Machine Learning in Retail

Machine learning (ML) is one of the most disruptive technologies across consumer facing industries today. Artificial Intelligence (AI) is not – as many believe – replacing humans in business, but rather enhancing our capabilities to serve customers.

The retail industry in particular has benefited greatly from the use of machine learning algorithms, thanks partly to the shift towards digital payment solutions and mainstream FinTech applications. Cloud-based payment services, be it for eCommerce or a bricks and mortar store, have allowed companies to capture vast swathes of data about their customers’ spending habits.

Add in social media, web tracking cookies and various API integrations of data points across the web, and the value of data we have today is phenomenal if applied correctly. Machine Learning can use this in multiple ways for retailers to capitalise on upcoming market trends and non-financial incentives.

It’s not just about plugging personalised ads to potential customers at the right time. AI applications in retail can provide competitive insights, boost sales and reduce costs. Machine learning can improve the retail operating model across all functions with data-driven insights.

Here are some key features of Machine learning in the retail industry.

Applications of Machine Learning in Retail

Understanding consumer behaviour at the point of sale is one way that retailers can optimise pricing strategies for maximum revenue. There’s a number of variables in the customer journey that could lead to a sale – or that could lead to abandonment.

Digital payments solutions and point-of-sale (POS) systems allows retailers to capture financial transaction data in both online and offline shopping scenarios. Insights into transactions when mapped across other factors such as time, price and even external social trends can be used to predict future buyer behaviours.

Machine learning helps retailers to test new pricing points, run offers and promotions and even try out a new product mix. Understanding why a customer has made a purchase allows businesses to compete in non-price competitive ways.

This predictive modelling is crucial to improving sales cycle conversion rates, customer life cycle management and maximising revenue-per-customer.

Revenue is only one side of the coin, though. Driving down costs and finding efficiencies in the business model is another way to generate  profit, without necessarily increasing sales. Machine Learning can take into account seasonality and external shock events to allow retailers to apply real-time stocking and supply chain management. Improving stock turnover through real-time data can improve cash flow without the need for acquiring debt or finance facilities.

Anomaly Detection in Retail

Detecting anomalies in your dataset will make your machine learning model more accurate over time. But the real value in spotting anomalies lies in risk mitigation and even strategic decision making.

Fraudulent payments in the retail industry account for billions of dollars of lost revenue every year. Spotting anomalies and applying automation rules in real-time can help to stop fraudsters before a transaction has been processed.  Additionally of course, these anomalies or divergent data records can be used to forecast new ways of prevention with machine learning algorithms.

Shocks in the market can impact retail both in consumer behaviour and supply chain management. Stocking levels might need to rapidly change in response to say, a natural disaster, to keep up with demand or reduce inventory waste. Who’d have thought toilet paper would have been so in demand pre-2020? It’s this kind of unpredictability that can cost retailers if not handled correctly.

It’s worth highlighting here that detecting anomalies and then applying them to data models is not an easy task. Continuous iteration of an algorithm and manually discounting false positives can be time-consuming. Machine learning, however, can go one step further and compare different algorithms to each other, gaining better insight into the variables being used and the interactions between them.

Avora’s anomaly detection helps retailers find unusual behavior in time-series data so that problems can be solved before they cause significant losses of revenue.

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Root Cause Analysis in Retail

Cause and effect is not an easy thing to establish in retail when there are so many variants that impact financial success. What’s even more difficult is understanding the weighted impact of specific influences, and using this to make strategic decisions.

Root cause analysis with machine learning takes the guesswork out of business operations. Product and technology teams behind an eCommerce brand can utilise machine learning to prioritise workload based on consumer impact rather than, say, a list of pain points. Algorithms take data analysis one step further by visualising the true cost of a problem and how best to respond to it.

Whether a physical store or an eCommerce platform, retail infrastructure and the customer journey will never be 100% optimal. The perfect sales cycle cannot exist across all customers and will continue to change due to external markers or social factors. But what machine learning can do is to optimise sales metrics by spotting warning signs and trends so that businesses can act early.

Spotting a problem is easy, but understanding its importance is not. There are internal and external factors a retailer must consider. A dip in sales could be caused by an unsuccessful marketing campaign, or a competitor launch of a new product. Machine learning can quantify what a human can only assume.

Predicting Customer Behaviour in Retail Analytics

 What is becoming increasingly true in today’s world of vast consumer choice is that products require continuous improvement to stay relevant. Demographic trends are adopted more quickly through viral posts and social media – longevity of retail brands is something machine learning can better inform.

Retailers no longer need to rely on marketing assumptions and focus groups to build customer personas. These can be modelled accurately using historical data to forecast how customers will behave in the future.

Customer insights can now be enriched with further data sources, such as social media activity and even browsing history to build a full picture of customer wants and needs. Machine learning algorithms can help retailers to spot future trends.

More immediately however, predicting customer behaviour is key to maximising sales through upselling and cross-selling of relevant products. Machine learning can go beyond price and urgency (typically what drives consumer behaviour) and provide insight into more psychological factors.

Recommendation engines are one example of this, using algorithms to scrape website and search activity to display suggested products. The use of AI can present products that customers are likely to desire, even before the customer had considered it themselves.

Machine learning provides a number of advantages to retailers and can be applied across any business function. Data-driven insights, particularly into consumer behaviour, can inform business strategy on both short and long-term timescales.

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