Retail Analytics: The Secret Weapon (Wiley and SAS Business Series)

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Retail Analytics: The Secret Weapon

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SAS Retail Analytics Cruise

Srini R. Srinivasan 1 Prof. Brijesh R. Sharma 2 Abstract With the advent of technology major aspects of our life is getting digitized. We are leaving digital footprint on a large scale.

Cox, Emmett

Retail business is not immune to this. Retail data is the new black. The footprint in the retail context is ubiquitous hence humongous. Use of machines to decipher and get insights from this data into something meaningful and for further action is the realm of Retail analytics. Conceptually this fundamental is axiomatic. The paper is an exploration to develop a retail analytics framework which can become a rallying model for further discourse in Retail analytics.

Retail managers have a number of operational levers to influence these interactions, including store design, assortment planning, pricing, inventory control, and staffing. Retail managers also have some visibility into what transpires in the store. Historically, this visibility has been limited to inventory positions, staff schedules, and, since the emergence of barcode technologies in the 's, point-of-sale POS data. Recent years, however, have seen a heightened interest among practitioners in store visibility how a retailer can gain clearer visibility and how it can best use this visibility for operational and marketing advantage.

Citing opportunities brought by existing and new retail data sources, a recent report by the McKinsey Global Institute highlights retail's tremendous upside potential across the industry for individual players to expand and improve their use of big data" McKinsey Global Institute Factor behind the increased interest in retail visibility is the emergence of modern technologies for in-store data collection as well as information technologies for capturing, storing, and analyzing data from these sources.

These technologies bring the promise of a revolution in retail operations by offering visibility at a more granular level of detail and a finer time scale. Examples of such technologies include radio-frequency identification RFID and traffic counters, which have existed for a number of years but whose uses are still being explored and evaluated, and new technologies such as smart shopping carts, video monitoring, and cell phone tracking. Customers leave footprints — whether they actually make a purchase or not.

Those footprints come in the form of browsing your Web site, questioning your staff, and, of course, making purchases. The shelf- and space-management department sees it as just placing these products on the appropriate shelf where they fit best. Real estate managers, those individuals who buy parcels of land for new stores, look for population size to support total store sales growth goals.

As you can see, very few areas take the whole picture into perspective. The analytics team plays an important role in keeping a holistic view of all parts of the process. Objective The discussion around Retail analytics is intuitive and can be understood by all and sundry. The application of analytics needs a nuanced understanding of the retail domain and a domain of analytics.

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Retail Analytics: The Secret Weapon - Emmett Cox - Google Livres

The objective of the paper is to simplify and develop a framework to facilitate a meaningful discussion on retail analytics. To get more insight videos from you tube and ted. Identifying Retail Business Analytics Pillars Retail Business analytics relies on four technological and informational pillars on which they can build a strong decision support system.

These pillars are.


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  • Business intelligence BI uses querying, reporting, analysis, scorecards, and dashboards to make it easier for business users across the organization to find, analyze, and share the information they need to improve decision-making. Analytic applications A new trend in BI and reporting is analytic. Analytics applications include software that measures operational performance — and sets the stage for better results. Analytic applications rely on historical data, along with software tools that crunch numbers and slice and dice data from different perspectives, to offer results that give decision makers the actionable insights they need to improve retail functions.

    Analytic applications package business analytics capabilities, data models, process workflows, and reports to address a particular domain or business problem. Examples of these areas include understanding buying behavior during the holiday shopping season, getting insight into which stores or channels are the most productive, which suppliers are slow to deliver product, which lines are driving the greatest profits. Financial performance and strategy management Financial performance and strategy management tools and disciplines aim to drive greater profit and growth across the retail operation.

    These business analytics tools accomplish this task by simplifying, structuring, and automating dynamic and sustainable financial performance and strategy management practices. This area of business analytics deals with, budgeting and planning, financial consolidation, scorecarding and strategy management, financial analytics, related reporting capabilities.


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