Powering local-level campaigning with Hope not Hate with 20,000-person MRP analysis

HOPE not hate’s campaign work rests on deep understanding of local-level opinion trends. This means having the ability to intimately understand local communities, often on a street-by-street basis, but also obtain results fast – in order to react to flash changes in opinion.  

HOPE not hate needed a research partnership that could deliver these highly granular estimates of public opinion and a deep understanding of the public opinion factions that make up the country.

Achieving this level of on-the-ground insight is often patchy, slow and prohibitively expensive. This is because it requires large sample sizes but also large enough panel coverage to target the most hard-to-reach groups.    

Focaldata designed a survey to capture the different dimensions of public opinion and attitudes across the UK. We used our platform’s targeting capabilities to survey a cross-sectional, 20,000-person sample of the UK public. This sample was carefully constructed to be representative across both traditional dimensions (age, gender, ethnicity, education tenure, past vote) but also attitudes, cross-checked against longitudinal panel data.

We used this sample to support a highly granular form of geographic modelling, powered by the advanced statistical technique MRP. The team used our proprietary MRP data pipeline to provide Hope not Hate with public opinion estimates at output-area level (equivalent to ~500 people). By layering these estimates onto dynamic mapping software, Focaldata provided Hope not Hate with a campaigns toolkit to better allocate their resources and power door-to-door campaigning.

With the help of Focaldata modelling, Hope not Hate is able to pick out public opinion trends on a community-level, street-by-street basis. Our mapping tools enable them to interrogate the data fast, prioritise their campaign efforts and anticipate public opinion flare-ups accordingly.