Behaviour & Psychographics Behaviour

This data product provides information on consumers’ interests, values, opinions, attitudes, interests, and lifestyles.

Datasets modelled using Numeris’ RTS survey*

Household Energy Conservation Patterns
*This marketing research information is the result of Manifold’s models using the Numeris RTS Canada survey, it is not intended to duplicate Numeris data.

This data product describes consumer actions toward environment and energy conservation, for example, propensity score of

Owning energy-efficient appliance, HVAC

Using low water showerhead/toilet

Using programmable thermostat

Using appliance in off-peak times

Recycling

Composting

Taking public transit

Upgrading windows/doors, insulation.

Utility companies use this data product to target their market better. Government agencies can use it to refine economic plans. Manufacturers and retailers can use the data to gain consumer insight and tailor communications with them.
Household Home Improvement Patterns
*This marketing research information is the result of Manifold’s models using the Numeris RTS Canada survey, it is not intended to duplicate Numeris data.

This data product describes consumer behaviour of home improvement and renovation, for examples, the propensity of

Adding living space

Renovating deck/fencing, floor, garage, window, HVAC, …

Remodeling bathroom, kitchen, other room

Roofing, landscaping, …

Renovating by do-it-yourself or with family/ friend or use contractor/tradesperson

It also includes information on

Household spending on home improvement in the last two years

Length of residence

Plan for moving

Area intend to move if plan to move

Home buy/rent plan if plan to move

Ownership of cottage/recreational properties.

Retailers and manufacturers of home hardware and moving companies can use this data product to gain customer insight; perform trade area analysis; estimate market potential; tailor communication and identify the best prospects.
Consumer Leisure Activities
*This marketing research information is the result of Manifold’s models using the Numeris RTS Canada survey, it is not intended to duplicate Numeris data.

This database provides a propensity score of consumer leisure activities, including:

Video/DVD rentals: channel, frequency, and spending

Purchase of lottery tickets: type, frequency, and spending

Visit of Casinos: type and frequency

Attendance of schools and learning centers

Search for jobs

Dating activities

Live events like weddings, a child born, retirement, promotion, career change

Attendance of local attractions: type and frequency

Attendance of concerts/theatre: type and frequency

Attendance of live sports events: type and frequency

Visits of consumer shows: type and frequency

Participation of general leisure activities: type and frequency

Participation in sports activities & events, e.g., golfing, skiing, jogging, …

This data product reflects consumer lifestyles from various perspectives. Through leisure activities, companies can gain a better understanding of what their customers may like, value and participate, and then optimize their approaches for engagement. This database is indispensable for companies striving for holistic engagement with their customers and prospects. It can help companies segment their customers better and tailor their products/services by lifestyles.

This data product is mainly based on 8 years of the Return-To-Sample survey by Numeris (formerly Bureau of Broadcast and Measurement). We have also integrated research publications on consumer lifestyles and behaviour, and Manifold’s predictive modeling techniques. We identified robust consumer leisure activity patterns in the survey data and extrapolated them into the propensity score for each of the 6-digit postal codes.
Consumer Psychographic Patterns
*This marketing research information is the result of Manifold’s models using the Numeris RTS Canada survey, it is not intended to duplicate Numeris data.

This database provides information on consumers’ interests, values, opinions, attitudes, interests, and lifestyles. It includes propensity score on consumers’

Attitude about advertising

Health consciousness

Opinion about new products

Brand loyalty

Cost sensitivity

Social networks and activities

Lifestyle

Opinion about work

Interests and family life

Attitude regarding self-esteem.

The psychographic information in this data product is complementary to the demographics, spending, behaviour data products. It tells companies what customers may think and value their products and services. Thus leveraging the power of psychographics can help companies position their products and services to the right consumers at the right time (life stage and location).

This data product is mainly based on the Return-To-Sample survey by Numeris. We have also integrated research publications on consumer psychographics, lifestyles and behaviours, and Manifold’s predictive modeling techniques. We identified robust consumer psychographic patterns in the survey data and extrapolated them into the propensity score for each of the 6-digit postal codes.
Consumer Travelling Patterns
*This marketing research information is the result of Manifold’s models using the Numeris RTS Canada survey, it is not intended to duplicate Numeris data.

This database describes the propensity score of consumer traveling activities, including

Spending on vacations

Accommodation by types: resort, bed, and breakfast, boat, camping, cottage, cruise ship, hotel, motel, package tours, …

Vacation by location: Vancouver, Banf, Ottawa, Toronto, PEI, Alaska, Hawaii, …, and over 40 domestic and oversea locations

Booking methods: full-service agent, direct via hotel, online, etc.

Use of major airlines: Air Canada, British Airways, Asian Airlines, etc.

Traveling distance as driver or passenger

Usage of local bus and streetcar.

This data product is not only useful to the travel industry. It also reveals consumer lifestyles, e.g., people who prefer package tours, cruise ship and use full-service agents are quite different from those who like camping and travel more locally. Insight of consumer’s traveling pattern can help companies to engage their customers more effectively.

This data product is mainly based on the Return-To-Sample survey by Numeris (formerly Bureau of Broadcast and Measurement). We have also integrated publications of provincial tourist ministries, and Manifold’s predictive modeling techniques. We identified and confirmed robust consumer product usage patterns in the survey data and extrapolated them into the propensity score for each of the 6-digit postal codes.
Consumer Work Patterns
This marketing research information is the result of Manifold’s models using the Numeris RTS Canada survey, it is not intended to duplicate Numeris data.

This database describes consumers’ working related activities, e.g., the propensity of

Time driving to work by the minute

Number of people in the vehicle when driving to work

Public transit time to work by the minute

Commute to work by mode of transit

Involvement of business purchase decisions and value

Involvement of business purchase decisions by category:
  – Traveling
  – Computer hardware, software, education/training
  – Convention arrangement
  – Courier services
  – Handheld communications
  – Long-distance telephone services
  – Office equipment
  – Office food catering
  – Office supplies and phones
  – Office furniture

Security services

Business size

The number of business trips by mode of transportation: Air, rail, car, bus, etc.

This data product is important for public transportation and economic development. It is also very useful for B-to-B companies to identify decision-makers. Knowing driving time is strongly correlated to radio listenership, companies can use information in this database to identify best prospects reachable via radio.

This data product is mainly based on 8 years of the Return-To-Sample survey by Numeris (formerly Bureau of Broadcast and Measurement). We also integrated Census and NHS 2011 (National Housing Survey), and Manifold’s predictive modeling techniques. We identified and confirmed robust consumer product usage patterns in the survey data and extrapolated them into the propensity score for each of the 6-digit postal codes.
Household Restaurant Visits
This data product provides a propensity score of consumer restaurant visits, including:

Frequency of coffee restaurant visits, e.g., Coffee Time, Tim Hortons, Starbucks, etc.

Frequency of fast food restaurant visits, e.g., A&W, Burger King, McDonald’s, Subway, Wendy’s, etc.

Frequency of restaurant visits, e.g., Boston Pizza, Pizza Hut, Swiss Chalet, etc.

Frequency of restaurant visits by type: breakfast style, burger/pizza restaurant, high quality formal dine-in restaurant, etc.

Usage of food services like take-out, home delivery, online order, etc.

Spending on restaurants: business and personal.

Information in this database is used by food and catering companies for customer insight, trade area and competitive analysis, and optimization of flyer distributions.

This data product is modelled by combining inputs from Numeris RTS Survey and proprietary Manifold data including CanaCode lifestyle clusters. We identified and confirmed robust consumer restaurant visiting patterns in the survey data and extrapolated them into the propensity score for each of the 6-digit postal codes.

Datasets modelled using Vividata’s Survey of the Canadian Consumer**

Restaurant Visits
This dataset tracks dining habits over the past 30 days, including restaurant visits, ordering methods, and cuisine preferences.

Dining Frequency: Delivery, take-out, drive-thru, and eat-in usage.

Restaurant Types: Fast food, casual dining, bars, buffets, and more.

Cuisine Preferences: Popular choices like pizza, sushi, and steakhouse.

This data product is based on the Vividata survey on Consumer Product Usage and Behavioural Patterns. No personal or private information was used.

Information in this database is useful to food and catering companies for customer insight, trade area and competitive analysis, and optimization of flyer distributions.
Psychographics
This dataset provides the opinions, attitudes and beliefs of Canadians spanning the following topics:

Communications and media

Advertising

Personal Motivations

Finance

Personal views/Interests

Food

Automotive

Luxury

Shopping

Travel

Health

Personal appearance

Environment

Social views

News

Print

Media and products

This data product is based on the Vividata survey on Consumer Product Usage and Behavioural Patterns. No personal or private information was used.

This psychographic database tells companies what customers may think and value of their products and services. Leveraging the power of psychographics can help companies position their products and services to the right consumers at the right time (life stage and location).
Leisure Activities
This dataset describes the leisure activities of individuals through the following variables:

Frequency and recency of participation in leisure activities such as:

Sports & Recreational activities

Bingo/Video lottery terminals

Lottery tickets

Frequency and recency of attendance to:

Movies

Public sporting events, shows, or concerts

Restaurants

Casinos

This data product is based on the Vividata survey on Consumer Product Usage and Behavioural Patterns. No personal or private information was used.

Through leisure activities, companies can gain a better understanding of what their customers may like, value, and participate in, and then optimize their approaches for engagement. This database can help companies segment their customer better and tailor their products/services by lifestyles.
Real Estate and Home Improvement
This dataset captures home ownership and rental trends, property characteristics, real estate intentions, home energy usage, and spending on home improvements, furniture, appliances, and gardening tools.

Home owners and renters

Dwelling type and value

Vacation home ownership, type, and location

Real estate (type of property owned, intention to buy/sell, etc.)

Energy used for home heating

Spending on home improvement

Home improvement items & tools

Where furniture was bought and the amount spent on furniture

Type of gardening tools bought, amount spent, and where they were bought

Type of large household appliances & durables and where

Other info on small appliances, general household accessories, paint, etc.

This data product is based on the Vividata survey on Consumer Product Usage and Behavioural Patterns. No personal or private information was used.

Retailers and manufacturers of home hardware, moving companies, and storage companies can use this data product to gain customer insight; perform trade area analysis; estimate market potential; tailor communication and identify the best prospects.
Business Decisions
This dataset captures business purchasing and leasing decision-making involvement over the past 12 months across various categories. It categorizes respondents based on their level of influence in purchasing decisions and their spending levels. Key aspects include:

Company type

Work location

Business Purchasing/Leasing information sources used/Amount spent

Home Office/Business

This data product is based on the Vividata survey on Consumer Product Usage and Behavioural Patterns. No personal or private information was used.

This database is useful for businesses operating especially in B2B sales and relationships, trying to find populations that have a say in business decisions to allow them to partner or sell a resource.
Influence of Advertising
This dataset provides insights into consumer behaviour after seeing advertisements across various media channels. It captures the actions taken by individuals after exposure to ads, their recall of recent advertisements, their perceptions of ad influence, and the types of ads that capture their attention. Key variables include:

Actions taken after seeing ad by media channels: TV, radio, newspaper, magazines, outdoor/billboard

Last noticed an ad: yesterday, past week, past month, or longer

Where the ad was seen: TV, streaming platform, social media, email, search pages, mobile apps, outdoor billboards

Ad Influence Perceptions

Ad Characteristics the capture attention

This data product is based on the Vividata survey on Consumer Product Usage and Behavioural Patterns. No personal or private information was used.

This database can be especially useful for Ad, marketing, media, and consulting agencies looking for individuals whose purchases are influenced by different Ad types.
Life Events
This dataset tracks significant life events that individuals have either experienced in the past 12 months or anticipate occurring in the next 12 months.

Getting Married

Became a Parent

Became a Grandparent

Retire

Lost Job or Laid Off

Change Job

Start your own business

Moved out of Parental Home

Buy First Home

Sell or Change Principal Home

Separated/Divorced

Become Caregiver

Graduated High School

Quit Smoking

Get New Pet

This data product is based on the Vividata survey on Consumer Product Usage and Behavioural Patterns. No personal or private information was used.

Major life events, such as weddings, funerals, and graduations, often lead to shifts in purchasing behavior. Businesses that cater to these milestones can leverage data to identify where their past and potential customers are located, allowing for more targeted marketing and engagement strategies.

* This marketing research information is the result of Manifold’s models using the Numeris RTS Canada survey. It is not intended to duplicate Numeris data.

** This marketing research incorporates information from the Vividata Survey of the Canadian Consumer. It is not intended to duplicate Vividata data.

Most Recent Update

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2024

Available Geographic Levels

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6-digit postal code, FSA, DA, CT, CSD, CD, and custom geography

Update Frequency

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Annual

Methodology

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This data product is based on the Return-To-Sample survey by Numeris, publications of Statistics Canada and market research companies, and Manifold’s predictive modeling techniques. We identified robust consumer media usage patterns in the survey data and built hundreds of predictive models to extrapolate them into the propensity score for each of the 800,000 6-digit postal codes in Canada.

Validation is performed with in- and out-of-phase survey data to ensure accuracy of the predictions.