MilliOnAir interactive Magazine April 2017 - Page 100

Provide context to learn and grow

It seems like an obvious point, but the more data a bot has about a shopper, the richer and more relevant it’s able to make an interaction with that shopper. “We use a range of technology to detect the intent of a shopper and make heavy use of the circumstances surrounding that particular shopper and their request at that moment, in order to determine intent. We have data and context about each shopper, when they ordered the item, when it’s coming, what they’ve purchased before, their return history, when they attempt to interact with customer service, etc. We know who they are and their reasoning. Our bot will constantly update the shopper profile and change behavior based on it,” says Alessandro.

In order to be truly useful to customers, your bot needs to have the capacity to learn from its interactions with them, both individually and at scale. A successful bot is able to draw on the customer data you’ve already collected and analyzed and also add to it and adapt its behavior based on this new data. If you haven’t figured out how chatbots and conversational channels integrate into your quest for a single customer view, you aren’t setting yourself up for success.

Make the human-to-bot hand-off seamless

“The problem with most chatbots is if they don’t understand what you’re saying, they don’t do a good job of transferring your issue to a human agent to be solved,” says Alessandro. This results in customers getting frustrated with the bot and either looking for another channel through which to engage with your brand or, worst case scenario, deciding not to bother engaging further. “At the present stage of the technology, we’re looking at some sort of hybrid experience between humans and chatbots. A chatbot should be smart enough to know when it doesn’t know and to escalate to a human,” Alessandro notes. Linc’s own bot has a mechanism to allow a human agent to take over the conversation, which is triggered by specific cues that indicate a shopper is not getting what they need — the bot is unable to understand a human’s messages, the shopper indicates a response is not helpful through a CTA attached to most of the responses, or it does detect the shopper’s intent, but knows a human agent can best solve the problem. “The key is to be sure to forward the inquiry as soon as we detect the shopper would be better served by a human agent, there’s avoiding any frustration and avoiding the common stigma associated with dysfunctional and ‘useless” bots,’ says Alessandro.