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Chatbots Sell Four Times As Much As Their Human Counterparts

From Sephora to Amazon to Dominos Pizza – major companies have employed chatbots to interact with millions of customers from around the world.

These artificial intelligent systems have been found to outsell human employees by four times and at almost zero marginal costs.

However, new research has found that when consumers realize they are conversing with a bot, they are less likely to make a purchase because they feel it is less knowledgeable and less empathetic to their needs.

Chatbots are conversational artificial intelligence (AI) that were developed to simplify human interactions with computers – and they are now being used in customer service.

Many service-based corporations have adopted these systems to help with the workload of communicating with thousands and even millions of customers from across the globe.

The AIs use voice commands or text chat, have almost zero marginal costs and have been found to outsell most human employees by four times.

However, it has created one question –why aren’t these robots used more often?

From Sephora (pictured)  to Amazon to Dominos Pizza ¿ major companies have employed chatbots to interact customers

However, machine-customer conversation reduces purchase rates by more than 79.7 percent when the human consumer discovers they are speaking to a chatbot

A team of researchers set out to uncover the answer and found that machine-customer conversation reduces purchase rates by more than 79.7 percent when the human consumer discovers they are speaking to a chatbot.

The study involved over 6,200 customers from a financial service company, who were randomly assigned to either humans or chatbots.

The disclosure of the bots varied from not telling the consumer at all, to telling them at the beginning of the conversation or after the conversation, or telling them after they’d purchased something.

‘Our findings show when people don’t know about the use of artificial intelligence (AI) chatbots they are four times more effective at selling products than inexperienced workers, but when customers know the conversational partner is not a human, they are curt and purchase less because they think the bot is less knowledgeable and less empathetic,’ said Xueming Luo, a professor and Charles Gilliland Distinguished Chair at Temple University.

‘Chatbots offer enhanced technological benefits, reduced customer hassle costs and increased consumer welfare (offering the product at lower cost because bots save money on labor),’ added Luo.

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‘This data empowers marketers to target certain customer segments to cultivate customer trust in chatbots.’

Although human consumers may be turned off by the current chatbots, scientists are working on technology for AI chatbots to help these systems better understand the links between language and how we feel.

And this could lead to the creation of ever smarter home assistants like Siri and Alexa, as well as laying the foundations for emotionally engaging robots. 

A team of researchers has developed an emotionally intelligent bot called the Emotional Chatting Machine that can reply to messages from human users with an appropriate response based on their mood (process flowchart pictured) - to improve human-machine interaction

EMOTIONAL CHATTING MACHINE

The Emotional Chatting Machine (ECM) was developed by a team of Chinese researchers.

Their aim was to create an AI bot capable of replying to chat messages with an appropriate emotional reaction.

Users currently have to manually select what kind of mood they are in from five options.

But in the future, AI software could learn how to select the proper emotional response for themselves.

The researchers found that over 61 per cent of people who tested the system preferred its responses to those of a traditional chatbot.

The Emotional Chatting Machine (ECM) was developed by a team of Chinese researchers at Tsinghua University in Beijing and the University of Illinois.

Their aim was to create an AI bot capable of replying with responses that are not only relevant and grammatically accurate, but also express an appropriate emotional reaction.

Users currently have to manually select what kind of mood they are in from five options such as happy, sad, disgusted and angry.

But in the future, AI software could learn how to select the proper emotional response for themselves.

And the researchers found that 61 per cent of people who tested the system preferred its responses to those of a traditional chatbot.

Speaking to the Guardian, Professor Björn Schuller, a computer scientist at Imperial College London who was not involved in the paper, said: ‘This will be the next generation of intelligence to be met in daily experience, sooner rather than later.

‘It is not a question whether they are desirable – they clearly are – but in which applications they make sense and where they don’t.’

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Chatbots are AI programs that help users communicate with businesses and other organisations.

The chatbot studied a data set containing millions of real social media interactions. Each was labelled as one of six emotional categories - anger, disgust, happiness, like, sadness and other (pictured)

HOW ECM LEARNS

ECM studied a data set containing millions of real social media interactions.

By learning the type of language associated with each of these categories, ECM was then able to assign this emotional content in real time conversations.

Given the input ‘Worst day ever. I arrived late because of the traffic’, a traditional chatbot replied ‘You were late.’

But using its emotional intelligence ECM offered the following responses: ‘I am always here to support you’ (like); ‘Keep smiling! Things will get better’ (happy); ‘It’s depressing’ (sad); ‘Sometimes life just sucks’ (disgusted); ‘The traffic is too bad’ (angry)

They can carry out automated one to one tasks, including customer service and interactive games.

By teaching ECM about the emotional content of messages and their responses, it is able to tailor its own responses appropriately.

To learn how to do this, the chatbot studied a data set containing millions of real social media interactions.

These were given an emotional label based a sample of 23,000 sentences collected from Weibo, a Chinese social media site.

Each sentence had been manually labelled as one of eight emotional categories – Anger, Disgust, Fear, Happiness, Like, Sadness, Surprise, and Other.

Fear and surprise were disregarded as they contained too few responses, but the remaining labels were applied to the data set.

By learning the type of language associated with each of these categories, ECM was then able to assign this emotional content in real time conversations.

Given the input ‘Worst day ever. I arrived late because of the traffic’, a traditional chatbot replied ‘You were late.’

But using its emotional intelligence ECM offered the following responses: ‘I am always here to support you’ (like); ‘Keep smiling! Things will get better’ (happy); ‘It’s depressing’ (sad); ‘Sometimes life just sucks’ (disgusted); ‘The traffic is too bad’ (angry)

TRAINING DATA SET
Emotion Responses
Anger 234,635
Disgust 689,295
Happiness 306,364
Like 1,226,954
Sadness 537,028
Other 1,365,371

Source: Dailymail

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