ABSTRACT Foundational consumer data management is the key to a successful localization strategy. CONSUMER DATA MANAGEMENT Authors Trent Edwards, Ryan Liebman, Tom Mech, and Patrick Pawlicki The key to a successful localization strategy
Page 1 Introduction Localization is an active, consumer-centric, data-driven strategy used by companies to improve top-line revenues and influence the retail ecosystem and consumer journey at a granular level. Localization enables retailers to offer a subset of products and services that align with diverse consumer and vendor predispositions. While the attempts of localization strategies at large-scale retailers have been well documented, implementing the strategy includes new challenges that retailers haven t adequately addressed. Who are the local consumers? Is it possible to find local products and merchants without disrupting existing business models? How frequent must the stores plan to refresh or change their product offering so that shelf space does not sit empty? How will supply chain and inventory be managed if production isn t taking place at an industrial level? Addressing these questions requires a more complete understanding of the localization strategy, the resources required to execute it, and the tools and techniques that should be used to effectively mitigate operational risk and maximize sales. Ultimately, all answers are driven by foundational consumer data management. Localization Strategy In order to localize, retailers need to perpetually engage with the consumer to optimize offerings, constantly adapting to the preferences that they express. Retailers need to be more agile within the ecosystem to enable proper strategies across different regions and manufacturers; in many cases, smaller merchants and manufacturers typically cannot operate with the same merchandising and supply chain scale as national production. Successful management of a localized merchandising portfolio, which includes well-tended relationships with merchants and vendors, inventory management, and an understanding of the stocking cycles, promotes the retailer s abilities to capitalize on the reputation of the brand that they represent while demonstrating their commitment to understanding their consumers. One of the key differentiators between Localization and other sales strategies is the context through which the consumer is understood. Consumers exist within the communities that retailers serve and those individuals choose to live in communities based on its identity, which may include similar cultures or ethnicities, professions, or general interests. If retailers are able to understand these communal drivers, they can engage with their consumers and actively serve them by stocking their shelves with the products that are best suited to those demands. To successfully identify with a community, and be able to identify the right merchants and products to localize with, retailers need to understand the geographic, cultural, and behavioral boundaries for that community and their values. With many multi-dimensional communities around the world, establishing a genuine understanding to create a meaningful connection can prove challenging. Personalized Data Fortunately, capabilities and new technologies exist today that enable retailers to access consumer data in ways that were not previously available. Companies have the ability to gather individual opinions through social media platforms (i.e. Twitter, Instagram, Facebook), as well as have a data warehouse of online digital footprints for individual users enabling a more personalized and unique buying opportunity which creates loyal, life-long consumers. Consumers are more accessible today than ever before. Retailers only need to know how to hear them and how to apply what they hear. Harnessing Personal Data For several years, many businesses have been embracing the data-driven culture as a means to manage operations objectively. Creating, analyzing, and communicating data has promoted a fact-based understanding of vendor and
Page 2 employee performance and has led to the rise of targeted key performance indicators and daily reporting. Retailers are now able to leverage consumer and sales data to generate insights that explain buying patterns as they relate to consumer behaviors. These insights are driven by the capability to maintain, analyze, and manage big consumer data. Information across the retail ecosystem is estimated to produce 40 gigabytes of data every second. Decisions informed by data analysis have been shown to be more accurate and scale more effectively than those relying on anecdotal experience. Localization, particularly, benefits from a data-driven culture. Operational Localization Localization campaigns require co-ownership by enterprise and local store management teams. Through the use of data management at the most granular level, retailers can create standardized processes and analysis to build more personalized and localized experiences. Overtime the operational efficiency can be realized by creating purchasing teams, which are skilled in identifying, locating, and securing the local products to offer. Locally, management teams play an enormous role in understanding the consumer s preference immediately. While creating localization strategies, local store management and on-floor employees play the crucial role of quality assurance for the localization model. Given that these employees are locals themselves, they are responsible for providing context to the analysis and validating or refuting the findings before the purchasing teams are deployed, avoiding costly purchasing errors. Retailers can (and should) start small, initially rolling out a localized offering to select locations to create a baseline understanding of how frequently inventory turns, the number of local merchants and different products needed to remain in-stock, and even identifying whether local products should be showcased as part of the enterprise portfolio. This also enables a proof of concept around the implementation of the retail ecosystem. Key Elements of Localization Implementing a localization approach shouldn t be a drastic departure from normal business practices - in many ways, it optimizes on already in-place practices. Successfully executing the localization strategy requires a data architecture and operating model that focus on the analysis lifecycle and can ultimately prove to be transformational assets. Data Architecture The purpose of a sound data architecture is to enable more opportunity for retailers to better understand their consumers motivations and finding new and creative ways to engage them. The focus is on streamlining and automating data collection, transformation, and pre-analysis preparation, eliminating redundancy, and guaranteeing confidence in the quality of the data. Operating Model Data Architecture benefits should be reinforced by the operating model, which emphasizes feedback from a ll participants to confirm that the data and analysis activities always align with the mission of the business. The operating model should clearly lay out the relationships between, and responsibilities of, the people, processes, technology, and data. A structured and organized communication protocol to explain findings and solicit feedback is vital to the operating model because it allows on-floor employees to gain an understanding of the stocking decision drivers, provide valuable contextual feedback to the analysis teams, and ultimately allows retailers to be responsive to local consumers while keeping shelves well stocked with relevant products.
Page 3 Using technology to systemically reduce the hours spent on repetitive and manually-operated tasks creates opportunities to take the business to the next level. Local employees, who had previously been assigned to those tasks, can collaborate with the analysis teams by explaining features of the consumer buying behavior. Their insight can prove invaluable when developing hypotheses, measuring the results, and interpreting how the feedback should be incorporated for the next set of tests. Their first-hand knowledge and understanding of the consumer experience can help drive the selection of locally sourced products offered, shelf layout, and advertising campaigns. Analysis Retailers should seek to take advantage of the expanding capabilities of data management and analysis technologies when conducting analysis to inform localization decision making. Understanding the local population sentiment can be achieved through Natural Language Processing analysis; real-time supply chain and inventory management can utilize developments in database technologies; data accessibility can be enhanced through the use of cloud storage and computing services; and parallel processing and machine learning algorithms can be developed using robust analysis and visualization tools to optimize operational processes. All of these opportunities exist at a low cost (and in some cases, free) and s hould be incorporated into the retailer s analysis toolkit. Conclusion Among the many appealing features of properly implementing localization is the control that an enterprise has on where and when to roll out the strategy. Once management fully understands how the strategy works within the context of their business, it shouldn t be long before they are able to expand the strategy to additional locations with ease. Using data to build a consumer journey from the consumer perspective enables retailers to build an efficient, meaningful, and targeted retail ecosystem. CONTACT US Start your localization strategy today. Tom Mech, Partner Sagence 111 South Wacker Drive, Suite 3900 Chicago, IL 60606 tom.mech@sagenceconsulting.com (312) 635-1280
Page 4 BIOGRAPHIES Trent Edwards: Trent is a Senior Principal at Sagence. He has over seventeen years of industry and consulting experience creating, delivering, and leading customer-focused, data-driven products, services, and analysis for the communications, technology, financial services, and industrial products industries. His focus has been on leading teams to deliver innovative growth strategies, business models, roadmaps, and product launch efforts for his clients. Trent holds a Masters of Business Administration (MBA) from Duke University, The Fuqua School of Business and a Bachelor of Science degree from the University of Cincinnati in Mechanical Engineering. Prior to Sagence Trent was a Principal at Diamond Management and Technology Partners. Trent has also worked at Dishable and Motorola. Ryan Liebman: Ryan is a Manager at Sagence. With a strong focus on management consulting for the retail industry, he brings significant experience in multiple phases of work within strategy, business process design, functional design, integration, and customization. He is known for successfully translating client needs into business strategy and requirements, leading to integrated solutions through a functional approach. Ryan holds a business degree from Emory University, Goizueta Business School in Finance and Organization & Management. Prior to Sagence, Ryan worked for Accenture, Ross Stores, and Macy s. Tom Mech: Tom is a Partner at Sagence. He has over 20 years of business and technology experience, with emphases in analytics and analytical capability development, program management, strategy development and operational/organizational analysis. Tom s current focus is on helping clients build analytical capability. Tom holds an MBA from the Kellogg School of Management (Northwestern University) and a Bachelor of Science from Duke University with a double major in Electrical Engineering and Economics. Prior to Sagence, Tom worked for United Airlines, Diamond Management and Technology Partners, and Andrew Corporation. Patrick Pawlicki: Patrick is a Manager at Sagence. His core focus is in developing strategies for employing analytics to drive business process efficiency and customer retention, as well as developing data analysis tools to deliver business insights. He has served clients across a number of industries including Financial Services and Health Care, where he is experienced in acting as the liaison between the business and technology. He has participated in a variety of roles that have given him exposure to the full life cycle of a project. Patrick holds a Bachelor of Arts in Economics from the University of Notre Dame. Prior to joining Sagence Patrick worked for Mattersight Corporation.