Machine Learning in Retail - Can it compete with E commerce?

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While the internet and automation are almost solely to blame for the mass closings of brick and mortar stores across the world, those same factors may actually be the saviors of physical store locations.

The first wave of machine learning in retail is unlikely to be what many people may think... Helpful in store robots that make suggestions, answer questions or restock shelves.

Rather, the first wave of AI will involve much less physical automation and direct replacement of human workers and much more augmentation of the current retail experience with data and decision-making.

"To survive in the tough, tough retail market, you have to start to turn your business, and make predictions, based on learning from your historical data," said Paul Winsor general manager of retail at DataRobot. 

Leveling the playing field

E commerce companies tend to hold an advantage over traditional retail in there ability to optimize analytics, marketing and product placement etc.

There ability to do this lies in their 'stores' or websites and apps. These stores are set up to detect every click, every scroll, time spent on a product page and so on.

Now, with the help of machine learning, brick and mortar locations will in the near future be able to compete with their online counterparts in terms of marketing and product placement, stocking and inventory and behavioral tracking/theft.

Lets take a look at how traditional retail, aided by machine learning can start to compete again with eCommerce.

Marketing and product placement

Stores will inevitably become equipped with more cutting edge technology, the first of those will most likely come in the form of sensors. These sensors will be adept at tracking valuable information, stores will be able to encourage sales and cart value in many new ways, while additionally preventing theft.

Two applications most likely in the near future...

1. Detecting walking patterns and gaze of customers - Used to analyze the interest in various products or to restructure store layouts or if a customer picks up an item etc..

2. Picking up on demographic differences between retail locations -  This information can be used to adjust product placements (for example, if elderly people shop much more on weekdays, then products that sell well to those groups may be placed or promoted more prominently on weekdays).

Retail Stocking and Inventory

While eCommerce does still have its hassles in regards optimizing inventory. There is a logistical problem on this front which privileges physical store locations however. Optimizing per location of retail chain...

Two applications most likely in the near future...

1.  Purchase data to predict inventory needs in real time - This information may prompt a daily dashboard of suggested orders to a purchasing manager.

2. Using machine vision for cameras - inventory systems of the future will be able to generate accurate, real-time estimates of all products in a given store. A system like this could notify a manager of unusual patterns of inventory data, such as suspected theft.

Theft.

The same sensors used for marketing and product placement can also be trained to detect whether an item has been placed in a backpack or under a shirt.

Four applications most likely in the near future...

1. Detection of any item that is hidden or concealed by the individual in the store

2. Detection of individuals deemed statistically likely to steal

3. Detection of “sweethearting” – when checkout clerks skip over items or avoid scanning them.

4. Detection of individuals deemed statistically likely to steal (based on training data of previous confirmed thieves inside the same store)

Future of AI in retail

In-store robots, augmented reality, chatbots… there are a lot of potential future applications that will alter the retail experience.

There will be a general shift towards “instrumenting” retail stores to make real-time decisions based on data, relying less on “how it’s always been done” and more on constantly adjusting the retail environment to cut losses and produce gains.

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