Customer insight workshop c steve postlethwaite and sam hepenstal

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Customers at the heart of Total Place event 18th March 2010 Workshop C
Hampshire CC presentation on tackling ASB through customer insight

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  • - identified neighbourhoods where fears are concentrated – and shows Mosaic profiles within these areas – these can form the focus of targeted communications strategies to reduce fears. – not necessary to blanket areas when can identify particular neighbourhoods demonstrating most of fear Concentrations of unsafe responses – confirmed predictions in a number of cases – deprived locations, social housing, relatively high crime and ASB – demonstrated fears as expected. – for these cases analysis is useful in overlaying Mosaic household profiles to inform communications – analysis added to these expectations including info from street surveys – situational context of fear and driving factors. Some anomalies – areas which would not have been found – identified questions for future work – role of SNT/Neighbourhood watch Hypotheses drawn out from research for potential drivers of fear - to be tested in further work
  • Customer insight workshop c steve postlethwaite and sam hepenstal

    1. 1. Hampshire County Council: Customer Insight Project, Community Safety Steve Postlethwaite and Sam Hepenstal
    2. 2. Customer Insight Project: Community Safety <ul><li>Project Aims </li></ul><ul><li>Key Findings </li></ul><ul><li>Overview of process, methodology and findings </li></ul><ul><li>Identification of Initiative areas </li></ul><ul><ul><li>Examples from Havant </li></ul></ul><ul><li>Use of Mosaic Data </li></ul><ul><li>Communications strategies – future direction </li></ul>
    3. 3. Customer Insight Project: Community Safety <ul><li>Project aims: </li></ul><ul><li>The initiatives are primarily concerned with tackling national indicator 21; Dealing with local concerns about anti-social behaviour and crime by the local council and police. </li></ul><ul><li>Confidence is a complicated measure: A literature review informed the selection of the fear of crime as a key driver influencing this indicator. </li></ul><ul><ul><li>Strong correlation exists between postcode Place survey responses feeling unsafe and feeling the police and local authorities deal with crime and ASB by Mosaic group, 2008 (excluding group K ‘rural areas’) </li></ul></ul><ul><ul><li>Ipsos MORI research – feeling unsafe impacts upon many other influences of perceptions around police and local authorities dealing with crime and ASB. </li></ul></ul>
    4. 4. Initial Research <ul><li>Preliminary research seeks to identify neighbourhoods which can be targeted with communications initiatives aiming to reduce the fear of crime. The potential driving factors behind fear of crime in each neighbourhood will be considered. </li></ul><ul><li>Neighbourhoods identified can be targeted with communications, specifically tailored to the Mosaic profiles located within each area – to ask more questions and get a better understanding of fears for the next stage of analysis. </li></ul>
    5. 5. Key Observations <ul><li>Fear of crime is not spread randomly or uniformly across residences or neighbourhoods, but is related to particular situational and environmental influences </li></ul><ul><ul><li>Mosaic Profiles cannot be targeted uniformly </li></ul></ul><ul><ul><li>Fears relate to environmental and situational features </li></ul></ul><ul><li>Concentrations of residents feeling very unsafe have been identified </li></ul><ul><li>Confirmed predictions – but some surprises </li></ul><ul><li>Areas identified for further research: Initial development of a toolkit, role of Mosaic in identifying drivers linked to other factors. (NHW) </li></ul>
    6. 6. Data sets <ul><li>The Place survey, Sept 08 – Dec 19 th 08 – Question “How safe or unsafe do you feel when outside in your local area after dark?” was used to demonstrate fear of crime, given past literature on the subject. </li></ul><ul><li>Crime and ASB incident data – Between September 2007 and 19 th December 2008. Incident types selected based upon literature review of driving factors for fear. </li></ul><ul><li>Mosaic profile portraits – 2008, upgraded to 2009 categories. </li></ul>
    7. 7. Methods <ul><li>Spatial analysis: </li></ul><ul><li>Clustering – Identifies concentrations of point locations. i.e. Very unsafe Place survey responses, crime recorded incidents, and ASB recorded incidents. These cluster areas can form target neighbourhoods for communications and initiatives. </li></ul><ul><li>Overlays – Visual comparisons of data sets to observe correlations and relate Mosaic data to fearful areas. </li></ul><ul><li>Street surveys – To investigate situational characteristics influencing fearful responses. </li></ul>
    8. 10. Bedhampton:
    9. 11. Use of Mosaic and future work <ul><li>Residences within the areas identified can be targeted with communications based upon Mosaic Profile data. This will tell us how to communicate – but not what message </li></ul><ul><li>Research recommendations - To form communications strategies for the areas identified, based upon Mosaic profiles. </li></ul><ul><li>Future work – Role of Mosaic as proactive tool (requires further research, including more detailed questions and hypothesis testing) </li></ul><ul><li>Recommendations of research: The use of good practice street surveys is strongly recommended </li></ul>
    10. 12. What else do we need to know? - What message to communicate? <ul><li>If we select by mosaic groups/segmentation does this provide us sufficient information? </li></ul><ul><li>Our identified areas are different but with some similarities </li></ul><ul><li>We know neighbourhood watch [NHW] are active and communicate regularly with its members in these areas </li></ul><ul><li>Is NHW good/bad? </li></ul><ul><li>Who is fearful of what and why? </li></ul><ul><li>Are the communications good/bad? </li></ul><ul><li>What makes them fearful? too much/too little communication? </li></ul><ul><li>What is this telling us? </li></ul><ul><li>The more information collected the better our customer insight is </li></ul>

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