A customer-centric approach sets you on a path for business success. But to truly make customers the heart of everything you do, you need to clearly identify their needs and expectations.
Analyzing what motivates your customers to buy your product or service is key to enhance the customer experience. It can also lead to innovation and reveal gaps in the competition, that you can exploit and improve upon.
And, of course, to get to know your customers, you have to listen to them. There are many forms of customer feedback you can learn from, including surveys, product reviews, and social media posts.
In this article, we’ll review the basics of a customer needs analysis and show you how you can make the most of your customer insights using out-of-the-box machine learning tools:
Customer needs analysis is the process of identifying what motivates a customer to buy products or services. Knowing which features, attributes, and benefits are relevant to customers can help businesses adapt their product development and marketing strategies.
Doing a customer needs analysis involves drawing insights from a variety of customer data, like surveys, social media posts, product reviews, and more.
When you look into customer feedback, you can detect unmet needs and opportunities for improvement and innovation. Maybe your customers suggest a new feature that you can add to your product roadmap. Or perhaps they point out some flaws in your customer service that you need to fix.
Understanding what customers need allows you to deliver a great customer experience at every step of the buyer journey. Also, knowing what’s important to them can help you rethink the way you market your products, and focus on particular customer pain points.
Customer needs are what drive purchase decisions. When customers buy a product or service, they are trying to solve a problem. They have certain expectations, requirements, and specific things that are important to them.
Let’s take a look at some of the most common customer needs:
Price: customers want to buy products or services that adjust to their budget. They expect the price to directly reflect the value and quality of the product. But a product’s value is also determined by intangible factors, like a brand’s perceived worth and the overall customer experience. In fact, 86% of customers would pay more if that means they will get a better customer experience.
Functionality: the product or service should be able to solve a customer’s problem. There are certain features or characteristics that customers expect from a product. For example, a customer buying a gaming PC will probably look for a fast central processing unit (CPU), a powerful graphic card, and lots of storage space.
Usability: this is related to the way a product is designed and the experience it provides to the customer. Users tend to prefer products with a short learning curve, that are easy to use, and help them accomplish tasks in a seamless way. For instance, customers expect a mobile app to be intuitive, error-free, and easy to navigate.
Reliability: the product has to be able to perform its function consistently over time, without failure. Reliability can be also defined as “quality over time.” A company that makes reliable products will have a positive reputation and high levels of customer satisfaction.
Support: customers crave good customer service. They expect to reach out to customer support through the channel of their choosing to get fast, personalized responses; and be treated with empathy. When it comes to buying a SaaS solution, a poor customer experience relates directly to customer churn.
Security: when they interact through digital channels, customers want to ensure that their transactions are safe, their personal data is protected, and their information will not get lost. Getting an error message after entering your credit card information on an e-commerce site, for example, would definitely turn customers away.
Effectiveness: customers buy a product or service because they want to solve a problem. So, their main interest is buying something that actually does the job it advertises.
A customer needs analysis should provide insight into your customer’s pain points and challenges. It can inform all of your internal teams ‒ from sales and marketing to customer support ‒ to create data-driven strategies to improve your business.
Here’s how to do a customer needs analysis in 5 steps:
Engage with your customers and ask them for feedback. Depending on the type of business you have, choose the best channel to send surveys, whether it’s via email, phone, SMS, or website pop-ups.
Take time to carefully plan your survey, and make sure to include open-ended questions so your customers can go into more detail. Online survey tools, like SurveyMonkey, Typeform, and Google Forms make creating and sending surveys easy.
You can also collect unsolicited customer feedback, like social media posts mentioning your brand, online product reviews, and more. The opinions that customers spontaneously share online are key to learn how they think and feel about your brand.
Finally, you can gather data from customer emails and chats with your customer support reps or from CRM platforms, like Zendesk and Freshdesk.
So, you’ve collected customer feedback. Now, you have this huge amount of unstructured data – all the free text data containing your customers’ opinions – that you’ll need to organize. Sorting all this data comes down to two options:
Manually tagging feedback is time-consuming, tedious, and prone to error and inaccuracies. Machine learning tools, on the other hand, provide fast and consistent results no matter the size of your dataset.
MonkeyLearn is a machine learning platform that can help you analyze customer feedback to uncover customer pain points, previously unknown customer needs, and missing points on the customer journey. MonkeyLearn also comes with pre-trained models that are ready to use, like this free survey analyzer that classifies NPS responses by topics, Customer Support, Ease of Use, Features, and Pricing.
But to get the most our of your customer data, you should consider building your own classification models through a user-friendly, no-code interface, often in just a few minutes.
Creating a custom tool means you can classify feedback according to the categories and criteria that you consider relevant to your business and goals. Then, you can train your model to identify if a piece of feedback relates to a customer need like “price”, “functionality”, or “customer support”, and add the proper tag. Once your model is smart enough, you can easily integrate it with your favorite tools.
Read this tutorial to learn how to build a custom classifier with MonkeyLearn.
Sorting your data into categories gives you an overview of the main topics that appear in customer feedback. For example, you may learn that a large part of your customers make comments about your product’s features. But how do you know how they actually feel about this topic?
Power up your analysis with advanced techniques that will give you even further insight into your customer’s needs, such as:
Here’s an example of how a keyword extraction tool can help you understand customer feedback:
Data visualization is essential when it comes to sharing the findings of your customer needs analysis with your internal teams and stakeholders.
With MonkeyLearn Studio, you can create visually impactful dashboards and charts that will take your analysis to the next level. Visualization tools make your data easy to understand at first glance, but also help you spot trends and patterns.
Here’s an example of how you can organize customer feedback ‒ in this case, customer reviews about Zoom ‒ in an insightful dashboard:
You can show each category by sentiment, see how sentiment evolves over time, visualize relevant keywords in a word cloud, and more.
A customer needs analysis examines if your business is meeting customer needs and shows opportunities to improve your product or service.
The results of your analysis should drive your internal teams in the right direction, by providing solid evidence for them to make informed decisions.
Maybe you find out that customers are not using your SaaS product to the fullest, because they find some of your features too confusing. Your product team can use this data to create a better user experience and improve product usage. Also, your sales team can offer new customers a demo, so they can learn more about the product and how it can help them.
A customer needs analysis sheds light on some fundamental questions:
By analyzing customer feedback, such as survey responses or product reviews, you can learn what customers expect from your brand and what you need to improve to stay on top of their requests and requirements.
With the help of machine learning tools, you can make sense of qualitative data in a fast and cost-effective way. MonkeyLearn Studio provides an all-in-one solution that allows you to analyze text using no-code tools and create striking visualizations to share with your internal teams.
December 10th, 2020