93 per cent of businesses cannot use analytics to predict individual customers’ needs

Despite a wealth of good intentions, including a big push towards using artificial intelligence (AI) to improve the customer journey, too many customers are being left in their own ‘digital shadows’. They are being served with communications and offers using incomplete data or data that is no longer relevant to their current interests or lifestyles. Even where the data is relevant, often backward-looking analysis is carried out meaning the organisation is not establishing the ‘next best action’ for that customer.

“No matter how many organisations say they’re using artificial intelligence and predictive analytics to improve their customer experience, the reality is clearly far behind the talk,” commented Tiffany Carpenter, Head of Customer Intelligence at SAS UK & Ireland. “Too many companies are not using all the information available to make accurate predictions about their customers’ latest tastes and circumstances, trapping them in the digital shadows of their past selves. As a result, businesses are missing out on new revenue streams, not to mention the risk of damaging their customer relationships.

“Regardless of the industry, most consumer-facing organisations admit they are still driven by internal sales and targets over customer experience. They need to implement predictive analytics to avoid leaving customers in a rut,” she continued. “It’s essential to incorporate as much data as possible – internal and external, online and offline – in real-time analytics engines to ensure the insights they produce are as accurate as possible. Only then can you achieve the kind of intelligent personalisation that modern consumers now demand.”

Mistaken identities

There’s a clear gulf between what companies think they can do and what they actually deliver. Although they are quick to sign up to buzz topics like AI and real-time customer engagement, many are failing to make good on their promises. For example, although a quarter (25 per cent) claim to calculate new offers based on real-time context, only 10 per cent purposefully introduce new products to see whether customers will try new things. The majority (61 per cent) base recommendations solely on historical data and previous purchases. In other words, customers are likely to receive recommendations based on things they bought previously, no matter what they are looking for now.

For many, this problem is compounded by an inability to personalise their marketing in real time. Although over half (54 per cent) of businesses say they can optimise suggested actions based on real-time behaviour, in reality only 10 per cent calculate customers’ ‘next best action’ on the go. The vast majority cannot even tell when customers have a major life event like getting married (74 per cent), having a baby (77 per cent) or retiring (86 per cent), leaving them unable to change their service offering to match. Businesses must pay more attention to their customers’ immediate circumstances if they’re to provide a truly personalised service.

Other key findings from the survey include:

• Only eight per cent of companies can view their customers as a ‘segment of one’, while a third (33 per cent) do not segment their customer base at all 
• A third of companies (30 per cent) use less than half of the customer data they hold to personalise the customer experience
• Around 70 per cent of organisations are typically not collecting meaningful data to personalise digital experiences. For example, only a quarter (25 per cent) are analysing previous transactions, and only a fifth are using CRM data
• Only 10 per cent can use online and offline analytics to personalise the digital experience in real time
• Only 16 per cent are currently prioritising customer experience over internal product and sales targets.

Artificial Intelligence in reality

It’s not all doom and gloom, however. The report also found that a majority (69 per cent) of respondents are planning to enhance their customer experience by implementing AI within the next three years, and a significant minority (14 per cent) already have an AI programme in place for this.

In practice, cognitive engines and machine learning are the most common types of AI. Over a third (35 per cent) of respondents are using or plan to use cognitive engines to create chatbot-style solutions that provide human-like interactions, while 28 per cent of companies use or plan to use machine learning to automate analytical insight.

More companies are leveraging the power of AI to analyse massive volumes and types of data in seconds, and augment customer experiences with convincingly human-like communication. Increasingly, AI will be a powerful asset to help build meaningful relationships between individuals and companies.

GDPR – a fine balance

Most companies are planning to collect less customer data as a direct result of General Data Protection Regulation (GDPR). Information about customers’ physical location and personal contact details will both see an eight per cent drop, with basic demographic information and web browsing behaviour close behind at six and seven per cent respectively. That will impact companies’ ability to effectively understand their customers as individuals and tailor their offerings as a result.

GDPR presents an opportunity to improve data hygiene as companies reduce their data collection habits, which could benefit the customer in the long term. Nevertheless, companies will have to balance the need to ease GDPR compliance by collecting less customer information with the need to maintain enough data to enable effective customer analytics.