Text Analytics Terms You Need to Know

Whether you’re a seasoned pro or just getting started in the world of customer experience (CX) and employee experience (EX), you need to be fluent in the language of text analytics.

However, that’s more easily said than done. With technology evolving so quickly, it’s hard to keep up with the latest and greatest. That’s why we’ve put together this quick text analytics glossary. Check it out below!

Top Terms

Accuracy: The combination of precision and recall for a given tag or model. 

Emotion: A measure of positive/negative feelings. Must be strong and clear-cut enough to be categorized as a specific emotion.

Human Translation: This translation method has a human translate each comment individually as the customer submits it.

Intent: Intent identifies what the customer is trying to achieve based on their response.

Keyword: A word or term that occurs in unstructured customer feedback data.

Machine Translation: Translation done by a machine that has been trained by humans.

Native Language Model: A text analytics model that is purposely built for a specific spoken language.

Natural Language Processing: A field of computer science and artificial intelligence that draws intelligence from unstructured data.

Precision: Correctness; represents how often a given concept is correctly captured by a specific tag. 

Recall: Coverage; refers to how thoroughly the topics or ideas within a given tag are captured. 

Sentiment: The expressed feeling or attitude behind a customer’s feedback. Categorized as positive, negative, or neutral.

Sentiment Phrase: Also referred to as a Sentiment Bearing Phrase or SBP. A phrase or sentence identified with positive, negative, or neutral sentiment.

Sentiment Score: A measure for both the polarity and intensity of the sentiment within a given comment.

Tag: A label generated from text analytics that groups together similar customer comments around a specific concept or topic.

Text Analytics: The methods and processes used for obtaining insights from unstructured data.

Text Analytics Model: A natural language processing engine that uses tags to label and organize unstructured data.

Theme: A dynamically extracted concept from a collection of comments, generated by an unsupervised machine learning algorithm.

Unstructured Data: Qualitative data or information that is not organized according to an easily recognizable structure. Can include comments, social data, images, or audio recordings

Making the Difference with Text Analytics

We hope this quick glossary helped you on your journey to find the best solution for your business. After all, text analytics make the difference between getting a meaningless score from your data and getting actionable intelligence. And without that intelligence, you can’t make experience improvements in the moments that matter. That’s why it’s so important to get your text analytics right!

If you want to learn more about world-class text analytics solutions, including new approaches like custom layered models and adaptive sentiment engines, you can check out our full eBook on the subject here!

The Shortcomings of Comment-Based Surveys

Comment-based surveys can be effective for immediately gathering feedback from customers. And when it comes to customer experience (CX), timeliness can make or break an organization’s ability to act on that feedback.

However, there are several arenas in which brands use comment-based surveys when another survey type would yield better intelligence. Today, I’d like to dive into several shortcomings that can make using comment-based surveys challenging for brands, as well as a few potential solutions for those challenges. Let’s get started.

Outlet-Level Analysis

As I discussed in my recent article on this subject, comment-based surveys are often less effective than other survey types for conducting outlet-level analysis. In other words, while brands can see how well stores, bank branches, and the like are performing generally, they usually can’t determine where individual outlets need to improve .

The reason for this has as much to do with the feedback customers leave as the survey design itself. From what I’ve seen across decades of research, customers rarely discuss more than 1-2 topics in their comments. Yes, customers may touch upon many topics as a group, but rarely are most or even a lot of those topics covered by singular comments.

What all of this ultimately means for brands using comment-based surveys to gauge outlet effectiveness is that the feedback they receive is almost always spread thin. The intelligence customers submit via this route can potentially cover many performance categories, but there’s usually not that much depth to it, making it difficult for brands to identify the deep-rooted problems or process breakages that they need to address at the unit level if they want to improve experiences.

(Un)helpful Feedback

Another reason that brands can only glean so much from comment-based surveys at the outlet level is that, much of the time, customers only provide superficial comments like:“good job”, “it was terrible”, and the immortally useless “no comment.” In other words, comment-based surveys can be where specificity goes to die.

Obviously, there’s not a whole lot that the team(s) running a brand’s experience improvement program can do with information that vague. Comments like these contain no helpful observations about what went right (or wrong) with the experience that the customer is referring to. The only solution to this problem is for brands to be more direct with their surveys and ask for feedback on one process or another directly.

How to Improve Comment-Based Surveys

These shortcomings are among the biggest reasons brands should be careful about trying to use comment-based surveys to diagnose processes, identify employee coaching opportunities, and seeing how well outlets are adhering to organization-wide policies and procedures. However, none of this means that comment-based surveys should be abandoned. In fact, there’s a solution to these surveys’ relative lack of specificity.

Brands can encourage their customers to provide better intelligence via multimedia feedback. Options like video and image feedback enable customers to express themselves in their own terms while also giving organizations much more to work with than comment-based surveys can typically yield. Multimedia feedback can thus better allow brands to see how their regional outlets are performing, diagnose processes, and provide a meaningfully improved experience for their customers.

Click here to read my Point of View article on comment-based surveys. I take a deeper dive into when they’re effective, when they’re not, and how to use them to achieve transformational success.

What Customers Say the 2020 Holiday Retail Season Will Look Like with COVID-19

Summer has passed, school is back in session, and Halloween is just around the corner. You know what typically comes up next: the holiday shopping season.

The only thing is that 2020 is anything but typical. There were very few summer road trips, kids are wearing masks or taking classes from home, and trick-or-treating might be off the menu to limit COVID-19 spread. So what can retailers expect—if anything—from the holiday shopping season?

Well, at InMoment, we believe that asking customers is the best way to understand their expectations and perceptions, so our Strategic Insights Team is here with the answers! Enter our brand new report, “What Retailers Can Expect from Customers in the 2020 Holiday Season.”

In this study, we asked over 5,000 North American customers all about the 2020 holiday shopping season, including:

  • When they will shop
  • What they will be shopping for
  • Whether they will be shopping in store or online
  • If they expect to attend Black Friday doorbusters
  • And more!

Typically, you’d need to download the full report to access the findings, but we’ve decided to give you a sneak peek into our findings! Keep reading for insights that will get you prepared for the upcoming season.

How Will the State of the Pandemic Affect Shoppers Feelings and Habits?

If it’s one lesson we’ve learned so far this year, it’s that we need to expect the unexpected. When many of us started working from home at InMoment in March, we never imagined that we wouldn’t be able to work in the office for months. Customers know this, but they are still feeling optimistic that circumstances with the pandemic will improve in the next few months according to our research.

In the unstructured data accompanying these questions, customers went into their feelings in more detail:

  • “I don’t think it will get better until 2021…but that will not stop my [upcoming holiday shopping].”
  • “I think things will remain the same for a while…we just have to get used to this [new normal].”

Though we all hope that we will see improvement in the next few months, we have to face the reality that there is a possibility COVID-19 will be with us through the new year. With that in mind, we asked how this possibility would affect likelihood to switch from in-store shopping to online.

In this case, customers were especially wary of their personal safety and health if the pandemic is still among us in the holidays, with the majority (65%) stating they are more likely to shop online. Still, 35% said they would still shop in stores; these customers described:

  • “I think [brands] are doing enough right now to make sure I’m safe when in their stores.”
  • “As long as the [COVID measures] are still in place, I will be going to the stores.”

It should definitely give retail brands a boost to know that they are making their customers feel safe, and that the in-store experience is so important in the eyes of their customers. 

Looking Forward

Preparation is key, especially in such a busy season. But add in a global pandemic and being prepared seems to be almost impossible. 

However, if retailers are armed with information directly about their customers about what they will do in the event that the pandemic worsens, whether they’ll be shopping in store or online, and more, they will know where they need to dedicate their time and resources to succeed. 

Looking for even more detailers on what your customers are expecting this holiday shopping season? You can download the full report for free here!

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