The Internet of Things (IoT) is here today in the devices, sensors, cloud services, and data your business uses. Microsoft delivers a flexible cloud-based approach that enables enterprises to capitalize on IoT by gathering, storing, and processing data centrally. The link below (click on the image) is an interesting talk by Sam George at MSIgnite on the Microsoft Vision for IoT:
One of the areas that the talk covers is a Business Maturity Model for IoT based on customer research. In the first stage from a Retailer perspective, I see retailers deploying sensors to monitor telemetry around the following scenarios:
- Monitoring in-store traffic to determine what days of the month or week are busiest and what time of day is the busiest. It could certainly trigger alerts when in-store traffic exceeds pre-configured thresholds. A great example of monitoring in-store traffic, is the Que Vision system at Krogers.Kroger says its shoppers are waiting an average of just 26 seconds to start the checkout and this is key to building customer loyalty.
- When refrigeration units fail in stores, it can mean significant losses in food that needs to be thrown away. Monitoring and reporting temperatures in the refrigeration units across all stores enables retailers to monitor and respond in time to issues. Again, an example of what is being done in Kroger as explained by Chris Hejlm in this interview.
- Tracking on-shelf availability of products could be another scenario that is critical to retail. Using shelf sensors, RFID and other sensor devices, retailers could get real time information about product availability on every shelf in every store.
- It could also mean engaging with the devices that customers in stores already have, like mobile phones. Through an opt-in mechanism, retailers could potentially engage with customers using personalized messaging and offers. VMob is a great example of a personalization platform that enables retailers to engage with customers using their mobile devices.
Retailers that are on the next stage of the IoT Business Maturity Model are the ones that are using a lot of Business Intelligence & Advanced Analytics capabilities with the telemetric data being collected. They are looking for patterns and predicting behaviors from the collected data.
In the examples that I mention above, it would really be about applying advanced analytics method to the data for some of these possible scenarios:
- The in-store monitoring data could be used in combination with several diverse data sets like weather data, information about events within a specific radius of the store, holidays and so on to more accurately predict in-store traffic over time. This prediction could help with workforce optimization, inventory and campaigns.
- In addition to monitoring refrigeration units, the detailed telemetric data collected from them could be used for Predictive Maintenance of the equipment to ensure that downtimes are either eliminated or minimized. This has major implications to store performance, especially for Grocery and QSR’s.
The companies that use the data and analytics to power new services and revenue streams are the ones in that third stage in the IoT Business Transformation Model. Using in-store traffic data to make decisions on workforce optimization could an example of that. Some examples:
- Using in-store traffic information to plan & optimize workforce
- Combining Weather, Social & other public data with In-store Traffic Data to better predict demand.
- Sharing the telemetric data from refrigeration units with the manufacturers/service companies to enable increased uptime.
- Use behavioral data to design store and shelf layout
One of the examples that I found to be very powerful/interesting is the Uber and Starwood partnership to let riders get Starwood points for Uber rides. This enables Starwood to obtain information it didn’t have in the past: where do you stay when you travel, if its not on a Starwood property. Check out this article.
Another example that is interesting as well is the experiment where logistics technology company Fleetmatics teamed up with George Mason University to analyze 74 billion data points logged from GPS-tracking devices installed in 552,000 vehicles owned by small businesses across the country. The goal was to determine whether the movement of goods-carrying trucks can be corralled to provide a timely view of the direction of consumer spending, business investment and other economic indicators. The data & patterns they found from the IoT implementations on the trucks was that they could predict retail sales trends earlier than the US Census Bureau reports. See the article here
I would love to hear about other companies in the Business Transformation stage with IoT, forging new partnerships or driving new revenue streams.
Some Microsoft Case Studies of IoT implementations in Retail can be found in my blog post Microsoft IoT Case Studies in the Retail Sector
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