By Sebastien Rancourt
Canadian privacy laws set ground rules on how organizations may collect, use and disclose personal information. Under the Personal Information Protection and Electronic Documents Act, for example, personal information can only be collected when it is gathered with the knowledge and consent of the consumer-and only used for the reasons for which it was gathered.
Despite these data challenges, marketers and strategic planners have found effective ways to understand customer needs and create actionable customer segments. These insights and best practices-while particularly germane in Canada-are relevant to anyone looking to improve results by targeting more effectively.
Today’s leading solutions begin with geo-demographic clusters. While cluster segmentation strategies have existed for decades, contemporary clustering methods use robust statistical data and advanced analytical power to capture, create and measure more precise customer segments based on geography, demographics and lifestyles. With the right data and analytical tools, organizations can characterize the behavior of every clustered customer-from their favorite movies and foods to their preferred attire and avocations-enabling users to more accurately predict customers’ responses to every campaign.
Professionals in retail, financial services, media planning, real estate and restaurants, among others, rely on cluster segmentation to improve decision making and business results. Yet with the enhancements made in recent years, some marketers have yet to incorporate the latest advances which can boost overall performance. In speaking with experts across Canada, we’ve identified a series of best practices to help guide your next steps.
Segment by neighborhood, not postal codes. Some segmentation strategies rely on postal codes, which can lead to problems down the road. Each month, as many as 5% of the roughly 850,000 six-digit Canadian postal codes change, as Canada Post updates this system solely on the basis of their mail delivery needs. Not only does this taint campaigns in the short-term, it makes it nearly impossible to manage year-over-year modeling and analysis.
The best neighborhood segmentation clusters begin with census data at the dissemination area levels-which are the lowest levels for which reliable census data are published-providing hundreds of reliable data variables. In addition to data accuracy, these neighborhood-based models offer year-over-year consistency, so marketers can build on past success over time.
Incorporate household-level insights. This past year, leading cluster models have found ways to use more comprehensive household level data, incorporating consumer information that goes far beyond census findings. These inputs, which conform to Canadian privacy laws, represent an unprecedented level of detail and behavior-based data-and create a more high-definition view of customers and prospects.
Maximize data points. Not all household level data is the same. Some cluster models are built extrapolating data from as few as 8,000 surveys across the full population of 33 million Canadians. More reliable cluster models will analyze self-reported data from as many as 10 million individuals-providing for more accurate targeting and a lot less guesswork.
Overall, organizations that employ these best practices will benefit from a multidimensional framework that makes it possible to sort through the complexity of Canadian consumer culture without having to manipulate literally hundreds of census and survey variables.
One such solution is PSYTE HD, the Pitney Bowes Business Insight segmentation system created using an innovative two-step clustering process. The 59 clusters identified, including Canadian Elite, Joie de Vivre, Urban Verve and Next Gen Rising, leverage the largest and most robust repository of Canadian consumer intelligence to date-making it easier for organizations to locate new opportunities, connect with customers and communicate more efficiently. We invite you to learn more and look forward to your feedback.