Knowing who your customers are and what drives them to buy your product or serviceare key to business growth and success.
Through customer analysis, companies can identify different groups of customers and the needs of those customers to help them build more personalized customer experiences, and stand out in a competitive marketplace.
Customer analysis is the process of analyzing customer data to extract insights and inform business decisions. Customer analysis uses market analysis techniques to understand existing users in order to reach new customers.
By analyzing all sorts of quantitative and qualitative data, brands can divide customers into groups based on shared characteristics, uncover customer pain points, and understand how their products or services solve customer needs. This allows businesses to create personalized experiences based on factors like buying behavior, gender, age, interest, and beyond.
Customer analysis is all about understanding the needs, satisfaction, and pain points of existing customers, so you can improve the customer experience (CX) of customers you already have and use predictive analysis to attract new ones.
When you consider that it’s 5 times more expensive to acquire new customers than retain your current ones, it makes sense that you need to need to find out why they may be leaving and focus on keeping them.
No matter how much time you’ve spent on product development and testing, you won’t really know how well your product or service works until it’s out in the real world. You need to understand how your business actually serves your customers with direct surveys and analysis of customer support interactions, social media data, and online reviews.
Understanding the voice of customer and implementing a customer feedback loop are key to always staying in touch with your customers and improving products and services.
Sometimes just letting your customers know that you’re listening to them can be a huge boon to business, especially when data tells us that 58% of customers would leave a company if they don’t get the support they expect. That’s why it’s always important to close the customer feedback loop by sending a message once a problem has been solved. This will let your customers know that they’ve been heard.
Customer analysis may seem like a daunting task, but there’s more customer data available than ever, and when you put the right processes and tools in place, you can be certain that you’re doing everything possible to truly understand your customers.
Follow this 5-step process for customer analysis and get the results you need for top-of-class, data-driven decisions.
The more you understand your customers and the nuances of demographics, the better you’ll be set up to complete the subsequent steps of customer analysis.
The 4 basic types of market segmentation are:
Demographics are all about who your customers are. The demographics you need to focus on will depend on what kind of business you have. Is your product or service generally occupation-specific or targeted to a certain age group? Different demographics include: income, age, gender, occupation, family status, and education.
Where do you customers live? And do you want to research new markets for potential customers?
What are the personalities of your customers? What is specific to their lifestyles? What are their values and beliefs? Psychographic segmentation can be quite subjective because it’s often about customers’ own perceptions of who they are. But it can offer a lot of insight because it’s often quite specific to individual products and markets.
This is where hard data, like sales figures, purchase patterns, and brand awareness come in. Behavioral data should be a fairly easy segmentation because you’re either already collecting it or you can use surveys and focus groups to uncover it.
When diving into customer analysis, data is usually just a few clicks away. This is where you want to focus on voice of customer (VoC) to understand your customers’ holistic view of your brand and your products or services.
You can pull product reviews from all over the web: Consumer Reports, TrustPilot, and ConsumerSearch, for general product reviews; TrustRadius, Capterra, and G2, for software; and TripAdvisor, Yelp, and Google My Business for hospitality.
You might also gather your social media data and send customer satisfaction surveys at different stages of the customer journey to learn about your customers’ preferences, opinions, and experiences.
Or explore customer data that’s already in your CRM, sales database, or with your website analytics.
What’s NOT working for your customers? Where do they have problems with your products or services?
Voice of customer analysis and customer feedback loop will allow you to follow the entire customer journey and find the pain points along the way.
With SaaS companies, for example, onboarding is often an issue. Maybe you just need to educate your customers more thoroughly or use FAQs and chatbots to handle their most frustrating issues.
CSAT and NPS surveys can be helpful here to ask the questions you need answered at any point on the customer journey – have them pop-up in your app or on your website after customer journey touchpoints, to find out what is causing customer pain.
When your customers are properly segmented and you’re collecting the data you need, you’ll have it covered.
Once you’ve followed the above steps, it’s time to create buyer personas that will guide your analysis. Different customers buy your products for different reasons, so you can create a number of personas based on buying trends and motivations.
Use the data to uncover patterns. What is the value each persona gets from your product or service?
This is where qualitative data from open-ended survey responses, product reviews, and social media responses can be particularly handy. Use quotes and opinions in your customers’ own words to help explain and define individual personas.
Uncovering buyer personas leads directly into market fit. What are the challenges each customer group is facing and how does your product or service help overcome them? If your company is solving the problems of some personas but not others, then the latter isn’t a good product-market fit.
You might analyze product reviews, mentions on social media, and open-ended survey responses to learn what customers like and dislike about your product. Maybe your customer service is amazing, but users feel your mobile app is hard to navigate.
Using machine learning tools can help you identify prospective customers, in email responses to your sales or marketing campaigns. Then, you can use this data to analyze your conversion rates and detect upsell opportunities. Companies like SugarCRM have already implemented AI to gain predictive customer intelligence, so they can predict when a customer is likely to convert or leave.
AI-guided machine learning text analysis tools, like MonkeyLearn not only make customer analysis a lot easier, but also help you gain insights about your customers.
With MonkeyLearn Studio, you can easily build custom text analysis tools to analyze your customer data, and visualize your customer analytics in a striking dashboard, like the one below:
Performing a thorough customer analysis is one of the most useful things you can do for your business, and it will help in myriad ways. Here’s how to put it to work across your company:
If you know who your customers are and which channel of communication they use most often, you can reach out to them in a more effective way, lowering customer acquisition costs. By targeting customers that you know will be more interested in your products (and, therefore, more likely to buy them) you can deliver messages that resonate with them.
Use customer feedback to find out what works for your customers and what your product might be missing. You may even discover new use cases and new features that you’d never considered based solely on customer feedback. Finally, prioritize your product roadmap based on your customer analysis by detecting which features or updates customers mention most often – perhaps there are features from a select group of customers that you want to prioritize.
Knowing what drives customer satisfaction in each segment and what makes customers churn can help you design data-driven strategies to boost customer retention and brand loyalty.
By segmenting customers, you can identify which group provides a higher return for your company, allowing you to focus more on nurturing those customers.
Once you understand who your best customers are and what the ideal product-market fit is, you’ll know which are the ideal customers to target in the future. And, by following customer feedback from all over the web, you’ll know exactly where to find them.
Customer analysis gives you valuable information to drive your business forward. By identifying customer segments, and understanding the needs of different customers, you’ll be able to create meaningful experiences and attract the right clients.
Knowing what influences your customers’ buying decisions can offer you valuable insights to boost sales. Also, keeping an eye on trends and understanding customer needs can help you come up with new products to serve your customers better.
Text analysis tools can help you perform customer analysis on your text data in a fast, reliable, and accurate way. Schedule a demo with MonkeyLearn and we’ll show you how to better understand your customers.
September 9th, 2020