Understanding your audience is an integral part of building a product or service that will delight customers and keep them coming back for more. There’s no better source for feedback and valuable insights than from your customers themselves. By collecting and analyzing customer feedback, businesses can identify areas for improvement and customer feature wish lists.
It’s in pursuit of these insights that many companies set about collecting customer feedback through surveys and questionnaires, ending up with mountains of raw data that, unless analyzed and actioned upon, is ultimately useless.
This post will answer common questions like:
- What is customer feedback analysis?
- What is quantitative data?
- What is qualitative data?
- Why is customer feedback analysis difficult?
- How do you analyze customer feedback?
- What is customer feedback analysis?
- What is quantitative data?
- What is qualitative data?
- What makes customer feedback analysis difficult?
- How do you analyze customer feedback?
- How do you analyze customer feedback using a script?
- How do you use software solutions to analyze customer feedback?
- How do you leverage artificial intelligence to analyze customer feedback
- 4 steps to customer feedback analysis success
- Start analyzing customer feedback today
What is customer feedback analysis?
Customer feedback analysis is the process of categorizing and interpreting data provided by customers to identify and understand their frustrations and needs. The goal of feedback analysis is to improve customer loyalty and satisfaction.
Using multiple feedback channels including, but not at all limited to, proactive feedback from customer surveys and questionnaires, businesses can take pulse checks and collect information to help guide their decision-making.
Feedback data can come in all sorts of formats, from numerical scores and scales to long or short-form text responses. Ultimately, some types of raw data are easier to process than others, and it’s easy to put the difficult ones on the back burner.
The two main categories of data are quantitative and qualitative, and understanding their nature will help you understand how to analyze them and unearth the valuable insight hidden within.
Quantitative data is numerical information that can be counted and measured.
What are examples of quantitative data?
Some examples of quantitative data that might help you understand your customers include revenue in dollars, number of return customers, average shopping cart value, or units per transaction.
Quantitative data gives us the ‘what’ with nice numbers that fit very pleasantly on pivot tables. But it doesn’t give us the ‘why.’ We can make assumptions, but they are just assumptions. That’s where qualitative data comes in, and where the heart of customer feedback lies.
Qualitative data is a way to describe qualities or characteristics. In formal terms, it’s the culmination of descriptive and conceptual findings collected through questionnaires, interviews, and observation.
What are examples of qualitative data?
Some examples of qualitative data are observation notes, semi-structured interviews, participant journals, focus groups, case studies, and open-ended surveys.
In terms of customer feedback, this is the data that tells you in no uncertain terms how your customers feel about you, your product or service, pricing, and more. This is obviously incredibly valuable information for product and marketing teams.
Through both positive and negative feedback, qualitative data is how you can identify your customer pain points, understand why they chose your product in the first place, as well as what they need from you in order to stay.
There are many ways to collect customer feedback. Unlike with the simplicity of numbers, however, analyzing customer feedback isn’t always as simple or straightforward as plugging in a formula in a spreadsheet to devise the average response.
What makes customer feedback analysis difficult?
Many businesses find it difficult to analyze qualitative customer feedback data because, to date, most analysis has been time-consuming and complicated. Humans use complex language, and it’s not easy to summarize and sort what people are saying into categories.
For example, if you ask a dozen people how they feel about a button in an open-ended questionnaire, they may all have the same feelings about the shade of blue you’ve chosen, but they’ll express it in a dozen different ways”
- “I don’t like it.”
- “That blue is too bright.”
- “I would pick a different color.”
Ultimately they’re all saying the same thing, but in very different ways, which makes sorting and tagging the information a laborious task.
Multiply that by hundreds or thousands and you have yourself a wealth of data that no one wants to sort through.
Worse still, not all feedback is of the same caliber. Some will be unhelpful or generic. Anyone who’s read through survey responses has likely seen the throwaway comments that people make in the feedback fields, probably assuming that no one will actually ever read them.
Real, useful customer feedback is imperative to accomplishing the gargantuan task of improving customer experiences. Having actionable insights means seeing the path to improved products and experiences. But it’s hard to get there in the face of so much work.
How do you analyze customer feedback?
There are four main methods for analyzing customer feedback that vary in levels of automation and resource expenditure:
- Manually using spreadsheets
- Using scripts
- Using software solutions
- Leveraging artificial intelligence
How do most companies analyze customer feedback?
The status quo is to dump a bunch of verbatim comments into a spreadsheet and have an unlucky junior analyst read through it to try to summarize survey results.
The only way that this process is manageable is if you only have a few comments to read but not a large enough sample to be statistically significant or indicative of any real trends.
Unfortunately, if you have enough data to make a real difference, reading through every comment and understanding all of the information is unrealistic. It’s a ton of work and at the end of the day you really only internalize a fraction of responses.
How do you analyze customer feedback in Excel?
Managing customer feedback analysis in Excel, Google Sheets, or any other spreadsheet application is a labor-intensive and time-consuming process that requires reading and categorizing every single piece of feedback.
In order to analyze customer feedback manually, you start with two spreadsheets.
The first spreadsheet is for your code frame of themes and subthemes. On this spreadsheet, you create a framework where you assign letters or numbers values.
The second spreadsheet is for coding each piece of feedback according to your code frame. Essentially, next to the piece of feedback, you would put the number or letter associated with the category that the data most closely fit into.
You go through each and every comment and piece of documented feedback, read it, then assign it a code or a tag. You could use a similar method to assign hierarchy levels or category identifiers like usability issues, bugs, or feature requests.
Once you have everything tagged and coded according to your framework, you can filter and summarize your spreadsheet to identify how popular different feedback themes are.
What are the downsides to manually analyzing customer data?
Manually processing customer feedback takes a lot of time and resources and is prone to human error. All it takes is a few mistyped codes to skew your data and make it problematic to work with.
It’s a difficult process to scale and a lot of the nuance from the customer’s comment is lost.
Automated feedback analysis can save your company valuable time and dollars spent on manual analysis.
How do you analyze customer feedback using a script?
Some of the more tedious aspects of manual feedback analysis can be mitigated using keyword extraction scripts. This method is a form of simple automation that helps you identify common keywords within the text, then automatically categorize feedback based on those keywords.
What are the downsides to analyzing customer data using a script?
There are a few major downsides to using the script method for customer feedback analysis. For one, recurring phrases are not necessarily themes. In order to merge and organize them further, you need to expand your script.
Similarly, though you may be able to identify popular terms or categories, the nuance of expression is lost because it’s based on keywords. Ultimately, the feelings and insights behind those themes may be skewed or altogether lost without manual investigation as well.
Finally, if you’re unfamiliar with coding, this method requires the help of a data science expert, drawing extra resources. As with manual data processing, having a headcount and an entire salary dedicated to data analysis in-house or the cost of having an analyst on retainer may not make sense for many businesses.
How do you use software solutions to analyze customer feedback?
Third-party customer feedback analysis software automates the process of gathering, organizing, and analyzing customer feedback, empowering data analysts and category managers to easily understand customer sentiment.
SaaS services focus on deriving actionable data and insights from all forms of customer data–not just survey results. Every touchpoint becomes a valuable feedback source, from chat logs and forums to surveys and Net Promoter Scores.
Under the hood, many of these solutions use basic categorization scripts; however, their algorithms are designed to interpret customer feedback text because that’s their sweet spot.
What are the downsides to software solutions for customer feedback analysis?
Quite literally, not all SaaS solutions are built the same. Some software solutions just do basic data interpretation using text analysis, and don’t consider the nuance of natural language.
When it comes to processing customer feedback at scale, it’s important to use the best tool for the job, and that’s where artificial intelligence comes into play.
How do you leverage artificial intelligence to analyze customer feedback
Artificial intelligence opens the window to your data’s soul. It’s more accurate than any other manual method and provides faster results. It does this by going beyond scripts and leveraging natural language processing algorithms to get to the heart of unfathomable amounts of text.
Whether your customers’ feedback are short and curt or they leave you extensive diatribes, artificial intelligence and machine learning can make it all meaningful and actionable.
Using machine learning and natural language processing, artificial intelligence can not only sort through thousands of data points, but can help you extract the real meaning from the text, provide clear analysis, and help you take action on the information.
Artificial intelligence solutions can help you find valuable insights in every interaction and identify nascent and recurring themes, track issues, and create visual reports to make it all make sense. Data is only as useful as it is understood, and if the information lives and dies with an analyst, it’s not helping anyone. Easy-to-understand dashboards make it all matter.
Solutions like Idiomatic’s leverage artificial intelligence and machine learning to help you understand your customers’ voices, are easy on resources, fast, free from internal confirmation biases, and easy to use and access.
Not sure if AI-driven feedback analysis is for you? Try our ROI calculator → to see how much you can save on manual data analysis.
What are the downsides to using artificial intelligence for customer feedback analysis?
There are two main concerns that businesses have when it comes to using SaaS solutions that leverage innovative technology like artificial intelligence.
The first is around data privacy and safety. Customer feedback and information are highly valuable, so your vendor’s security policies really matter in this arena. This concern can be handled by keeping an eye out for vendor security certifications and only sharing data, but not personally identifiable information.
Idiomatic values your privacy, maintaining SOC 2 Type 2 certification—a rigorous third-party audit of a vendor’s customer data controls. Our tool also avoids pulling fields that contain personally identifiable information such as names, email addresses, etc. We go a step further by using machine learning to remove any identifiable information that customers include in their feedback text.
The second concern for some organizations is cost. At the end of the day, you get what you pay for. Using an advanced system may come with a higher initial cost than setting up two spreadsheets and telling the intern analyst to get to business.
You have to ask yourself, however, if the labor and opportunity cost of having a person dedicated to categorizing data, and potentially miscategorizing and misunderstanding the information, is worth the delays in action and potential customer frustration as you miss the boat on taking their feedback to heart.
It is also worth noting that setting up our own in-house proprietary machine learning algorithms is a huge time and money cost. So unless you’re going to leverage the expertise and power of scale provided by a third-party solution, using artificial intelligence for customer feedback analysis isn’t feasible or sustainable.
4 steps to customer feedback analysis success
1. Make it easy for customers to provide feedback
Customer feedback is ultimately about the customer. Some companies will set up recurring Net Promoter Score surveys and sit back, content to watch the scoreboard because qualitative data is difficult to work with manually. But if you only consider survey responses, you’re working with a significant blind spot. Net Promoter Scores are helpful, but they don’t tell the whole story.
Make it easy for customers to provide feedback in many different ways, and make sure that it is always on, unfiltered, and open-ended. Some of the many ways to collect customer feedback include in-app surveys, app reviews, customer satisfaction scores, long form-based surveys, website feedback widgets, customer interviews, customer support channels, transactional emails, community boards, and website chatbots.
2. Collect customer feedback
Leave no stone unturned in your quest for actionable insights. Not every customer will feel compelled to tell you what they think of you directly, so it’s imperative to collect customer feedback from every possible source and include it in your dataset.
Pull in information from social media posts, app reviews, customer surveys, support requests, online forums, and product reviews. If a wheel is squeaking about you instead of at you, it should still get attention, and ultimately, grease.
3. Analyze the qualitative data
Use a SaaS solution that leverages machine learning and natural language processing to tell you what your customers are really saying. Instead of devoting countless hours to categorizing and re-categorizing based on rudimentary keyword extraction scripts, spend your time assessing trends and big picture insights.
4. Interpret, understand, and action.
When you use the right tool for the job, the data is presented to you in straightforward, intuitive dashboards that tell you what the problems are and how to fix them.
All that’s left for you to do is empower your product and marketing teams and provide actionable insights from your customers’ feedback and let them get to work.
Start analyzing customer feedback today
At the end of the day, data is only as valuable as it is understandable and actionable. Instead of letting your customer feedback and the potential it holds languish in spreadsheet purgatory, harness the power of artificial intelligence and machine learning to put your customer data to work by automating analysis to spur action.
Request a demo today with Idiomatic today to automate customer feedback analysis and unearth meaningful, actionable insights from your customer base.