same time, result in a highly efficient infrastructure,
which enhances IT economics by optimizing analytic workload performance on all the relevant
information available to the retailer. As the approach is extended throughout the retail
organization to its suppliers and customers, decision-making can be accelerated by delivering
intelligence where it’s needed, shortening the time to value delivered by the analytic systems.
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Retail CIOs and line-of-business managers should consider adopting a Smarter Analytics
approach if:
• The organization typically relies on information that is weeks or days old
• More management time is spent looking back at historic data than at real-time findings or
predicting probable outcomes
• Analysis is limited to looking at lists of data output, rather than looking at exceptions,
proactive alerts, and graphic visualizations of findings
To be successful, retailers must become more relevant to their customers and proactively
create competitive advantages, and this will propel profitable growth. Following a Smarter
Analytics approach to create an informed, insight-driven strategy can help achieve these aims.
REFERENCES
1
NRF Foundation and KPMG LLP, Retail Horizons: Benchmarks for 2011, Forecasts for 2012, February 2012. 15
February 2012 release. http://www.nrf.com/modules.php?name=News&op=viewlive&sp_id=1312. Retrieved 20
April 2012.
2
“Autonomy CEO: H-P Deal Marks IT Shift,” Wall Street Journal, Aug. 30, 2011. http://blogs.wsj.com/tech-
europe/2011/08/30/autonomy-ceo-says-h-p-deal-marks-fundamental-shift-in-it/. Retrieved 04 April 2012.
3
Experian QAS. “The Dilemma of Multichannel Contact Data Accuracy.” 19 July 2011 release. http://www.qas.
com/about-qas/press/experian-qas-releases-latest-research-report-the-dilemma-of-multichannel-contact-data-
accuracy-985.htm. Retrieved 01 May 2012.
4
IBM. The Essential CIO: Insights from the Global Chief Information Officer Study, Retail Industry Highlights. May
2011. http://public.dhe.ibm.com/common/ssi/ecm/en/cie03099usen/CIE03099USEN.PDF. Retrieved 09 May 2012.
5
NRF Foundation and KPMG LLP, op cit.
XBL03021-USEN-00
This report was developed by Frost & Sullivan with IBM assistance and funding.This report may
utilize information, including publicly available data, provided by various companies and sources,
including IBM. The opinions are those of the report’s author and do not necessarily represent
IBM’s position.
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