Feedback is crucial to continuous improvement. When an employee wants to be more effective at their job, they would benefit from knowing what they’re good at and where they need to improve. The same goes for your products. To consistently offer quality products, knowing what your customers think is key.
Customer reviews are a goldmine of valuable big data and insights that all retail organizations should tap into. However, analyzing these is not as simple as calculating the average of ratings on a 5-point scale. Most feedback consists of text, after all. And unstructured data calls for more advanced analytics methods.
Let’s talk about how you can extract value from customer reviews—whether it’s feedback left on an e-commerce site, a comment left on your social media page, or an email sent to your Customer Service team—through machine learning and text processing techniques.
How to analyze textual, unstructured data
It’s easy to infer what customers mean when they leave a 3-star review on your product, and even one text-based review is pretty straightforward and easy to understand. But what happens when you need to identify the general sentiment of your customers?
Customer feedback is usually text-based, so reviews are considered unstructured data and aren’t organized in a predefined manner. This means that in order to analyze reviews from multiple customers and various online and offline channels, you’ll need to adopt sophisticated analytical methods—especially if time is of the essence.
There are many different algorithms used in machine learning, but let’s talk about two kinds of models that are commonly used when it comes to text processing:
1. Text clustering
Using unsupervised learning, clustering groups in a set of similar information. A group is called a cluster, and each cluster has a class label that unifies all the data points assigned to it. The labels are not predefined. This model is great for grouping similar posts, tweets, or comments related to your product or service.
2. Text classification
Classification is the method of assigning predefined categories to new textual data, with the help of training data. This model is great for organizing and structuring information based on categories such as urgency, product type, or review topic.
While these techniques don’t always give a specific result in terms of customer sentiment analytics (positive, negative, or neutral), they’re extremely useful for getting valuable insight into your customers and purchasing trends.
Text analytics dashboard: 7 clusters created through Advanced Analytics, presented through word clouds.
For example, in an analysis done on customer reviews left on a certain e-commerce site during a specific time period, the seven clusters above were created. The first cluster covers perfume products, the second cluster covers nail polish products, the third cluster covers skincare products, and so on. The words you see in the visualizations refer to the prominent words in the cluster, arranged according to frequency.
Must-read: 4 ways Text and Sentiment Analysis can benefit your business
What kind of Advanced Analytics insights do you get?
By analyzing written customer reviews, you’ll get valuable and actionable insights for better decision-making in product development, customer service, marketing, and other enterprise areas.
In our e-commerce example alone, you can expect the following findings:
- Text clustering results – This will provide valuable retail trends to take note of based on the most frequent / prominent words used.
- The most and least bought items based on the number of reviews – This will help you identify what products to markdown or push and determine what kind of products to prioritize developing in the future.
- Customer with most frequent reviews – This will help you determine high-value customers to target and retain with promotional efforts and messages.
- Product with low/high ratings – This will give you insight into pressing quality issues and provide visibility into attractive and effective product features.
- Customer with low/high ratings – This will be useful for customer segmentation and targeted retail marketing efforts.
Related: Adapting to shoppers with predictive analytics
Growing your retail organization, one review at a time
Just as important as analysis is creating avenues for customers to tell you what they think. Creating a culture of feedback not only provides insight but can boost your reputation as well. In fact, according to a local consumer review survey by BrightLocal, customers read 10 reviews on average before they can fully trust a business.
Reviews also highly affect purchasing decisions. In the same survey, customer say that they take time—13 minutes and 35 seconds on average—to read reviews before they decide on a purchase. What’s more, the purchase likelihood for a product with 5 reviews is 270% greater compared to a product with no reviews.
Don’t forget to make sure that the reviews you do get are of high-quality. After all, only 53% of customers would consider buying or employing a business with reviews that are rated less than 4 stars.
Customer reviews are undeniably a great way to employ proactive customer care, provide better offerings, and create more effective sales and marketing campaigns. By providing opportunities for customer feedback and making use of the insights gained, your retail organization can only scale higher.