Quantitative Market Analysis
At this point, the assumptions, vision, and general direction of the retail node in question should have already have been defined at a macro level. Without this guiding process, it is easy to lose sight of reality under the immense quantity of data available in most retail markets. The purpose of a quantitative market analysis should be to test a hypothesis and not scientifically to “discover” unknown retail phenomena.
Trade Area Delineation
The first stage is to delineate a retail node’s “trade areas”. Trade areas refer to the geographic regions which will likely contribute the majority of retail sales. Factors influencing trade areas include:
- Customer Travel Time
- Geographic Barriers
- Psychological Barriers (e.g. crossing a bridge)
- Retail Competition
It is never possible to include all future customers within delineated trade areas. In North America, market analysts working for regional mall developers typically use a 30 minute drive time as the extent of their analysis. Retail sales attributed to residents and tourists originating beyond this point are treated as “inflow”. North American regional malls generally have an inflow of approximately 20%, but individual circumstances can change this figure dramatically. The same thinking can be applied to an organic retail street but on a much smaller scale.
Trade areas are often subdivided into Primary, Secondary, and sometimes Tertiary. There is no hard and fast rule about how to delineate these areas – the general goal should be that every customer within a given district is positively and negatively influenced relatively equally by the same factors.
Demographic / Psychographic Analysis
The next stage of analysis is to determine demographics by trade area. It may be possible to purchase Geographic Information System (GIS) mapping software to automatically generate profiles. Data may be available for free through census publications and can be manually recorded if budgets are an issue. Typical variables include average age, household size, household ownership, ethnicity, gender, income, population, education level, occupation and retail expenditures. Growth rates should also be obtained if possible.
The variables most needed to run the actual quantitative calculations are population and per capita retail expenditures. The other variables are useful for painting a more colourful picture of the region and its characteristics.
Demographics are often complimented by psychographic variables (personality, values, attitudes, interests, or lifestyles), which may be categorized as groupings such as:
- Kids & Cul-de-Sacs
- Home Sweet Home
- Young Influentials
- Suburban Sprawl
- Urban Achievers
- Blue-Chip Blues
- Domestic Duos
- New Beginnings
- Suburban Pioneers
The following is a sample profile of one particular category:
Kids & Cul-de-Sacs
Falling under this category are upscale, suburban, married couples with children, with an enviable lifestyle in recently built subdivisions. With a high rate of Hispanic and Asian Americans, this segment is a refuge for college educated, white collar professionals with administrative jobs and upper middle class incomes. Their nexus of education, affluence and children translates into large outlays for child centered products and services.
Social Group: The Affluentials
Lifestage Group: Young Accumulators
US Households: 1,687,777 (1.52%)
Median HH Income: $70,223
- Buy children’s video games
- Go to Chuck E. Cheese
- Read Parenting
- Watch Nickelodeon
- Drive a Honda Odyssey
- Ethnic Diversity: High Asian, Hispanic
- Family Types: Families
- Age Ranges: 25-54
- Education Levels: H.S./College
- Employment Levels: Prof, White-Collar
- Housing Types: Homeowners
- Urbanicity: Suburban
- Income: Upper Middle
The benefit of psychographic data is that it balances out the “averaging” which often occurs when finding the demographics of a larger region. What exactly does knowing that your primary trade area is 5% wealthier than the city average tell you? Psychographics would let you know that 50% of the population of your primary trade area are lifestyle targets which match exactly with the intended upscale profile of the retail in question, whereas the remaining groups are bringing down average income and expenditures.
Market Penetration Analysis
There are two primary ways to estimate market penetration. The first is basic feasibility testing mechanism, while the second is a more complex series of calculations called gravity modelling.
Basic Feasibility Testing
Having completed the earlier assumptions, an estimate should already have been made of the general magnitude of new retail floorspace for the retail node in question. Market data on sales performance allows you to estimate roughly the amount of turn over stores would need to be financially viable. Typically, most new developments in North America see a sales performance figure ranging from $350 to $700 per ft2 annually, depending on location and retail category. Average street retail outside of main shopping streets and older malls might average $150 to $300 per ft2 annually. In a more detailed analysis, retail sales are subdivided by merchandise category. At a minimum, total floorspace should be broken into the major retail categories (Convenience, Comparison, Food & Beverage, Entertainment).
Total estimated required retail sales are divided by total retail expenditures of the delineated trade area. The resulting figure is the “capture” of retail expenditures that must be absorbed. $10,000,000 in annual sales in a $200,000,000 retail expenditure market would require a 5% capture rate. Determining whether this capture rate is reasonable is the real challenge. Intercept studies at existing retail centres provide excellent insight into consumer behaviour, but generally only mall managers and retail consulting specialists have access to this type of data. An easier way of conceptualizing capture rates is to consider the likelihood someone will visit a centre. How often will local residents visit per week? What are they most likely to spend on? For example, customers will travel only a short distance for convenience products, but might travel much further for comparison shopping. Conducting an intercept survey at a nearby retail development can shed a lot of light on these issues.
Gravity modelling is a more complex method for calculating the future sales of a retail node. The methods vary, but generally the process divides a district into numerous subparcels (i.e. Census Dissemination Areas). Each parcel contains X amount of retail expenditures for each merchandise category. Retail competitors are added to the equation and their level of attractiveness and relative distance to each sub-parcel is entered. The subject retail node is also factored in, along with its relative distances and level of attractiveness. Just as the name “gravity” implies, the attractiveness power of each retail node diminishes exponentially with increasing distance. The projected sales created by this model can then be analyzed to see if it meets the threshold required to make the retail node financially feasible.
The model is a useful tool, but nothing more. Every model has a major assumption hidden at some level. Every model is missing critical data, or overly simplifying a complex situation. This is not to say that models should be discredited, but rather to highlight the importance of combining these tools with critical thinking and full due diligence.