The Future of AI and Machine Learning in Retail

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By Published On: December 17, 20190 Comments

We’ve all heard the doomsday prophecies — sales in bricks and mortar stores are in decline and online shopping is threatening retail as we know it.

You, therefore, may be surprised to learn that it’s projected 83 percent of goods purchased globally in 2022 will be bought in-store. A big contributor to the revival of bricks and mortar retail is new in-store technology that’s revolutionising the shopping experience — artificial intelligence (AI) and specifically, machine learning.

At its simplest, AI is computer activity that can mimic or simulate human thought or behaviour. It’s machines carrying out tasks in a way that we consider ‘intelligent’. Machine learning is a specific application of AI based around the concept that we can give machines data and they can learn for themselves. Machine learning is responsible for the most exciting applications of AI, think smart phones that recognise people’s faces, self-driving cars, and the personalised TV recommendations served to you on Netflix — these are all thanks to machine learning.

AI and machine learning hold enormous potential for the retail industry and businesses are wasting no time getting on board. According to IDC, global spending on AI and cognitive technologies will reach $97.9 billion in 2023, more than two and a half times the $37.5 billion that will be spent in 2019. Locally, AI and machine learning is becoming a top priority for CIOs in Australia, with spending in this area increasing 22 per cent per year since 2016 and expected to reach $1.98 billion by 2025.

Particularly appealing to millennial and Gen-Z consumers, machine learning has the potential to transform the customer experience by making it more personalised and engaging, and by bridging the divide between virtual and physical shopping channels in a way that’s never been done before. Retailers that successfully integrate machine learning with in-store environments and make customers’ shopping experiences more enjoyable and efficient as a result, stand to reap the benefits.

A great example of a retailer leading the way in this area is Sephora, whose Color IQ technology scans a customer’s face and provides personalised recommendations for foundation and concealer shades. Hardware chains are also ahead of the curve and are using machine learning to help customers identify what product they need for a particular job and where it’s located in the warehouse. Behind-the-scenes applications are just as important, with H&M using machine learning to ensure it has enough stock of in-demand products by analysing receipts and returns. From this information, H&M’s algorithm informs stores which items to promote and stock more of in certain locations.

While fun and attention-grabbing applications of AI and machine learning can be good for publicity, at the end of the day if they do little to enhance the shopping experience, they won’t support a sustained increase in traffic and revenue.

Not only does machine learning enhance the shopping experience for existing customers and help to attract new ones, it can also make businesses more efficient. By gathering information on sales and trends, machine learning can help business owners to make more informed decisions, allocate resources more strategically, and free up staff for more high value tasks like assisting customers and building relationships with them.

While the potential applications of machine learning are seemingly endless, its abilities are directly tied to the breadth and quality of data informing it. It learns to recognise and predict patterns from the data it’s fed, so if the data is poor quality, this will be reflected in the technology’s performance.

Retailers wanting to realise their AI and machine learning ambitions need to become data-driven businesses if they aren’t already. According to Forrester, 60 percent of decision makers at companies adopting AI cite data quality as challenging or very challenging.

To ensure the necessary volume of data, retailers need to analyse how data is being captured — when and through what channels are customers being prompted for information, are browsing patterns being monitored, how is sales and inventory information being collected? Equally important as the volume of data is the quality —it doesn’t matter how much data retailers have if it isn’t accurate, complete or relevant. To maintain data integrity, automated data cleansing needs to be undertaken regularly, including at the point of capture, and not as an afterthought. Third-party tools are an effective way to do this.

Machine learning and data are intrinsically linked, so before retailers embrace this new technology, it’s critical that they automate and streamline their data collection and management processes. Without these, AI-powered technology can’t work to its full capacity and errors risk being proliferated during the automation process.

AI and machine learning have the potential to make retailers more competitive by maximising resources, improving customer service, and offering enhanced product offerings, but like any new technology, it can be executed well, or it can be done poorly. Good data is the foundation for getting it right.

This is article is by Tunc Bolluk, Regional Director APAC Validity Inc., a world leader in data quality, email marketing, and sales solutions

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About the Author: Power Retail

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