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A trade area is often defined as the geographic area that encompasses the majority of a firm's sales. Although intuitive and defendable, the idea of hard trade area boundaries is also criticized as being much too rigid and doesn't account for the complex and dynamic behavior of real life shoppers. From an economic viewpoint, the trade area provides the foundation to understand regional supply and demand. Consumer demand data explicitly implies its own specific trade area (including surveys) and retail store data (supply) also implies a specific trade area. Market research is really just understanding the relationship between supply and demand while the trade area framework needs to be able to quantify and account for the complex nature of human buying behavior. Trade Area Solutions generally separates market research into two levels. The first level is the trade area or macro level. The macro level is an analysis of the entire trade area which helps to spatially understand the total supply (your competitors) and demand (your customers) in the market. Or put another way, it identifies how the total demand for goods and services is being serviced by competitors (inside and outside of the trade area). Because both the supply and demand have very different and potentially very complex trade areas, this level of market research basically reconciles the differences between the supply and demand trade areas. In site selection studies, the macro level market research analyzes the supply and demand for goods and services of potential sites and identifies, evaluates and ranks these potential markets. This level of research identifies which community (small to medium sized urban centers) or which market within a community (large large urban centers) to locate a facility. We believe that the macro level market research forms the foundation of all market research because it provides an accurate understanding of the whole supply and demand picture. This is the focus and strength of Trade Area Solutions. The second level of market research is consumer or producer research at a more micro level. This is the level of market research that dominates the industry with a wide variety of tools and data. Consumer research is aimed at better knowing and understanding your customer. Criticisms of the rigidity of trade area analysis has led to much market research being done at the consumer level, including the use of surveys, without explicitly understanding the macro or trade area level research. However, whether trade area analysis is explicitly carried out or whether it is implicitly assumed, all market research implies a trade area. The most common market research mistakes overemphasize the micro research level with little or no understanding of how the total demand is being serviced by competitors inside or outside the trade area (the macro picture). For example, an exaggerated focus on micro factors such as land costs, traffic, parking and egress can lead to wrong location decisions. Many of these factors don't actually add sales, just removes sales when the factor has a negative impact. What is often overlooked, however, is market understanding regarding how the foundational supply (your competition) is servicing the demand (potential customers). In fact, it is not uncommon for retailers to only analyze the micro level and assume the macro level. We have seen this lead to poor business decisions. In site selection studies, this micro level of market research identifies the actual physical location within the trade area identified by the macro level research. Our experience is that most retailers have strengths at the micro level but need support at the macro level. Although we can do both levels of research, our focus and strength is macro level market research. We provide site selection at any scale from a single store with a single product to a nationwide network with a large basket of goods and services. Conventional trade area estimation techniques Ring/radius analysis is the most simplistic and likely the most common conventional estimation technique and involves a predetermined radius around a location (i.e. 5 km). It's used most often in situations where no customer sales data is available and/or when a "quick and dirty" trade are is required. One of its limitations is that it's only a theoretical general indicator which requires an arbitrary radius assumption. In addition, circles don't account for barriers such as rivers and the road network. Drive time or distance analysis has become popular with the use of GIS software and is widely used for convenience oriented goods and services where customers are expected to travel to the closest or most convenient location. Drive time analysis is really just a more sophisticated version of ring analysis that takes into account logistical barriers. It is also used when customer sales data is not available. It's limitation is that it still requires an arbitrary distance or time assumption. In addition, the accuracy of the trade area is further limited by the availability of an accurate road network. As in ring/radius analysis, these trade areas are only theoretical representations of an actual trade area. Gravity models estimate trade areas based on their ability to attract customers relative to other trade areas. The idea is that people are more attracted to larger retail centers because of an increased selection of goods and services. The attraction or pull factor is normally based on retail size parameters (square footage), population and income factors while the resistance is the time and distance of the establishment from the customer. Each retail location is normally modeled with its own trade area based on its relative pull factor. This becomes unrealistic when competitors are in close proximity to each other (i.e. across the street). The limitation of gravity models is that they remain largely theoretical and are often not "truthed" to reflect actual consumer behavior. They do, however, offer the most promise for trade area definition. Huff-type models are one of the common type of gravity model. These probability models can assign proportions of a neighborhood to more than one store location. Although often considered to be a trade area model, these models are not true trade area models because they still require defined neighborhoods or a trade area as input. More accurately, they are generally called "site models" or "spatial interaction models" (SIM) which are designed to help understand customer flows better (micro level market research) and predict the flow of customer purchases from within the trade area to the retail location. The weakness of SIMs is that they require intensive local primary research as input. When numerous sites are involved for site selection studies and network planning exercises, SIMs become an expensive alternative while still require trade area assumptions. Customer point-of-sale (POS) analysis has become a popular method for trade area analysis when the data is available. It is considered to be the most accurate because it uses actual customer sales data. Market firms differentiate their trade area analysis based on the variations of their POS derived trade areas. Its limitation is that it can't be used to evaluate new sites where no POS data is available.
The problem with conventional trade areas
is that it can't account
for purchases outside a trade area.
This is a result of two
issues. First, the data to quantify the purchases between
primary trade areas is not directly available. Or put another
way, a complete estimate of
the consumer underlying supply and demand picture is not complete at
the regional or micro level.
The only data available comes from
proprietary retail point-of-sale data that does not include
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These
consumer purchases outside or between primary trade areas are
sometimes called "leakages" and are not included in conventional trade
area analysis and assumed to be insignificant or approaching zero.
These "leakages", however, continue to grow and can routinely exceed
50% of the total market in many situations. Signs of significant
"leakage" often show up as confusing network performance
or
market research results. For example,
situations where marketing and sales trends don't confirm estimates of
untapped market potential suggest a
lack of fundamental
understanding into the economics or purchase behavior within a trade area.
Accounting for this trend in consumer buying behavior is the major
problem with conventional market analysis and site selection.
This can lead to poor and costly business decisions.
a
customer's other purchases to competitors within and outside the
primary trade area. As a result, market purchases between
primary trade areas remains unknown. Or put another way, spatial
supply and demand equilibrium at the regional level remains unknown. Although purchases
between primary trade areas occurs in convenience oriented goods and
services, the trend becomes acute when analyzing the trade area
for higher order shopping oriented goods and services. This data
gap cannot be ignored because it is foundational in virtually all
business market research projects.
Second,
the conventional market analysis framework can't explain or
quantify the purchase of goods and services between trade areas
because each trade area is estimated and analyzed as an isolated stand-alone trade
area. Even the best conventional trade area analysis techniques,
including the use of sophisticated customer point-of-sale data
techniques, can cause large spatial supply
and demand equilibrium errors. These errors can lead to serious business
decision errors. The following 