Knowing who your customers are, what drives them to buy your product or service – and what motivates them to come back time and time again – are key to business growth and success.
Through customer analysis, companies can identify target customers, anticipate different customer's needs, build engaging and more personalized customer experiences, and stand out in a competitive marketplace.
To be truly competitive, however, you need to be fast at detecting trends and patterns in customer data, whether looking at customer demographics or real-time conversations on social media platforms.
Read on to find out how to perform customer analysis and grow your business.
Customer analysis is the process of identifying your target customers, detecting their pain points, and examining how your product or service solves their needs.
By analyzing all sorts of quantitative and qualitative data, brands can divide customers into groups based on shared characteristics. Allowing businesses to create personalized experiences based on factors like buying behavior, gender, age, interest, and beyond.
Overall, customer analysis can help you:
Customer analysis can be used in marketing plans or to improve your overall business strategy. Either way, you’ll need to go through the following steps when performing customer analysis:
The first step in customer analysis is to collect data from multiple sources that help you define characteristics of existing customers. Then, you’ll need to analyze this data to identify segments, that is, sub-groups of customers with shared attributes.
Here are some ideas for you to start gathering and making sense of customer data:
Explore customer data that’s already in your CRM, sales database, or delve into website analytics, for example:
Segmenting your customers can help you adapt the tone and content of your messaging, and create more personalized and effective marketing campaigns, rather than following a ‘one size fits all’ approach.
Finally, discover which channels you should prioritize to communicate with existing and potential customers and understand exactly how customers in different segments use your product.
Now that you have a clear customer profile, it’s time to draw out their pain points. What problems are they trying to solve with your product?
The way you present your business must be entirely aligned with the specific needs of your customers. But, how can you learn what’s important to them?
Analyze customer feedback (such as social media posts or survey responses) to identify which aspects of your business are mentioned most often by your customers. Creating word clouds from your data can be a great starting point, as they provide a quick overview of frequent words and expressions.
For a more advanced analysis of your customers’ needs, you could perform topic analysis, a text analysis technique that automatically sorts customer feedback by topic. It allows you to see what customers mention most often: Ease of Use, Customer Service, Pricing, etc.
For even more granular insights, you could extract keywords from customer feedback once it has been sorted by topic. This way you can really drill down into your analysis to discover new feature recommendations, frequent queries, and any other topics (or customer needs) that you may not have considered.
The last step in customer analysis consists of assessing how your product is helping customers solve their problems.
By using text analysis with machine learning, you can automatically sort topics customers mention into positive or negative, and pinpoint your strengths and weaknesses.
You might analyze product reviews, mentions on social media, and 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.
Another source of insight comes from analyzing customer intent.
Again, 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.
Text analysis tools 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:
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.
Text analysis tools can help you perform customer analysis on your text data in a fast, reliable, and accurate way. Explore MonkeyLearn’s demo to discover how you can better understand your customers.
September 9th, 2020