Quantitative Skills in Advertising
Of all business disciplines, advertising is probably the one where the need for quantitative skills is least
expected. In fact many “right brained tendency” students are attracted to advertising because they
think their lack of quantitative skills will not be a problem. If those students complete the Advertising
Management major at PSU, they experience a different reality.
The need for quantitative skills in advertising (or marcom*) falls into five broad categories:
1. Use of Indexes
2. Development of weighting models
3. Calculation of cost efficiency metrics
4. Computation of various ROI metrics
5. Creation of Infographics
Indexes
Indexes are analytical tools that help compare numerical attributes to a baseline. All indexes are
baseline 100. Brand Development Indexes (below) show the sales potential in a metro market. The
higher the index over 100 the better. BDI’s help ad managers decide where to spend advertising dollars
market by market.
The example below shows the computation of the BDI for Nordica ski boots in Los Angeles.
Brand Development Index=BDI
A BDI is a numerical indication of how well a specific brand is selling in a spot market. In this
case the brand of ski boots is Nordica.
% of a brand’s total sales in Los Angeles (8.35) X 100 = a BDI of 164
% of total US population in Los Angeles [5.09]
A 164 BDI means that the Nordica brand is a good sales performer in a good ski boot
market. If Noridica’s BDI was an 86, then it would be an under-performing brand in an
over performing market.
*Marcom stands for “marketing communications” and is interchangeable with the term “advertising.”
Another common use of indexes in advertising is in the analysis of possible target audience demographic
characteristics. These indexes are found by the thousands in product and media consumption tables
created by Simmons and MRI. An index (again baseline 100) indicates the probability of a specific
demographic to behave in the manner described in the tables. An index more than 100 indicates a
higher probability of a specific consumption behavior when compared to a larger but related
demographic population.
The MRI table below shows how different demographic subgroups index for the light, medium and
heavy use of gelatin dessert products. See the D columns in each usage level cluster.
Page 308
Weighting Models
These quantitative analytical tools inform advertising strategy decisions ranging from target audience
and media selection to advertising budget allocations. Such models often include multiple decision
factors with weights of importance attached to them. Model processing of multiple numerical factors
result in a series of numbers that mean nothing except in comparison to each other.
The Sum of Factors Weighting Model below shows how population, Effective Buying Indexes and
category sales are processed in a prescribed manner to inform how an advertising budget should be
allocated over four markets in Georgia.
Sum of Factors Weighting Model
Market
Albany
Athens
Atlanta
Augusta
Columbus
Macon
Savannah
Population
(000)
147.6
158.5
1,906.7
222.9
237.1
248.4
219.9
Effective
Buying
Income (EBI)
($000,000)
1,009.8
957.8
14,773.3
1,727.7
1,186.3
1,236.0
1,304.3
Sales of
Furniture/
Furnishing/
Appliances
($000,000)
37.9
35.2
293.9
45.9
36.4
28.7
59.7
Sum of
Factors
1,195.3
1,151.5
16,973.9
1,996.5
1,459.8
1,513.1
1,583.9
Total for
top four
markets
22,067
FourMarket
Percentages
77%
9%
7%
7%
100%
Adjustments may be made in the allocations if budget minimums apply.
Assignment 7C uses a 15% budget minimum rule. So money can be shifted in one of
two ways to meet that spending guideline.
Cost Efficiency Metrics
Some 80-85% of all advertising expenditures are for paid media (digital and traditional). The industry has
developed a series of cost efficiency metrics, four of which are Cost per Thousand Impressions (CPM),
Cost per Rating Point (CPP), Cost per Effective Reach (CPER), and Cost per Inquiry (CPI).
All four of these metrics are computed the same way, as shown below.
Cost per Thousand Impressions =
Cost per Rating Point =
Cost per Effective Reach=
Cost per Inquiry=
Cost of media buy
Impressions delivered
Cost of media buy
Rating points delivered
= (CPI) x 1000 = CPM ($0.00)
= CPP ($000)
Cost of media buy
= CPER ($000)
Cume reach within Effective
Frequency range
Cost of marcom program
=CPI ($0.00)
Number of measurable responses
ROI Metrics
Advertising agency executives must impress upon clients that sales performance should not be the
sole nor even the primary ROI metric for advertising campaigns. A 2011 publication from the American
Association of Advertising Agencies (AAAA or 4A’s) advanced an expanded and more relevant set of ROI
Post-campaign Results:
Dimensions of Brand Health for
State Farm Insurance
Category Relevance
Importance of an agent
State Farm
Customers
Competitors’
Customers
No Current
Provider
Average
Variance
+2.5%
+14%
+23%
+13.2%
+10%
+24%
+12%
+15.33%
Trustworthy
Responsive to Questions & claims
Looks out for my best interest
Keeps up with the times
State Farm Brand Strengths
Customer satisfaction
+19%
+3%
+5%
+16%
+14%
+11%
+20%
+10%
+14%
+20%
+29%
+18%
+15.67%
+11.33%
+18.00%
+11.33%
+5%
+18%
+18%
+13.67%
Strong local presence
Best overall value for price
Likeliness Factors (State Farm)
Likely to remain a customer
+3%
+5%
+20%
+13%
+17%
+15%
+13.33%
+11.00%
+8%
+8%
Likely to personally recommend
Likely to try other SF products
Likeliness Factors (Competitors)
Likely to consider State Farm
Likeliness to talk to a SF agent
Brand Health Scores (out of 100)
+18%
+5%
+18%
+5%
Pre-Campaign Exposure
Post-Campaign Exposure
Overall Change
Ideal Brand Attributes
Price competitive
State Farm
Customers
+16%
+16%
Competitor
Customers
+15%
+15%
No Current
Provider
+15.50%
+15.50%
Average
Score
78.0
80.5
+7.5
57.3
69.3
+12
61.8
74.0
+12.2
64.03
74.60
+10.57
metrics. Titled “ADVERTISING METRICS AND ROI: A Guide to Improve Agency Accountability and
Effectiveness” two global analyses demonstrate the importance of qualitative skills at the top of the
advertising food chain.
The Brand Health Model above shows critical changes in consumer attitudes toward a brand after
exposure to an ad campaign.
The Marcom Financial Analysis table below is used to track the productivity of advertising programs
over time. It uses ratios of advertising expenditures to key performance indicators. Cost efficiencies for
different fiscal periods are computed and then productivity indexes indicate forward progress or
slippage.
Financial Analysis of Marketing Communications
Key Ratios
Current
Period
Prior
Period
Cost Per:
Current period
Cost Per:
Prior period or 3‐year
rolling average
Productivity
Index*
Advertising to
Sales
$50mm
$500mm
$47mm
$460mm
$100,000 in adv.
expense for every
$1,000,000 in sales
$102,200 in
adv. expense
for every
$1,000,000 in Sales
+102.2
Advertising to
Market Share
(SOM)
$50mm
32%
$47mm
29%
$1.56mm
$1.62 mil
+103.8
Advertising to
Mind‐share
Share of Voice
(SOV) to
Share of Mind
$50mm
66%
38%
66%
$47mm
60%
37%
60%
$75,800
$78,300
+103.3
.58 to 1 ratio
.62 to 1 ratio
+106.9
Share of Voice
(SOV) to
Market Share
(SOM)
38%
32%
37%
29%
.84 to 1 ratio
.78 to 1 ratio
+107.7
Brand SOV CPP to
SOM CPP
$1.31mm
$1.56mm
Competitive
Pooled
SOV CPP to
SOM CPP
$1.33mm
$1.62mm
.84 Efficiency
Factor
.82 Efficiency
Factor
*Indexes above 100 mean that the most recent ad spending was more productive within the
metric than the previous measurement period or a rolling 3‐year average.
+102.4
Infogrpahics
Advertising has not been immune from the impact of “big data.” More and more marketing
strategies are built on data-driven assumptions and the proof of advertising claims in ads and on
websites increasingly rely on graphic displays of data, also known as Infographics. These tools help
tell a brand’s story or simply show quantitative information is a more interesting, visual manner.
The key to good Infographics is knowing how to interpret the data in the first place. A solid
background in statistics is critical for team designing Infographics. If you want to learn more about
Infographics, go to http://en.wikipedia.org/wiki/Information_graphics
In Conclusion
As perhaps shown here, there is a lot more math and application of quantitative analysis in advertising
than is thought. That said, the computations are pretty straight forward: percentages, ratios, indexes,
cost efficiencies, and combinations thereof.
This report submitted by Don L Dickinson, retired Director of the PSU Advertising Management program
and author of the AAAA publication cited on page 4.