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Retail Analytics 2018-04-11T08:25:23+00:00

Commercial Success and Adoption of Retail Analytics

In today’s retail market, day-to-day contact points evolve into short-term and long-term challenges, as the region’s information technology infrastructure is maturing significantly, funding has been increased to solve all forms of business, tactical and strategic processes. As for the current scene of retailing, it seems fragmented. And it is difficult to identify the starting points and solutions that must be addressed first.

Retail Analytics & Retail Business Intelligence

With the implementation of business analytics in the retail market sector problems facing companies can be addressed and solutions can be found. The traditional retail model is a combination of forecasting, ordering, warehousing, and selling. Many retail operations are based on management experience of the experimental sales performance.

Abilities to place the right product on time for the right customer often require retailers to specialize at the store level, or store a lot or all the options given; which in turn raises the daily operational costs.

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The current retailer must undertake all these traditional business processes simultaneously by optimizing each of the five categories of strategic, namely planning, storage operations, marketing, supply chain management, and promotion. Each of these operational functions can benefit from retail analytics, and take advantage of the space needed to drive higher returns in parallel with reducing operating expenses.

Retail Customer Analytics

The success of any retail store can be seen once the retail analytics solution is adopted and the automation is integrated to perform one or all five classified functions. With the ability to create a complex data collection, analysis, and decision making process that enhances the retailer’s productivity and dramatically increases efficiency, the automation platform allows traditional retailers to evaluate and estimate profit and retail performance by hour, day, week, month, and year.

Data Management:

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Over the years, Xpandretail powered by Savant has witnessed the transfer of large amounts of data through applications and tools to cloud infrastructure, and data management has been more complex. For technology and operations, we imposed strict control features on staff that have access to data and analytical reports.

Analysis and Distributed Data versus Centrality:

From the point of view of the retailer, data and effective analysis are just a combination of analysis, experimental data and centralized storage.

For corporate buyers, centralized data and analysis are in demand to be purchased in the right directions based on external data as well as data from all business locations. This ensures that distribution sites are able to maintain inventory to supply incoming orders while reducing inventory at hand. Distributed data is to manage data inventory, with short-cycle time to identify products that need to be reduced in price according to the local market trend. From our study, based on the Xpandretail customers, we noticed an increased accumulation, quality and data diversity from different data sources owned by retailers. Whether owned, acquired through a third-party data source, or coupled with a demand for decentralized and centralized analysis analytics will push retailers to switch to individual analytical platforms instead of solving the problem separately.

Business analytics are on the path of growing maturity, with retail analyzes providing supportive gains at various levels. The form below is used to give an overview of the process that companies are going through, in which 80% of our customers are in the forecast stage.