1 / 3

How Merchandise Analytics is Empowering Retail Industry

Retailers today are increasingly inundated with data and are always under pressure to make quick decisions based on that data. With each data-point, there is a possible adjustment in strategy. Retailers are realizing the importance of effective business intelligence.

renytamang
Télécharger la présentation

How Merchandise Analytics is Empowering Retail Industry

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. How Merchandise Analytics is Empowering Retail Industry Retailers today are increasingly inundated with data and are always under pressure to make quick decisions based on that data. With each data-point, there is a possible adjustment in strategy. Retailers are realizing the importance of effective business intelligence. They are using merchandise analytics software to stock the right products at the right time and the right place. Merchandise analytics empower planners to align their merchandising decisions based on customer expectations. The key areas of merchandise analytics are demand forecasting, space allocation, assortment planning, planogram analytics, product adjacency, location-based assortment, and more. Analytics is the key to optimising assortment. For every Stock Keeping Unit (SKU), retailers can identify several attributes like brand, flavour, package size that are useful to customers. Retailers can subsequently use the sales of these SKUs to anticipate demand in the future and use these estimates to predict the demand for a combination of attributes, including those corresponding to new products that the retailer is planning to add to its assortment. Analytics lets them discover new products that are expected to perform.

  2. Key Features and Benefits of Retail Merchandising Analytics Most retail merchandising analytics solution gets rid of data silos and enables self-service business intelligence to business users. It ensures that insights can be drawn from customer journeys through integration accelerators, certified packaged integration, and integration stubs. Here are some of the key features and benefits of retail merchandising analytics: •Advanced insights into retail performance. •Offers focused visibility and greater support for various customer shopping journeys with pre- built metrics, dashboards, and reports. •Industry-specific solution for enabling insights into merchandising operations. •Set of compatible and comprehensive retail-specific BI apps. Steps to Create a Successful Analytics Powered Merchandising Strategy 1. Leverage big data to improve granular customer insights Through big data, retailers will be able to gauge customer perceptions and get a better understanding of each stage of the path-to-purchase cycle. By leveraging big data to know customer priorities and preferences, retailers can develop a detailed blueprint to optimize their assortments. The failure to recognize customer preferences revolves around the fact that retailers do not have a structured approach to help them serve their customers properly. 2. Create attribute-based retail assortments Through big data, it is easier to define demand from the customer perspective, but eventually, retailers must start a detailed analysis of product placement. By concentrating on attribute-based analysis, retailers can assess merchandise internally and consider other external features that may impact a product category. 3. Localize and streamline assortments To streamline and localize product categories, retailers must correlate local customer demands and purchase histories to enhance where specific merchandise should be placed. This will help improve sales, particularly among the slow-moving inventory. How can Retailers Adopt Analytics? While several retailers have begun their analytics journey, many are still struggling to identify the appropriate model for sustainable implementation. Retailers can adopt analytics using the following options: Hybrid model: The development of in-house analytics capability will take place through an external consultant who will develop an analytics center of excellence for retailers and co-create several analytics models on their premises.

  3. Primarily outsourced: Here, the whole analytics work is outsourced to an external vendor that performs the analysis and sends the insights and results back to the retailer. This approach will deliver quick results but can prove to be expensive in the long run due to its reliance on external vendors. Inorganic in-house: Purchase an analytics company that will start working on analytics projects across the enterprise. You are likely to face integration issues in this approach and the analytics resources might take time to comprehend the processes and culture. Organic in-house: Expand in-house analytics team steadily and introduce analytics in a phase-wise manner. However, this is a slow process and the time to first insight increases considerably.

More Related