Who does a business serve? Its CEO, its investors, or its customers?
When looking at our year-on-year performance, it makes sense to look through the lens of the people whose opinions matter most: the customers.
Typically, performance is measured financially. But while your finances can be a decent indication of performance, the reality is more nuanced. There can be many things that skew your financial data. For example, your business investments may have long-term profits that aren’t yet reflected in your finances, or a new competitor has entered the market that has affected your usual growth. Though they’re easy to measure, financial metrics don’t always directly reflect your performance.
First and foremost, your performance relates to how well you serve your customers. So their opinion on your business is vital to measuring performance and getting key insights on how to improve for the year ahead.
In this blog, we look at how and where to gather customer feedback, and how to perform deep analysis on your data so that you can get useful and detailed insights into your business performance.
Gathering Customer Feedback
Where can you get customer feedback? One traditional method is to run customer surveys. Online customer surveys are easy to set up with platforms such as SurveyMonkey and Typeform. You can target participants online through your website or your email subscription list.
One of the main benefits of customer surveys is that you have control over what questions are asked of your customers. If you have a specific topic you’d like to explore then surveys are a great option.
However, there are several issues with gathering customer feedback in this way.
- You may miss out on key insights that aren’t covered in your questions.
- The feedback is not organically generated. In order to respond to all of the questions, participants may have to form opinions on the spot which might not accurately reflect their experience.
- Surveys are subject to many biases including acquiescence bias – where participants tend to agree with the questions, or question order bias – where participants may react differently depending on where the question occurs in the survey.
Online reviews are a great way to source customer feedback data, and star ratings are just the tip of the iceberg. The most useful information is in the written review itself.
Unlike customer surveys, written reviews are open-ended and organically generated, meaning that customers can talk openly about their experiences without leading questions.
As we will discuss later, it’s now much easier to perform an in-depth analysis of review site data, thanks to advances in AI.
Social media posts and comments
Social media channels provide another source of organically produced customer content. For over a decade now, people have been turning to Twitter and Facebook to air their grievances and share positive experiences about companies online.
Like review site content, social media data is largely in the form of unstructured written text that until recently has been difficult to accurately measure and analyse.
If you’ve been systematically logging customer interactions into a CRM database, it’s a goldmine of useful content for customer feedback analysis. Qualitative CRM data, for example from email interactions and chatbot transcripts, can help to inform you of the motivations, attitudes and behaviours that relate to customers’ buying decisions.
Customer Feedback Analysis: More Than A Number
If a customer gives you a three-star review, you have questions. There’s no way that a fixed number is enough information to measure and analyse your business performance.
As analysts, numbers are attractive. They’re easy to tabulate and easy to compare. Numbers are a useful starting point, but they don’t tell the full story.
On the flipside, manually processing all available written data is prohibitively time consuming, if not impossible given the amount of big data that is turned over on an hourly basis. Reading a few reviews and comments can give you a snapshot of your customers’ experiences, but it’s difficult to know how representative these reviews are.
Thankfully, advancements in AI technology make large-scale customer feedback analysis not only possible but easy.
Symanto’s natural language processing (NLP) technology converts unstructured written data into metrics that can be easily analysed and compared.
Symanto technology empowers you to:
- Identify the main topics driving consumer behaviour.
- Delve deeper to explore how these topics break down into subtopics. For example, when people talk about the interface of your product, are they referring to its features, its usability or its layout?
- Explore the sentiment of each to reveal customer pain points and the successes of your product.
- Find out which words are most mentioned and whether they have a positive, negative or neutral sentiment.
- Read words in context to find out exactly what customers are saying.