A Crash Course in Machine Learning

How Machine Learning Enriches Customer Feedback

The terms “machine learning” and “AI” are being used more frequently. They are two terms that are often used interchangeably in marketing.  Sci-Fi movies often depict AI as some machine, the all-knowing oracle, capable of answering all questions posed by the film’s human cast members.

While there will be a day when Artificial Intelligence equals or even surpasses that of humans, the current sweet spot for machine learning / AI applications, remains prediction, and pattern recognition.

What is Machine Learning?

Remember the “Duck test”? The expression goes: If it looks like a duck, swims like a duck and quacks like a duck, it’s probably a duck. A simple example of abductive reasoning and an illustration of how a pattern in information leads to a predicted outcome – pattern recognition is the sweet spot of machine learning. Finding patterns and identifying them is what machine learning is all about.

In simple terms, machine learning (ML) involves training and prediction. The process uses modeling. Models are datasets that contain features (independent variables – patterns) and outcome (dependent variable – what the features represent)

Models use neural networks (statistical algorithms) to analyze “features” to predict “outcomes.” For example, if you have a dataset with many animal features, machine learning can predict the animal based on its feature set. While this is a simple example, the same principle applies to more complicated tasks such as detecting credit card fraud based on “features” of customers’ past purchasing history. To better train a model, you provide feedback, in the form of weights, to help increase prediction accuracy. The algorithms used can be one or more, depending on the type of data analyzed.

Analyzing unstructured data, in the form of customer feedback, starts by using natural language processing (NLP) to detect critical phrases, sentiment, themes, patterns, and entities. Using these techniques and others, Guest-Note improves classification and can automate tasks that save time. By identifying patterns within customer comments and inquiries, Guest-Note can sort through thousands, and even millions, of observations and detect common themes and improve issue classification.

How Guest-Note uses Machine Learning to Enrich Feedback

1. Categorization and Subcategorization of Customer Input.

Think of the computer adage “garbage in, garbage out.” The future value derived from data rests on the quality of that data. That means accurate classification and even subclassification of subjects customers are discussing increases the usefulness of the data. Guest-Note uses Machine learning to help detect when misclassified comments and suggest corrections. Moreover, Guest-Note can notice feedback for which there is no suitable category and recommend a new one.

2. Suggesting Better Solutions

Machine learning can offer up possible responses to customer issues. Using various data points like followup comments and PRS (Post Resolution Survey) data, past “Happy Customer” outcomes are used to help suggest better responses by your team members responding.

3. Chatbots

Based on similar techniques as those used to suggest better solutions, chatbots directly interact with customers, similar to Amazon’s Alexa. Able to provide customers with immediate answers to common questions. Chatbots become more effective over time by routinely assessing interaction outcomes.

4. Better Note Routing

Guest-Note works to automatically route customer comments and inquiries to the individual within a business best suited to provide the answer or benefit from the feedback. In the beginning, the program uses the assigned designations entered into the system. However, in using GN, it learns from modified routes. It then makes suggestions when a possible incorrect destination is selected—for example, it suggests the “buyer” for an out-of-stock issue and the default store manager.

5. Tagging

Through use, GN learns from the input-data and can automatically Tag notes appropriately. Tagging aids for a more prolific knowledge base and machine learning improves and even automates note tagging.

6. Red Flag Comments

Guest-Note comes with an extensive library of “Red Flag” terms and phrases such as “food poisoning,” “expired baby formula,” “dangerous condition,” such clauses would initiate a “Red Flag” – notifying the proper individual. As GN processes more data, its’ ability to accurately detect new Red Flag events improves with increased model training?

Machine Learning and AI have come a long way in the ability to digest and process unstructured text. Machine learning is the foundation for smart devices such as Amazon Alexa and Apple’s Siri. And, just as these two applications get better over time, the more GN is used, the better it becomes in helping your unique business help its customers.

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