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Text analysis by ReviewTrackers

It’s not unusual for a five-star Yelp review to include a candid comment about how some aspect of the product or service could use a little work.

It’s not odd either to come across a completed NPS survey form with a “not likely to recommend this business” response… followed by a few positive words and feedback in the Additional Comments section.

text analytics customer feedback

This isn’t to say that satisfaction scores and star ratings don’t matter — they do. And they can be extremely helpful when a business is trying to keep tabs on its digital reputation or working to understand customers better.

The big picture: a complete view of the customer

More companies, however, are beginning to recognize that the score or rating a customer gives a business won’t always provide a complete or even accurate picture of how exactly that customer thinks or feels about the business.

  • According to a report by eMarketer, 63 percent of executives worldwide agree that the ability to predict customer needs and desires is the leading benefit of predictive marketing in 2016.
  • 60 percent, meanwhile, see the improvement of customer experience and service as another benefit to achieving a more complete view of a consumer.

These trends underline the importance of not focusing exclusively on scores and ratings.

Your aggregated star rating on Yelp or Citysearch, for example, won’t necessarily reveal much about your customers’ buying trends and preferences. Neither will your TripAdvisor ranking or number of reviews on Google say anything about possible product flaws or new service opportunities.

When customers share feedback, how do you get to the heart of what they really mean?

Using text analytics to manage customer feedback data

This is where text analytics comes in.

In terms of managing online reviews and customer feedback, text analytics has quickly emerged as one of the most valuable tools for businesses today.

Companies that receive a lot of feedback — retail brands, for example, or enterprise-level organizations with multiple locations — can gain a significant advantage over competitors by implementing a text analytics program and analyzing information shared directly by customers.

It’s useful to categorize customer feedback data into two types:

  • Structured data is clearly defined and easy to report on. The customer’s name, age, location, income bracket, star rating, survey score, NPS label, etc.: these types of information can usually be tracked, managed, and analyzed with a listening platform, a spreadsheet application, or some other traditional statistical tool.
  • Analyzing unstructured data, however, is more challenging. This includes data from online reviews, social media posts, “additional comments” in surveys, E-mails, recorded customer phone calls, YouTube video reviews and comments, etc. It could be because of sheer quantity, or human error (misspellings and grammatical mistakes); it could be because the overall sentiment is hard to pin down (as in critical five-star reviews) or because the data contains multiple unrelated ideas — in any event, unstructured data is hard to analyze.  
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Text analytics helps companies “mine” unstructured data from customer feedback and make sense of it. This aids in capturing and interpreting the voice of the customer across multiple feedback channels, allowing organizations to build useful business intelligence and uncover implications and insights on purchase behavior, satisfaction levels, specific product or service issues, and the customer experience.

Simply put: text analytics helps your company develop the capability to understand — in seconds and at scale — what customers mean whenever they share their thoughts in their own words.

Looking to implement a text analytics program as part of your sales, marketing, or customer experience strategy? Here are great tips to help you get started.

Smart small, but take an enterprise approach

If your organization is new to text analytics, it’s probably best to start small and not take too much on. Roll out a pilot project first so it’s easier to gain experience, measure ROI, and get buy-in from your leadership team. Setting ten realistic goals is better than setting a hundred unrealistic ones.

At the same time, don’t hesitate to take an enterprise approach to text analytics. For example: most negative reviews of local businesses have little to do with whatever was actually purchased. Instead the reviews talk about your employees, customer service, pricing, facilities, ordering process, even your environmental practices.

If you’re only tuned in to product-level conversations or monitoring only specific keywords and items, you’re missing out on the opportunity to achieve a more complete understanding of what customers are saying.  

Focus on actionable insights

As you roll out your text analytics program, you may find yourself surprised (and overwhelmed) by the massive amount of customer feedback data suddenly available to your organization.

Not all of this will be critical to your business. More information doesn’t equate to smarter decisions.

Don’t make data collection your goal. And don’t waste your time designing fancy visualizations intended only to make you look good in your next executive meeting. (It probably won’t.)

Instead, prioritize your efforts on generating insights that you can act on. Set specific goals that you would like to achieve using text analytics. List down your recommendations and action plan based on your findings.

By focusing on actionable insights, you can enhance the efficiency of your organization’s text analytics usage and drive real business value from it.

Identify your most important data sources

Today’s text analytics tools are versatile enough to handle multiple data sources. But your ability to identify the ones that are most relevant to your business is key.

Customer feedback and “voice of the customer” data, for example, will include traditional sources like surveys, post-transactional comment forms, sales and purchase orders, focus group discussions, and E-mail and call logs.

But considering that today is a time when word-of-mouth has gone digital — and customers can quickly become online critics — you probably want to expand your list of sources to include online review sites, social media content, Internet yellow pages, blogs, and online photos and videos, helping you get a big-picture view of what your customers are saying.

Combine results with quantitative, structured data

The most valuable insights come from combining your text analytics results with the types of data that are structured and clearly defined.

Once you’ve analyzed text from your Google reviews or Facebook Page comments, bring in other data — your NPS scores, for example, or your aggregated ratings on social media — to validate results and determine a sound action plan.

Incorporating structured customer feedback data also means you can more easily measure your progress. Just finished analyzing E-mails and social media comments? Instead of waiting for the next set of customers to share their thoughts, take a look at your scores and ratings and see if any of the changes you made since the last analysis actually improved your performance.

Add location as part of your context

Franchise brands, chain stores, and multiple-location businesses can understand customers better if their text analytics strategy is structured to capture data at the point where it’s being generated.

Assign your data to a geographic location relevant to your brand. Not only does this enable you to discover opportunities and issues that can affect your company across all locations; it also allows you to more accurately identify trends, themes, and issues specific to one location, but which may not necessarily be occurring in other locations.

You may notice, for example, that on Yelp, your store in Chicago isn’t performing quite as well as the New York one. With location-specific text analytics, you can investigate the possible reasons for Chicagoans’ low ratings — “the morning receptionist is rude,” “the café next door has shorter lines” — and more effectively understand the thoughts and feelings of your customers based on where they are interacting with your business.

Stay engaged and responsive

With text analytics, there’s a temptation to let the tool or the machine take care of all the listening. But technological capability shouldn’t exempt your team from driving engagement and creating a positive customer experience.

Read the comments. Listen in on customer phone calls. Respond to reviews. Resolve issues. Your customers share feedback out of their desire to be heard individually and acknowledged personally. Text analytics can serve as your guide to customer experience management, but it won’t close the loop on your behalf.

Use text analytics to transform your organization

Text analytics initiatives are usually introduced and handled by marketing or customer experience teams. But the entire organization — from sales and human resources to R&D and executive teams — can benefit from the customer feedback data that you analyze.

As a tool, text analytics can equip your company with a rich, nuanced view of customer feedback. No matter the stage or scale, always keep in mind that the goal is to turn data into insights, and insights into ROI.

By involving your organization across all levels, you can grow your program beyond being a mere pilot project and mine customer feedback data in ways that make a positive impact on your bottom line.

Download the case study

A major automotive manufacturer with over 600 business locations in the US understands how customer feedback management and analysis can help foster improvements and drive dealership performance. Learn more about this company’s feedback data strategy and program in ReviewTrackers’ exclusive case study.

To download, enter your information in the form below:

Brian Sparker

Brian Sparker is the Head of Content Marketing at ReviewTrackers. Brian aims to solve problems through carefully crafted content, with the goal of helping businesses collect and understand customer feedback.

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