How Social Media Can Provide Insights for Customer Service Behavior

How Social Media Can Provide Insights for Customer Service Behavior

Great companies want to know their customers as well as they can and use this information in all their business decisions. Daily interactions with buyers, especially on social media, can give them customer insights they won’t find elsewhere. For example, they can find out what customers are saying about their products or services.

Businesses are using machine learning and natural language processing to help them quantify customer feedback. The data comes from channels such as social media comments, email messages, chat messages and call center recordings.

Natural language processing (NLP) is a branch of artificial intelligence that is helping businesses with a high volume of customer feedback to get the insights they need to improve customer service.

What is NLP?

Natural language processing translates human language into commands that computers can understand and process. Understanding human language does not mean just understanding words but also the context in which they appear.

People use language in messy and nuanced ways, which makes it very difficult for computers to understand. NLP helps to solve this problem by allowing clearer human-to-machine communication without the need for the human to know any programming language.

Any machine learning that is done involving natural language involves some form of NLP. At one extreme, NLP can be as simple as counting the frequency of certain words to compare writing styles. At the other extreme, it involves understanding human speech and text well enough to be able to provide useful responses.

Learn what customers think about products or services

Customer choices, preferences, answers to queries, complaints and more provide a great deal of unstructured data. Sentiment analysis along with other NLP techniques help businesses to understand unstructured data and reveal how customers feel about a product or brand.

NLP engines accumulate the unstructured data coming from various channels such as social media, analyze it, and assign a value to it. It may be classified as negative, neutral or positive. It may even be classified according to emotions like sad, happy or angry if the latest NLP technology is being used.

Segment different customers

There’s no more immediate source of customer insights than social media and brands need to be able to recognize different subgroups within their market and reach out to each one with the appropriate message. If they don’t, consumers simply go to a company that can.

Good social intelligence tools are able to group users together automatically and pull out recurring topics and keywords, so you know what they care about.

You may think you know all about the people who buy your products but you could be in for a surprise. Social media offers you the opportunity to confirm your suspicions or get some unexpected insights into who is mentioning your brand. These insights can transform the way you communicate with customers.

Get real-time, large scale customer insights

With real-time market research, brands are able to analyze many references to their products. They can also follow individual conversations and catch unexpected points of view. So many social media users tweet and post about what they think and feel every day, so it’s possible to get insights on a large scale, even when asking the most specific questions. NLP and machine learning work best with bigger sets of data.

Discover customer experience trends

Businesses need to understand customer service-related trends as these affect what their customers are looking for. Consumer satisfaction is one of the main drivers of change. As consumers have so many choices, businesses have to make the customer experience as satisfying as possible and the challenge is to drive initiatives that produce the desired results.

To produce results, social listening allows brands to pick up on valuable signals from social media data. Customer service representatives are unlikely to find it possible to deal with the content of conversations and still pick up on trends.

By automating the process, it’s easier to discover these trends. Social listening tools can take all the posts and comments and present them in ways you can actually learn from them. The very best tools use machine learning to improve results based on what you need to know.

Find out in what context and where customers are talking

You don’t just want to know whether people are making negative or positive comments about your brand. It helps to know the context of the conversation too. For instance, Nike would expect people to talk about their brand when speaking about basketball or fashion.

Apart from being able to identify your audience by location, you also want to know what platforms they prefer using. If your customers are mostly on Instagram, this is where you need to be. You need to know whether your current social marketing strategies make sense or whether you need to think about using other platforms to communicate with them.

Gain consumer insights from competitors

Businesses not only benefit from customer insights but also from their competitors’ public data. NLP can be used to tap into hundreds of social media and review sites so businesses can find out which brands are mentioned the most and towards which customers have the best sentiment.

By figuring out what your competitors are doing correctly, you can receive inspiration on what may work for you. This also gives you more context as you not only know what your customers look like but whether they match a wider industry audience. You may be ignoring some potential customers and targeting them could increase your sales.

A final word

An ever-increasing amount of social data is waiting to be explored and it can offer up customer insights that can transform customer service. However, businesses can be intimidated by the huge volumes of data. The challenge for most businesses is how to identify actionable insights from the data. Businesses are turning to machine learning and natural language processing (NLP) to harness the power of data and understand the behavior of their customers.

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