Title: AI for Summarization – Enabling Human-Consumable Information
In the first post in this series on Artificial Intelligence: Monster or Mentor? we saw that there are several key ways in which AI advances can improve human productivity in organizations. In this article, we’ll look at the first: Distillation.
Distillation is applying AI approaches to automate making large data volumes interpretable. Just like miners distill tons of raw ore into ounces of gold using machines, the goal is to automate the identification of value in big data. Here, we’ll focus specifically on how Distillation can be applied to the business problem of customer experience.
Companies interact with their customers in more and more ways, across ever-increasing numbers of service channels: call centers, web-chat, email, automated chat-bots, social media—the list goes on. A growing challenge is to understand your customer’s experience, even as they traverse this massive web of communications and interactions. Being able to distill answers to simple questions like the following can deliver enormous business value.
Why are they contacting us?
How can we most effectively interact in order to reduce service channel costs?
What can we do to make this a positive interaction?
Where/when should we intercede in the future to pre-empt the need for contacting us?
A brute-force, manual analysis of the raw interactions is just not possible. It’s true that a lot of insight can be gathered from analyzing certain specific interaction data. The challenge is knowing which needle in the haystack to focus on … or in this case which needle in the stack of needles to focus on.
Through combinations of network analysis, temporal pattern mining, and interactive analysis, it’s now possible to leverage AI-assisted technologies that enable humans to answer business-oriented questions like those above to identify service optimizations and cost reductions, and deliver a better customer experience at the same time.
For instance, as customers traverse a business’s service channels, network analysis metrics like betweenness centrality can identify “choke points” that customers are commonly funneled through. Such metrics let analysts focus their search and expose important interaction steps that can be optimized. As an example, these kinds of metrics can identify important patterns, such as cases where automated emails are key points of customer engagement. You might discover that having meaningful, customized emails instead of generic one-size-fits-all communications results in far-reaching impacts in customer interactions, because of the network effects such choke points create.
That’s Distillation for communication patterns. But beyond communication pattern analysis, AI approaches based on NLU (Natural Language Understanding) offer insight into the communications themselves. AI based on NLU provides opportunities to distill, and quantify, the meaningful aspects of natural language interactions (emails, call transcriptions, etc.) associated with the customer journey. As Narrative Science observes:
Until the last few years, NLP has been the more dynamic research area; the focus was on getting more data into the computer (e.g. teaching the machine how to “read” an email and determine if it’s likely to be spam). The problem has now flipped. Our computers have access to vast repositories of data, and the problem is trying to get actual value and insights back out from all that data.
That’s a brief look at Distillation. The next article will look at Categorization applications—the way data tends to percolate through organizations by moving from one bucket into the next, being enriched, processed, and actioned upon along the way. See you next time!
Roy Wilds is the Chief Data Scientist at PHEMI Systems, a big data warehouse solutions company.