S5 | 450: Black Friday Special: Real-time Data & AI for In-store Retail Operations, with Joe Shasteen, RetailNext

Digital StrategyTechnologyConversational AI

Nov 24

About the Episode

We are presenting our 2nd annual Black Friday special episode, where we focus on an aspect of retail, looking back on the current year, as well as to what retailers should be keeping in mind for next year.

With the prevalence of online shopping and growing ecommerce providers, brick-and-mortar retailers require business intelligence tools dedicated to in-store analytics to stay competitive in an omnichannel world.

Today we’re going to talk about real-time data and AI for in-store retail operations.

To help me discuss this topic, I’d like to welcome Joe Shasteen, Global Manager, Advanced Analytics at RetailNext.

About Joe Shasteen

Joe Shasteen is the Global Manager of Advanced Analytics at RetailNext, where he's been for over seven years. Using Retail Labs, Joe and the Advanced Analytics team at RetailNext help retailers understand in-store shopper behavior. This leads to the testing and design of the impact of in-store changes, ultimately improving the shopper's experience.

Resources

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RetailNext website: https://www.retailnext.net

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Synopsis

Real-time data analytics can empower employees to make better decisions and meet customer needs by providing a clear understanding of their preferences and behaviors. This enables retailers to adjust strategies and approach to deliver a personalized and tailored experience for each customer.

For instance, when a store experiences a high influx of customers, real-time data analytics can alert employees to crowded areas, allowing them to allocate additional staff members to manage queues and provide assistance. This ensures that customers do not have to wait for extended periods and receive prompt service.

Moreover, real-time data analytics helps retailers identify peak traffic hours, enabling them to plan non-selling tasks such as restocking or cleaning during slower periods. This ensures that employees are available to assist customers during peak times.

Additionally, real-time data analytics assists in optimizing operations by providing insights into staffing levels. By analyzing the data, retailers can determine the appropriate number of employees needed at different times of the day or during specific events, such as holiday shopping. This ensures adequate staff coverage to meet customer demands and provide a seamless shopping experience.

Overall, real-time data analytics empowers employees by equipping them with the necessary information to make informed decisions and effectively meet customer needs. By leveraging this technology, retailers can enhance the customer experience, increase operational efficiency, and stay competitive in the ever-evolving retail landscape.

Real-time data analytics also plays a crucial role in making better staffing decisions and adapting to changes in traffic and occupancy. By utilizing real-time data, retailers gain a clear understanding of their store's current situation and can make informed decisions about staffing levels. For example, if there is a sudden surge in customer traffic, an alert system can notify store employees to allocate additional associates to manage queues and ensure a smooth shopping experience. Additionally, real-time data analytics helps retailers identify peak traffic hours, such as during the holiday season, enabling them to plan non-selling tasks accordingly and avoid disruptions during high-traffic periods. Overall, real-time data analytics empowers retailers to optimize their operations by efficiently managing staffing levels and adapting to fluctuations in customer flow.

Effective use of real-time data analytics requires cross-communication and collaboration among different teams responsible for different parts of the customer experience. It is not just one team, such as operations or visual merchandising, that is responsible for utilizing the data. Instead, it is important for all teams involved in the customer experience, including marketing, advertising, and clearance areas, to utilize the data to drive the desired behaviors. For example, the data can be used to direct customers to specific parts of the store where key products are located, showcase certain advertisements or new displays with new products, or highlight clearance areas. By working together and utilizing real-time data analytics, retailers can optimize their operations and create a seamless and personalized customer experience.