Introduction

Data has become a cornerstone of business success in today’s digital age. Every day, companies collect massive amounts of data—which brings with it the challenge of how to best use and benefit from it.

We’re living in the age of data—what many now call the "new oil." Just like oil, data needs to be "extracted," "processed," and then "used" to unlock its true value. This isn’t just a catchy phrase; it’s something we’re seeing play out as more businesses rely on data to make decisions and improve their services.

Tech giants like Google, Amazon, and Meta built their success by turning data into insights—fueling smarter ads and better products.

This is where a CRM (Customer Relationship Management) system comes in. A CRM can be utilized as a comprehensive prediction tool for customer behaviour in addition to managing customer data, which helps any business meet the needs of its customers and increase income. Businesses are able to recognize patterns, predict trends, and provide highly customized experiences through the strategic use of CRM data.

Types of data provided by CRM and their role in understanding customer behaviour:

• Demographic data: The data describe the demographic characteristics of customers, such as name, age, gender, place of residence, marital status, occupation, and income level.
  Demographic data plays a key role in analysing customer behaviour based on factors like age, gender, or location. This enables accurate (segmentation) and customised marketing campaigns by grouping customers into similar clusters so that each segment receives highly focused advertising.
  

• Contact data: It includes customer contact information such as phone numbers, email addresses, or social media accounts. It is essential data in customer management systems because it serves as the link between the company and the customer, and is used to send messages, offers, or support. Analysing this data helps understand customer behaviour by identifying the communication channels customers frequently use to send marketing campaigns or reminders.
  

• Behavioural Data: This is one of the most important types of data, as it reflects the customer’s actual behaviour—such as the pages they visit, the products they’re interested in, their responses to messages and notifications, how long they stay on the site, and the times they’re most active.
  Its importance lies in showing what’s really happening rather than what’s expected to happen, making it a more accurate basis for evaluation and decision-making.
  For example, in the travel industry, it is a sign of interest when a customer often checks Turkey family travel offers, but does not make a booking. Based on this behaviour, the business can send a customised offer to encourage the booking.
  

• Transaction Data: Transaction data is all information related to a customer's transactions with a service provider, whether it's a purchase, reservation, payment, cancellation, or even a refund.
  It is a great source for understanding customer behaviour since it accurately reflects the actual actions of users while they are making financial decisions.
  The quantity and kinds of purchases or reservations, the monetary amount of each transaction, the dates and times of transactions, the payment method, the usage of coupons or discounts, and the history of cancellations or refunds are a few examples.
  Transaction data helps in evaluating the value and loyalty of customers to the business. It reflects recurring patterns in how customers interact and make decisions, which helps predict future customer needs, detect changes in their behavior, and design personalized offers that suit their preferences.
  
  

• Customer service data: This is the information collected from customer interactions with the support team through different channels. It shows how customers reach out to the business when they have a problem, a question, or need help, and it gives a clear picture of how satisfied they are and how they behave during these interactions.
  For example: how often a customer contacts support, the issues they raise, how quickly they get a response, how long it takes to solve the problem, customer ratings after each interaction, and so on.
  This data is really helpful for understanding customer behaviour in tough situations and measuring satisfaction in a real way. It helps the company spot problems, improve service quality, and offer faster, more accurate solutions.
  
  

How to Analyse This Data to Predict Customer Behaviour:

As we’ve seen, a Customer Relationship Management (CRM) system is a goldmine of data, storing detailed customer information. All of this data forms a robust analytical base that is later used in data analytics techniques to predict and interpret customer behaviour and formulate effective strategies.
To get the most of data, businesses need additional analysis tools to help interpret the numbers and transform them into actionable insights. The most important of these tools are:

How does CRM data help create smart strategies to understand customer behaviour?

Take a look at the infographic below, which highlights the key strategies that CRM helps build: