How In-store Analytics Provides Effective Guidelines to Optimize Staff Allocation and Scheduling

Indoor navigation technologies bring significant operations use cases for the retailing industry. It is known that e-commerce companies such as ASOS and Amazon have the channels that collect and arrange sophisticated, ready-to-use, easily displayed data. Likewise, it is not surprising that they benefit from the relevant and insightful data embedded into their operations. Indoor location data delivers the same measurability to brick and mortar retailers and department stores.

In-store analytics has the ability to provide frequency measurements and movement profiles. For example with indoor location solutions retailers can get and track a customer’s dwell time and motion inside a store. Therefore, physical retailers, like their online peers, can use navigation, localization; and drive customer traffic. Usage analytics, complete visitor profiles give retailers the ability to engineer data accordingly. Behind the doors, it is not only about the customers; indoor location data delivers businesses the ability to predict staffing needs. Retailers in every sector require cost-efficient operations to survive and gain competitive advantage. Thus, physical retailers should realize the importance of leveraging in-store analytics.

Indeed, operational indoor analytics with real time, transparent monitoring of customer traffic and staff behaviour such as conversion, traffic counting, how staff time is employed in stores, distances walked etc. can be used to instruct and notify staff in terms of scheduling. Additionally, in-store analytics can help to advance performance metrics of the staff. Staff optimization on shift is possible with in-store analytics. For example, in peak hours, in order to minimize congestion, an unattended till might be easily staffed after analysis. On the other hand, a real time surge in store traffic can be detected and can be handled appropriately with alerts sent to staff communication devices etc. Customer traffic and staff location conversion can show whether resource is adequate at any given time. Alternatively, in-store data analytics allows flexibility for stores to create personalized routes for different tasks and the continuous analysis of alternative paths to purchase.

Moreover, indoor store location analytics can enhance the visit engagement opportunities between the staff and the customers. In-store analytics is a sure-fire way to improve staffing operations as well as customer satisfaction. Companies can better comprehend their customers with detailed customer behaviour analytics, real-time contextual information, etc. After identifying patterns, retailers can merge patterns and historical or real-time in-store data together and then figure out optimal locations for their staff. The capability to locate and grasp customers individually creates the possibility to trigger consequential interactions as customers look and shop i.e. average visit duration, dwell time, paths, zones, repeat visits, etc. Hence, businesses can fine-tune their staffing operations based on the comprehensive data. Moreover, transaction data will measure aftermath of personnel reallocation or alteration.

In the end, increased measurability gives retailers supplementary tools to design effective layouts and assignments that upgrade the general quality and satisfaction. Data-driven decision-making and action- taking can deliver retailers a roadmap to success through a better appreciation of their clients and operations.

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