AI and Machine Learning in Social Data

Why is it that when Anne Hathaway trends on social media, Berkshire Hathaway sees a pop in the stock market? According to the CEO of Converseon, Rob Key, it is simply the result of primitive AI gone wrong. Hedge funds buying shares of Berkshire Hathaway based on an uptick in “Hathaway” mentions is a great example of how Artificial Intelligence gone wrong can sometimes mislead us.

Despite the recent rise in popularity, AI isn’t new. In fact, it has been around for decades. Millennials were interacting with “chatbots” back in the late 90’s with the rise of AOL’s Instant Messenger (AIM) and infamous chatbot, SmarterChild. As Madeline Parra, CEO and Co-Founder at Twizzo, pointed out, SmarterChild may have been a popular chatbot, but it’s biting sarcasm was too much for tricking your parents into thinking it was you.


AI and Human Interaction

That same challenge of blending AI and human interaction still exists today. As Joshua March, CEO of Conversocial, put it, “From a customer service perspective, a pure bot will fall over at some point.” Thus, the opportunity is really about combining the unique qualities of bots and human agents to create the best possible customer experience. The benefits of AI combined with human customer service can lead to faster response times and increased agent efficiency.

We’re talking about social data here, but really we’re talking about human communication. – Rob Key

Optimizing Content and Interactions

More and more complex customer service interactions are happening through chat. It’s clear that customers are increasingly choosing chat as a way to engage a company or brand. This has created a unique opportunity for AI to help support the customer service agent handling these conversations.

AI is moving beyond simple boolean rules to derive sentiment. The challenge now is, how do you correctly capture emotions through a simple sentence or 140 characters? Boolean queries offer a great starting point, but it must be handed off to AI for deeper analysis. The advancement of AI is enabling companies to capture expressions of feeling such as frustration and trust.

It can also surface important context, not just sentiment but the underlying reason for that sentiment.

Public vs Private Interactions

Twitter was one of the earliest to see organic customer service interactions take place on platform. As it is predominantly public, this incentivized companies to not only take notice, but try to resolve customer issues quickly and effectively. As these interactions move more towards private channels (e.g., DMs, Messenger), it becomes increasingly important for companies to have a strategy for public and private interactions.

As Jonathan Farb of ListenFirst Media explained, “Mistakes come from applying the same model and approach to both public and private context.” Private context enables companies to leverage customer data to help expedite the interaction. This opportunity for personalization will come from tapping into the company’s own data (e.g., CRMs). When done right, this personalized interaction can lead to an amazing experience for the consumer.

A Call for Standardization

Across technology as a whole, we’ve seen open source tooling made available to speed up development time and solve common problems. Within the AI space, that simply hasn’t happened yet. There is a lot of duplication of efforts going on. Everyone that does AI suffers from common issues, so having more of a community to manage what might be adult content for example, would greatly help out innovation of how AI gets adopted and rolled out.

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