This is a viewpoint from Duncan Keene, UK managing director of ContentSquare.
Conversational commerce offers great opportunities to improve the customer experience. But, the quality of that experience is not a given; behavioural analytics are essential if organisations are to maximise the value of chatbots and drive up both engagement and revenue.
Chatbots are at the centre of the conversational commerce movement and are fast becoming a part of everyday life for most ecommerce brands. They’re changing the way brands interact with their visitors and adoption rates are rapidly growing. Chatbot technology is becoming increasingly sophisticated and while the debate remains contentious about chatbots replacing human customer support agents, they are undoubtedly becoming more ‘human like’ in their ability to respond to a diverse range of commands, questions and even emotional sentiment.
The Holy Grail for many ecommerce teams is to truly understand how visitors behave and navigate through their sites on both desktop and mobile. Brands have been building towards this for over a decade with varying degrees of success; but in terms of analysing the behaviour of visitors when it comes to chatbot interaction, most teams are in the dark regarding the effectiveness of their automated bots. This is a huge grey area for the vast majority of organisations and is doubtless a source of lost revenue.
Aside from price and product, the quality of experience is proven to be one of the most effective ways to compete for consumers’ hearts and wallets: this continued lack of understanding regarding the way consumers interact with chatbots could be extremely damaging to a brand.
Understanding the UX
A poor chatbot experience will cause significant user frustration and risk damaging both brand engagement and perception. Organisations cannot afford to introduce chatbot technology without proactively monitoring and optimising the way in which visitors interact with chatbots from day one.
The ability to measure behavioural analytics is essential to understand how customers are interacting with chatbots across a website. It enables companies to rapidly identify problem areas and ensure they can be addressed, as well as pro-actively enhancing the experience based on trends in user behaviour. For example being able to see a visual map of visitors’ chatbot ‘journeys’ can identify high drop offs and site exit rates at a particular stage of a conversation.
Brands can also explore how specific visitor segments then behave after a bad chatbot experience – do they continue browsing on the site, end up making a purchase or instantly leave? The ability to rapidly identify and remedy such problems will be key to minimising revenue loss and brand damage.
Fully utilising chatbot UX analytics will also enable brands to explore the most effective time to deploy the bot on a visitor’s desktop or mobile journey; while further insight can inform the order of the questions surfaced, the effectiveness of various answers and the way different visitor segments behave when engaging with a chatbot.
OUI.sncf has developed a transactional chatbot where customers can book their train tickets. The bot is available on Facebook Messenger, Google Assistant (and Google Home by extension) and its own website. OUI.sncf is an early adopter of this technology and customers can do everything from searching for specific departure dates to booking season tickets.
We have been working on integrating a UX analytics tool so the digital UX teams can see how customers behave when interacting with the chatbots. This provides valuable insights which are key to improving this aspect of the customer journey.
Pascal Lannoo, digital customer experience director, OUI.sncf, believes conversational commerce is more than just a trend:
"It is a true new personal interface with our customers. We truly believe conversational will be a major channel for additional revenues, increased engagement and a stronger customer relationship."
He adds that using an analytics tool helps the company understand what users are experiencing with its chatbot, identify pain points and be able to react much faster.
Conclusion
While chatbots are becoming an increasingly mature and widely deployed technology, in reality organisations still have much to learn about the way consumers interact with chatbots to maximise the value of conversational commerce.
It is likely that as consumers become more familiar with day to day chatbot exposure, their behaviour will change and evolve – just as it has with websites over the past decade. The ability to track and understand this evolution will be important for companies to further improve that experience and fine tune the way in which chatbots are introduced within the consumer journey.
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