Location intelligence is now widely used to improve a variety of marketing and advertising activities. The tools and data now available enable decision makers to make evidence based decisions to improve their campaigns based on knowing who their target market is, where they live and work and how they spend their money. While location intelligence has an impact in improving the effectiveness in traditional marketing and advertising it can also have a significant impact in improving the measurement of the 3% of spend on outdoor advertising (billboards, buses/transit, bus shelters etc).
Location Intelligence provides the capacity to organise and understand complex phenomena through the use of geographical relationships inherent in all information. All evidenced-based decisions rely on data being available that is fit for purpose and a knowledge of the limitations of the use of a particular dataset has as much if not more importance than knowing its strengths. In regards to transit advertising (the advertising placed on buses and other vehicles) there are 3 key groups of data that are needed to create an effective transit measurement model for identifying locations and routes best suited to target markets. Knowing how to analyse and aggregate these datasets can be the key to finding that elusive competitive advantage. The 3 key datasets are;
- Vehicle routes and frequency
- Traffic volumes
- Census and spend data
Vehicle routes and frequency
To create an effective opportunity to see model (OTS) for transit you need to know the routes that the transit vehicles travel and the duration that these vehicles spend travelling along these routes. Collating and standardising transit vehicle data can be a time-consuming job as there is often no standard as to how this data is published assuming that the data is publicly available in the first place. Spatial data manipulation tools such as FME can help in building standardised data schemas where the source data can come in a variety of formats with varying levels of completeness.
Once the routes and frequencies have been established the next key dataset in calculating the OTS model is the daily volume of vehicles travelling along the transit routes. A robust OTS model will utilise traffic volume data to harness the strengths of the dataset while being aware of any limitations to model. You need to ask yourself, "what is that traffic volume data actually representing? Is it single or bi-direction travel? What time period? What vehicles are being represented? Is it an estimate or actual count". Knowing the answers to these questions is vital in understanding the strengths and weaknesses of the model.
Census and spend data
An OTS model can be built with transit route and associated traffic volume data but for the OTS model to provide the best recommendations on ‘right audience, right message, right time’ it needs to incorporate Census and spend data. Census data can be used to generate a high level profile of the types of people that live, work and travel within the 'catchment' of the bus routes while credit card spend data can be used to give a detailed picture on their spending habits. This data enables the optimisation of where and when transit advertising can be used to best reach the target audience.
When location intelligence is used in marketing and advertising it can provide a robustness and depth of complexity to analysis and modelling that may not otherwise be achievable. Companies like iSite Media are using location intelligence and spatial data analytics to build opportunity to see models that are transforming their business and enabling them to build a clear competitive advantage over their competitors.
To read more iSite Media’s partnership with Critchlow to create an award winning bus measurement system download the case study.