Feedback analysis helps identify customer frustrations, so you can improve their experience and retain customers. Latest developments in customer feedback analysis automate the process using artificial intelligence to facilitate analytics of large volumes of data in a timely way.
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Customer feedback can come from many different channels. Whether it's NPS or CSAT survey scores, social media comments and direct messages, or data logs from email, live chat and phone customer service.
The most powerful customer feedback is qualitative. For example, open-ended answers to questions in a survey or free-text from customer support conversations. However, at scale, qualitative feedback is hard to measure. Qualitative text needs to be turned into a quantitative measure so the severity of each mentioned problem can be assessed.
Imagine for a moment that your customer service team has 300 customer support conversations every day. Over 30 days, that's 9,000 conversations which are rich with customer feedback. The task of analysing 9,000 conversations by hand could take weeks—each conversation would need to be analyzed and tagged with a topic and sentiment. By that time, perhaps your customers have a different set of problems, making the analysis out of date and leaving proactive solutions on the table.
In this article, we'll address the answers to this problem and all your customer feedback analysis issues, like: Why is undertaking customer feedback analysis so important? How can you ensure the results of customer feedback analysis are actionable and unbiased? What tools and methodologies can you use to do customer feedback analysis? What use cases and case studies are there to learn from?
Customer feedback analysis is the process by which algorithms automatically make sense of large volumes of customer feedback and categorise it by topic and sentiment.
Most companies have large quantities of unstructured text data thanks to NPS surveys, customer reviews, and customer service live chats.
The insights uncovered by a sentiment analysis of this data is extremely valuable. Yet, making sense of large volumes of text is no easy feat for a human being.
There's a whole host of problems with customer feedback analytics done by a person—the main one being speed. A human can only do an analysis of so many reviews or live chats, usually meaning a potentially non-representative sample is taken.
Furthermore, a person typically has a the ability to do an analysis once a week, meaning they don't have a real-time understanding of customer complaints that can help them to proactively solve unusual source of customer issues.
Text analytics (read our complete guide to text analytics here) on the other hand, removes the need for human-powered analysis. Sentiment analysis, a subset of text analytics, categorises this text as positive, negative, or neutral and can even do 100,000s reviews in minutes. Whereas NLP can turn unstructured text into quantifiable data—for example, the frequency of reviews concerning the topic of late deliveries or broken packaging.
This kind of customer knowledge plays a vital role in business decision-making. Allowing firms to be responsive to their customer's needs and therefore reduce customer churn and remain competitive.
Customer feedback is the secret behind the success of companies like Amazon, Apple and Google. They listen intently to customer feedback to stay on top of changing consumer needs and to continuously improve existing products to retain happy customers.
With customer feedback, you can learn what your customers like and don’t like about their experience with you. If you know your customer's pain points with your product or service, you can tweak and improve them so they better serve your customers. Continuously acting on feedback like this means your customers won't start to look elsewhere for a company to fulfil their needs, which would lose you revenue. For example, in our research into Trustpilot reviews for a number of leading companies we discovered that products arriving damaged was creating angry customers who decided to shop online elsewhere in the future.
NPS is one of the most common ways to collect customer feedback. Understanding the drivers behind positive and negative NPS at scale is a powerful use case for customer feedback sentiment analytics. Although NPS surveys are now considered a biased form of feedback.
Reviews are a core driver of sales, as most customers look there before making a purchase. This makes understanding the reasons behind negative reviews even more important for business growth. A great example of a company taking advantage of their reviews is British Airways Holidays , who use sentiment analytics to proactively prevent customer complaints. Reviews are a more frequent form of feedback and real-time customer feedback analysis enables quick response-time.
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Support conversations are the most useful data source for customer feedback. Unlike reviews and surveys, the customer is likely in the midst of a problem so their memory is fresh. They are usually unbiased, because the customer is not aware their conversation will later be tagged and analysed. Customer conversation analytics is the latest use case for NLP and AI analytics.
In times of intense competition in customer service, a strong focus on turning customer feedback into product and operational transformation is needed to stay ahead. Customer feedback analysis done using NLP will provide you with immediate benefits, including a deep understanding of customer issues, a clear 'to do' list for your teams to prioritise, and a measure of performance over time.
The SentiSum team recently undertook a customer feedback sentiment analysis for two major industry verticals. Take a look below to see customer feedback analysis in action on reviews and you'll get a hint of what's possible when brands use this technology on customer support conversations.
How do you currently use SentiSum's customer feedback analytics platform?
We use SentiSum to actively listen to customer feedback so that we can take timely, objective action on any friction in the journey.
What's been the biggest impact on the way you work as a team?
We have saved hours of manual sorting of free customer text and achieved an objectivity which eluded us.
What's has been the biggest impact for your company-customer relationship?
We now have a clearer impact "to do" list for the Web Optimisation team in their efforts to reduce customer friction.
In what way has SentiSum been useful to you personally?
I get to quickly "take the temperature" of significant numbers of customers as I review trade, better understanding patterns over time which will lead the team to taking action on customer friction. Less friction means more sales.
There are numerous tools that can help you analyse customer feedback. Whether it's a holistic point of view of all your customer feedback channels or a specific sample of reviews that you want to tag, there's a tool out there for you.
A great place to start your search is on G2, a software review search engine. Insight Platforms also a dedicated environment for analytics software. For example, you can find a video of the SentiSum product demo on the Insight Platforms site.
SentiSum gives a holistic, real-time view of customer feedback channels, including customer reviews, NPS surveys and support conversations. Every data point is tagged automatically, providing insights on volume, topics and sentiment. SentiSum is a powerful method of creating customer-centric change. See pricing here.
Qualtrics is an SAP owned enterprise software that enables organisations to collect feedback at every stage of the customer journey. They enable enterprises to uncover trends and drive customer loyalty across multiple channels.
Chorus captures and analyses all customer calls, meetings, and emails to identify top performers and uncover insights that could be used as testimonials.
IBM offers many APIs for sentiment analysis based on NLP. The Watson Tone Analyzer, for example, which focuses on support tickets and satisfaction surveys and monitors agent sentiment – whether they’re polite and eager to help, and if they truly solved the customer’s issue.
Rosette has an API that uses AI to analyse natural language. They've branched out from social media to analyse entire documents, for example, the sentiment expressed by customers when they mention a specific product, company, or person.
32% of customers will now walk away from a brand they love after just one bad experience. This makes for a precarious environment for ecommerce brands. With the pandemic and other world shaking events, companies are left with an increased chance of negative sentiment at each touchpoint. In light of this, delivery, digital experience and the post-purchase brand experience have never been more important.
To stay on top of customer touchpoints, negate negative customer experience and inspire loyalty, there's three features we focus on to help you remain competitive.
Truly customer-centric change is achieved by putting actionable insights in the hands of the right team. We help you counter data silos and encourage cross-functional change by adding anyone to a daily email full of customer insight.
Knowing what impacts customers most, matters. Thanks to our sentiment score, you can see which topics are the largest contributors to negative sentiment. Accurate support ticket tagging also provides volume data, so you can quickly identify the biggest causes of complaints.
The speed of AI topic tagging enables real-time alerts. If there has been a sudden increase in a new topic, you'll know about it. Meaning you can work proactively with teams across your company to prevent the same problem happening to more customers.
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