Customer feedback analysis is used to measure and analyze customer reviews and feedback to improve product performance and customer support, and reduce churn.
Most businesses have established some form of feedback data collection. While this is a step ahead in capturing customer behavior for product improvement, this alone will not be effective in taking decisions based on the data. Even worse, the data collection often remains unstructured, so that information does not make sense during the analysis phase. This type of collection creates bottlenecks in data selection and interpretation, massively reducing the effectiveness of feedback analysis.
Why Should You Collect and Analyze Customer Feedback Data?
is undoubtedly the only real feedback that can be collected about the customer’s experience with your app or service. This is because any internal analysis, without actual user input, will fail to avoid some degree of variation or bias. It could simply be due to the difference in the method of data collection, the intent, the environment, or any other factor which can influence the output of the analysis.
Businesses have a need to understand the unique value that they bring to customers, and that makes them stand apart from the competition. Even if you were a unicorn in your product space, customer feedback helps you find the perfect ground to breed further innovation and facilitate expansion.
The concept of feedback collection and analysis is also embedded in helping customers find their voice and in letting their opinions be heard. Therefore, voice of the customer
programs have become an important faculty for making product developments match customer expectations.
Customer feedback analysis in the past was a complicated task, involving several teams to bring together data, select the relevant data points, and then create an analysis, all involving different tools and techniques. Needless to say, this is a cumbersome process that takes weeks or months. However, product analytics tools have simplified all the steps involved within a single tool, where you can even configure new changes
to the app on the fly.
The major perks of using product analytics for customer feedback analysis are:
Accurate interpretation of product impact on customer segments:
Product analytics apps have user segmentation
properties that help you understand where and from whom a feedback comes. Feedback and reviews have become excellent indicators of whether customer cohorts can be retained over a long period or if they are on the verge of slipping away
Analyzing feedback through customer segmentation will enable better targeting of customers. An example for that would be when a segment of high-value customers are rewarded for making purchases to improve their ratings for your product. Those loyalty points and rewards might make those customers stay longer with your business.
Customer segmentation will also help you understand the scope of the changes that you need to implement based on the feedback analysis. Sometimes, you may need to implement changes only for some sets of users. At other times however, the feedback prompts you to make a change globally, across your app. These may be region-based changes, value-based changes, behavior-based changes, etc.
Ultimately, segmenting user feedback helps you understand how particular sections of users respond to your product and the changes you bring to it.
Evaluation of customer lifetime value:
A study by Bain and Co.
found that a 5% increase in customer retention rates increased profits by 25% to 95%. This means that using feedback analysis to evaluate and increase customer lifetime value will pay off for businesses in the long term. Besides, customer strategy
think tank organizations also say that the cost of retaining existing customers is much higher than the cost of acquiring new customers.
Improvement to the user experience: Perceiving the fluctuation in user feedback and sentiment allows product managers to make decisions for change. For instance, an overall negative feedback can prompt businesses to change the tone of their content. Users can also report defects in the product through reviews that were previously not brought to the attention of the quality assurance team. Thus, feedback enables quick fixes that help make applications more stable.
But how do you collect feedback data in ways that will contribute to easier feedback analysis and a better product decision-making strategy? While there are many feedback data collection techniques that exist in today’s market, some of them are more effective and goal-oriented. They help you to apply dynamic and diligent processes to track how user feedback affects business outcomes.
There are three major ways in which you can gather customer feedback for product analytics.
They are - general surveys, NPS® for customized feedback, and ratings.
are direct and elaborate prompts aimed to achieve the most detailed insight into customer thoughts, emotions, criticisms, and suggestions. Depending on the frequency of interaction and/or the quantity of service delivered, you may draft different survey forms for customers. It’s therefore a good practice to make different versions of feedback forms – long or short, surface-level or qualitative - for different stages of the customer sales funnel and customer journey.
Since surveys are good at capturing a good chunk of information from users, you can pose multiple questions. For instance, your survey could be contained within a freeform template or span across multiple screens where users can go through questions one at a time.
On top of it, informing customers about how much time they could be spending in taking the survey gives them a clear idea about when and how they wish to take it. It helps in encouraging a higher number of responses from the general customer base.
Customers that are at a later and more advanced stage of product interaction may be more willing to give your product a more in-depth review. Meanwhile, first-time and new users would prefer to withhold giving proper reviews until they are more familiar with your product. So, you should aim to send requests for surveys at the right time in a customer’s conversion journey
or Net Promoter Scores can measure customer loyalty by asking the simple question, “How likely are you to recommend this product to your friends or family?”.
It’s become a popular feedback method owing to its ability to most accurately portray the true impact of a product on customers. Taken on a scale from 1 to 10, every customer rating will contribute to the overall score. To get an accurate score, customers are categorized into three groups - detractors, passives, and promoters.
Those who give a rating from 1 to 6 are considered detractors and not likely to speak positively about your product. On the other hand, those who give scores of 9 or 10 are promoters who will give you positive recommendations. And the rest are passives who don’t count toward our evaluation.
The formula for calculating NPS® is as follows:
NPS® = %Promoters - %Detractors
Ratings from NPS® are closely tied to how customers psychologically evaluate a product’s true worth as it affects their social standing, whether real or perceived. Customers, in general, care about the impact of a rating which might affect their friends and family, which makes them give a more truthful review.
An NPS® question can be presented to customers after a checkout, a customer support call, toward the end of a content delivery, or any other forms of successful customer service. If you are an e-commerce venture, every in-store experience can lead to a request for an NPS® rating. If you are a medical facility that serves customers online, you can request feedback after a visit to the clinic or an online consultation.
is a good indicator of customer loyalty
. According to Harvard Business Review, ‘Loyalty leaders grow revenues roughly 2.5x
as fast as other companies in their industries'.
NPS® is more effective than surveys in some cases due to the brevity of time demanded from the customer. But they are also more telling than ratings, which is our next method of collecting customer feedback.
are the simplest form of customer feedback that allows users to rate your product on a custom scale. The scales may be from 1 to 5, 1 to 10, emoji-based, or starred ratings. The simpler the UI for ratings, the easier it is for customers to leave a review.
A simple rating screen should ideally contain an easy query and an attention-grabbing interface. Users are also more likely to provide ratings if the prompt is limited to a single screen.
Ratings are most effective in gathering quick feedback from users, such as in the case of a customer reaching out to solve a problem with the customer support team, or for when a user is given a new suggestion about the type of content (e.g., a video, seminar, course) that they might be interested in.
They are also the most convenient form of user feedback collection as they don’t require customers to think further than their most recent interaction or spend more than a few seconds to form an opinion.
However, analysis of ratings can easily become confusing as they are the most frequent and diverse form of feedback collection. You can gather ratings from many instances of user interactions, and they can answer several queries ranging from quality, ease of use (CES), speed of resolution, etc. Some ratings may be influenced by the customer’s perception of your entire brand, while some may be based on just their current experience. Therefore, you can separate user ratings based on whether they apply to the entire product performance or whether they only apply to how a service was rendered.
Critical Success Factors (CSFs) to Measure Customer Feedback Success
CSFs are related to Key Result Areas (KRAs), which help you to identify whether customer feedback collection and analysis are paying off. Feedback analysis from product analytics dashboards should be a catalyst for continuous changes in the product marketing and development front. And measuring these effects of the changes appropriately matters according to where and how these changes occur and through what periods.
When you select the CSFs that you would like to measure, the goal should be to choose those factors which impact the entire organization. Simply looking at minor changes in your app or product will create a false sense of significance in your analysis. It can then undercut the important role that customer feedback plays in your product’s performance.
Hence, some of the most effective metrics for feedback measurement are:
Number of new users added within a month after implementing a highly requested product feature.
Rate at which customers churned during the 3 months in which your team proactively acted upon critical customer issues.
Percentage increase in revenue in the past year as a result of taking decisions based on actionable insights from your product analytics platform.
Laying down simple success metrics of measurement earlier in the process of customer feedback analysis will point out the biggest impacts that product analytics tools bring to your business.
Another great way to reap the benefits of customer feedback is to tie in other features of your product analytics tool, such as event paths
, user segmentation
, and source metric trackers
. In this way, you can increase the effectiveness of your product analytics in a manner that can influence customer-centric product development. There are numerous benefits to using product analytics features together that can eventually help in boosting your customer success.
Interested to learn more about product analytics features? Keep looking out for Countly articles on our blog
for more effective ways to use product analytics.