How Neural Networks & Deep Learning Can Help Categorize Customer Feedback
Machine learning is changing the way we do business. Nearly every facet of your company can benefit from machine learning of some type. Neural networks and deep learning can provide an unprecedented capacity to analyze and categorize data. Customer support and the analysis of customer feedback is one area where deep learning really shines. Customer feedback comes in the form of natural language, which is ordinarily difficult for computers to understand. Deep learning removes that difficulty and allows you to bring world-class customer satisfaction to your business.
Understanding Neural Networks
Let's briefly talk about what a neural network is. In computers, we are actually talking about artificial neural networks, a technology that is taking the business world by storm. A real neural network is a living brain, the theoretical model for this tech. Your brain consists of billions of neurons that are fired via electrical impulses. Each neuron has an activation threshold: if the electrical impulse is strong enough, the neuron fires.
When we learn, we are connecting the neurons and forming the threshold values in such a way that they complete the patterns needed for the task at hand. Artificial neural networks are a method of machine learning that mimics this behavior. They allow us to teach a computer by giving it data and letting it find the patterns rather than giving it a predefined list of patterns. This is especially useful when dealing with large data sets.
Most neural networks only consist of a few layers of neurons. Two for the inputs and outputs and then one or two more to train with. Some tasks, such as image or language processing, are very complex. The input for an image requires one or more neurons per pixel. For sentences, there needs to be at least one neuron per word. A few layers of middle neurons (called hidden layers) isn't enough for a computer to encode meaningful data about such a large input. The architecture for these types of tasks has extra layers to provide it with the computing capacity to effectively learn. This extra depth of neuron layers is where the term deep learning comes from.
Deep learning can examine text in much the same way that humans do. This allows you to automate many of the tasks that would normally require humans to spend time doing. Because a computer can read and analyze text much faster, you'll save money on labor costs and improve customer outcomes. Let's look at some ways text analysis can help you.
Deep learning can detect the sentiment of the text, to know whether your users are happy or angry with you or your products. It can tell you what type of tone your customer support agents are using with the customers and how the customer's tone and satisfaction level evolves during their conversation. It can do this by examining emails, chat logs, or transcripts of telephone calls. Deep learning based solutions can even automatically transcribe your recorded telephone audio.
Beyond their interactions with support staff, you'll be able to gauge user sentiment across any channel where they can communicate with you. Pull in data on what is being said about your business or product on social media, review sites, store pages, and anywhere else customers gather in the digital space.
In addition to giving you a better overview of how your customers feel about you and the pain points they are experiencing, monitoring customer sentiment over time is a great way to track the effectiveness of your customer support efforts. You can specifically target your efforts to get problems resolved quicker, with less frustration on the customers' end, and with friendlier communications from staff.
You may already use a CRM that will use clustering to segment your customers into distinct buyer personas that your marketing staff can build a sales strategy around. That same ability to examine large amounts of data and group it into clusters can be great for categorizing your customer feedback. By organizing your feedback by question type, department, topic of conversation, or whatever other grouping makes sense to your business, you'll be better organized and more equipped to handle customer feedback promptly.
When grouping is combined with sentiment analysis, the tool becomes even more powerful. How do customers feel about your user interface? Product reliability? Are you sending too many, or too few, notifications? Without the need to bother your customers with frequent surveys, you'll be able to see how they feel about the different aspects of your business.
Another substantial advantage of successfully integrating neural networks and deep learning into your customer feedback operations is the ability to auto-label your data. Auto-labeling functionality, like what Yogi offers, allows your AI and ML tools to easily and efficiently sort your datas into functional buckets. Properly implemented, auto-labeling will automatically grab new customer feedback data and place these into the right topics and groupings based on the network’s previous understanding.
Refine Buyer Personas
Speaking of buyer personas, tapping into the communications that you have with your customers is a great way to gather even more insights into who your users are. When you use this data to better understand your users, not only will you be able to provide them with better service, but you'll be able to further refine your personas. This will make your marketing more effective and allow you to gain more customers to wow with your great support efforts.
Neural Networks & Deep Learning: Powerful Tools
Technology has allowed customer service to grow to a level that surpasses what was possible just a decade or so ago. As a result, customers expect more today than they have in the past. Deep learning is a key factor in staying ahead of the curve and understanding your customers and their feedback at the level required to compete with the service level that your competitors are providing. With Yogi's powerful tools, you'll be able to understand your customers like never before.