Allegiance Blog

The amount of customer feedback coming from social media channels is growing rapidly. Companies who don’t capture and analyze this feedback are missing or ignoring a large percentage of the valuable information that could be helpful to their business. Therefore, many businesses are turning to text analytics systems and technologies to automatically process and analyze text in all its forms and transform it to be utilized in identifying trends, early warning signs, product issues, suggestions for improvement, and cries for help from customers.

In applying text analytics to gathering customer feedback from social media, many new challenges must be considered. The number one challenge is that there is so much text out there, yet only a fraction of it is actually relevant to your business.

Even if you use traditional keyword filtering, it is still going to yield inconsistent and inaccurate results. For example, if you were evaluating comments about American Airlines, you would find some people who say, “I flew on American Airlines,” while others say, “I flew on American.” Think of the number of matches you will find if you just use the term “American”!

To manage this challenge, VOC programs using social media need to be able to apply smart filtering techniques and select only the relevant information from the mountain of available data. Text analytics technology based on Natural Language Processing (NLP) can also be utilized in the development of these smart filters, but due to the relatively new emergence of social media, very few are commercially available. With the popularity of social media, many of the leading text analytics and customer feedback technology providers are rapidly developing systems to overcome this challenge.

The power of text analytics will allow companies to quickly and accurately identify actionable issues and then adapt in real time by taking immediate steps that will boost customer retention, differentiate their business and quickly grow revenue.

Traditional business intelligence systems that analyze structured data are very good for statistically reporting the current state of customers and markets. Sales are up or sales are down. Customers are more satisfied or customers are less satisfied. This region seems to be performing better than that region. Although these are important facts to understand, the key insights that are missing are why those things are happening now. Answering the “why” behind the data is typically not possible, even with investments in interpolation, modeling, and statistical analysis on traditional structured data.

However, when you combine structured data with unstructured data, such as freeform replies to open-ended survey questions or comments on the Internet, you add another layer of depth that can give you a complete picture. For example, you can see what customers are saying about a poorly performing product, why customers in a specific region for a specific type of product and for a specific time period are unhappy, and what were the key issues that drove low satisfaction.

Text analytics is the key to understanding these questions. Well-designed surveys will typically ask for customers to rate products or services, then ask “Why did you give us that rating?” or “Why were you dissatisfied with our service?” The answers to those questions provide powerful insights. However, until recently this has been difficult to analyze. Businesses have traditionally relied on verbatim coding systems where outsourced vendors or analysts manually review a random sample of a few hundred responses, and then create codes to categorize them into common issues.

Although manually reviewing a sample of responses provides some level of accuracy, there are some inherent flaws in that process. First and foremost is that you are not looking at all of the data. If you have thousands or hundreds of thousands of responses, you are only able to cost effectively analyze a small fraction of the available information.

The second flaw is human bias. Whenever humans are making decisions about the data, there is always a tendency for people to respond and categorize based on the way they are feeling that day. Eye strain and fatigue also play a role in delivering inconsistent results. One day an analyst may categorize a particular issue as a customer service problem, the next day or week they may think it is more of a product problem.

In addition, customers may have complex issues that are not easily categorized with traditional coding schemes. In this case, you may need multiple interdependent codes, but that can make it even more difficult for human analysts to be consistent. All of these challenges to analyzing freeform, open-ended comments in surveys are prevalent today. Text analytics delivers the capability to automatically process and analyze large volumes of freeform text with consistency and accuracy.

Coming Soon: The new way to do text analytics in a VOC program

Eric Weight is Director Text Analytics Products at Allegiance, Inc.

Currently, there is an explosion of freeform text information being generated by consumers. Studies show that as much as 80% of the information that is created in a corporation is freeform or text in nature. At the same time, computer technology can not accurately process and understand language in its traditional form because computers are made to simply match patterns, compare and sort. Therefore, companies are missing or ignoring a large percentage of the valuable information that could be helpful to their business.

Since the revolution of the Internet, paying attention to this type of information has become even more important. Consumers now generate an incredible amount of online content by posting comments that are publicly available to everyone. Most compelling, this is information that is not being said to the companies themselves, but to the world at large.

Companies have numerous internal systems such as call centers, email and automated feedback systems to gather and manage customer information. However, public Internet comments are posted for all to see, providing low-cost access to relevant customer thoughts and feelings about a company and its competitors. Businesses and their competitors can use this information to do competitive research, understand general market trends, and pinpoint emerging problems early on in the product development lifecycle.

However, due to the freeform nature and sheer volume of this information, it is an expensive and cumbersome process to gather and understand unstructured data. Transaction or feedback surveys typically contain one or more verbatim questions such as, “How can we improve?” or “Please describe the problem you had.” Responses to these are typically very helpful individually. But what if you had a few thousand? How would you summarize them?

For these reasons, businesses are turning to text analytics systems and technologies to automatically process and analyze text in all its forms and transform it to be utilized in identifying trends, early warning signs, product issues, suggestions for improvement, and cries for help from customers. We will be getting into more details about text analytics in the future….so stay tuned.

Join the American Marketing Association for the free March 23 webcast titled, “Social Media – The New Frontier of Customer Feedback.” Speakers include Matthew Bowman, former CEO of Wi5Connect (a social media company) and Eric Weight of Attensity (text analytics expert). To register, go to http://bit.ly/ajOQop