BackgroundPendleton & Son* is?a local butcher based in North-West London. Established in 1996, the company has enjoyed a steady customer base and good reputation for two decades. Almost two years ago, when the local library closed down, a supermarket chain store moved in. Located on the same street, the new store affected overall footfall and revenue for the small butcher shop.
What problem is big data helping to solve?While founder Tom Pendleton was certain his shop offered superior quality and choice compared to the supermarket, the trouble was conveying this message to the public and getting customers through the door. Trying to compete on price wasn?t working and, with falling income, son Aaron Pendleton turned to data to help keep the business afloat.
How is it used in practice?The Pendletons worked with a consultant who suggested installing simple, inexpensive sensors inside the store window to monitor footfall and measure the impact of window displays and promotions. Using these sensors, the firm were able to measure how many people walked past the shop, how many stopped to look at the window display and sandwich board sign and how many people then came into the store as a result. Armed with this information, they were able to refine their displays and messaging based on what interested customers the most. The sensor data also pointed to an unexpected new revenue stream for the business. As two popular pubs were located at the end of the street, the hours of 9pm to midnight proved particularly busy in terms of passers-by ? almost as many as the busy lunchtime period. So the Pendletons decided to trial opening at night and serving premium hot dogs and burgers to hungry folk making their way home from the pub. In order to decide on what products to offer at night, Aaron analysed trend data from Google Trends to see what food items were particularly popular. This led to the creation of their pulled pork burger with chorizo. Going forward, the butchers are hoping to expand their use of data in order to increase knowledge of customers even further. They have just started to pull in weather data to predict demand even more accurately and have plans to introduce a customer loyalty app which gathers information on who customers are and what they purchase. This data will allow the butchers to email customers with targeted and seasonal offers. Once they have built up some customer data, surveys will allow them to delve even deeper and gain insights that can improve their products and service.
What were the results?In this case, the sensor data showed that meal suggestions on the sandwich board outside the shop, backed up by simple recipe sheets available inside, proved more popular than messages centred around price; for example, on a blustery autumn day the sign outside would read: ?How about venison sausage & bean stew? Pop in for our special sausages and recipe.? In short, the Pendletons found that local customers favoured inspiration and ideas over cheap deals, which were available every day in the supermarket. They were able to use this insight to improve their messaging and get more people through the door ? and those who entered the shop were far more likely to make a purchase as a result. In addition, the late-night openings proved enormously popular and the company decided to make this a permanent feature on Fridays and Saturdays. Not only did this provide much-needed additional revenue, it also introduced the company and their products to more customers.
What data was used?The Pendletons worked with data from a small sensor placed outside the store window, plus other internal data such as transaction and stock data. They also made use of freely-available external weather data to help them plan the meal suggestions and recipes for the week ahead.
What are the technical details?For the cellular phone detection, Pendleton & Sons installed cellular phone detection sensors which detect the presence of phones through the Bluetooth and WiFi signals that phones emit. The sensors work for iPhone and Android devices and pick up the MAC address of the phone, the strength of the signal (which helps you to understand the distance from the sensor), the vendor of the smartphone (e.g. Apple, Samsung) and the type of device. For the analysis, Aaron used the cloud-based business intelligence platform that the sensor vendor provided.
Any challenges that had to be overcome?For Aaron, the first challenge to overcome was convincing his father it was worth investing in data in the first place. It was important to make a firm business case that set out how data could help a small business like theirs. Relating data to the business?s challenges and goals helped enormously with this. Aaron set out what the business wanted to achieve (i.e. increasing customer awareness and revenue), what was stopping them (competition from the supermarket and lack of information on what customers wanted) and how data could help them overcome the current challenges (by gathering the information they needed to attract more customers). Armed with a strong business plan, it was easier to argue the case for introducing data to their decision-making process. The next challenge, which is a common one for small businesses, was knowing where to start. With limited resources and manpower, the Pendletons were always going to need someone to handle the data side of things for them. They turned to a big-data-as-a-service (BDaaS) provider who had experience of working with smaller businesses and, as they only paid for the work needed (as opposed to investing in new systems and staff with data experience), the initial outlay was minimal. They found the sensors themselves were surprisingly cheap (and they?re getting cheaper all the time), and there was no need to invest in additional software as the BDaaS provider did all the analysis work for them.
What are the key learning points and takeaways??This case study shows how big data isn?t the sole domain of big corporations but is making a difference to businesses of all shapes and sizes. And while this type of data project isn?t necessarily always seen as big data, it certainly is enabled by our big data world. Sometimes it simply means accessing and using the big data that is out there to inform your decision making. In the end, it doesn?t matter how much data you gather and analyse: it?s what you do with it that counts. *Please note that I have changed the name of the business and people in it in order to protect their anonymity. ?Bernard Marr is founder and CEO of the Advanced Performance Institute, a big data think tank.
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