Understanding Employee Sentiment at Scale With Machine Learning

Are you drowning in reviews? If you’re a business that has reviews on sites like TripAdvisor, Yelp, Glassdoor, the answer is yes. You’ve got reviews and people are leaving their opinions – very, very blunt, honest feedback about your business. The bigger your brand is, the more of these you have. Listening to customers and employees is one of the most important things we do, but what if we can’t keep up with the volume of reviews? We’re going to miss critical insights – customers liking or disliking a certain thing, employees disliking or liking a certain thing – and we may not know what until we see massive business impact:

• Our hiring pipeline diminishes to a trickle.
• Our customers start talking about competitors instead.
• Our business sharply declines.

… and we don’t know why.

We could. We do, in a way. We have the data, stored in these reviews, in an unstructured collection of conversations. So, how do we access them? How do we turn a massive corpus of text into analysis and insights that guide our strategy and help us make better decisions?

The answer is machine learning. With machine learning,  specifically a set of technologies called text mining and natural language processing, we take a huge amount of text and digest it down into a summary, a readable form that we dig into and find anomalies. We find things that stand out, that we didn’t expect to see, but guide our business and help us make better decisions.

In our latest case study, we digested more than 2,500 employee reviews of a US restaurant chain, Olive Garden (an Italian casual dining franchise), and ran them through text mining software to understand what employees say about working there. We found, of course, the usual, expected complaints like hours, pay, and managers amongst some of the complaints, but we also found something unusual, something atypical.

In hundreds of these reviews, one dish was cited as the source of woes: the $7.99 all-you-can-eat soup, salad, and breadsticks dish. Customers love the nearly unlimited nature of this particular dish. Employees hate it with a passion because of logistical reasons. By finding this one insight, we were able to extract and create strategic guidance about how to make employees happier: solve this particular problem.

Download the case study read through it, and see how this could work for your business. What large quantities of text do you have that you’re not reading, but could contain valuable insights?

• Your customer service inbox?
• Your reviews on Yelp or TripAdvisor?
• Employee reviews on Glassdoor?
• The notes in your CRM?
• The feedback in your employee suggestion box?

Chances are, you’re sitting on the data right now. You may look at one or two, here or there but there’s been no thorough, comprehensive analysis of it.

See how you could apply the same ideas – machine learning, text mining, natural language processing – to go through all that data and turn it into actionable insights that make your business better today.

Get the case study here:

Olive Garden Case Study (PDF)

Christopher S. Penn
Co-Founder, Trust Insights