You?d be seeking to use tests that give you the largest number of
samples to differentiate between H0 (no advertising) and H1 (cases
with advertising). You have the following data sets:
Baltimore H0, 2000
St. Petersburg H0, 2000
Baltimore H0, 2001
St. Petersburg H0, 2001
Baltimore H0, 2002
St. Petersburg H0, 2002
Baltimore H0, 2003
St. Petersburg H0, 2003
Baltimore H1, 2004
St. Petersburg H0, 2004
Baltimore H1, 2005
St. Petersburg H0, 2005
LOOKING AT YOUR SALES DATA
Despite our attempts at clarification, it is not entirely clear what
you have for sales. If you have sales in units and/or dollars per
sale, you potentially have many data points, allowing tighter
measurement of statistical significance because there are more
?degrees of freedom?.
If you do NOT have detailed sales data, the same tests can be applied
for gross revenues ? but with fewer degrees of freedom. This is only
logical because a few large sales might be skewing the total revenues
database. For example, if we?re trying to measure sales of commercial
apartment buildings and each of the two markets has only a half-dozen
or dozen sales ? but they represent hundreds of millions of dollars.
?Differences between Two Means (Independent Groups),? (undated)
TEST 1: The data set from 2000-2003 should allow some judgment on the
relative size of the Baltimore and St. Petersburg markets, potentially
with a large sample size in N(Baltimore) or N(St. Pete).
Let us assume that the results are different in those four years: at
this point you?ll be seeking a correlation or cause for the
difference. It is at this point that you?d be looking for the closest
Sales = ? + ?(FACTOR) + e
WHAT FACTOR IS CRITICAL?
In trying to estimate whether population, per capital income or some
other factor is important, it is important to understand the
underlying population, as Peter FitzRoy notes in the ?Advertising?
section of his book, ?Analytical Methods for Marketing Management.?
If we?re dealing with an impulse purchase or a minor consumable,
perhaps it is better to look at the total population of the two
cities. Thus, for shampoo sales or newspaper sales, population might
provide the best FACTOR.
If it is a product aimed at retired people, perhaps the best statistic
is the number of people above age 65 in each market. So, for denture
cleaners or supplemental Medicare plans we can look to test that data
as a FACTOR. Census numbers for 2005 show that the over-65 population
of St. Petersburg is 50% larger than for the Baltimore metro area.
If the product being sold is being consumed by only upper income
groups, per capita income may be the easiest FACTOR to use. Boat
sales and possibly even mortgage closings might all be very responsive
to this measure.
Luckily, the U.S. Census has started to speed the analysis of major
metropolitan areas by conducting annual surveys, providing much more
rapid demographic data than the 10-year census does. Note that
Baltimore data is under Washington-Baltimore before 2005 but is
In addition, the aggregation of St. Petersburg with the larger Tampa
market may cause some measurement problems for you:
U.S. Census Bureau
American Community Survey home
Whatever predictive FACTOR you?re using, it should be one of the
elements of the 2004 and 2005 analysis, especially because
traditionally the St. Petersburg population sizes have been growing
more rapidly than the Baltimore SMSA. Baltimore?s population grew
5.1% between 1995 and 2005, while Tampa-St. Petersburg grew by 20.4%
during the same 10 years.
IMPACT OF ADVERTISING
TEST2: You?ve already drawn some conclusions about Baltimore and St.
Petersburg markets, results that will PARTIALLY account for
differences in 2004 and 2005 sales results. Now it is time to add in
advertising to see what impact it is having:
Sales = ? + ?(FACTOR) + ?Ads + e
Here the H0 hypothesis is that the means are different -- ?0 for St.
Petersburg and ?1 for the Baltimore case. The ? value should be
carefully considered: it might be the NUMBER of ads (if uniform); the
total DISPLAY SPACE; or possibly even the AMOUNT spent on advertising.
Note that there are reasons for excluding the ad firm?s creative fees
if they are upfront costs unrelated to starting a campaign and NOT
related to number of exposures. But there are also reasons to include
them if a campaign changes messages often ? or if you?re trying to do
a profitability analysis.
You should be able to tell quickly now whether the advertising is
having a significant impact by measuring the difference between ?0 and
?1 with a T-test.
And you should expect the advertising analysis to yield some
? assuming that advertising is roughly equal, year 2 advertising
should show an improvement in effectiveness due to diffusion effects
and lags in advertising response, among other impacts. If it is not,
the strategy and tactics bear re-examination.
? the analysis of the impact of advertising should allow an analysis
of the profitability of advertising by examining the increased
spending vs. increased profitability. In one well-known marketing
analysis of retail done by Doyle and Fenwick, ?Planning and Estimation
in Advertising,? in the Journal of Marketing Research (1975), they
found that retailers benefited from increased sales as advertising
expenditures rose ? but that profits actually declined for the top 25%
Google search strategy:
sales advertising "statistical test"
?difference between means?
advertising diffusion effect
There are likely to be aspects of this analysis that appear unclear.
Please don?t hesitate to ask for a clarification request before rating