Marketers and advertisers expect a lot from their data, and rightly so. Data drive decisions that make or break bottom lines.
At CiG, we spend a lot of time talking to marketers and advertisers across Canada about what they value most in terms of data. From the hundreds of conversations we’ve had, it really boils down to these five attributes:
- Relevance: Is the data relevant to their brand and products?
- Accuracy: Does the data accurately reflect consumers and consumer behaviours?
- Currency: Is the data updated with a frequency that reflects current market opportunities and insights? (Data are like groceries; they have a best-before date!)
- Reach: Does the data cover the entire market? Does it drive through all marketing channels?
- Privacy Compliance: Does the data comply with legal privacy requirements while aligning with the brand’s commitment to customer trust and transparency?
We designed our data methodology for intelligentVIEW by integrating industry best practices with innovative solutions, informed by the invaluable feedback of Canadian marketers. To showcase this innovative approach, we sat down with CiG’s CEO, Tim Leys, to discuss what makes our data methodology truly unique and valuable to our clients and partners in the marketing and advertising industry.
What are the key differences and advantages of CiG’s data methodology?
There are two major differentiators in how CiG builds and manages data:
1. Ecosystem Approach
At CiG, we treat all data sources as interconnected components of a single system, or “ecosystem.”
What we mean by an ecosystem approach is that data sources are linked geographically and statistically. When combined, the strengths of one dataset can enhance the effectiveness of others, improving the overall quality and utility of the data.
Additionally, this data ecosystem approach allows for streamlined updating processes. Since the data sources are interconnected, a single data team can update the data across the entire ecosystem. This process also ensures consistency across different data attributes and supports the maintenance of our intelligentSEGMENTS personas within the same workflow.
2. No Projection from Segments
Secondly, we do not use our intelligentSEGMENTS to project any of our data. intelligentSEGMENTS are built from the data, not the other way around.
Using segments to project important data attributes like media consumption, shopping behaviours, and psychographics is commonly done in the industry but results in broad national profiles that offer little differentiation to market to. For example, when you use segments to project data, the media profile of a segment in two entirely different markets will look the same. Meaning that they listen to the same radio stations and watch the same programs, which typically isn’t true. It also doesn’t allow for the inclusion of any local media.
Projecting data independently from segmentation is critical for accuracy and differentiation in marketing and advertising.
What data sources contribute to CiG’s master data table of 30,000 attributes?
Here are the three types of data sources that we use and their importance to the ecosystem. The real power comes from integrating these data sources, enabling marketers and their agency partners to gain timely and actionable insights.
1. Complete Market Coverage Data
The first type of data source has complete market coverage but at large geographies. An example is the Census from Statistics Canada.
The advantage of this data type is that it is generated using a robust methodology at a national scale. For example, nearly 2,000 census attributes cover demographics like income, age, language, dwelling information, and more.
However, these data sources have limited value for marketers because they are collected at five-year intervals and offered in large geographic aggregations that are too big for practical marketing purposes.
2. Consumer Behaviour Data
The second type of data source contains robust consumer information like shopping habits, media consumption, psychographics and brand preferences. An example is the Numeris RTS panel data.
While very robust in terms of data most relevant to advertisers, the information exists on less than 50,000 Canadians.
3. High-Frequency, Granular Data
Our third type of data source captures very little information but captures data frequently across most or all households or postal codes. Examples include real estate listing data, Canada Post points of call, rooftop geographies and mobile movements.
These data form anchors to identify market changes, such as growth, contraction, and migration, at a very granular level and with high frequency.
Why did CiG develop its data ecosystem?
There were four key reasons: consistency, quality, frequency, and flexibility.
1. Consistency
As we’ve expanded the number of attributes in intelligentVIEW, we’ve ensured that the data make sense relative to the profiles our customers generate. For example, the types of vehicles people drive should align with their socioeconomic segment.
Consistency in audience segment size is also important. We use Canada Post points of call to show exactly how many households we’re reaching. Anchoring digital IDs to those households makes sure our digital and offline audience numbers are in sync.
2. Quality
Our processes are designed to validate against source data, markets, and industry-reported data, so whether you’re using intelligentVIEW data for profiling, powering your dashboards or building a model, you can be confident in the results.
Owning our quality control and process also allows us to incorporate customer data analyst feedback, identify inconsistencies or errors, and correct them in real-time so our master database, Snowflake customer share and intelligentVIEW are corrected quickly.
3. Frequency
intelligentVIEW used to rely on data licensing from other third parties who only update data on a 12 to 24-month cycle. However, a lot can change in a quarter, let alone over two years.
Our ecosystem allows us to publish updated data several times a year, with new updates appearing in intelligentVIEW within hours of release.
4. Flexibility
Finally, the CiG data ecosystem provides the flexibility to quickly evaluate, ingest, and project additional data sources, further expanding the ecosystem and driving value for our users.
And, frankly, it makes our cost-effective subscription pricing model possible and allows our customers access to all of our data, not just the data product licenses they have budget for.
What methodology or statistical processes does CIG use?
Probably all of them. More seriously though, CiG’s data ecosystem is a structure of many mini systems, from micro clusters to various regression techniques applied to discrete sets of attributes or geographies to nearest neighbour processes. Some systems are applied hyperlocally, some regionally and some globally across the entire database.
Technological advances and even artificial intelligence are increasing the sophistication of our data ecosystem at every iteration.
Each data source contributes its unique value to the ecosystem, ensuring that our clients receive the most comprehensive and actionable insights available.
The Only Real Audience Prospecting Platform for Canadian Marketers
Ultimately, CiG’s data methodology is the key to making intelligentVIEW Canada’s only audience prospecting platform.
To be an audience prospecting platform you must have three things:
- Canadian data
- Automated analyses to granularly differentiate audiences for targeting
- A way to connect and reach those audiences throughout the marketing funnel and across all media channels
Unlike other solutions on the market that offer data or targeting, intelligentVIEW is the only solution to combine everything in a single platform, allowing marketers to effectively find and reach their next best customers in one place.
Learn more about CiG’s one-of-a-kind Canadian consumer data ecosystem here or request a sample report to see how our platform can help you drive data-driven marketing success.