How are retailers using artificial intelligence right now?

(Image credit: Image Credit: Enzozo / Shutterstock)

By now, most of us are comfortable with the idea that artificial intelligence (AI) is here to stay and that it holds enormous potential to change the way the world works. However, to the average person, the conversation can seem dominated by visions of the future, with experts keen to discuss what AI will do, rather than what it does right now. AI is making a practical impact as we speak, and for industries such as retail, it is already forming a fundamental part of a business strategy.

AI learns from data and in particular, it learns how to predict. These predictions can be used in many applications and then integrated into business systems and processes to automate and truly harness the power of AI. Here are some exciting ways that AI is already being used in retail.

1. Product recommendations

  • Artificial intelligence trends for 2019

One of the most powerful applications of AI in retail today is in personalised product recommendations. AI systems are capable of drawing on multiple sources of data to learn from the behaviours and habits of every single customer to create a marketing experience tailored just for them. AI systems allow retailers to hyper-personalise and automate marketing campaigns, recommending the right products at the right moment in a customer’s buying journey.

This is a vital practice for retailers looking to stay competitive because customers now expect personalisation with brands at every touchpoint. Amazon credits 35 per cent of all sales as being driven by its recommendation algorithms, whereas Netflix claims 75 per cent of all content is consumed based on AI-driven recommendations. At Peak, we’ve seen great success with our customer Footasylum, which saw an 8400 per cent marketing return on ad spend (ROAS) when it introduced AI into its social media advertising campaigns.

2. Optimising advertising spend

Not so long ago, retailers had little insight into how effective their marketing was. But with AI, the days of spending money on advertising with little idea what actions have resulted in sales are gone. Using AI, retailers are able to gather data on their customers’ tastes, predict the lifetime value of that customer, and make appropriate adjustments to the level of spending dedicated to acquiring this customer. Combining this with the business’s objectives such as revenue or profit growth means that AI is able to ensure the efficient use of marketing funds for the maximum return possible.

3. Lead scoring and predicting customer churn

Lead scoring is one area where AI is already helping retailers to more efficiently target new business opportunities. For retailers searching for new leads, AI systems can take information based on past successful pitches, and combine it with available information about the sales prospect to predict the lifetime value of the prospect and the likelihood of winning the business. This is invaluable to teams looking to prioritise their pitches and spend the maximum time possible speaking to receptive warm leads. This approach is already popular in businesses which sell occasional but high-value items; car dealerships, gyms and insurers to name but a few.

The other half of the equation is applying the predictive abilities of AI to spotting early signs that you may be about to lose an existing customer. Businesses models based around subscriptions have been the early beneficiaries of this strategy, allowing them to make offers to customers at the right time in order to retain them. However traditional retailers are fast catching up, driving greater customer retention, increasing lifetime customer value and building deeper customer relationships.

4. Propensity and preference scoring

AI is also able to make informed predictions about how a consumer will behave. For example, an AI system could anticipate if a customer will purchase a basket of goods or just one specific item. This technique is known as propensity scoring, and it is another tool in modern retailers’ arsenal to understand if a customer is looking to buy, and how they will behave. Preference scoring builds on this and uses the behavioural data of all buyers to ascertain their individual tastes and preferences. For example, this system can generate an insight that an individual customer is likely to prefer blue sweatshirts from Brand A rather than grey sweatshirts from Brand B. Both of these techniques can be used to convey to and reach customers who are searching for particular items at specific times.

These examples are just the beginning of what AI is capable of achieving for retail. It is clear to see that artificial intelligence is playing an ever-increasing role in the retail sector. In contrast to the usual doom and gloom foretelling the end of the high street, those pursuing an AI-driven strategy are already achieving things which are unthinkable in more traditional businesses. It is far from too late for these businesses to change, and deliver strong returns.

Mylo Portas, Retail Customer Success Manager, Peak