Smarter Analytics for Retailers

Transcription

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

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 Conclusion 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 Frost.com 3

4 Frost & Sullivan sessions, product reviews, or social media postings. These can be rich sources of valuable insight, as up to 85 percent 2 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 Know what price point will draw customers and increase profitability mix Customer Insight Sell what customers want at their preferred point of purchase Sales Insight Adapt new services and new ways to sell based on how customers are buying Marketing Insight Deliver personalized promotions and optimized marketing spend Merchandising Insight Optimize inventory investment with minimal stockouts Business Success Drive maximum revenue and 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 4 Frost.com

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 customercentric 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. Frost.com 5

6 Frost & Sullivan 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-ofbusiness 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 insightinformed 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, 6 Frost.com

7 Smarter Analytics for Retailers 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 Care Perishable/ Volatile Goods Inventory Non-perishable/ Non-volatile Goods Inventory Month-to-Month Sales Comparisons Year over Year Comparison/ Trends Fraud Detection Store Sales Figures Advertising 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-forpurpose 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. Frost.com 7

8 Frost & Sullivan 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 8 Frost.com

9 Smarter Analytics for Retailers to create a fast and easy-to-deploy, end-to-end business intelligence environment. The fit-forpurpose 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 Analyticsinspired 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 Frost.com 9

10 Frost & Sullivan 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 topline 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. 10 Frost.com

11 Smarter Analytics for Retailers 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 February 2012 release. Retrieved 20 April Autonomy CEO: H-P Deal Marks IT Shift, Wall Street Journal, Aug. 30, Retrieved 04 April Experian QAS. The Dilemma of Multichannel Contact Data Accuracy. 19 July 2011 release. com/about-qas/press/experian-qas-releases-latest-research-report-the-dilemma-of-multichannel-contact-dataaccuracy-985.htm. Retrieved 01 May IBM. The Essential CIO: Insights from the Global Chief Information Officer Study, Retail Industry Highlights. May Retrieved 09 May 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. Frost.com 11

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