Reading: Types of Marketing Information

Illuminating the Marketing Picture

There are three primary types of marketing information organizations use to generate insights for strategic decisions: internal data, competitive intelligence, and marketing research.

Internal Data

Internal data refers to the information companies collect from their own operations, customers, and interactions. Marketing departments track leads, customer demographics, online behavior, and engagement preferences. Sales teams capture purchase histories, buying patterns, and customer lifetime value. Accounting and billing systems provide financial insights such as spending levels and payment timing, while customer support systems record satisfaction trends, complaints, and usage challenges.

Modern companies integrate this information through shared databases, enterprise platforms, and Customer Relationship Management (CRM) systems. A CRM system such as Salesforce consolidates data from multiple touchpoints into a unified view of the customer, supporting more precise segmentation and personalized engagement strategies. With the advent of Big Data and AI-powered analytics, organizations can process massive amounts of structured and unstructured data—from website clicks and IoT devices to social media interactions—and use algorithms to uncover hidden patterns and predictive insights.^[1]

An example of a leading CRM software is Salesforce.  This video shows how Salesforce combines multiple data sources throughout the organization to present a 360° view of the customer.

For example, Trident Marketing applied marketing analytics across CRM records, customer-service interactions, and external data sources to improve sales targeting and predict customer churn. These insights enabled precise recommendations about when to contact prospects and which salesperson should engage them, leading to a nearly 1,000 percent increase in revenue over four years.^[2]

Today, organizations increasingly use AI-driven personalization engines to analyze internal data and deliver real-time recommendations. Netflix and Amazon, for instance, rely on predictive models to suggest content or products tailored to individual users, illustrating the power of internal data for driving retention and sales.^[3]

Competitive Intelligence

Competitive intelligence (CI) provides insights about competitors and market dynamics. It helps answer questions such as:

• Who are the key competitors, and what are their product strengths and weaknesses?

• What is their pricing strategy, and how does it compare with ours?

• How are they positioning themselves in the market, both in advertising and online search?

• What share of the market do they hold, and how is this evolving?

Marketers gather CI by monitoring competitor websites, press releases, social media activity, and industry reports. Tools like SEMrush or Similarweb analyze digital footprints, while AI-based platforms scan large volumes of public data for emerging competitive signals. Some firms conduct win/loss analyses to capture insights from sales opportunities, helping refine product features, pricing, and promotional strategies.

Although companies closely guard sensitive data, a disciplined CI practice helps organizations anticipate competitor moves and adjust their marketing mix—product, price, place, and promotion—accordingly.^[4]

Marketing Research

Marketing research is a structured process for solving marketing problems or identifying opportunities through systematic data collection and analysis. The process typically includes:

1. Defining the research problem or opportunity.

2. Determining the information needed.

3. Collecting and analyzing data.

4. Generating insights and reporting results.

Marketing research addresses a wide range of topics:

• Environmental factors such as economic trends, technology shifts, and cultural changes.

• Customer attitudes and behaviors, including satisfaction levels and decision-making processes.

• Product research, such as concept testing, feature prioritization, and pricing studies.

• Promotion effectiveness, including ad testing and segmentation analysis.

• Corporate strategy, covering brand reputation, partnerships, and long-term positioning.

Research can involve primary data (collected directly from customers) or secondary data (sourced from existing studies). Increasingly, companies employ AI-enabled tools to conduct sentiment analysis on social media, automate survey coding, or simulate market scenarios, accelerating insight generation at lower cost.^[5]

Pet Valu

A notable example comes from Pet Valu, a major chain specializing in pet food and supplies, leaned heavily on

marketing research and internal data to strengthen its loyalty program. In a challenging retail environment marked by rising costs and inflation, the company analyzed customer behavior to understand what drives spending and retention. They discovered that loyalty program members—making up a significant portion of their customer base—visisted more frequently and spent notably more:

• Loyalty members visit five times more often and spend four times more than non-members.

• With over 2.8 million active loyalty members, these insights reflect a deep well of customer data and engagement metrics.^[6]

These findings guided Pet Valu to double down on tailored loyalty marketing—offering targeted promotions, consumable bundles, and personalized messaging. The result: loyalty-driven revenue provided critical resilience amid the broader retail downturn.

Creation note: This content was updated with the assistance of ChatGPT, a language model developed by OpenAI, and was subsequently reviewed and edited by the author for clarity and accuracy.