How to Identify 3 Data Categories in Predictive Analysis

As a result of doing business, companies have gathered masses of data about their business and customers, often referred to as business intelligence. Predictive analysis uses this data. To help you develop categories for your data, what follows is a general rundown of the types of data that are considered business intelligence:

Behavioral data derives from transactions, and can be collected automatically:

  • Items bought

  • Methods of payment

  • Whether the purchased items were on sale

  • The purchasers’ access information:

    • Address

    • Phone number

    • E-mail address

  • All of shoppers have provided such data when making a purchase online (or even when buying at a store or over the phone).

Other types of data can be collected from customers with their co-operation:

  • Data provided by customers when they fill out surveys

  • Customers’ collected answers to polls via questionnaires

  • Information collected from customers who make direct contact with companies

    • In a physical store

    • Over the phone

    • Through the company website

In addition, the type of data that a business collects from its operations can provide information about its customers. Common examples include the amount of time that customers spend on company websites, as well as customers’ browsing histories. All that data combined can be analyzed to answer some important questions:

  • How can your business improve the customer experience?

  • How can you retain existing customers and attract new ones?

  • What would your customer base like to buy next?

  • What purchases can you recommend to particular customers?

The first step toward answering these questions (and many others) is to collect and use all customer-related operations data for a comprehensive analysis. The data types that make up such data can intersect and could be described and/or grouped differently for the purposes of analysis.

Some companies collect these types of data by giving customers personalized experiences. For example, when a business provides its customers with the tools they need to build personalized websites, it not only empowers customers (and enriches their experience of dealing with the company), it also allows the company to learn from a direct expression of its customers’ wants and needs: the websites they create.

Basics of attitudinal data in predictive analysis

Any information that can shed light on how customers think or feel is considered attitudinal data.

When companies put out surveys that ask their customers for feedback and their thoughts about their line of businesses and products, the collected data is an example of attitudinal data.

Attitudinal data has a direct impact on the type of marketing campaign a company can launch. It helps shape and target the message of that campaign. Attitudinal data can help make both the message and the products more relevant to the customers’ needs and wants — allowing the business to serve existing customers better and attract prospective ones.

The limitation of attitudinal data is a certain imperfection: Not everyone objectively answers survey questions, and not everyone provides all the relevant details that shaped their thinking at the time of the survey.

Basics of behavioral data in predictive analysis

Behavioral data derives from what customers do when they interact with the business; it consists mainly of data from sales transactions. Behavioral data tends to be more reliable than attitudinal data because it represents what actually happened.

Businesses know, for example, what products are selling, who is buying them, and how customers are paying for them.

Behavioral data is a by-product of normal operations, so is available to a company at no extra cost. Attitudinal data, on the other hand, requires conducting surveys or commissioning market research to get insights into the minds of the customers.

Attitudinal data is analyzed to understand why customers behave the way they do, and details their views of your company. Behavioral data tells you what is happening and records customers’ real actions. Attitudinal data provides insight into motivations; behavioral data provides the who-did-what — the overall context that led to customers’ particular reactions. Your analysis should include groups for both types of data; they are complementary.

Combining both attitudinal and behavioral data can make your predictive analytics models more accurate by helping you define the segments of your customer base, offer a more personalized customer experience, and identify the drivers behind the business.

Now let’s compare attitudinal and behavioral data.

Characteristics Attitudinal Behavioral
Data Source Customers’ thoughts Customers’ actions
Data Means Collected from surveys Collected from transactions
Data Type Subjective Objective
Data Cost May cost extra No extra cost
Basics of demographic data in predictive analysis

Demographic data comprises information including age, race, marital status, education level, employment status, household income, and location. You can get demographic data from the U.S. Census Bureau, other government agencies, or through commercial entities.

The more data you have about your customers, the better the insight you’ll have into identifying specific demographic and market trends as well as how they may affect your business. Measuring the pulse of the demographic trends will enable you to adjust to the changes and better market to, attract, and serve those segments.

Different segments of the population are interested in different products.

Small businesses catering to specific locations should pay attention to the demographic changes in those locations. All neighbors have witnessed populations changing over time in certain neighborhoods. Businesses must be aware of such changes; they may affect business significantly.

Demographic data, when combined with behavioral and attitudinal data, allows marketers to paint an accurate picture of their current and potential customers, allowing them to increase satisfaction, retention, and acquisition.

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