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3 Steps to act on customer experience feedback after you get it

3 Steps to act on customer experience feedback after you get it

To celebrate the launch of the SurveyMonkey Technology Ecosystem Program (STEP), we’re highlighting one company that we’ve partnered with—in one way or another—every day in August. This is a guest post from Joao Alves of Chattermill. Check out all our integrations partners here.

The reasons for collecting customer feedback need no explanation; to create the best possible customer experience you need to understand what people think in their own words about your products and services at each point of the journey. 

It’s critical to be meticulous and systematic about how you collect and array your feedback data. Our quick data collection guide explains the thought-process you need to get through this first challenge. 

Nevertheless, it becomes less obvious what to do with your customer feedback once you’ve received it. It is often the case that you have collected multiple surveys and sources of feedback such as NPS surveys, post order surveys, customer support feedback and reviews, all of which will likely contain free text responses. Once you have collected a lot of these, how can you consistently and effectively make use of this rich qualitative data?

We’ll cover the three key steps you can take to extract insight and actionable insights from your customer feedback data using our analysis approach.

The first step to successfully making feedback insights actionable is categorizing the collection of the feedback data. 

Many professionals go straight from collection to analysis without making sure that the data is categorized the right way. But when you’re staring at numbers on a spreadsheet all day, it can be easy to get lost in the data. Once you have your data, you must be able to convert all that open-ended, qualitative data points into quantitative insights. The clear obstacle being the ambiguity of language.

In this regard, there are three main solutions you can follow.

1. Coding - Transforming qualitative data into quantitative data. This is done by; firstly, categorizing the piece of feedback (is it related to price, quality, service, etc.?); secondly, then by determining if a piece of feedback is positive, negative or neutral, and; thirdly, assigning a value (usually 1-5) of how negative or positive that piece of text is.

2. Rules-Based AI – A rule-based approach involved human-crafted and curated rule sets. The interpretation of positive and negative words over a large sample size defines whether a sentence is generally positive or negative, despite the ambiguity inherent to language that changes with culture and context.

3. Sentiment Analysis – These programs, powered by deep learning algorithms, are taught to recognize certain key phrases and go through your customer feedback data, tagging and categorizing them automatically, turning them into quantitative data points. However, instead of analysing the topic or intent of a piece of text, the AI analyses the emotion or sentiment behind the words.

Overall, the goal here is to find out what your customers are talking about, how they feel about it, and what they want from you, quickly and at scale. There is no one-size-fits-all approach here. The best move will be specific to your company and stage; or, perhaps, the best move will be a combination of the methods presented above. That’s what makes this challenge so fascinating.

Categorization done well allows you to better understand which types of customers stay and why, who churns and why, who are the adjacent users, who are the loyal users, etc. That’s the type of vital information we all need to strategically and confidently innovate and improve our customers’ experience.

The following step is to analyze all the feedback data that you’ve efficiently collected and categorized to make it actionable. 

Once you have all the data on a spreadsheet, you’re able to uncover insights around some of the following:

  • Unexpectedly high- or low-quality ratings on certain products
  • Different demographic trends, such as product preferences based on age or location
  • High amounts of customer service complaints in a specific location or via a specific channel

Doing so allows you to objectively look at what customers are feeling when they say what they’re saying and what customers feel about which aspect of the product or service. That type of information is invaluable. Every comprehensive CX analysis must design a full view of the customer journey by understanding what is driving positive sentiment with your CX and what’s letting it down.

Once you’re able to identify trends and ensure the data is statistically significant, you can start to roll out small, data-backed changes to your business with confidence. To dive deeper into how to do this, check out our Customer Feedback Analysis whitepaper, where we go into depth using practical examples and show you how AI and text analytics empowers your analysis.

The third step to actionable customer feedback analysis is identifying the root cause behind customer issues.

If you’re receiving a lot of niche complaints about a specific product, it’s worth taking a deep dive into that product to see if it needs to be overhauled.

For example, if multiple customers leave reviews about a product all complaining about different things, such as “I couldn’t get the export function to work” or “My computer crashes every time I open this,” you may investigate the product and learn that the onboarding manual file is broken. Customers weren’t able to learn how to install the software properly in the first place, leading to the variety of complaints. Remember, you don’t know what you don’t know.

Though the goal of customer feedback analysis is typically to identify overarching trends and themes, sometimes a variety of small problems can point to the rate limiting step that is stopping your company from growing further or being able to innovate.

Here you must know what the right questions to ask are. A list of some of our favourites are:

  • What themes stand out right away? For example, a larger-than-expected amount of negative or positive responses to something.
  • What are respondents saying about your new product/service?
  • Which responses are surprising you? Can you drill down further to figure out any patterns?
  • What is the most common positive feedback? What is the most common negative feedback?
  • Where in the customer journey do customers drop off and experience the biggest pain point?

When faced with these insights, we’re prone more than ever to bias. Therefore, indispensable tools to have by your side here are checklists. We have designed and organized a list of practical checklists that will guide you through difficult decisions with discipline and logic. 

Keeping in mind that the overall objective of collecting customer feedback is to make changes to your business that build better customer experiences, the analysis approach you take becomes a significant driving force toward reaching that goal. Using the methods we’ve mentioned and following our three simple steps will ensure you are acting on your customer feedback after you’ve collected it.

Chattermill provides a robust customer experience dashboard that brings advanced analytics to your survey responses. Bring all your feedback together in one place.