What is Market Intelligence Data? 4 Easy Examples & Sources

The most successful companies today leverage data to optimize customer experiences. While understanding one’s customers is key, small and medium-size businesses earn a strong competitive edge when they use data to understand market conditions and competitors.

In a sentence, market intelligence data consists of tabular details addressing a market’s customers, competitors, suppliers, and macroeconomics, using both quantitative and qualitative information. In this sense, it’s one of the most comprehensive data sets in a company’s arsenal.

The purpose of market intelligence data is to provide insights for company decision-making. Most companies collect small amounts of market intelligence data internally and turn to external providers for the majority of it.

Market intelligence data examples

Market intelligence can be split into four types:

  • Supplier data
  • Customer data
  • Competitor data
  • Macroeconomic data

Each of these types has the following market intelligence data examples. For expository reason, I’m not putting raw data titles on every line. Instead, these are the categories of information that market intelligence analysts focus on. Explanation follow them to provide additional reference.

  1. Supplier data
    • Number of suppliers in industry. It’s useful to know the total number of suppliers, as it could help understand the total size of the market in which the company competes. For example, if there is only one supplier in the industry, and if both suppliers are small, analysts can infer that the market is rather small. A nuance here is that this logic is valid only when the suppliers deliver a small number of products. If they’re a kind of conglomerate, then the inference is harder to make.
    • Geographic locations served. If the identified suppliers are only local representations of a larger number of suppliers in other locations, then you can infer that the market size is quite large. A good example of this is farmers. If you own a bakery, no amount of analysis on the number of suppliers will provide valuable insights because there are hundreds of thousands of wheat farmers in the world, if not millions.
    • Clients served. Information about the clients your providers serve can provide enormous insight on your competitors. In fact, if you and your competitors share a common supplier, you know that the products they receive are made from the same costs of goods sold. This means they have no material-based competitive edge. In addition, knowing the clients served can help you get an idea of the size of your competitors.
    • Supplier names. Knowing the names of all suppliers in your industry can help you organize your competitive research around them over time. The time element is key here, since markets change, and suppliers go through their own competitive challenges.
    • Supplier products. Knowledge about the different products available from suppliers helps your place your company amongst direct competitors in the larger indirect market. One of the big threats to companies is the risk of incumbent competitors. For example, if your company sells 8 inch disk drives and your supplier makes parts for 4 inch disk drives, you know there’s a market of potential incumbent competitors.
    • Supplier technology. Suppliers have their own set of competitive technologies. Knowing what makes them competitive from a technology perspective could help identify better potential providers with superior providers. For example, a common example of this is commodities. Copper providers, for example, carry a host of different copper types and technologies to deliver it. Companies that refine copper are constantly on the lookout for suppliers who sell cheaper because they found better delivery technology (in bulk with other metals vs copper alone, for example).
    • Supplier business model. In addition to technology, supplier business models provide valuable insights on pricing. My favorite example of this is money transfer company TransferWise. They modified the bank-dominated money transfer model to eliminate technology interfaces. You simply send your money to a local account, and Transferwise sends money from their account in the destination region. If you can find a supplier with a better business model, they’re likely less expensive.
    • Unique traits of suppliers giving it competitive advantage. There are a host of reasons why your supplier is the one you choose. Maybe it’s price. Maybe it’s quality. Maybe it’s speed. Whatever the reason, market intelligence analysts should be aware of this so they can analyze the marketplace and ensure your company is getting the best deals on its CoGS.
  2. Customer data
    • Customer IDs (for data protection reasons, you should never identify a customer directly). Customer IDs are assigned the moment a new customer enters the company database. For privacy reasons, customer names should be mapped to Customer IDs in a high-security database. In your work, never refer to customer names. Not only is it illegal in many political regions, it’s also unethical. Your customers do not agree to let you examine and manipulate their private information, even if you get them to click a box somewhere saying otherwise.
    • Customer age. Like most customer data points, age is a useful metric because it helps identify purchasing patterns based on age groups. Typically, age groups are divided into 6-year subsets, such as 19-24, 25-31, 32-38, and so on. In this way, customer age is considered a clustering variable, which is a part of a special data analyst exercise called clustering.
    • Customer declared gender. Knowing how your customer identifies him/herself is another important dimension that can be used to cluster, or group, them based on shared traits. An easy way to envision this is that those identifying as male are more likely to purchase items associated with masculinity, whereas the inverse is true for those identifying as female.
    • Customer purchases. Once a company is established for some time, it collects a record of what purchases each Customer ID has made. This shows a trend in product sensitivity, another dimension is customer profiles.
    • Customer campaign sensitivities. Campaign sensitivity is closely related to product sensitivity. If campaigns have identifiable traits, such as a discount code, they can become another part of the customer profile. At the same time, you can use product sensitivity to understand which campaigns to pitch to which customers based on the existing profile.
    • Customer race. Knowing customer race adds another level of detail to the profile. This dimension is most valuable in markets where there are racial subcultures that have a strong impact of spending habits. A good example of this exists in big cities. Supermarkets in New York City, for example, might provide more asian products in Chinatown, Italian products in Little Italy, etc. NB: while this is a useful metric in the aggregate, it should not be used to target individual customers. This would be unethical.
    • Customer socioeconomic details. Understanding in which socioeconomic group your customers fall can provide even more insight into their spending and consumption habits. However, finding and using this dimension is something of a hot topic. How do you define socioeconomics? Is it ethical to use an arbitrary metric? Is it ethical to even request details that would help you understand this information.
    • Customer economic details. As a single dimension, economic details about customers is most useful for luxury products, where customers are almost exclusively of a certain income level.
  3. Competitor data
    • Competitor products. In market intelligence, data on competitor products is a gold mine. Understanding exactly what the products are and what customer groups they target is for many the essential actionable intelligence in a company’s strategic plans.
    • Competitor pricing. Pricing is one of the three key sales and marketing criteria. No matter how good or bad your product is, pricing can make the difference between purchase and no purchase. When you understand your direct competitors’ prices, you can incorporate it into your own pricing. That said, there are limitations. Most established industries do not compete on pricing, but value proposition. This is because the industry becomes optimized. The only way to make pricing changes is to change the industry with technology, which we’ll talk about below.
    • Competitor geographic regions. Knowing in what regions your competitors sell allows you to strategically position yourself. Especially when product delivery costs are high, you can sweep entire regions with lower prices.
    • Competitor product sub-niche. Sub-niches are types of one product. A great example of this is smartphones. The iPhone occupies a premium niche, while Huawei occupies a cheaper one. When you understand the competitor’s sub-niche, you can either hit it head on, or shift to work on another sub-niche.
    • Competitor indirect or direct status. Having a list of competitors based on a direct or indirect status can help as a classification technique for other dimensions.
    • Competitor technology. As with suppliers, understanding competitor technology, its strengths, and its weaknesses, can help you position your technologies to either compete head-on or to pivot towards another technology — usually one that works better at a less expensive price.
    • Competitor business model. As with suppliers, understanding competitor business models can provide insights into how they make their margins, where potential weaknesses may be, and how to attack them.
    • Competitor unique value proposition. Understanding what part of the product makes the competition more attractive to some customers than your product is arguably the most important market intelligence data. This is variable by industry.
    • Unique traits of competitor giving it competitive advantages. This is any other unique value that the competition is able to bring to its customers for reasons other than sub-niches, technology, or business model. It changes by industry. The first example that comes to mind is brands. Coca Cola, for example, leverages its brand against Pepsi, though the products are quite similar at the end of the day.
  4. Macroeconomic data
    • Correlated macroeconomic indicators (GDP, Employment rates, discretional income, etc.). By identifying correlated macroeconomic indicators, companies have external references for internal metrics. They then use government-projections on the indicators to gain a better understanding of future company performance in comparison to the market at large. Macroeconomic data is the most data-analytic-heavy part of market intelligence.
    • Historical data. To be more precise, historical data on these macroeconomic indicators is a must for any predictive analysis.

Market intelligence data sources

Arguably the biggest challenge when working with market intelligence data is finding it in the first place. While some third-party data aggregators exist, they often lack completeness or correctness. In the end, market intelligence data is so vast that analysts end up doing a lot of manual work.

For this reason, I’ve compiled a list of market intelligence data sources analysts can use to get the data they need:

  1. Supplier data
    • Supplier websites
    • Industry associations and chambers of commerce
    • Direct contact with suppliers
  2. Customer data
    • In-house sales and marketing databases
    • Social sciences demographics databases
    • Focus groups
    • Questionnaires
  3. Competitor data
    • Competitor websites
    • Third-party providers such as Capital IQ and Pricewatch.
    • Direct contact with suppliers
  4. Macroeconomic data

About the Author


Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.


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