{"id":1111,"date":"2021-10-29T08:40:23","date_gmt":"2021-10-29T08:40:23","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/10\/29\/machine-learning-in-retail-know-your-customers-customer\/"},"modified":"2021-10-29T08:40:23","modified_gmt":"2021-10-29T08:40:23","slug":"machine-learning-in-retail-know-your-customers-customer","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/10\/29\/machine-learning-in-retail-know-your-customers-customer\/","title":{"rendered":"Machine Learning in Retail: Know Your Customers\u2019 Customer"},"content":{"rendered":"
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This guest post is authored by Stephen Kinns<\/strong><\/a>,\u00a0CEO and Co-Founder at\u00a0CatsAi<\/strong><\/a><\/em>, who will be presenting this use case on November 3, 2021<\/strong><\/em><\/em> in<\/em> our upcoming webinar about Machine Learning in Retail <\/a><\/strong><\/em>.<\/p>\n

Are you in food and beverage retail (FnB)?\u00a0Do you want to reduce waste? Here\u2019s a common situation in FnB that you may recognize.<\/p>\n

\"\"<\/a><\/figure>\n

Your business delivers to clients or sells at more than 10 sites.\u00a0Maybe even up to the 1000\u2019s.\u00a0 You might be geographically spread or locally dense. Your business may sell fresh produce,\u00a0 artisan, or prepacked. The product line maybe a handful or hundreds.<\/p>\n

You have long term plans for strategy, perhaps medium term for steering resources, and short term operational forecasts to line up the products and people ready to deliver.<\/p>\n

You\u2019ve been using the same planning and forecasting process for a few years and it works absolutely fine. You\u2019re broadly content with the performance. There\u2019s likely room to improve, but not really seeing a driving need to change anything.\u00a0\u00a0<\/p>\n

On the other hand, your retail sites throw away food on a daily basis due to slight over supply.\u00a0 Your customers, your investors, and your employees want to know why. Perhaps they\u2019re financially challenging the situation, or more likely in these times of climate change awareness they\u2019re looking to you to make a positive contribution.<\/p>\n

So you\u2019ve got some waste that worries you. Then you\u2019ve also somehow got empty shelves. Customers are demanding you fix the problem with your deliveries. Their own customers are going elsewhere. And you know an empty shelf is a missed opportunity \u2013 another sale gone.<\/p>\n

So how do these line up?<\/strong><\/p>\n

You\u2019ve got the baseline forecasting but you\u2019re not getting it right on the day at the location. The likely reason is it\u2019s a naive forecast. That\u2019s not to be rude; that\u2019s the technical term. If you\u2019re forecasting on a basic statistical level, or applying a \u201clast week+10%\u201d or \u201caverage of last 6 weeks\u201d then that\u2019s normally called a naive forecast.<\/p>\n

So what?<\/strong><\/p>\n

A naive forecast is unable to respond quickly enough to real-life.<\/p>\n

At every location, for every product, in the next week things will deviate from what was originally planned.\u00a0 Your customers may respond to adverts, the weather may change, local traffic or events may change access times to locations. Each factor, at each location for each product has a different outcome to sales. Millions of possibilities.\u00a0 Too much for humans, or even most advanced software.<\/p>\n

And the answer?<\/strong><\/p>\n

Hyper-Focused Predictions. <\/strong>This is a new type of forecasting and it\u2019s very short term. It\u2019s only thinking about the critical next few days<\/strong>. It\u2019s hyper-local, and hyper-agile. It can take thousands of influencing factors and adapt to the changing environment. Every day without fail, learning and adapting.<\/p>\n

It\u2019s a tool that sits alongside your existing forecasting helping you adapt.\u00a0\u00a0<\/p>\n

There\u2019s only one way available to do this \u2013 and that\u2019s Machine Learning. Unfortunately, that\u2019s normally a very expensive proposition if you choose to build it from scratch as talent, software, and hardware costs can run very high. Fortunately, there is one company that has encapsulated all of that into a solution with a timeline to positive ROI counting in days\/weeks not years for any size of firm.<\/p>\n

We will be revealing more about how this alternative forecasting process works with a peek into the associated machine learning workflows in our upcoming webinar: Machine Learning in Retail on November 3, 2021<\/a><\/strong>. So be sure to reserve your spot<\/a><\/strong> today.<\/p>\n

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