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Customer analytics

Data analytics

Web 2.0, mobile, social, and artificial intelligence advance rapidly, and businesses have an obligation to prepare customers, employees, and customers to whom they are accountable for that new world.

Cumulatively, companies are increasing their R&D expenditures on average per employee, and they are spending more time acquiring new customers. To make up for the loss of revenue, companies are increasing costs, increasing compliance, and developing new services. These costs are concentrated among key revenue channels, placing greater financial strain on targeted markets. As a result, companies are increasingly turning to analytics to understand how they are growing, how to respond to market shifts, and how to attract and retain high-value customers.

What is customer analytics?

Customer analytics is the systematic examination of data to determine what has and has not been achieved. Marketers use customer analytics because it allows them to visualize and analyze data to make better decisions. It also helps them make decisions based on data, as they are no longer free to create a poor marketing message or market an ineffective solution.

Why do companies use customer analytics?

Customer analytics helps businesses break big problems into manageable answers. Marketing spend can increase by many percent when not trying to understand customer behavior. However, corp. cost per customer can be significantly reduced by analyzing data to determine what has and has not been achieved. When companies have access to data about their customers, they can improve their customer experience by using that data to their advantage.

Customer analytics helps companies make better decisions because it allows them to visualize and analyze data to make better decisions. Marketers can learn to utilize data about consumers anytime and anywhere. They can collect information from various ways of performing everyday tasks, from mobile devices used to log shop activity to web click streams. That’s why companies do use customer analytics.

How do customer analytics work?

Customer analytics puts business decisions into perspective. It helps companies make decisions based on data, which means that they need a way to leverage the data they have and how they are being collected.

The key to effective customer analytics is the application of data science, which involves the application of statistical and machine-learning algorithms to data, to predict customer behavior. This application of data science is key to understanding how to influence behavior and developing responsive marketing programs.

The business decision process

The business decision process is comprised of the following steps:

  1. Identify

  2. Define

  3. Apply


That means that through some process of business definition, industry standard, something has been defined or determined that needs to be kept as a core value or standard. Without explicit business definition, this could mean forgetting about something that is not obvious to everyone, or it could mean defining something vaguely enough that it may or may not apply, or overly depending on a gut feel. What does that mean? On average, it is less optimal for clients and the company.

Rather, customer analytics should be business / solution-oriented and data driven. In the digital age of constant customer connectivity, it is essential that businesses maintain close relationships with customers. It is also essential that businesses maintain open lines of communication so that they can hear what customers are saying, and hopefully do something about it. Without open lines of communication, it is difficult to segment customers, predict future buying behavior, or anticipate future events. Embedded within these business goals are the concerns of brands and individuals about their image and brand value. Without strong consumer relationships, businesses will be unable to predict customer behavior and will often make poor decisions.

How to implement customer analytics?

Customer analytics is often implemented in a track-based fashion, where the company defines a problem, analyzes data, and then proposes a solution based on the data. Marketers may utilize machine learning algorithms, statistical methods, or any other analytical tool that puts data into context. In sum it is:

  • Analytical / data driven

  • Business / solution-oriented

Companies that use data driven methods are more successful, have better customer relationships, and are more competitive. Data driven methods also tend to be easier to understand because the company collects and analyzes data in a structured and understandable fashion.

Business solution-oriented tools to implement customer analytics

With the right tools, every business has access to all types of customer data, but you may not know which is the most relevant for your business purpose. Data driven analytics collects data in ways that are intuitive but which provide insight into human behavior. For example, customer analytics can be triggered automatically when a customer taps a certain button, providing insight into how a customer process should be structured, how the customer will be identified, and how to segment the customer.

Data driven analytics also comes in the natural language form of question marks, which help define problems, identify correlations, and draw conclusions.