Gravio Blog
May 12, 2021

[Case Study] Improving small brick and mortar retail experiences with Gravio

What we learned when deploying Gravio in a small Swiss Cheese Shop in Central London.
[Case Study] Improving small brick and mortar retail experiences with Gravio

Retail is one of the most exciting opportunities to innovate using IoT. In a retail environment customer interact a with products and services. That means it is crucial for a retail business make these interaction experiences as pleasurable as possible for the customers. Furthermore, it is important to drive people into a retail environment, and make the shopping or consumption experience as easily accessible as possible, increasing footfall. There are a number of optimization areas in this field: 

  1. Making the shopping experience for the customer, more pleasurable and remarkable.
  2. Making the retail staff more efficient and reduce their workload.
  3. Ensuring that all the retail systems are working smoothly, reliably and efficiently at their best capacities.

In this post, we want to explore how the installation of Gravio in a small Swiss cheese shop in London changed the way, not only the customers, but also the staff are experiencing the space. The shop had three issues to deal with. 

  1. Because of the kitchen, being at the back of the shop, sometimes the staff did not notice that there was a customer in the shop front, and customers could sometimes feel left alone. 
  2. As a cheese shop, the business has a duty imposed by government food health and safety rules to keep track of the temperatures of the refrigerators, and making sure that the cheeses would not exceed a certain temperature. In most shops this is achieved by measuring the temperature with a thermometer twice a day, and manually recording the temperature on a paper chart. 
  3. The shop wanted to provide a memorable, and pleasurable experience to the customers coming into the shop. This makes the shopping experience more remarkable and ultimately draws more people to come in.

What we built

We used Gravio Basic with the USB Dongle, an external USB speaker, four sensors and a Raspberry Pi 4. We connected the Raspberry Pi to the shop’s Wifi that they used for their POS system.

After installing Gravio, we connected Gravio via the USB dongle to a PIR motion sensor that we placed near the entrance of the shop and to 3 temperature sensors temperature sensors inside three different cheese refrigerators inside the shop to measure the fridge temperatures. We used an off-the shelf standard USB speaker and connected it to the Raspberry Pi.

We then created a number of Gravio Actions. One action was triggered if a person walked through the main door of the shop. Upon motion detection, it would add a new line to a CSV file including the timestamp and play one of 10 random audio files as a “doorbell sound”. To match the topic of the cheese shop, we chose 10 random alpine sounds such as cowbells, moo-ing cows, yodelling, goats bleating, birds chirping and so on. The doorbell sound is chosen at random using a shellscript that was triggered by Gravio. The CSV was useful to detect trends and find out when the busiest times were. Any such data is useful for future optimisations. And because the doorbell sound was quite loud, it could also be heard by staff back in the kitchen back room.

Now, because we have ultimate flexibility in the way we created this doorbell action, we also created an alarm system that would turn the doorbell into alarm during certain times at night. If motion is detected during that time, the system would play a very loud alarm sound and through a connection with the third party Twilio, a sms and telephony platform, Gravio would send out notifications to the shop keepers.

The three remaining environment sensors (which measure temperature, humidity and air pressure) were placed in the various refrigerators of the shop and the storage at the back. They would pick up the temperature in real-time and also save them to a CSV file. Equally, if the temperature would exceed certain thresholds, Gravio would send out text messages to the respective shopkeeper to investigate.

There are only two functionalities that require interest in this setup:

  • Any integration with Twilio, the phone alert system, i.e. the burglar alarm system and the temperature alarm system
  • The weekly reporting system that would send the week’s CSV files containing footfall and temperature data to a dedicated e-mail address, ready to be imported to Excel

As a result of this installation the shop can operate much more efficiently. There is also a lot of customer feedback that people enjoyed the variation of the various greeting sounds when they enter the shop, and they find it a very pleasant experience. Much to our surprise, it was the also staff themselves, who found this unique doorbell a very pleasant experience. They told us that they very much welcomed the variety of the different doorbells in their day and that they appreciated the fact that temperature-taking is easier. But most importantly it seemed to us, the unique experience made the staff members proud that the shopping experience in their shop is different from most other shopping experiences in town. 

As a conclusion we learned that a small installation such as the one described here can make a huge difference and have a substantial effect on the customer experience, ultimately drawing happy customers into the space. In fact, it can become part of a whole new “corporate identity” making customers and staff members happy alike. 

Temperatures are recorded
The system keeps track of the fridge temperatures

Customers entering trigger one of 10 audio files being played

Gravio Hub(エッジゲートウェイ)
A USB speaker attached to the Raspberry Pi plays the sound

Gravio HubKit(サービス)が作動
The Raspberry Pi is located in a central place in the shop

PIR motion detectors detect if a person enters the shop

Total Shop Door Activity(グラフ)
Statistics are locally created and sent to the shop keeper as csv e-mail every week

Feel free to view the YouTube Video for more details on this case study.

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