Interpreting Survey Results

Interpreting survey data is a multi-step process requiring significant human, technology, and financial resources. This investment pays off when teams are able to change directions, delight customers, and fix problems based on the information gained.

The type of survey or question sets the direction for responses and for the resulting survey data. Understanding the options and what kind of perspectives they can reveal is critical to accurately interpreting the responses.

Types of Survey Questions

Before beginning, review the survey’s goal and the type of survey questions selected. This will help when interpreting the results and determining next steps. The following are frequently used survey types, though many more options exist.

The Net Promoter Score (NPS)

NPS is a widely used survey question that provides insights on how happy customers are with a brand. The result is a number on a scale which answers the primary question “How likely are you to recommend [business or brand] to others in your social circle?” Thus the NPS reports how strong a brand’s word-of-mouth message is at a given time.

Some NPS questions follow up with an open-ended comments box. This allows customers to explain or identify the factors that influenced their choice. These verbatim comments can help organizations hone in on where their products or services are exceeding expectations and where things may be breaking down.

Post-Purchase Follow-Up

Asking customers about how satisfied they are with the purchasing process and/or with products or services after they make a purchase can be helpful for businesses seeking to improve their customer journey. Questions include how satisfied the customer is with the experience, how happy they are with their purchase, and whether they’ve had any challenges getting the most out of their purchase.

This type of survey helps companies prevent customer unhappiness by improving the path to purchase, identifying weaknesses in the product or service that can be addressed, and opening up a conversation with unhappy customers early. Proactive customer support can transform a customer’s negative experience into a positive one and create a strong advocate in that customer.

Customer Support Contact Follow-Up

When customers contact a support resource, this kind of survey asks whether the customer’s problem has been fixed. It shows the ability of customer support to solve problems and identifies when the problem isn’t user error but inherent issues within the product, service or customer experience. Knowing this answer helps organizations address the right issue at the right time so their customers remain happy even after they purchase and begin using a product or service.

New Products and Features

A product may perform perfectly within the controlled environment of a lab or development area, only to flounder when tested in a real environment. Inviting customer feedback during the planning stages can save a company significant time and money as they bring a new product, service, or feature to market. In addition, businesses satisfy their customers better when they develop the updates and features that their customers want. Asking for input from customers allows companies to see which updates are most important and therefore belong higher on the priority list.

Step Two: Organize the Survey Data and Results

After choosing the type of survey, it’s time to deploy it and review the results. Depending on the survey chosen, organizations may receive only numerical data (such as a net promoter score), numerical and comment data, or comment (textual) data only.
When data results come in, organizing it first helps facilitate further data analysis. Questions to consider when organizing data include:

  • Did the survey target a particular product or customer segment?
  • Did the survey go to people making an initial purchase or to repeat customers (who are already at least somewhat loyal to the business)?
  • What are the unique characteristics that separate data sets from one another? Tag the feedback based on these criteria in order to compare and contrast the results of multiple surveys.
  • What overall themes appear in the survey data? Do a majority of the responses focus on a particular feature or customer service issue? A significant number of references to the same topic indicates something that is particularly positive or negative.
  • Is the emotion in the verbatim comments positive or negative? This is sentiment analysis of text responses and gives context to numerical rankings. Sentiment can show where businesses need to focus improvement effort.

Step Three: Analyze and Interpret the Data

Once the data is organized, the next step is to analyze it and look for insights. Until recently, data analysis required manual processing using spreadsheets or similar applications. Not only is manual analysis tedious and time-consuming (taking weeks or months with large data sets), it is also inconsistent.

Manual analysis requires human coders to review all of the data. No two people will review information the same way. The same person may assess it differently on any given day as well. Too many factors can change how an individual assess a set of data, from their physical wellbeing (Hungry? Tired? Coming down with a cold?) to their emotional state. This inconsistency makes it challenging to detect trends and significant changes over time (which businesses need in order to determine whether improvement efforts are working).

Text Analysis Tools

Text analysis tools automate many tasks associated with reviewing survey data, including recognizing themes and topics, determining whether a response indicates positive or negative emotion (sentiment), and identifying categories for this information.

Today, companies like Ascribe have developed robust and extremely efficient survey analysis tools that automate the process of reviewing text. Ascribe’s software solutions include sentiment analysis of verbatim comments, which offers greater visibility into customer feedback in minutes, not weeks. Advanced text analytics solutions such as Ascribe’s CX Inspector with X-Score can deliver high-level topic analysis or dig deep into sentiment using accelerated workflows that deliver actionable insights sooner.

CX Inspector can analyze and synthesize data from multiple channels in addition to surveys, such as social media, customer panels, and customer support notes. This creates a multidimensional big picture of an organization’s relationship with its customers, patients, employees, etc. These tools also drill down into the information through topics and categories. For companies with international products and services, Ascribe’s text analysis and sentiment analysis tools can also perform multi-lingual analysis.

X-Score is a patent-pending approach to customer measurement that provides a customer satisfaction score derived from people’s authentic, open-ended comments about their experience. X-Score highlights key topics driving satisfaction and dissatisfaction, helping identify the actions needed to improve customer satisfaction quickly and easily. In this way, companies can cut the number of questions asked in half and reduce the size of the data set without compromising on the quality of data they receive.

How Automated Text Analysis Tools Work

Ascribe’s cutting-edge suite of solutions work by combining Natural Language Processing (NLP) and Artificial Intelligence (AI). This powerhouse duo can sift through slang, regional dialects, colloquialisms, and other non-standard writing using machine learning. The software uses machine learning to interpret meaning and recognize the emotions behind the words, all without human intervention.

CX Inspector improves data quality by removing gibberish and profanity, and the software also can improve data security by removing personally identifiable information.

What to Do with Survey Results

The reports provided by CX Inspector appear in easy-to-read charts and graphs. Visuals highlight the challenges that respondents face with an organization’s products or services. They also draw attention to high-functioning areas receiving consistently positive feedback. These reports can reveal repeated topics and trends, eliminating guesswork and allowing companies to resolve specific complaints about a product or service. Positive feedback highlights strengths and helps businesses capitalize on them (and avoid making changes to things customers are happy with).

Neutral sentiment can also be an action item, as it identifies customers who are either apathetic or ambivalent about their experiences with a brand. This category of customer may respond well to extra attention, or they may simply not be the ideal customer. Knowing which is true can keep a business from wasting effort on customers they cannot please and help them stay focused on the customers they can.

People like to feel heard and to know that their opinions are valued by the companies and brands they do business with. Surveys and reviews give them the opportunity to give feedback. But surveys by themselves do not satisfy customers, patients, and employees. The highest value of these surveys comes from interpreting the data correctly, learning from it, and using it to improve experiences, loyalty, and retention.

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