Customers don’t care about your shiny new tech, only about what improves their experience with a brand. That’s the thing to remember when setting up communication channels: tech solutions are not necessarily going to make your customers happier, and they can have the opposite effect.
That was the subject of a recent survey of over 2,000 U.S. consumers commissioned by Invoca.
Among its findings was that over half of customers (52 percent) report feeling frustrated with companies that offer “no option for a human interaction” and close to a fifth (18 percent) go so far as to express anger about it. That indicates a setup that relies exclusively on automated communication incurs more negative than positive feeling, as only 16 percent report that they enjoy the experience.
It’s not a big surprise that Invoca finds variations between age groups with respect to trust in AI. Generally, the younger they are, the more they trust. “AI generated advice (80 percent of 18 to 34-year-olds vs. 62 percent ages 35 plus) — especially when it comes to financial services and healthcare, with 22 percent (each) of 18 to 34-year-olds trusting AI-generated advice for healthcare and finance compared to 10 percent (each) of 65-plus year olds.”
The proliferation of chatbots over the last decade may give the impression that they are only a product of the internet. In truth, though, the roots go all the way back to 1966 when Joseph Weizenbaum a German computer scientist and Professor at Massachusetts Institute of Technology developed a program he called ELIZA.
The all caps make it look like an acronym, but ELIZA doesn’t stand for anything. Instead, as explained in the original Stanford article about it: “Its name was chosen to emphasize that it may be incrementally improved by its users, since its language abilities may be continually improved by a ‘teacher.’”
The reference there was to the character of Eliza in George Bernard Shaw’s Pygmalion (more likely recognized by people today as the character in the musical version My Fair Lady). Eliza was hoping to convince others that she was something she was not -- a well-bred lady. Likewise, the program was designed to come across as a human therapist and convince users “that they were having a conversation with a real human being.”
While ELIZA definitely counts as the first chatterbot, the term was only born decades later. In 1994
Michael Maudlin invented a program he named Julia and called the function of a chattering robot “ChatterBot,” and the term soon got shortened to chatbot.
Chatbots now While users enjoyed their conversations with those early chatbots, most of us would not mistake them for actual people. But today’s chatbots are a different story.
They’re able to carry on much more natural-sounding conversations thanks to the application of machine learning, artificial intelligence , and natural language processing. Adding in ML and AI enables them to learn by identifying data patterns and then to apply their knowledge to answer questions and carry out tasks without any human intervention.
Their greater functionality translates into far more use by businesses and their customers. Today businesses use bots for a range of communication needs, ranging from customer service to product suggestion, scheduling, and various forms of marketing designed to engage the audience.
Juniper also predicts that by 2024 more than half of retail chatbot interactions will go through automatically and that “80% of global consumer spend over chatbots will be attributable to discrete chatbots” that are used through a mobile app rather than a browser.
On that basis, the report “urges retailers to implement chatbots as part of a wider omnichannel retail strategy in order to maximise their presence on a number of key retail channels.”
The problem with human moderators is that they have human limitations that cannot keep up with a
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huge influx of comments. That was the problem the New York Times faced in balancing reader comments demand with an editorial standard of civility for all published comments. Its solution was a partnership with , an incubator owned by Google's parent company, Alphabet.
Back in September, the Times announced the partnership in an article that set out the challenge faced by its 14 moderators tasked with reviewing the comments on the 10% of articles that do allow them. That alone amounted to 11,000 comments a day. As the article invited readers to try their hand at moderating, it was entitled Approve or Reject: Can You Moderate Five New York Times Comments?
I took the test. The official summary of my results were: "You moderated 4 out of 5 comments as the Community desk would have, and it took you 81 seconds. Moderating the 11,000 comments posted to nytimes.com each day would take you 49.5 hours."
That summation was followed by this: "Don't feel too bad; reviewing all of these comments takes us a long time, too." According to my calculations, however, the Times actually allows more time for their moderators. Given 14 people working 8 hours a day, the number of working hours each day would be 112, or more than twice the number of hours they said would be required for my rate of moderation.
That investment of so many hours is not something they regret, as they regard it as a requisite part of building up "one of the best communities on the web." However, they recognize that needs must dictate a new approach. That's where the machine assistance enters into the picture, enabling the same number of humans to effectively moderate a much larger number of comments and reduce the delay for reviewing time.
