The success and profitability of your company depends on how well you understand your customers’ wants, needs, and motivations. The customer experience you provide must be at the center of every business decision you make. A well-thought out, well-executed customer data strategy is an essential part of this.
Customer data allows you to understand your customer better. This understanding means you can offer them products and/or services that fit their needs, creating a customer-centric organization.
Because of that, it’s essential that you know what customer data you want to collect, why you are collecting it and what to do with it.
Here we’ll get into exactly what customer data is, go through the main customer data types, outline how best to collect it, then show you best practices for analyzing your data to improve your customer experience.
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Customer data is any information that you have about your customer. This can range from simple information like their name, to detailed information regarding their purchasing habits or how they use your product and/or service.
Customer data can be collected in a number of ways. These include, but are not limited to: direct survey feedback, web analytics, and transactional information.
Customer data helps you run your business in a customer-centric manner. This way, the business decisions you make lead to positive outcomes for your customers and repeat business for your company.
Research performed by Gallup showed that companies who apply customer behavioural data outperform their peers by 85% in sales growth and more than 25% in gross margin.
When you have the right data about your customers, you know what they like and dislike. From there, it's easier to offer them new products and services that they are likely to want or need. It also lets you know where your customer experience needs to be improved.
There are a number of different kinds of data you can collect, each telling you something different about your customer. Let’s dive deeper into the different types:
This is your customer data starting block and encompasses any kind of basic data relating to your customer’s identity.
Personal data includes:
Descriptive data goes beyond just personal data and covers more in-depth insight regarding your customer and their lifestyle.
What this data looks like, and what you choose to gather, will vary depending on your industry. If your company is a pet food brand, you’re more likely to gather information regarding their pets, rather than, say, the number of children they have and their ages.
Descriptive data includes:
Quantitative data, also known as customer behavior data, covers information relating to their transactions with you. These are customer insights you gain as your customers move through the customer journey.
Quantitative data includes:
Qualitative data is everything related to how your customer feels about your organization. It's their subjective opinion about your company and products. This is also known as attitudinal data.
Qualitative data includes:
Now that we’ve covered the main kinds of data, the next logical step is to learn how you can collect these different kinds of data.
There are a lot of ways you can collect customer data. However, before you begin the process, it’s important to consider why you want to collect it.
The first step is to think about what you’ll do with it and what your end goal is. There is no point in collecting data that is not related in any way to your organization’s product or your future vision for your company.
When you start with clear goals, you’ll go into your data collection process with more purpose, choose the correct collection method for your needs, and ultimately get better results.
It’s also worth noting that data collection has ethical and legal implications. There are a number of different data privacy laws, like the General Data Protection Regulation (GDPR) in Europe. These vary depending on the country and are put in place to protect citizens. It’s important that you are well-versed in these regulations as you build out your data collection strategy.
With all that said, let’s go through five of the most practical ways to gather customer data:
You can collect a breadth of useful data through web analytics. Examples of the type of data you can gain through this method include acquisition data, landing pages, time on page, exit pages, and more.
By collecting customer analytics data you can track how people are using your webpage. This matters because you can see which pages or interactions lead to success, like a sale, and in which instances you are losing customers. You can then put in place informed measures to make the most out of their experience.
Transactional information is data you can collect that is connected to customer purchases, exchanges, and returns.
This data is highly specific and can let you know more about your customers' purchasing habits like what products they like the best, which products they wanted to return, whether they repurchased or continued a subscription, etc.
This feedback can come in the form of surveys, interviews, and feedback forms. Examples of these kinds of survey metrics include the Net Promoter Score, Customer Effort Score, and Customer Satisfaction Score.
The benefit of these surveys is that you can design them in a way that allows you to collect both qualitative and quantitative feedback, providing additional information for your customer profiles. You can also pinpoint specific areas of your business that you would like to gain insights on.
Social media provides a wealth of customer insights. These can largely be broken down into two categories:
These are insights that you can gather by monitoring engagement metrics like comments, shares, and likes. These metrics can also show you how your customer perceives your brand.
By seeing the kinds of content and companies your customers are engaging with online, you can learn a lot about them. Monitoring this information will help you to understand your customer better and learn about their personalities, interests, and what it is that captures their attention.
The interactions that your customers have with your customer service team can tell you a lot about how satisfied they are with their customer experience journey. Tracking your customer service data will include complaints and queries your customers have made and the dates and frequency with which they make them.
Based on this information you can detect patterns and common pain points you can fix for a better customer experience.
The data that you collect from your customers is filled with insights that you can use to make better business decisions to improve your customer experience journey. However, in its raw, unstructured format, that information is not very workable. Here is where customer data analysis comes into play.
The different customer data types out there, and the many ways they can be collected, mean you have the potential to receive immense amounts of data. What’s more, these large data sets will be rolling in continuously in real-time. Manually processing this data is not scalable, and it will result in a subjective, inefficient analysis.
The most efficient way to process large amounts of customer data is through AI. Specifically, machine learning and natural language processing. Machine learning is accurate, objective, and fast — three essential qualities when you’re dealing with data that can improve your business.
MonkeyLearn offers low to no-code text analysis tools that automatically sort through masses of unstructured data, in seconds. With tools like our keyword extractor or our sentiment analyzer, you can analyze your data to gain immediate insights. Then with our interactive Studio dashboard (see below) you can visualize it all in one easy-to-read dashboard.
Without customer data, you can’t create a customer-centric strategy, and nowadays if you’re not making decisions with your customer at the forefront, you are falling behind.
To create an efficient customer data management strategy you need to implement customer data platforms that allow you to make the most out of the data you collect.
September 15th, 2021