A key KPI to measure the average time spent in your local areas on a daily basis. This is represented in a bar chart showing the average time in minutes.
Analyse what is affecting the usage of your local area. Do events, the weather, aesthetic or marketing increase the time people spend in your local area? More time spent equals higher engagement with the area.
Dwell time should represent the total time spent in an area your are analysing based on the purpose of the area I.e. excluding residents that live in flats above a store on your hughstreet if you are analysing high street dwell time.
The longer the duration of visits to towns and centres, the more things and the more engaged visitors are when they travel there. Dwell time is an established leading indicator for economic performance at the local area level.
Dwell time is measure of the time spent between entering an area or radius around a point and leaving. Our products report the average daily visit duration in minutes.
There are no real constraints to how fine or how broad a measurement area, or point, can be. Customers typically provide points at which to measure dwell time, or polygons representing their areas of interest to us as inputs. We then report dwell time according to each location.
There is no limit to the number of locations available for measurement within our products. Our pre-existing data coverage exists across the whole of each geographic market we serve. This flexibility allows us to exactly meet your measurement requirements.
Huq’s mobility data goes back as far as 2016, although most products provide footfall data history starting in 2019. Mobility data history for your selected locations will be present within your product as soon as it is set up.
Mobility data allows researchers to determine where visitors travelled from, what they did / how long they spent there, and where they went next. It also provides a measure of the speed at which visitors travel. These qualities enable us to differentiate between visits and transits to an area within the metrics that we produce.
We associate profiles within our mobility data with census and other demographic classification data. This allows us to add dimensions such as income, age and gender to the mobility measures we make. This helps researchers discover more about who is visiting their centres and why.
Our experts will guide you through what is contained within the data, provide samples, support and get you started with Huq.