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 Four- Market Percentages 77% 9% 7% 7% Total for top four markets 22,067 100% 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 of media buy = (CPI) x 1000 = CPM ($0.00) Impressions delivered Cost per Rating Point = Cost of media buy = CPP ($000) Rating points delivered Cost per Effective Reach= Cost of media buy = CPER ($000) Cume reach within Effective Frequency range Cost per Inquiry= 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 State Farm Customers Competitors Customers No Current Provider Average Variance Category Relevance Importance of an agent +2.5% +14% +23% +13.2% Ideal Brand Attributes Price competitive +10% +24% +12% +15.33% Trustworthy +19% +14% +14% +15.67% Responsive to Questions & claims +3% +11% +20% +11.33% Looks out for my best interest +5% +20% +29% +18.00% Keeps up with the times +16% +10% +18% +11.33% State Farm Brand Strengths Customer satisfaction +5% +18% +18% +13.67% Strong local presence +3% +20% +17% +13.33% Best overall value for price +5% +13% +15% +11.00% Likeliness Factors (State Farm) Likely to remain a customer +8% +8% Likely to personally recommend +18% +18% Likely to try other SF products +5% +5% Likeliness Factors (Competitors) Likely to consider State Farm +16% +15% +15.50% Likeliness to talk to a SF agent +16% +15% +15.50% Brand Health Scores (out of 100) State Farm Competitor No Current Average Customers Customers Provider Score Pre-Campaign Exposure 78.0 57.3 61.8 64.03 Post-Campaign Exposure 80.5 69.3 74.0 74.60 Overall Change +7.5 +12 +12.2 +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 $50mm 66% $47mm 60% $75,800 $78,300 +103.3 Share of Voice (SOV) to Share of Mind 38% 66% 37% 60%.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 Competitive Pooled SOV CPP to SOM CPP $1.31mm $1.56mm $1.33mm $1.62mm.84 Efficiency Factor.82 Efficiency Factor +102.4 *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.
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.