Skip to content

Human And Machine Learning: Chat Sales Solutions For Businesses

Share this article: Facebooktwitterredditlinkedinmail

The key to chat sales solutions for businesses is building these algorithms —as we saw in this closed loop diagram— building the classifiers and issuing recommendations and feedback to the live chat agent. And then —and this is the key— we monitor the user behavior. We A/B test the recommendations and in some cases we tell the live chat agent to apologize. In some cases we say, you should offer something for free.

In chat sales solutions for businesses, the live chat agent is the mediator. We, the Data Scientists, don’t have direct interaction with the customer. If the live chat agent decides not to follow any of the recommendations our analytics are worthless. So we need to do live chat agent behavior modification —this is behavioral economics. We consider, how can I display this message in a way that gets across to this person? Is it how I position my response? Is it adding colors? Do some people respond better to, I think you should do this as opposed to this or this conversation is going really poorly, so take this approach.

We’re constantly A/B testing all of these things because for chat sales solutions for businesses to be effective, we need to understand the live chat agent as well as the customer. Ultimately we A/B test different approaches and we see how the visitor reacts. We predict what they would have done, then we see what they do. We do enough of that to generate a pattern to observe in the data. That’s when we’re able to learn causally what’s driving good and bad outcomes.

Information about the live chat outcome feeds back into the system. We score the recommendations that were mediated by humans with decision trees, and then push them back into the machine. Should the live chat agent go down this path or that path with the customer? Given a sampling of responses, the live chat agent chooses one and then autocorrects the bot response. The live chat agent tweaks the bot responses uses them with the customer.

When you take the human out of the loop, this is the result, taken from a customer/bot interaction from the airline Indigo:

Customer: “Thank you for sending my baggage to Hyderabad and flying me to Calcutta at the same time. Brilliant service.”

Bot: “Glad to hear it, keep flying.”

Customer: “Are you serious?”

It’s a bad interaction right? Eventually we’ll get to the point where the bot can pick up on those sarcastic comments. This is a nudge to Microsoft, but everyone knows about Tay the bot they released into the wild. Microsoft was very excited about this but people found ways to make Tay say things that were racist and homophobic. This is what we need to be careful of. The way you do that is having the humans and the machines work together, teaching humans how to interact better, having humans close the loop by teaching machines how to interact better, and then ultimately, we can build a truly bot-enabled world.

Transcribed from Dr. Michael Housman’s Lecture at UC Berkeley Business School in May 2017.

Learn more about the live chat agent training from the team at RapportBoost.

Follow us:Facebooktwitterlinkedinrssyoutube
Tony Medrano

About Tony Medrano

Tony started his career as a technology entrepreneur out of his Stanford University dorm as co-founder and President of DoDots, the first desktop-to-mobile app platform. He built the company to 100+ employees, raised $25M in venture capital and managed all company operations, personnel and deals. He was most recently CEO of Boopsie, a mobile Platform-as-a-Service company that he grew for 4 years and successfully sold to a strategic partner in 2015. Tony has also served as Vice President of Sales / Business Development at Reply! and SmartDrive Systems. Tony has dedicated his career in technology to leading sales teams and understands the need for automation tools that help sales reps increase efficiency, close more business and increase margins. Tony received his MBA and JD from Stanford, M.A. from Columbia and B.A. from Harvard.

1 Comment

  1. […] Another way live chat benefits online brands is through significant cost savings related to customer support and sales operations by enlisting bots to help live chat agents with simple, repetitive tasks. But Julie Ask, Vice President and Principal Analyst at Forrester points out that, “The enabling technology isn’t fully adopted. Consumers don’t want to chat with machines that ask them dumb questions. Only one in four enterprises surveyed by Forrester even use location data to make mobile services more relevant — let alone insights built on the immense context available.” Bots can speed up conversation times by automating tasks such as concern classification and checkout. But for successful automation and cost savings, it’s crucial for companies to keep humans-in-the-loop. […]

Leave a Comment

Scroll To Top