Beyond the Shopping Cart

A case study of using offline data to find your best online customers

By Jesus Mena

Continued from Page 1

ZIP Code Analysis

We started our analysis by appending ZIP Code demographics from the CACI's ACORN neighborhood segmentation system. This includes a breakdown of all U.S. ZIP Codes by the number of households, grouped by multiple consumer segments. Using a segmentation technique, we found that our client's online products were being purchased in neighborhoods where almost 40 percent of its residents were foreign-born, and many spoke a language other than English at home.

Our segmentation analysis found that a high percentage of online sales were from neighborhoods where the demographic profile consisted of a rich mix of ethnic and racial groups: Neighborhoods where nearly 60 percent were married-couple or single-parent families; with a median age of 37.9 years; households where workers had commutes; with over a third of them crossing county or state lines on their way to work. The consumers were affluent with a median income of $48,900, well educated, and gainfully employed, most of them held a bachelor's or graduate degree, and worked in professional or managerial positions.

We found that the Web site's online customers were primarily renters from the urban canyons of large cities, living in high-density, high-rise, pre-1950s apartment buildings. They tended to be urban, mobile, apartment dwellers from densely populated, central city locales in the largest metropolitan areas on the East Coast. This indicated that apartment dwellers were purchasing window air conditioners for older buildings that did not have central climate control. Our ZIP Code analysis found that these consumers were very highly concentrated in New Jersey and New York (see Table 1).

Household Analysis

To further define the profile of these online air conditioner customers, we appended demographics from Acxiom to their physical addresses. We did this in order to obtain consumer information at the household level (see Table 2).These demographics represented important lifestyle information, which could reveal hidden associations in relation to air conditioning products, such as type of dwelling, and a consumer's age or income level. In order to explore these relationships we used a rule-generating inference tool to segment the data. We found that sales tended to be higher to households in multifamily dwellings:

IF Single dwelling YES
THEN Sale 12.1%
IF Multi-family dwelling YES
THEN Sale 18.8%

This confirmed the findings of the ZIP Code analysis and the high concentration of renters. We also validated higher sales rates to households with single adults:

IF number of adults
per household is 1
THEN Sale 17.9%
IF number of adults
per household is 2
THEN Sale 13.9%
IF number of adults
per household is 3 or 4
THEN Sale 11.4%
IF number of adults
per household is 5 or 6
THEN Sale 13.8%

We also found that most of their online customers did not own automobiles:

IF value of all
vehicles owned is 0
THEN Sale 15.0%
IF value of all
vehicles owned is >$10,000
THEN Sale 12.7%

Lastly, we found that the age of their online customers also was a factor affecting their sales; we observed this reoccurring pattern in the ZIP Code analysis and then again in the household-level analysis:

IF age range of 
individual is 0 to 30
THEN Sale 13.3%
IF age range of 
individual is 30 to 37
THEN Sale 16.3%
IF age range of 
individual is 38 to 46
THEN Sale 14.1%
IF age range of 
individual is 47 to 50
THEN Sale 10.2%
IF age range of 
individual is 51 to 58
THEN Sale 13.2%
IF age range of 
individual is 58 to 98
THEN Sale 16.1%

These findings coincided with the findings from the ZIP Code analysis: sales were primarily made to single individuals in their 30s. We found that sales were higher to households in multiple-family dwellings; in other words, sales were highest to apartment dwellers, most of whom do not own automobiles, but instead used public transportation.

Real Property-Level Analysis

We also appended real property data from DataQuick based on information extracted from county assessors' and recorders' offices. This data provided detailed information about property ownership, age, size, and structural dwelling type. This analysis was designed to find associations between the air conditioner sales and the type of structural buildings or homes of this e-commerce site's online customers. Using their physical addresses, we appended the attributes to their Web data (see Table 3).

The purpose of this analysis was to see if unique, physical, real property features were affecting this air conditioner site's online sales. One of the first segments we discovered was the following IF/THEN rules, which coincide with a ZIP Code analysis that identified the neighborhoods of these consumers to be high-rise, 1950s apartment buildings:

IF structure 
age year Prior to 1957
THEN Sale 22.5%
IF structure 
age year 1957 to 1975
THEN Sale 12.4%
IF structure 
age year 1976 to 1978
THEN Sale 17.1%
IF structure 
age year 1979 to 1988
THEN Sale 11.9%
IF structure 
age year 1989 to 1999
THEN Sale 9.6%

The following rules found significance based on dwelling size:

IF dwelling size
in square feet less than 4,000 
THEN Sale 12.0%
IF dwelling size 
in square feet greater than 4,000 
THEN Sale 26.1%

The dwelling size indicated these buildings were primarily rental units. Yet another set of rules found an association between rate of sales and the size of the building structures:

IF home size range 
in square feet less than 3,500
THEN Sale 10.8%
IF home size range 
in square feet 3,500 to 7,000
THEN Sale 36.6%

Again, the real property analysis confirmed the trends discovered in the ZIP Code and household-level analyses; sales were higher than average for multiple family dwellings, which physically represented rental units.