Utilizing natural language processing to improve product experience
Customers first they say, right? Listening to their opinion can help you improve your products and ensures you provide an exceptional experience.
But what is Product Experience exactly?
The product experience is part of the broader user experience and focuses on the time when a customer is using a specific product, from beginning to end.
It starts from the first time log in until they end using the application. Everything in between contributes to the product experience.
Nowadays, to provide an intuitive product experience is more important than ever. A poor experience will chase users away or make them stop using again a product. Meanwhile, a good experience increases usage, builds loyalty, and generates a positive word of mouth.
How to improve the Product Experience?
Understanding the experience gives you actionable insights to improve customer retention. So understanding the customers’ needs is key to keeping them happy.
But how do you run a product experience analysis?
Product teams can use analytics to identify the reasons behind specific user actions.
Which behavior leads to using a particular feature? What results in a service cancellation? You can monitor how long it takes customers to perform certain tasks, find out when and where users struggle to navigate through an app or a website.
Along with quantitative data, you need to seek qualitative data. Survey tools make it easy to create surveys to gain feedback. You can also download customer data from chatbots and emails or from Customer Relationship Management systems. Internal customer-facing teams represent a mine for insights.
On top, social listening allows you to collect product feedback from social media, online reviews, forums.
Collecting qualitative data should be ongoing, but this may pose a real challenge as it may be time-consuming to run analysis.
Well, that’s where Natural Language Processing comes into the game.
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language.
To start with NLP, you do not need to have very high technical skills. On the market there are various no-code/low-code tools available that can help you analyze and derive insights from feedback. Such tools mimic the human ability to comprehend texts and extract meaningful information. Once you feed your data into the tool, tag and train your model, the tool will work to understand the data.
What can you learn about your product with NLP?
- What are the customer pain points?
- What do customers need and expect?
- What makes a product unique?
- Why are customers using the product?
- How are customers using the product?
Sentiment Analysis
Sentiment analysis is the process of detecting positive, neutral or negative sentiment in a text. Automatically analyzing customer feedback allows companies to learn what makes customers happy or unhappy so that they can tailor products and services to meet customers’ needs. This is a first way to gain insights and feeling on products.
Word Cloud
While a sentiment score can show either a positive or negative feedback, a word cloud can help analyze which are the actual words used. Developing a word cloud can give a better understanding of feedback themes or topics. For example, you can discover the specific words that commonly convey positive feedback around your product.
Topics Extraction (or Analysis)
Topic extraction from text is a technique that organizes and understands text data by assigning “categories” according to each individual topic or themes. As a user, you can specify topics you expect the machine to extract, such as specific features, designs, price, etc.
Extracting topics implies the machine knows the topics before starting the analysis, therefore, you need to tag your data in order to train a model. Even if this involves time investment with this extra step, the strategy pays off in the long term.
Conclusion
Through this article, we discussed the value of customer feedback for building cool products and how Natural Language Processing can help you analyze feedback fast so that you can focus on your product development. Looking forward to your feedback!
Do you want to know about another NLP Use Case? Check out the next article on Chatbots use cases here!
Useful links: https://hyperight.com/how-nlp-is-used-for-better-customer-experience/, https://www.uxlift.org/topics/nlp-guide-for-product-and-user-experience-simply-explained/
