Customer and Consumer Behavior Analytics and Big Data

Big data presents a tremendous business opportunity in consumer behavior analytics for the companies that are best equipped, ready, and able to transform raw data into insightful information. Customer analysis is expanding as technology and users expands, so most businesses are adding to their usual customer analysis techniques. In this article, we’ll discuss customer behavior analysis, its challenges, and the role data preparation. 

Customer Analysis Today

Customer analysis is the process of determining who is most likely to buy or utilize a company’s product or services. With the technology growth of recent years, businesses have more access to tools to understand their customers better than ever, especially because of online shopping. The process of analyzing a customer base can usually be broken down into three stages: 

  • Identify current customers. The first stage is to fully identify who the current customers are and why they utilize the service or buy the product. Analysts compile data from websites, social media, surveys, and other sources of data to determine who these customers are, including demographics and psychographics. Understanding current customers can help with retention as well as determining why people choose a product.
  • Segment the customers by needs. The next stage is to determine what different groups of customers there are. These groups should be segmented based on needs because that will help organize customers by what drives them to a product. From there, it’s easier to determine need patterns within the industry and among competitors. 
  • Determine how to help the needs. The final stage is to determine what connects the first and second stages. How does the product meet the needs? The analysts then need to determine what other pain points and needs the product meets and how they can communicate that to the consumer base.

These steps are just an overview of what can go into the process of customer analysis. However the analysis is done, one key factor is the analysts gather a lot of data that needs to be handled smoothly. 

Customer Analysis Example

Nearly every organization handles customer analysis on some level. One example of using customer behavior analytics is Apple. Apple is a major corporation that sells tech problems, and its analysts have used customer analysis to develop products and maintain retention. Here’s an example of how Apple uses customer behavior analysis for each product launch: 

  • Apple determined who its current users are through surveys, its website, and more. From there, analysts discovered why Apple users choose Apple products. They figured out why some people choose their products and why they want to, based on their pain points and needs. 
  • Apple then determines needs and pain points. The analysts determine what the industry needs and pain points are. They gleaned information from competitor websites and forums on design issues. They used this data to determine what people wanted from a product. 
  • Apple determined how to bridge the gap. The product developers determined what kind of product could bridge the gap between the needs and the current customers. They figured out what design problems needed solving and then solved them while adhering to what current users love about the company. 

This Apple example happens on a smaller scale at nearly every modern business. It’s the key to understanding a consumer base. 

Challenges with Customer Analysis

The main challenge with customer behavior analysis is that it involves a lot of data. Gathering data about customers and the industry can result in large and unwieldy datasets that can hinder analysis and results. The first step in getting to improved customer behavior analytics is data preparation. The process of preparing data and transforming large and diverse datasets into useful information has long posed a number of very complex problems for companies’ analytics teams and IT staffs. Forrester reports that data analysts will spend up to 80% of their time preparing data for analysis. Because it can be so time consuming, many companies miss the benefits of data preparation and, as a result, the benefits of improved customer analysis.

Making the Most of Your Data Faster with Designer Cloud

Designer Cloud offers a better way to prepare data for customer behavior analytics. It’s an easy to use data preparation solution that can streamline the process of preparing data for business analysis. Designer Cloud helps companies attain more accurate insights faster through customer behavior analytics. We help you put decisions about data into the hands of the people who best understand how to make use of it. Designer Cloud can be used by anyone in your organization, from IT users to data analysts to business users, you can depend on Designer Cloud to:

  • Reduce the time spent on preparing data for analysis
  • Understand the business value of your data faster 
  • Empower your analytics team to directly engage the customer behavior data
  • Enable data analysts to uncover data trends through data discovery and visual profiling

Uncover more about how Designer Cloud can help your business find new value and insights in customer behavior analytics. Download our eBook introduction to data wrangling, Six Core Data Wrangling Activities.