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Location Analytics
GIS & Location Intelligence
We blend business data with geographic data to reveal the relationship of location to people, transactions, facilities and assets. We know that real issues which our clients encounter are multidimensional in nature and in many cases present high levels of uncertainty. To deal with those customer requests, we bring together comprehensive datasets based on public, commercial and government data, then engage multi-criteria analysis to distinguish the importance of different data inputs, and dynamically work through "what if" scenarios.
Ad Hoc Analysis
One-off research projects based on geo-spatial data and relevant methods.
Decision Support
Improved location-based decisions by combining various geographic and business data.
Data Collation
Aggregating the required data from various sources and filling the gaps.
Live Dashboards
Visualisation and sharing of geographical data using the latest GIS and BI tools.
Data Sources
We work with a large number of data sources to perform multidisciplinary location searches at local, state and national levels. Data categories that we utilise include biophysical, socioeconomic, infrastructural, and administrative information. Extensive use of spatial data allows us to answer questions specific to site cadastral boundaries or define business-specific regions. Additionally we can analyse temporal dimensions of user requirements and bring together historic, current and strategic data layers to analyse trends and increase certainty in decision making processes.
Typical Project
We help organisations to turn location data into business outcomes through enrichment of data, visualisation and iterative analysis. Below is an outline of steps we undertake to complete a typical location analytics project:
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Assess
Identifying the requirements and opportunities.
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Acquire
Getting the data from multiple sources.
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Expand
Filling in the gaps and enriching the raw data.
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Model
Preparing the data model for analysis.
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Analyse
Performing the multi-criteria analysis.
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Deliver
Providing the results as reports or dashboards.
See Location Analytics in Action
We created the example below to demonstrate our approach to Location Analytics.
A fictional business case features an online bike shop looking to improve their sales in Melbourne.
First, we made a research to identify the target demographics.
The analisys showed that people with a Graduate Diploma, Bachelor Degree or Postgradute Degree, aged between 20 and 59 years,
and living within 10 km from their work place, are most likely to ride a bike.
Then we identified Melbourne suburbs with substantial population (over 2,000) matching the above criteria.
Finally, we matched this information with the last year's sales of the fictional company.
This clearly demonstrated which suburbs are performing well and where the sales could be improved.
On the map below, larger bubbles represent the suburbs with more potential cyclists.
The colour reflects the sales performance, with the red colour showing the lowest sales per capita and the green being the highest.
It is easy to spot the quick wins like Northcote, Hawthorn and St Kilda, which have no sales and a relatively large target audience.
This information could be directly actioned by the sales and marketing department.
Please feel free to investigate the map further by zooming in/out and hovering over individual bubbles to view their details.
See Location Analytics in Action
We created the example below to demonstrate our approach to Location Analytics.
A fictional business case features an online bike shop looking to improve their sales in Melbourne.
First, we made a research to identify the target demographics.
The analisys showed that people with a Graduate Diploma, Bachelor Degree or Postgradute Degree, aged between 20 and 59 years,
and living within 10 km from their work place, are most likely to ride a bike.
Then we identified Melbourne suburbs with substantial population (over 2,000) matching the above criteria.
Finally, we matched this information with the last year's sales of the fictional company.
This clearly demonstrated which suburbs are performing well and where the sales could be improved.
On the map below, larger bubbles represent the suburbs with more potential cyclists.
The colour reflects the sales performance, with the red colour showing the lowest sales per capita and the green being the highest.
It is easy to spot the quick wins like Northcote, Hawthorn and St Kilda, which have no sales and a relatively large target audience.
This information could be directly actioned by the sales and marketing department.
Please feel free to investigate the map further by zooming in/out and hovering over individual bubbles to view their details.
Use Cases
Here are a few real-world scenarios of how location analytics could help a business achieve their goals or gain competitive advantage.
Sunshine Coast, QLD
We were contracted to help finding a freehold land parcel located in Sunshine Coast Hinterland. Using the criteria provided by the customer (size, soils and hydrology, proximity to transport corridors), we made a selection of 20 lots (chosen from over 1 million), thus helping the client make the final decision.
Brisbane, QLD
A company contracted us to help identifying QLD suburbs that have the highest potential for expanding their business. Using the data available both internally and externally, we designed the criteria and analysed the business growth potential based on the existing and potential customers in each suburb.
North Coast, NSW
Our client required an analysis of potential markets for expanding their partner network. We collated information from multiple data sources, including the current sales by area and potential customers (based on industry workforce stats). This helped the client decide which areas require attention.
Sydney, NSW
An international investor required a property within 10-20 min drive from multiple suburbs with a specific ethnic profile. We used a number of criteria (such as available infrastructure, steepness, size and shape) to short list 25 properties (from over 1.5 million available parcels) for further consideration by the client.
