There are two sets of advertising data reflected in our reports:  Revenues received by local media companies and amounts spent on advertising by local businesses.

The statistical model underlying the advertising numbers in our reports is used by more than 1,000 media companies in North America.  It has been under continuous development since 1990 as a basic model for gauging advertising spending in any geographically defined market. The methodology is based on the concept that advertising expenditures are essentially equal to advertising receipts at the national level. The heart of the methodology is the manner in which these totals are allocated across business categories, media types and individual counties.

We pay particular attention to online advertising and spend much of our time collecting and analyzing data from this media segment. Our model is founded on two databases:

  • Database 1: An estimate of ad spending by all 15 million U.S. companies — by size and by Standard Industrial Code (SIC) — across all media channels
  • Database 2: An estimate of online ad receipts by ALL U.S. media companies

The model recognizes the fact that advertising money spent by advertisers located in one market may go anywhere. Similarly, a portion of ad spending from any other market may end up in the market measured. Therefore, the model separates ad spending that is coming into a market from ad spending that is going out of the market. This enables us to measure ad spending that is:

  • Generated and spent in a given market  
  • Directed to a market from elsewhere  
  • Generated in a market but spent elsewhere

Database 1 (Ad Spending) is compiled from sources that include Dun & Bradstreet, Interactive Advertising Bureau, AdRelevance, the IRS and several other government agencies. There are also more than 30 secondary sources, including industry research and reports as well as articles from a variety of trade publications. We then adjust the preliminary version of the Spending database in two ways:

  1. To fit a market's specific media demand pattern according to Nielsen, Claritas and other sources.
  2. To be based on per-employee spending, rather than on total company sales.

The per-employee basis is an important aspect of the model. As businesses get larger, the absolute level of their ad spending increases, but the per-employee amount of their ad spending rises and falls as the business grows through distinct stages of advertising behavior. The per-employee metric adjusts for company size and is therefore more reliable.

Database 2 (Ad Receipts) is based primarily on Dun & Bradstreet, the annual financial reports of media companies, and our own database of more than 4,300 local media company Web sites that participate in our annual survey. Numerous secondary sources include reports from industry and trade associations such as the Newspaper Association of America, Direct Marketing Association and Interactive Advertising Bureau, as well as surveys and articles from various magazines and online sources, such as Media Week, D&B and Advertising Age.

After we compile and adjust the databases, we compare them with estimates generated by companies such as McCann Erickson, Deutsche Bank, Morgan Stanley,  Veronis Suhler the NAA and various trade associations. Discrepancies are analyzed to ensure that differences are due to differences in theory or methodology rather than data error. 

When the Spending and Receipts databases agree, the spending estimates are then distributed by SIC among all U.S. counties. This process involves three steps:

  • Step 1: Allocation: The estimates are allocated to each county using the weighted values of several variables, including retail sales, households, Internet usage, median income, population and median age.
  • Step 2: Replacement: Whenever possible, actual known information replaces allocated estimates. Typically, 10 percent of the estimates are replaced.
  • Step 3: Recalculation: After replacement, the sum of the estimates will no longer fit to the original national totals. So, all un-replaced estimates are re-indexed and recalculated.

The process outlined so far produces estimates of online spending directed to each county. At this point, we still don't know how much of that originates locally.

To estimate this final piece of the puzzle, we take the ad spending generated in a county (from Database 1) and add to it the amount spent nationally to reach that county, and then subtract the amount spent by local companies on national ads. This leaves us with the ad spending directed to the county. We do this for each media type.

This methodology produces the local ad spending reports that our clients have relied on for years. Management consulting firm Booz Allen said, "It's the only methodology that could work."

Borrell Associates has compiled thousands of advertising intelligence reports for individual markets for more than ten years.  Media companies use them to understand the flow of advertising through their local markets, to target specific advertising categories for sales campaigns, and to develop compelling and informative sales presentations for individual advertising prospects.

How Borrell Forecasts

Borrell's forecast uses information from BLS, Woods & Poole Economics, and Dun & Bradstreet to estimate online media spending by ad category for each year during the period being forecast. The forecast mechanism is a five-step process:

  1. Woods & Poole forecasts for market economic growth (Retail Sales, GRP, etc) are drawn. Trend data are calculated and normalized to meet the growth estimate for the forecast years.
  2. These forecasts are applied to Bureau of Labor Statistics forecasts for job growth by industry for the current 10 year period. The result is a forecast for employment growth by ad category for each year.
  3. The forecast from step 2 is applied to the latest Dun & Bradstreet market counts for each ad category. The result is an annual forecast of employees per ad category in the market.
  4. Since the Borrell model utilizes ad spending per employee as a major input, a media forecast for the market in each annual year forecast is generated.
  5. The media forecast is joined to the employee forecast, creating a compatible estimate for ad spending in that year.

Every Forecast set is based on a set of assumptions, which are used to give weight and proportion to the estimates generated. The assumptions used to produce this estimate set are:

  1. The local and national economies will retain current rates of growth and inflation. Unemployment rates —  local and national — will follow trends suggested by the most recent Bureau of Labor Statistics data.
  2. Business activity and consumer sales will follow the same trend established during the past 4-5 years. 
  3. The relationship between sales and employees per business will remain roughly constant.
  4. Costs of raw materials (newsprint or steel, for example) will not change remarkably.
  5. No major changes in government policy (taxation, for example) will take place.

To obtain detailed advertising spending data for your specific market, contact us at 757-221-6641 or This e-mail address is being protected from spambots. You need JavaScript enabled to view it .