Data & Files FAQs

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What is the difference between Customer vs Market Files?

All Audience Files within intelligentVIEW can be categorized as one of the following:

Customer File
Processing a Customer Audience Files file keeps duplicates in order to provide the appropriate weighting for accurate profiling. In this type of file, each row represents a single customer or household. For example, if you are analysing 12,123 customers, your Customer Audience File will have 12,123 postal code records (or 12,123 FSAs if only first three digit postal codes are available).

Market File
Select Market File to include all households in the FSAs or postal codes loaded. This is useful if your territories or trade areas that you want to analyze and profile are groups of postal codes or FSAs. Processing the file removes duplicate FSAs or postal codes. rows since selecting ‘market file’ allows you to upload entire trade areas of postal codes/FSAs which include every household in these postal codes/FSAs for analysis. FSA Map tool output is defaulted to Market File.

What kind of customization options are available for the pre-built data modules?

We offer the following customization options:

Add your own segmentation or personas:
Many customers have invested in creating custom personas. Since we don’t use intelligentSEGMENTS to project other data (like media behaviours), integrating your own segmentation is fast and inexpensive.

Profiling using your own data:
intelligentVIEW is built on templated configuration files. This means that reporting on any data aggregated to postal code is easily accommodated and accessible only to your team.

Custom Audience Builder:
Secure audience-building UI accommodates a view of your proprietary data to allow non-technical users the ability to quickly build custom audiences.

Is the ‘raw’ intelligentVIEW data in sync with the intelligentVIEW platform?

Yes! Our data is updated multiple times per year and our process to load data into the intelligentVIEW platform takes a few hours, not months.

So, if you’re using intelligentVIEW data in your modeling or dashboards, it is the same data vintage in the intelligentVIEW platform

How does intelligentVIEW handle data discrepancies from different sources?

intelligentVIEW handles data discrepancies by cross-validating data from multiple trusted sources.

The platform integrates data into a unified ecosystem where statistical relationships between sources are exploited to maintain consistency and robustness.

This approach minimizes data discrepancies and ensures that the data provided is reliable and accurate.

How often are the data and projections in intelligentVIEW updated?

New projections are made every quarter, ensuring that you have access to the latest consumer and market data.

This frequent updating helps maintain the accuracy and relevance of the consumer insights provided by the platform.

Can I integrate my existing data with intelligentVIEW’s platform?

Yes, proprietary segments, personas, and customer-based data are easily integrated for either building proprietary audiences through our audience builder technology or for reporting module templates.

How is data privacy maintained in intelligentVIEW?

Data privacy in intelligentVIEW is maintained through strict adherence to Canadian privacy legislation.

All data is anonymized to protect individual identities, and comprehensive documentation for all data sources is compiled and maintained.

Additionally, intelligentVIEW ensures that all data partners comply with Canadian privacy standards and can work with you to meet any additional privacy requirements you may have.

Can we get access to intelligentVIEW data to use internally?

Yes! You can access all intelligentVIEW raw postal code data to power your own models, and dashboards.

This data is available through Snowflake as an add-on to your intelligentVIEW subscription. Contact your CSM for details.

How are your data methods different from others?

CiG’s methodology begins with our unique approach that pools our data sources into an ecosystem where sources are used to cross-validate and contribute to each other’s robustness.

We treat our data as a part of an ecosystem, rather than independent tables or data products, which means that statistical relationships are exploited, bases (populations, etc.) are consistent, and each data source is a valuable contributor to the maintenance of the many.

Download Data Methodology