Best Purchase Intent Datasets, Databases & APIs

What is Purchase Intent Data?

Purchase intent data is a sub-category of consumer data. A key factor in determining which products to sell or even produce lies in determining what the users of the product or services will most likely spend money on. Purchase intent data is key in marketing strategies as well as the creation of market segments and the production arm of any industry needs to understand what exactly it should focus its resources on.

How is Purchase Intent Data collected?

Purchase intent data is collected just as any other consumer data is collected. There are various ways to do this, such as first-party data, which is data collected directly from the consumer when they interact with retailers and producers. It is usually info centered on personal consumer details. There is also third-party data, which refers to information on a consumer that is collected from third party sources. This type of data is spread across various platforms and includes details on how customers function on these platforms, which is recorded and used to determine purchase intent.

What are the attributes of Purchase Intent Data?

The details which can be obtained about an individual which indicate purchase intent are limitless - from demographic details such as age, location, gender, to behavioral information, such as shopping habits and brand preference. This information could relate to both offline and online activities or environments. Purchase intent information could also include consumers’ personal interests, financial information and purchasing habits.

What is Purchase Intent Data used for?

Purchase intent data, just like any other consumer data, is used by many different businesses and companies. It is used by companies to create profiles for consumers. These profiles help in advertising campaigns and fine-tuning targets to appeal to the right customers. Other companies can use it to improve the rate of response to consumers and increase opportunities for sales. Generally, company or business profitability can be greatly improved by perfect analysis of purchase intent data, as companies understand their consumers and market better.

How can a user assess the quality of Purchase Intent Data?

Purchase intent data always makes for a wide dataset, considering the variety of data that is available. The data should be tailored to users’ specific needs. Policy assessment can be attained by data sample testing, as this helps to determine the exact dataset needed by the user. Reviews from other purchase intent data buyers are also important as they inform you on how reliable the providers are.

Where can I buy Purchase Intent Data?

Data providers and vendors listed on Datarade sell Purchase Intent Data products and samples. Popular Purchase Intent Data products and datasets available on our platform are Online Purchase Data | Aggregated Transaction Patterns for In-Person and Online by SafeGraph, MotivatorIQ by AnalyticsIQ - Consumer Purchase Intent Data USA - 251M Individuals by AnalyticsIQ, and Above Data | Automobile Consumer Purchase Intent USA & Canada - aggregated by make and model by Above Data.

How can I get Purchase Intent Data?

You can get Purchase Intent Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Purchase Intent Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Purchase Intent Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Purchase Intent Data?

Purchase Intent Data is similar to Shopping Intent Data, Online Purchase Data, Purchase Panel Data, Purchase History Data, and Purchase Behavior Data. These data categories are commonly used for Purchase Intelligence and Intent Based Marketing.

What are the most common use cases for Purchase Intent Data?

The top use cases for Purchase Intent Data are Purchase Intelligence and Intent Based Marketing.