5 ways AI can reduce risk in retail

AI can use past retail information like sales or fraudulent behaviour to predict when it may likely happen again.  By using this type of AI, also known as predictive modelling, you can plan better and build a resilient business. 

For the less technically-minded among us, predictive modelling seems like magic. Somehow, data is pulled in, analysed and turned into eerily accurate forecasts. Of course, it’s not magic – it’s maths.

A mathematical equation expresses a relationship between variables. A predictive model uses these equations to make sense of massive datasets, 'learn' the variables that correspond with specific outcomes and make predictions based on that information.

If it’s done well, predictive modelling is a powerful tool for business leaders. No wonder it’s being used in an increasing number of applications – from supply chain management to equipment maintenance.

Here’s what you need to know:

How can AI help retail leaders?

Predictive modelling involves using past data to determine what will happen in the future. AI and machine-learning programmes usually involve predictive modelling in some form – the applications use models to sift through past data and learn how certain variables connect.

For example, an AI model might compare online browsing behaviour with sales – which behaviours correlate with the most sales? Is there a specific pattern of behaviour in common? That information is then applied to future customers and used to target advertising in the right places.

With predictive modelling, the key is regular feedback and testing – data gathered and tested years ago, or even months ago, won’t necessarily apply to current market or economic conditions. The more frequently you update your models, the more accurate they will be.

Ultra-modern AI modelling programmes are updated continuously – so they use real-time data to forecast future trends. In today’s variable, fast-moving economic environment, that kind of flexible modelling can be incredibly valuable.

5 ways AI can reduce business risk

Here are some of the ways you can apply predictive modelling in a business setting:

Improve your Supply chain

AI and predictive modelling can streamline your supply chain by giving up-to-date predictions of what needs ordering.

If you don’t know how much product to order, when and where to purchase it or when it will arrive, your business is in trouble. Unsold inventory is a financial drain, delays can lead to lost sales, and ordering needs to keep up with fast-moving trends. In this area, predictive modelling is growing more and more sophisticated, with applications integrating real-time and third-party data and tracking individual items across the globe.

INCREASE Marketing ROI

AI modelling can be used to track and analyse browsing behaviour and target specific types of customers. It predicts which customers are likely to churn and targets them with retention strategies. It also tests marketing strategies and specific pieces of content.

Better Fraud detection

AI and predictive modelling are used to analyse bank transactions leading to fraudulent activity, so banks and retailers can be alert for future suspicious behaviour. If your bank has ever called you about an offshore purchase or an unusual series of smaller transactions, predictive modelling probably played a part.

caluculate Risk

Algorithms determine risk levels for vehicles, people and property, so insurers can set rates fairly. Similarly, lenders use predictive modelling to assess the risk level of businesses applying for loans.

improve operations

In businesses with large fleets and crucial equipment, modelling applications use past data – and sometimes data collected from the machines themselves – to forecast and schedule maintenance. This minimises downtime and keeps expensive, vital equipment up and running.

How Datamine can help reduce your risk with AI

It’s useful to build your understanding of predictive modelling, but it doesn’t mean you’ll suddenly be able to create a model from scratch. That’s where we come in – predictive modelling and analytics are our specialties, and we have decades of experience behind our expertise. We can work with you to build predictive models for your business, create analytics programmes and give you the data you need to make better decisions every single day.

Ready to get started? Talk to the Datamine team now.