Retailer Argos is helping online customers make choices about what products are best for them by offering live chat assistance powered by predictive analytics as they shop online.
Working in partnership with intuitive customer experience firm [24]7, sophisticated predictive technology is used to identify only those customers who could benefit from additional help to make a buying decision.
The approach has been welcomed by customers, who are more satisfied with their eventual purchase, and resulted in improved conversion rates for the company.
Neil Tinegate, head of digital innovation at Argos said: “Our primary concern here is to offer human assistance to customers who need it, at the right time in the shopping cycle for them. We have seen this work in digital stores where colleagues are on hand to help customers get what they want, and this is a natural extension of that. Customers tell us they appreciate the help, so we plan to continue to offer the experience.”
The partnership with [24]7 includes the introduction of predictive digital assistance on selected product lines.
It understands consumers’ online behaviour and uses data-driven insights to predict which customers require help, when to engage and what to offer them.
Nick Mitchell, managing director, EMEA at [24]7, added: “We believe in encouraging more contextually relevant conversations with those customers who truly need assistance, accurately predicting when to offer assistance and using real-time cross-channel behaviour to know what to offer. Results from the programme show this is allowing Argos to have smarter interactions with its customers, which in turn is driving measureable increases in incremental revenue.”
Argos offers around 50,000 products through its website, growing mobile channels, stores and over the telephone.
It claims to be the UK’s largest high street retailer online with around 123 million customer transactions a year through its stores and 738 million website and app visits in the 12 months to February 2014. It employs 29,000 staff.
