How Topic Modeling Can Change How Brands Interact with Customers
Nobody likes a Monday morning quarterback. He’s the guy who always knows exactly how to run a play to score a touchdown…the day after the game. As much as the postgame know-it-all gets under everyone’s skin, many brands are Monday morning quarterbacking with one of their biggest assets: their customers.
Often, companies take a reactive approach to important decisions about their customers because “hindsight is always 20/20.” The problem with hindsight is that it doesn’t always tell brands how to fix issues until it’s too late.
Many companies are using speech analytics to look for exact answers within customer conversation data before taking action but, in the midst of massive amounts of information, that can mean many verbal cues go unnoticed, and those cues point to a bigger picture. But, what if brands can determine even the seemingly unpredictable trends before customers have specifically stated the problem? With topic modeling, it’s possible.
Many brands are using speech analytics to gain insights into customer sentiment and needs. Conversations with customers are recorded, speech is translated to text, and brands receive alerts when a pre-determined keyword or phrase is spoken.
Analysts can then drill down into conversations to further understand what customers are saying so brands can make appropriate changes to a product, service, or marketing campaign. While this is important, it’s impossible to classify every keyword or phrase that may be useful, making it difficult to identify some issues until it’s too late.
For example, several customers of a restaurant chain may call to let the restaurant know that they aren’t feeling well. However, until they start saying specific words and phrases like “nausea” or “food poisoning,” the company may be missing a larger issue. By the time the issue is finally discovered, they’re already far behind.
With topic modeling, brands can see beyond the actual words being used to discover trends and get ahead of potential problems. Topic modeling uses machine learning and natural language processing to find abstract topics in large pools of data so brands can identify the larger trends in the midst of conversations, including topics that aren’t even on the radar.
In topic modeling, algorithms analyze the speech-to-text conversation data, find related words, and extract themes from the word clusters. These statistical models determine the connections between those themes and how they change over time. As the data set expands, the algorithms grow, change, and learn to better process information, so the themes become more accurate.
