The market research problem - How conventional trade area analysis can lead to poor business decisions!

Consumers purchase outside their trade area.  Today's retail reality continues to change at an increasing pace.  The growth of the big box and power center retail concepts reflects the reality that consumers are driving further and routinely traveling outside their primary trade area to purchase goods and services.  In addition, the size and offer of some of the big box retailers is also growing to super size proportions.  The basket of goods in a typical consumer shopping trip continues to grow and normally includes both convenience and shopping oriented goods and services.  This same reality also exists in North American agribusiness.

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. 

The problem with conventional trade areas is that it can't account for purchases outside the trade area.  Click here for a more detailed background to the different methods of conventional trade area analysis including ring/radius analysis, drive time/distance analysis, gravity models, and customer point-of-sale analysis.  The problem 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.  For example, a firm may have data showing its own milk sales but it does not know how much milk its customers are purchasing from competitors inside or outside its 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.  Spatial equilibrium occurs when all the demand for goods and services inside a trade area is supplied by competitors inside the trade area.  Spatial equilibrium virtually never occurs without a spatial economic framework to adjust and analyze for it.  This data gap cannot be ignored because it is foundational in virtually all 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 case studies show three real life situations where conventional trade area analysis has led to wrong and expensive business decisions.

Our solution

Our solution is to build a new spatial economic framework.  The new framework allows us to estimate and fill in the data gaps. 

Closing the data gap ensures that the supply and demand picture is complete providing the foundation for further market research.  It becomes the basis for accurately understanding and quantifying the economics of a trade area.

Our framework does not analyze a trade area in isolation from its surrounding trade areas. We use a whole-of-system approach using multiple trade area layers to quantify and analyze the interaction within a firm's existing network as well as your competitor's network. This makes it an ideal tool for retail network planning and optimization.

The Trade Area Solutions market analysis framework involves an advanced gravity model that simulates purchase behavior. The foundational economic theory is that retail establishments and or groups of retail establishments have a different attractions or pull factors relative to other locations and competitors. The strength of these pull factors determines the size and shape of the trade area.

The gravity model, however, differs from convention in two ways.  First, instead of using simple population and/or retail square footage variables to determine the attraction or pull, we use a much more detailed and sophisticated approach that gets at the core of the pull or attraction.  In fact, we often incorporate econometric or other modeling techniques in a two stage approach to ensure the validity and explanatory power of the variables we use.  The idea is to allow market factors to determine the size and shape of a trade area without any preconceived assumptions such as drive times or radius distances. 

When available, customer point-of-sale data is used to validate and analyze the performance of a firm's current network.  Our framework bridges economic theory with reality by understanding and quantifying the ability of a trade area to attract customers based on actual customer point-of-sale data.  Because customer point-of-sale data is incorporated, the framework accounts for logistical barriers and is capable of explaining consumer behavior for all types of goods and services. The framework can also be used directly in analyzing new potential sites, without arbitrary drive times, distance, and other imposed parameters.  If customer point-of-sale data is available from an existing network, the framework can utilize these data to calibrate the model for estimating trade areas in site selection studies.  As a result, the trade area estimation techniques can be used seamlessly between situations where customer point-of-sale data is available and also in new potential site analysis where sales data is not available. 

The second way our gravity model differs from convention is that trade areas are estimated as a system of multiple demand layers.  For example, the primary layer consists of the major purchase flows and major competitors for the goods and services being analyzed within a trade area.  This trade area will account for a certain proportion of the total supply and demand for the specific goods and services being analyzed.

The second supply and demand trade area layer is the secondary trade area.  These are more regional trade areas that involve minor competitors often situated near the boundaries of the primary trade areas.  Competitors in these trade areas often carry more convenience type than shopping oriented goods and services.

The third supply and demand trade area layer is the tertiary trade area layer which involves the smallest of competitors in the hinterlands of the primary trade area.  Competitors in these trade areas most often compete only at the convenience level for goods and services.

The last trade area layer is a spatial equilibrium test that quantifies and fills the data gap of consumer and producer purchases between trade areas.  This allows Trade Area Solutions to understand and quantify market migration or the growing trend of customer purchases outside their trade area.  As a result, regional supply and demand spatial equilibrium is ensured meaning an accurate regional economic picture that can be validated and calibrated using customer point-of-sale and even survey data.  This proprietary framework and  analysis techniques create a level of understanding and accuracy conventional market analysis simply can't match.

Large urban trade area example

Small town trade area example

Your benefits

The benefits of our solution are simple: an optimized network with higher profits and the ability to identify and rank market opportunities while minimized the risk of expansion.

Our market analysis framework offers the following abilities and advantages:
• Completes the foundational macro supply and demand picture for virtually all market research;
• Provides the framework for combining proprietary client data with publicly available data from various sources such as survey and customer point-of-sale data;
• It is the only technique that fully accounts for the impact of competitors. It can also estimate and compare the subject trade area with a competitor's trade area;
• Analyzes the entire market including the competition, not just a single factor or location;
• Ideally suited for network planning and site selection studies because it identifies and automatically ranks market opportunities;
• Capable of understanding and quantifying the market for both lower order convenience and higher order comparison shopping oriented goods and services;
• Verifiable accuracy.  The analysis framework incorporates client point-of-sale data, the accuracy of the trade areas can be validated and verified;
• The analysis framework tests and analyzes for spatial equilibrium: market migration or the flows between trade areas is quantified as a separate demand layer;
• Decomposable - since the framework and analysis process is based on an underlying supply and demand balance sheet, one can drill down into each trade area which yields a new higher level of understanding of market dynamics and improved predictive power;
• The framework is dynamic and capable of modeling ‘what ifs’, such as changes in supply or demand, or changes in market structure or competition.