Defining AI and Machine Learning for Enterprise CX

The market is seeing a significant emphasis on the need for predictive solutions, ranging from machine learning capabilities to artificial intelligence (AI). And while many companies claim to offer these exciting new technologies, rarely do they deliver on the promise.

True prediction requires more than a click of the mouse. We want to think technology has advanced to this stage, but the reality is we need more than one methodology to truly predict human behavior. To more accurately reflect the capabilities of predictive software, we must define and outline what AI and machine learning means for the CX industry’s current state of technology.

AI/Predictive Definition

AI for the CX practitioner is defined as deep insight generation curated from advanced analytics and machine learning technology that identifies patterns in human behavior which can be drilled-down to the individual customer, ultimately determining future actions of key market segments. This is accomplished through the combination of both human and technological elements, as AI is an evolving capability needing both forms of intelligence to determine future actions that impact business outcomes.

Levels of Prediction

Since AI and prediction span an array of capabilities from self-driving cars to determining customers’ future decisions, it is important to set a precedence as to what you can expect from CX vendors:

  1. Using Emotions and Intent Analysis: CX practitioners cannot compete in today’s landscape without a robust solution for mining unstructured feedback and then defining the deep insight from that feedback; a solution that can identify the emotion behind an experience and the intent behind the comment. Intent is the precursor to future action and should trigger action from your organization.
  2. Analyzing Audience Segment Patterns: Practitioners need a solution that aggregates all the structured and unstructured feedback and identifies patterns in customer behavior. This identifies trends within your organization that impact certain business outcomes.
  3. Identifying Future Actions via Predictive Modeling: The ultimate goal for an organization is to use a predictive model that identifies key attributes in historical CX data that is impacting business outcomes, so you can determine points in the customer journey that are likely to cause a future action. This allows a company to predict if a certain experience occurs, the customer will act in a certain manner.