For any brand, the bottom line is that you will need customers as long as you want to stay in business. So, a proper analysis of customer feedback is essential to a customer-centric business plan that will help you maintain current customers while gaining new ones. Customer feedback is not only to please the customers either. A robust analysis of customer feedback is as essential for a business as a navigational system is for a ship in the open sea. It not only guides you in your course but also prevents you from any imminent danger.
Ultimately, no matter if it’s good or bad, customer feedback is always beneficial. In fact, worse reviews may arguably be the most valuable ones since no one—yes, not even you—can judge your customer’s experiences better than your customers themselves. To prevent churn while improving customer experience, you must pay attention to your customer feedback.
Customer feedback is any information your customers provide regarding their experiences with your business. There are two main types of feedback from customers: direct and indirect.
Direct feedback is intentional and specific as companies design the inquiries to serve a particular goal. Surveys and complaint forms, for instance are the most common types of direct feedback. Businesses often request direct feedback from a predetermined group of customers through various mediums, which means the customer is conscious of the process.
Indirect feedback, on the other hand, is unprompted and occurs naturally. Businesses usually obtain indirect feedback by collecting data from social media, web analytics, and review platforms. Because this type of feedback is unsolicited and unsystematic, it is more honest yet less specific.
You can now use this data to customize your engagement with distinct segments of your customer base. By informing your product team about the opinions and needs of your target audience, you can create a product roadmap accordingly— or even develop an entirely new product! Thanks to feedback, you can optimize your customer journey. Even minor alterations to your website, UX, CX, purchase, and post-purchase processes could make a big difference.
Customer feedback can be the ultimate guidance when it comes to marketing and sales strategies since you would be learning about the customers’ priorities directly from the customer. What better way to show people you are listening, prevent churn, and retain customers? However, in this day and age, the different forms and amounts of data a business gathers may quickly become overwhelming and unmanageable. As a matter of fact, it is nearly impossible to do with mere human resources.
That’s where Machine Learning comes in!
Considering the amount of information people share online, sifting through raw customer feedback data to find helpful insights is humanly impossible in today’s world. Therefore, companies have been increasingly relying on the power of Artificial Intelligence—more specifically, Machine Learning. Analysis of customer feedback using machine learning relies on machine learning technologies like Natural Language Processing (NLP) and Named Entity Recognition (NER) to turn unstructured qualitative feedback of our everyday language into manageable data. Through a process called Text Classification, both NLP and NER work to make raw data more manageable for people and businesses who are looking to extract invaluable insights from online information. These technologies and processes are the perfect solutions to dealing with the exponentially increasing number of customer feedback opportunities.
This all may seem intimidating, but do not fret!
Customer feedback analysis using machine learning can be accessible even if you don't have the technical skills or coding knowledge. You can quickly get a customer feedback software like Kimola Cognitive to do the job for you with pre-built classifiers or a simple drag & drop.
If you’d like to see how a customer feedback analysis report sample works, you can find Kimola’s customer feedback samples and NLP datasets here! Now, let’s look at how to manage customer feedback analysis using NLP and NER.
The classification of data is one of the most complex parts. But it doesn't have to be! You can pick one of two ways: utilizing a pre-built classifier or creating a custom model.
As one of the best customer feedback SaaS, Kimola Cognitive has a gallery teeming with ready-to-use machine learning models for the most common cases of customer feedback analysis, such as Consumer Sentiment Classifier, Hate Speech Detection, and News Classifier. You can start discovering what your customers are saying about you by utilizing these machine learning models immediately once you sign into your Kimola Cognitive account.
The pre-existing models in the Kimola Cognitive Gallery may not be comprehensive enough for every business out there, in which case it may be more viable to create a custom machine learning model to analyze your customer feedback. With a custom ML model, you may choose the labels with which your material will be classified and continue to auto-classify new data.
Here is a step-by-step guide on how to create a custom machine learning model on Kimola Cognitive. You can also watch our Youtube video below on how to create a custom machine learning model with Kimola Cognitive.
Basically, the first step is to determine the “labels” to auto-classify texts with machine learning. Choosing the right labels for text classification is essential for successful qualitative research because the primary purpose of classification is to summarize the raw dataset according to relevant content. To find the correct labels you must know what your consumers are talking about. Look at the first 200 data and determine their labels. You can set a label for any category you can think of: Price, Customer Service, Stores, Design, and so on. Afterward, you need to collect the feedback into separate excel sheets according to their respective labels. You need around 500 feedback data for each label.
Next, you will drag and drop the Excel document—which comprises an average of 7-8 labels and 3500-4500 lines of classified customer feedback— into Kimola Cognitive’s system for initial machine learning training purposes. By doing this, you will essentially be creating a custom machine learning model tailored to your business needs. After creating the model, all you need to generate a continuous feedback loop is to keep the new customer feedback in Excel and automatically label your data with your custom model on Kimola Cognitive.
See how quick and easy it is to generate data sources for customer feedback with Kimola’s Airset Browser Extension! You can even acquire data from customer feedback from your Instagram posts.
Eventually, you can monitor what people have said about your business and use a monthly customer feedback analysis report to compare your mentions. Did the customer talk about your pricing, stores, or customer service? The possibilities are infinite and might be unexpected. For instance, in addition to learning what the customer thinks about your business, with customer feedback using NLP, you can select one of the customer feedback sentiment analysis models to observe how the customer feels.
As one of the most common text classification tool features, Kimola Cognitive has Sentiment Analysis to scan and analyze texts. Based on the information extracted through sentiment analysis, you can categorize your customer feedback as positive, negative, or neutral, as well as determine the writer's mood, emotions, and context. It can even detect sarcasm in advanced cases!
Take a look at how Kimola Cognitive conducts sentiment analysis for customer feedback!
Customers are the backbones of businesses. The happier you make them, the more likely they will return the favor. And, like in any relationship, listening is the key to reciprocal loyalty. Given the amount of free online customer feedback data and the easy-to-use customer feedback analysis software at your service, there is no excuse for you to miss out on all the insight you can gain from listening to what your customers are saying.
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