Consumer IoT 13 MIN READ

5 IoT Use Cases in the Retail Industry

By Jennifer
August 16, 2019
Woman's hand opening the door of a convenience store IoT refrigerator shelves to pick a product
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Shaping up the future of retail with IoT & AI

IoT use cases in the retail space can affect store owners and employees, as well as commercial equipment manufacturers. However, discussions around digital transformation in the industry tend to focus on the shopper/customer experience

Connected equipment is producing more data than ever before with about 5 quintillion bytes of data per day. Unfortunately, these massive pools don’t always translate into successful business outcomes. But smart equipment data, when integrated with advanced IoT analytics solutions, is redefining the retail space. Companies can now create new business models, drive revenue growth, achieve operational efficiency and increase customer engagement.

In this blog post, we’re looking at the real-life example of a connected commercial fridge. Equipped with sensors, it tracks the inventory and internal temperature to ensure optimal conditions. It also measures temperature, humidity, number of products per shelf, compressor information and GPS location. 

The data generated by these sensors empowers store owners and manufacturers with several IoT-driven use cases:

  1. Asset Management: Reduce the number of fridges disappearing without permission
  2. Predictive Maintenance: Anticipate diagnostic issues and reduce downtime
  3. Predictive Replenishment: Forecast sales with usage and consumption data
  4. Customer Engagement: Use data from multiple sources to deliver a personalized experience
  5. New Revenue Streams: Enable dynamic pricing to charge for shelf space. 

Asset Management in the Retail Industry

In the retail industry, space management is extremely important. It allows companies to increase productivity, reduce operating costs and increase financial performance. In stores, fridges are strategically placed to maximize exposure and drive sales. But once installed, there is no system in place to track their whereabouts if moved.

Mnubo AIoT Platform tracks the assets’ status and location. In turn, manufacturers can flag assets with impending faults as well as identify missing or misplaced assets. Dynamic maps highlight the distribution of registered fridges as well as the distribution of fridges sending events. Drilling deeper, users can identify fridges that are sending events from a location that is different from where it is registered.

The business outcome is twofold. Operational efficiency is improved and the number of lost or stolen assets is reduced.

Blurred IoT refrigerator in a supermarket aisle with fully stocked product shelves

GPS tracking is essential, but IoT data allows for much more. The next use case leverages internal data to anticipate equipment failure.

Predictive Maintenance

In the retail space, commercial fridges typically follow a time-based maintenance (TBM) schedule. In other words, maintenance is performed every now and then, and is purely triggered by time. However, by equipping their equipment with sensors, companies can predict maintenance issues that might affect power consumption. They can also monitor temperature fluctuations to ensure food safety.

Mnubo AIoT platform leverages real-time and historical data to predict when assets will require maintenance. This effectively allows manufacturers to move from time-based to predictive maintenance. The service dashboard considers multiple factors, and sorts fridges by “asset health score” so companies can better understand their install base. This allows them to prevent unplanned downtime.

Predictive maintenance in retail allows companies to improve operational efficiency, and to reduce downtime.

The first use case empowered manufacturers with visibility over their install base. The second one allows them to better understand the health of their equipment and make data-driven decisions regarding maintenance. The next use case uses data to predict replenishment.

Predictive Replenishment

Typically, beer and other merchandise are sold to stores on a calendar basis or as a reaction to a purchase order. However, it is not based on consumption. But consumption is only one element, and there are multiple others that influence sales: holidays, sports events, weather. By failing to consider these, stores risk running out of inventory.

Mnubo AIoT Platform considers multiple factors to improve demand forecasting for the next period. Thus, the marketing dashboard sorts stores by “predictive replenishment”. It highlights the stores that will run out of beer in the next two days.

Predictive replenishment leads to increased profitability for both the beer company and the retail store, while also optimizing the supply chain.

IoT beverage refrigerator near brown pendant lamp in a convenience store

Companies can effectively predict when they will need to replenish their shelves. The next IoT use case looks at ways to improve marketing and customer engagement.

Engagement and marketing insights

Throughout the customer journey, there are many touch points to influence the purchase decision. However, seventy percent of consumers choose their beer at time of purchase. Point of purchase therefore remains a challenging conversion point.

Mnubo AIoT Platform scores users based on consumption and engagement. The marketing dashboard filters users with engagement scores between 0.6-1.0. The brand can then suggest personalized coupons and discounts when consumers are in front of the fridge.

This is highly beneficial to companies. Indeed, they can grow their revenue and create new sales opportunities.

Commercial IoT refrigerator in retail space with shelves full of dairy products and milk

Engagement and marketing insights are only one way for companies to increase their earnings. Our next use case looks at the future possibilities for companies to generate new revenue streams.

New Revenue Streams

IoT in the retail industry has the potential to create new revenue streams. For example, dynamic pricing is now becoming a reality. Also known as surge pricing, it is a strategy in which product prices continuously adjust.

Let’s look at our commercial fridge. Research demonstrates that sales are higher when a product is placed on the middle shelf (compared to the top or bottom). However, it is challenging to measure shelf value when relying strictly on sales data. Companies also need to consider location and the fact that no two stores are identical.

Dynamic pricing models enable the brand to charge for shelf space depending on location and aisle.

Asset management, predictive maintenance, predictive replenishment and personalized marketing are transforming the retail space from product to customer-centric. Mnubo AIoT platform empowers brands with real-time visibility over their connected install base.

Get in touch with us today, and start transforming your business tomorrow.

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