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Agenda

Views of Commercial Users Barry Leventhal MRS Census & Geodemographics Group ONS Summer Workshop 24 th July 2013. Agenda. Introduction – the MRS CGG How will commercial users be analysing the 2011 Census data? What kinds of analysis should ONS be producing? Conclusions.

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Agenda

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  1. Views of Commercial UsersBarry LeventhalMRS Census & Geodemographics GroupONS Summer Workshop24th July 2013

  2. Agenda • Introduction – the MRS CGG • How will commercial users be analysing the 2011 Census data? • What kinds of analysis should ONS be producing? • Conclusions

  3. The MRS Census & Geodemographics Group (CGG) • An advisory board of The Market Research Society • CGG represents the interests of researchers in census and population statistics • Formed in 1989 – has helped to shape the last three censuses, and is now looking beyond 2011 • 20 members from market research agencies, census distributors, targeting consultancies and other commercial users • Websites: • www.mrs.org.uk • www.geodemographics.org.uk • Linkedin Group: MRS Census and Geodemographics Group Network

  4. How will commercial users be analysing 2011 Census data? • Continuous use of census data throughout the commercial sector • Applications in research & marketing include: • Geodemographics • Targeting local markets • Targeting customers • Designing market & social research surveys Unashamed plug: CGG Seminar Wednesday 6th November on early use of Census and other open data - further details on MRS website

  5. Geodemographics ApplicationsDeveloped by value added resellers such as...

  6. Geodemographic Analysis • Geodemographic classifications, e.g. ACORN, MOSAIC • Applying cluster analysis to small area data • Profiling catchment areas (and customers) • Comparing profiles of target area vs. national profile • Estimating Retail potential • Applying market research estimates to area profiles • Input to further products & services, e.g. site location analysis • regression and gravity models • And other applications of geographical information

  7. Targeting Local Markets • Retailers use census data to help make multi-million pound investment decisions, e.g. • Where should new stores be opened? • Which existing outlets should be closed, refurbished or rebranded? • What range of products and services to offer, to meet local needs? • Which local media to employ? – newspapers, posters, radio, leaflets? • Census analysis includes: • Measuring size of catchment area population • Profiling catchment area population by demographics, such as age, social grade, ethnicity, religion, and by geodemographics • Estimating market size - by applying market purchasing rates to catchment area profile

  8. Example: Growth in Sainsbury’s store network since release of 2001 Census data March 2003 March 2010 • Between 2003 and 2011, Sainsbury’s opened over 450 new stores and extended over 100

  9. Targeting Customers • All types of companies with domestic customers use geodemographics as an input to targeting, e.g. • Which customers to target for cross-sell or up-sell? • Which customers are likely to churn? • Which customers should be sent catalogues? • Where to place advertising for gaining new customers? • Geodemographic analysis includes: • Profiling target groups of customers, e.g. churners vs. non-churners • Building and deploying propensity models • Analysing media profiles

  10. Designing market & social research surveys • Market researchers use the Census to design, control and enhance sample surveys: • Sample design & execution • Custom analysis, e.g. using Census Microdata • Profiling research data by geodemographics, e.g. products and media • Modelling/integrating Census data with research and customer data

  11. Sample Design & Execution • A unique benefit of the Census is availability of demographic information at small area level • Allows analysis/understanding of areas beforeresearching them • Researchers use census data for: • Profiling areas and planning fieldwork • Estimating penetration rates • Stratifying samples • Setting quota targets • Survey weighting

  12. Social Grade Approximation for the Census • “ABC1” Social Grade used to be a key omission from the Census for market researchers and other commercial users • Government classifications such as NS-SEC have not been adequate substitutes • MRS CGG has developed a model to derive approximate Social Grade based on Census variables • Model was first built for 2001 Census, redeveloped for 2011 • Social Grade model was implemented by ONS as derived variable • Tables on Approximated Social Grade are included in Census outputs from Release 2 onwards

  13. 2011 Social Grade Approximation was built and tested on market research data • Original and Approximation profiles match well for all markets examined • Similar targeting decisions would be made using Approximated Social Grade Source: National Readership Survey

  14. Example Results – Approximated Social Grade for Inner London Boroughs 2011 Census: Approximated social grade for local authorities in England and Wales Base: All household reference persons aged 16 to 64 in households Source: Office for National Statistics

  15. Integration of Census data with other sources • The value of Census data increases if it can be integrated with other sources • For example, new insights can be created by integrating... Census small area statistics Census microdata Insights Market Research survey data Customer database information

  16. Example - Generating small area estimates of demand for “eating out” Market research measurement of eating-out markets Census small area statistics Model demand by demographics Census microdata: estimate counts required for models demand estimates for all areas This method was applied to estimate and map the demand for 7 eating-out markets, at small area level

  17. What kinds of Census analysis should ONS be producing? • Data mining • Squeeze out every last drop of value from the Census • Things that users cannot easily do • e.g. Applying user-generated models to Census database • More data visualisation

  18. My personal wishlist (1)Modelling further variables onto the Censuse.g. Income estimates • Build a model to estimate an individual’s income, on a suitable survey, using demographics available in Census, e.g. age, occupation, region, hours worked • Apply model algorithm to Census database – generate income estimates for individuals and households • Summarise income results down to OA level • Average income levels • Distributions and cross-tabs by income ranges • Benefits: • Approximated income variable, as if collected on Census • Can then examine relationships with other variables • Other census users will require different modelled variables...

  19. My personal wishlist (2)Household Segmentation • Develop a household segmentation using either market research data or Census Microdata • Apply segmentation algorithm to Census database • Produce outputs down to OA level • Number of households belonging to each household • Make segmentation algorithm available to users – for application to their own surveys and data sources • Benefits: • Strengthens link between research data and Census • More accurate method of demand estimation than using geodemographics

  20. My personal wishlist (3)Profile and model Internet Completion • 16% of census forms were completed online – distribution seems to follow a geographical pattern • Profile Internet vs. non-Internet forms by all standard demographics • develop description of online completers • Build model to predict propensity of online completion • Apply model to other ONS and external surveys • Benefit: • Enables surveys to be targeted or stratified by likelihood of online completion

  21. More data visualisation, e.g... • Recent release of postcode-level Census headcounts enables dot mapping within OA’s • Example shows Oxford postcodes coloured by population size • Dot maps would be useful method for visualising population dispersion • See also US Census Dotmap Source: http://opendatareview.wordpress.com

  22. Conclusions • Commercial users are continuously analysing Census to underpin research and support investment decisions • ONS should focus on the kinds of analyses that users cannot easily do for themselves, including data mining on the Census database and applying user-generated analytical models • Squeeze out every last drop of value from the Census!

  23. Thank you! Dr Barry Leventhal BarryAnalytics Ltd www.barryanalytics.com barry@barryanalytics.com Tel: 020 8905 2634

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