How companies can reap the rewards of predictive analytics

For upon |For most companies the idea of predictive analytics fuelled by artificial intelligence to deliver better website performance and an improved customer experience has been more of a pipedream than a day-to-day business reality.

Research published by Econsultancy in partnership with RedEye has shown that companies have been hindered by legacy technology platforms, disparate data sources and the inability to turn insights into actions.

At the Festival of Marketing held in London earlier this month Matthew Kelleher, chief commercial officer at marketing automation platform RedEye, described how clients including Hotel Chocolat and Travis Perkins are reaping the benefits of AI-driven predictive analytics.

According to RedEye, predictive analytics can be defined as a set of algorithms that assess the likelihood that something will happen in the future, for example the probability that someone will make a purchase.

In the case of Hotel Chocolat, RedEye used predictive engagement modelling to help retain new and disengaged customers to lock in their lifetime value while increasing top-line revenue. Revenue increased by 25% even though 40% fewer emails were sent, according to Kelleher.

Among other examples given, building supplies retailer Travis Perkins increased retention by almost 9%, while increasing transactions nine-fold.

Kelleher stressed the importance of understanding the nuances of the customer journey, explaining: “If you don’t know where the customer is on the customer journey, you are going to be less targeted in what you put in front of them.”

Effective predictive marketing involves real-time access to a range of customer-related data sources relating to personal and demographic information, onsite behaviour, engagement, transactions, lifestyle and devices.

Central to RedEye’s approach is its customer data platform (CDP) that allows it to capture channel engagements with customers across different touchpoints and devices, building customer profiles and driving greater insights that enable companies to build customer value.

According to Gartner, by 2020, CDPs will power 20% of current multichannel marketing hub deployments.

As well as the rise of the CDP, artificial intelligence is another trend starting to have a tangible impact. Developments in AI and machine learning (ML) technology mean that better real-time decisions can be made as companies seek to provide the right blend of messaging and content in their quest to drive sales and extract maximum customer lifetime value.

According to Kelleher, many companies have adopted a ‘wait and see’ approach when it comes to the use of AI, and this is consistent with the findings of Econsultancy’s Digital Trends 2018 report. According to that study, 15% of companies surveyed said they were already using artificial intelligence, while a further 31% said that they were planning to use it.

It is clear that the brands that benefit the most from increasingly AI-driven predictive analytics in the coming months and years will be those that are able to integrate different data sources effectively, enabling them to generate the best possible insights and translate that information into effective marketing actions. This in turn will help them meet important commercial objectives such as increased customer lifetime value.

The last word goes to Paul Morris, the Global Ecommerce Director at Specsavers, quoted in the Embracing Predictive Marketing report: “Whilst most companies think they want advanced AI/ML, actually the most critical action all businesses need to undertake is to clean up and organise their data so that they are then ready to take advantage of, and embrace, the obvious tailwinds of machine learning and artificial intelligence. Without easy access to the right data of the right quality it makes it extremely hard to apply the services and APIs now available.”