That’s right, 50,000 interactions in the first week.
A year ago I worked on a project where, in 6 months, our team planned, designed, tested, broke, fixed, broke again, fixed again, and finally deployed a bot that in one week interacted with 50,000 unique users. I won’t get into bot details nor where you can find it, but I will share a couple observations I think can only be made when you look at the data produced from 50,000 chats captured in 7 days.
- People can be very polite to bots: You would be amazed how many people say thank you before they exit, or wish a bot good luck, say god bless or even compliment a bot. We gave our bot a personality, but it was still as a surprise! Amazing how many people say “TGIF” on Fridays to a bot! Perhaps it’s demographics, perhaps the bot is really that good, but it never occurred to us that there were that many people out there who felt compelled to show kindness to an informational bot with a cheery personality.
- People can be dicks to bots: Trolls are everywhere and they even take the time to troll a bot. A fairly high percentage of people either troll bots or get pissed when the bot doesn’t respond correctly, road rage pissed. We actually went back and enhanced the experience to respond appropriately to angry people and trolls, asking them to calm down or to stop bothering us – it actually worked pretty well.
- People are very open to suggestion from a bot, but not always to the types of suggestions you might think: This isn’t something new we discovered with this project. In fact, in marketing and sales, it’s quite well known that people are more open to suggestions in certain contexts than others. Our bot simply reinforced the importance of context and how it sometimes creates unexpected value that emerges from usage. Here’s a concrete example (not from our bot, but analogous).
My wife signed up for a huge nationwide gym two months ago and did so via a chatbot with a very simple conversation tree that asked questions and populated basic information into a registration form.
She was asked about her fitness, sports she liked to play, what sorts of classes she was interested in. Buttons and yes/no questions guided most of the experience, but there were several opportunities to answer in free form text. The bot had decent guard rails, keeping the conversation flowing towards completing sign up. If she tried to talk about things the bot was not designed for, it would politely redirect the conversation. In 4 minutes she was done.
Then she went to LuLu lemon & spent $200 on work out clothes. 😂
When is someone more likely to buy an umbrella? Just before it starts to rain. When might someone purchase $200 worth of work out gear? Within 30 minutes of signing up for a new gym. My wife talked to two entities that day in a way that would indicate a possible interest in stocking up on new work out gear.
Me and a bot…
Can you imagine if the owner of that chatbot could plausibly claim credit for the sale? Do you think my wife would have viewed a recommendation to purchase work out gear to be intrusive or obnoxious? Can you imagine if that chatbot signed 50,000 people up for new gyms a week?
Man, is that a missed opportunity.
And that’s the last thing I learned after that week, the concept of Emergence. A good passage from a post on Emergence…
As we continue to heighten connectivity and adopt analytical models that capture emergent properties, we may well see core business processes and conversational systems change dramatically. If we can observe, predict and react to user needs based on emergence, then we will need to rethink how conversational systems function at scale.
At massive scale, patterns, trends and unique patterns emerge. Often these emergent behaviors are difficult to predict until the actual scale is achieved and observed. I’m guessing at 50,000 weekly users, a fitness bot could sell a number of contextually relevant things other than gym clothes…
An interesting start up, cashbot.ai, is trying to help that gym signup bot (and all others), and more specifically the development team who runs it, to improve the understandings of user context and deliver unique channels to monetize those interactions when and where the time is right.
There are a lot of practical and technical challenges still needed for the chat and conversational ecosystem to mature. One of them is certainly being able to easily monetize interactions without ruining the experience, and that needs to happen one conversation at a time and at massive scale.
That’s it! I love you all.