Consumers give brands less and less time to get it right when it comes to Customer Experience.
A survey of 14,000 consumers around the globe published by consulting giant PwC last year found that almost a third (32 percent) would leave a brand they liked after just one bad experience.
That doesn’t leave much margin for error. Brands need to ensure they are using every tool at their disposal to understand what motivates customers and how their demands are changing. Knowing this can make all the difference to securing sales, driving up revenues, recruiting to loyalty programmes or hitting higher customer experience benchmarks.
The picture is complicated by the multiplicity of channels and platforms we increasingly use as consumers. Opus research in 2016 found that customers used an average of 3.5 channels to complete digital shopping and other brand-related tasks.
Retail organisations know that in the age of multiple channels and easy-come-easy-go consumer interactions they must get as close to customers as possible, understanding their changing requirements and responding to their concerns as quickly as possible.
AI turns customer data into a powerful source of insights
Almost every consumer-facing enterprise is now using analytical techniques to extract insights from transaction data, but until the advent of artificial intelligence (AI), it was not possible to analyse customer reviews as effectively. Firstly, they were too numerous and secondly, how do you obtain actionable insights from what customers think?
The answer is that AI uses natural language processing (NLP) and machine learning (ML) and together these two technologies make it possible to automate the analysis of thousands of reviews. This is about more than just picking out particular words such as “camera”. ML applied to NLP detects the sentiment behind the words so that what consumers think about the quality of the camera is revealed as well as particular aspects such as its lens or range of shutter speeds.
Companies with access to a platform that applies these technologies to the insight of real customers, obtain hugely valuable, easily-understood insights into what pleases or irritates customers. AI can spot trends in anything from fashion to festive toy-buying or travel destinations before consumers themselves are aware of them. On a personal level, real-time customer review insights enable customer-care departments to respond quickly to consumers who express their dissatisfaction about some aspect of a product or service, building trust in a brand as a result.
Uncovering the unknown knowns in customer data
Companies can use AI to uncover from customer data what may be eluding them through conventional means. If we take the example of two car dealers selling the same model of four-wheel-drive vehicle, they can deploy AI to analyse the feedback their customer’s leave, to discover why they generate very different levels of satisfaction in their respective sets of customers.
ML and NLP can rapidly drill down into the insight left by customers and blend all the available data to find out the likeliest causes of these problems from hundreds of possible factors. It can achieve in seconds what may take a member of staff many days, without the same guarantee of AI’s accuracy.
Blending customer data for greater insight and personalisation
The capacity of AI to digest masses of information and teach itself about significant patterns, means that customer review data can be blended with the other data that enterprises hold, transaction data being the most obvious. This can be combined to provide more wide-ranging insights that increase efficiency and responsiveness. The range of sources can include stocking and supply chain data, or details of finance deals, after-care packages, warranties, or product specifications.
This also feeds into the necessity for personalisation in customer engagement. With increasing numbers of consumers becoming inured to standard advertising or content delivered directly by brands, personalisation is necessary to cut through the noise. Individual transaction histories along with browsing habits or inquiry logs can be combined with review insights. Using this data, AI automation gives a company the ability to intervene with offers, notifications and discounts just as consumers are hovering close to the point of making a purchase or in danger of dropping out. At these moments, a single timely intervention can be decisive.
AI-driven insight is essential for you to compete with the retail giants
The retail giants such as Amazon are fast developing their capabilities, honing predictive analytics so that recommendations are personalised and based on the individual’s history, rather than on what other customers “also bought”. The rest of the retail world cannot afford to overlook AI when it is sitting on masses of valuable information in the form of customer insight – especially when they are from real, authenticated customers.
AI solutions are fast developing right across retail, such as the rise of conversational commerce through applications such as Google Duplex, or the emergence of visual product searching in which a mere image is sufficient to trigger a wealth of results.
Many AI solutions will struggle to make any impact in the real world of retail, whereas the insights delivered from the feedback of real customers are available now and can become the catalyst for transformed levels of customer engagement.
Customer Experience has become the main battleground of competition and personalisation one of the most effective weapons. No retailer can afford to ignore AI and the insights it delivers from real reviews if it wants to get ahead of the competition.