The future: Machine Learning & AI
The final part of our 3-part series of the biggest challenges facing retailers in 2019 and beyond focuses on the future of retail. We have discussed the importance of data and reporting and in part 3, we discuss the potential for artificial intelligence to automate back-end systems and warehouse processes and deliver personalised customer experiences more efficiently and effectively.
Before we begin discussing the future of AI, a quick guide to what we mean by the terms Machine Learning (ML) and Artificial Intelligence (AI) because there is still confusion about what AI is, how it relates to modern analytics, and how it can be used to transform the customer experience with true personalisation.
What is AI?
The term AI has been circulating, with different people having different definitions for what AI is, and its utility in the retail industry specifically. Depending on your source, AI is either the solution to every problem a retailer will ever face or a fashionable, expensive fad.
From our years of experience developing retail systems, we believe the answer lies somewhere in the middle. AI and analytics is likely going to streamline many operations and increase efficiency, but it will not replace every process and won’t solve every problem because it is not designed to do everything.
True AI will have the ability to learn and make decisions independent – i.e. without the need for human intervention and, ultimately, does not exist yet. It is unlikely that retailers are going to start trusting millions of pounds’ worth of stocking decision-making to lines of code in the next few years, due to the limitations of any system.
Any system that claims to utilise AI technology is likely using a sub-branch of AI known as machine learning and is much closer to what most companies will likely have in mind for digital transformations involving AI in retail, now and in the future.
What is ML?
Machine learning is often presented as an example of the potential of AI in practice for retailers. The early AI pioneers developed algorithms that were used to analyse vast amounts of data, learn about patterns in the data and then make predictions about something related to that data.
This technology is being used in some hospitals to identify symptoms and their related illnesses, the image recognition software on social media platforms and many more.
These techniques have developed and advanced over time but at a basic level, this means that a program or system is eventually able to learn how to perform a task without being hand-coded with a specific set of instructions.
The reason this is not considered true artificial intelligence is because, with machine learning, while the system is able to handle its narrow set of tasks and routines more efficiently than a human, it will not be able to perform anything outside of its specific design.
Where does AI fit in? The Future of Retail
The key question for retailers over the next few years is, where does AI fit in to the business? What role will AI assume in the business organisation and strategic processes?
There are a number of specific areas where AI and analytics can improve the industry – both for consumers and for retailers. Stock forecasting, warehouse management and reporting can all be improved to provide retailers with tangible benefits like reduced stock wastage, streamlined and cost-effective returns and delivery processes, and trend detection to better meet customer demand.
These systems can be used in advanced predictive analytics – the system is able to gather historic data and sales information during a certain period, for example the first week of June.
With this information, the system can then predict the necessary stock levels to prevent over/under stocking of products or predict what an individual customer is most likely to respond positively to, which can improve average customer spend and reduce overall marketing spend, as examples of practical applications of technology.
In part 1 of this series, we wrote about company ‘ABC’ and their customer ‘John Smith’ and how, with complete and accurate data records on John, ABC can tailor personalised sales messages that will appeal specifically John.
With machine learning, a system can be taught to understand John’s (and every other existing customer) likes and dislikes using existing customer data and countless plugins of contextual information (current season, predictable annual purchases, for example) to predict future demands.
As the system gets access to more data sets, these predictions can become more complex and accurate. This is why we believe data is the most important, and underutilised, asset that any retailer currently has access to.
In the future, AI will be able to be taught to give live suggestions to shoppers as they shop via devices in the store or enable shoppers to skip queues and walk out of a store, knowing what was in the customer’s basket and charge the customer automatically. Physical stores are likely to become more similar to showrooms, with consumers choosing items in store/trying items in store, possibly via VR, and then having those items delivered to their homes.
Currently, technologies like smart mirrors are not cost-effective and only time will tell if they evolve beyond a novelty to become part of the general customer experience in-store to justify the implementation/maintenance cost.
Ultimately, whether we call these technologies AI or machine learning, there are limits to what analytics can do, now and in the future, as well as what retailers will actually have a use for. But systems that utilise these technologies can improve many areas of a business today.
The time to lay the foundations for the digital transformation is now. Devising data management and data quality processes will enable organisations to jump-start their enterprise-level AI/ML platform.
As machine learning techniques become more advanced in the next few years, the abilities of these systems will only improve to become able to take over all tedious or repetitive tasks and the reporting needs of the organisation.
|Co-Written by Kevin Carrick and Pana Lepeniotis
Connect with Kevin and Pana on LinkedIn to discuss your data strategy.
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