Why is machine learning becoming important in the retail sector?

Machine learning is quickly becoming indispensable in the retail sector due to its ability to collect, manage, and analyse high volumes of data in a short space of time, using what it finds to optimise processes. The abundance of data generated in retail is daunting and includes everything from customer interactions to website traffic to inventory levels. Speedy but effective analysis at this scale is simply not possible with manpower alone - but humans can put these valuable ML insights to good use. 

The retail industry is at the mercy of ever-changing customer demands and uncontrollable outside influences, and as an individual business, you are likely surrounded by hungry competitors. Using ML to track trends and patterns can keep your metaphorical finger on the pulse of your industry - and this high level of responsiveness can often be the difference between sinking and swimming.

What are the key benefits of machine learning for a retail business?

Here we’ll take a look at some of the key ways in which machine learning can benefit a retail business: segmenting your customers; minimising lost opportunities; targeting customers more effectively, and increasing security. 

Segmenting your customers

Machine learning can segment your customers based on their various attributes, including behaviour, demographics and purchase history. Here are some ways in which segmented customer data can be broken down and used for different purposes within retail.

Predictive analytics

Lots of things fall under the umbrella term of predictive analytics, but all work on the principle of using past and current data to predict future events and behaviours. One area in which predictive analytics are particularly useful is in anticipating customer churn. At a time when every customer counts and retained customers are more profitable than new ones, this is an excellent way to utilise ML. By analysing customer behaviour and transaction history, ML can predict when a customer is likely to leave and identify when is the best time to implement a targeted retention strategy.

Basket analysis

By looking at items frequently found in your customers’ baskets, machine learning can recommend multiple engaging courses of action that might appeal to their needs. This includes everything from suggesting products that complement each other to applying tailored discounts to discourage basket abandonment. Even more specifically, machine learning can ensure items that are frequently purchased together are also restocked at similar times. This level of analysis would be time-consuming for employees, but takes no time at all for computers.

Calculating customer lifetime value

Similarly to both predictive and basket analysis, ML can take historical data and with the right model, use it to calculate customer lifetime value. Looking deeper than simply purchase to purchase, understanding the value that a customer can bring to your business is incredibly important - especially for customer retention which, as previously mentioned, is valuable. 

Using AI to minimise lost opportunities

Wasted products and missed opportunities are the bane of retail life. Thankfully these are also areas in which machine learning can make a big difference.

Predicting demand

Demand forecasting is an essential element of retail and, when done correctly, minimises the risk of wasting product while at the same time allowing you to prepare for favourable trends. Being able to accurately predict demand is essential for every area of a retail business.

Demand forecasting with machine learning is effective and easy - and, by the nature of machine learning, will only continue to improve itself over time. By feeding the right machine learning model the correct data, the model can easily be trained, tested, and fine-tuned until it’s ready to predict future demand for your business. 

Optimising prices

Optimising prices helps keep your business profitable and ensures that your products are priced appropriately for your target market. If a human were to calculate the optimal individual prices for every product you have on offer, it would be a huge drain on time and resources - especially as this price would fluctuate depending on outside influences. This is a calculation that can be carried out instantly and continuously through machine learning, which is capable of monitoring all of the influencing factors that go into deciding an optimal price.

This includes taking into account issues such as sales cannibalisation (where a discount on one item leads to a fall in demand for similar products) and the halo effect (when a promotion of one product leads to more sales in products it is commonly associated with). These factors can be hard for people to predict but are patterns easily identifiable by machines.

Machine learning algorithms can also monitor your competitors’ prices and market conditions and adjust your prices accordingly. This process can be automated, which removes the need for these changes to be monitored and implemented by individuals.

Target your customers more effectively

Machine learning is particularly useful for targeting your customers more effectively through personalisation, especially at scale. Even if you’re lucky enough to have millions of customers, you should still want to provide each of them with the best possible customer experience - and that calls for personalisation. Thankfully machine learning can take the hard manual work out of this process - and continually improve its own processes based on data it collects.

Personalising offers with machine learning

As mentioned above, ML algorithms can take customer data and use it to predict and respond to customer behaviour, at a huge, automated level. Here are some examples of machine learning helping to provide your customers with more enticing offers:

• Recommendation engines: Also known as recommenders, recommendation engines are data filtering systems that use machine learning to accurately predict and recommend products your customers may like, based on past behaviour and purchases.
• Personalised discounts: An extension of optimal pricing, machine learning uses data to create coupons and discounts that will appeal directly to individual customers. Using statistics, machine learning can create deals that walk the line between driving purchases and ensuring profitability.
• Feedback incorporation: ML and AI work on the basis of continually adapting and improving based on the feedback and data it receives. This is perfect for taking customer feedback and translating it into interactions and experiences that are guaranteed to engage and entice them. 

Keep your business secure

One of the main ways machine learning is used in many businesses is to help detect fraudulent activity before it can cause too much damage. Because machines are far quicker and more efficient in identifying unusual behaviour patterns than humans, they can detect and alert you to possible instances of fraud. This helps protect both your business and your customers and helps ensure that no time or energy is wasted on fraudsters posing as real customers. 

The bottom line

These days, machine learning is more than just an option - it’s a strategic necessity. The advantages are significant and many, and it can easily revolutionise the way retailers operate and engage with their customers. AI is becoming an unavoidable part of day-to-day life, and instead of seeing it as a threat, it should be embraced as an invaluable tool to use at our disposal.

Customer expectations of the businesses they buy from are higher than ever, and constantly in flux. At a time when customers are being described as ‘fast and fickle’, anything that can give your business a competitive edge should be welcomed with open arms. Whether this means saving your team time and energy by carrying out analysis too time-consuming to do themselves, or identifying trends and patterns that the human eye would have simply glossed over, machine learning is steadily revolutionising the retail sector.