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Since government and census data tends to be much less rich in Asia compared to the United States and Europe, getchee relies on methodology to create granular subsets (1KM2 - 500M2 grids) from very generalized published records.
Grid Segmentation — Governments typically provide demographic information at sparse intervals, and at very high levels. For example, there was a 10 year gap between China’s 2000 and 2010 censuses, and the most granular unit collected was to a city county level.
Our core value is our methodology and techniques for creating smaller, more granular units of measurement that can be used by strategic planners more effectively.
Grid Density — We use a color-coded system to show ranges of demographic density over digital maps. This is useful to quickly understand the distribution of market segments. With this method we can show a distribution of total population, working population, household number, affluent populations, and age.
In many of our engagements with our clients, we create customized distribution densities based on their business and target customer requirements.
Wealth Segmentation — Need to know the distribution of wealth across a city, region, or country? We model the distribution of wealth across geographies in Asia. This data gets consumed by retail and banking clients that need to understand how to better adjust their product and brand positioning in these markets. Ask us about the different kinds of wealth and affluent segments we have available in China, India, and Southeast Asia.
Retail Use — What product assortment do you feature at your retail locations? How do you develop a pricing strategy that suits the market? By understanding your markets in terms of wealth, you can better conduct sales, marketing, and expansion planning.
Geo-Coded Points — We use different techniques including web scraping, field surveys, 3rd party vendors, and crowd sourcing to collect POIs in different categories. These categories can be broken down into accessibility, business, lifestyle, residential, and tourist point data.
Need to know where all of the banks and ATM locations are in Bangkok?
Need to find all the major sports apparel brand retail locations in China?
Location Attributes — In many cases we collect more than just a latitude and longitude for our POI data. We collect critical information needed to conduct intelligent business and strategic analyses including, but not limited to, brand name, address, phone number, format, and size.
In China, for example, we have nearly every shopping mall in the top 50 cities mapped to a point. For most of these locations we can tell you the size, brand assortment, class definition, and the number of parking lots.
Competitor Locations — In most cases we likely have a large majority of your competitor locations mapped to a point with business attributes. Gaining access to this information is easy through raw data, a consulting output, or our web-based tool that allows you to visually see your locations in relation to your competitors.
Whether you are looking to expand your retail footprint, or better market to your target audience, you need to have a good understanding of where your competitors are in relation to your store network.
Besides tracking base map, infrastructure, and government related point data, we follow major brands in the categories seen here. So for example, if you need to know where all of the McDonald‘s, KFC, and Pizza Hut locations are in China, this information would be readily available. Perhaps you need to know where all of the Nike locations are, including inside of shopping centers and malls, this information would also be available.
Brand Synergies — By analyzing your store locations and performance data in relation to business POI, you can discover synergies that mesh well with your brand.
Service Area Overlap (Cannibalization) — Through a GIS analysis of your competitor locations, you can better understand how they impact the performance of your stores.
|Apparel & Clothing||30|
|QSR & Restaurant||10|
There are many different kinds of heat maps, but all of them share the same principle in understanding the density of a data set in relation to its geographic environment.
Accessibility — We use an aggregation of transportation infrastructure such as subway stations, bus stops, and train stations.
Business — We use an aggregation of business point data such as banks, registered business addresses, and office buildings.
Lifestyle — We use an aggregation of retailers such as coffee shops, malls, restaurants, and shopping centers.
Competitor Intensity — Using heat maps you can visually see and understand the distribution of your competitors across geographies. You don’t need a complex analysis to understand the output.
Industry Intensity — See how business locations of different industry types are distributed across geographies. How does this relate to your business? Are you in the right locations to capture potential?
Market Potential — We can customize a market potential heat map for your business based on your target consumers, key competitors, synergistic POI, and other market related factors.