While it may be a lot more fun to forecast revenues when the overall outlook for advertising is upbeat, last year’s turbulent ad market required ad sales executives to fine-tune their revenue forecasting models. The recent MFM Conference provided a forum for examining how we can apply those lessons in both good times and bad, and here are a few of the insights that resonated with our 2010 conference attendees.
As TV One’s Johnathan Rodgers once told an MFM conference audience, “It’s a lot more fun to focus on growing revenues than figuring out where to cut expenses.”
At this year’s MFM conference, Media Finance Focus 2010, attendees not only heard upbeat news on 2010 ad revenues, they also received advice from experts on how to more accurately forecast and optimize the revenues they expect their stations to generate.
While it may be a lot more fun to forecast revenues when the overall outlook for advertising is upbeat, last year’s turbulent ad market required ad sales executives to fine-tune their revenue forecasting models.
The MFM Conference session “Revenue Forecasting: Predicting the Future in Impossible Times,” provided a forum for examining how we can apply those lessons in both good times and bad, and here are a few of the insights that resonated with our 2010 conference attendees.
The first consideration addresses the models that may be used for revenue forecasting. Don Locke, COO of ShareBuilders, a company that tracks more than $4 billion in media buying, describes three methods that are commonly used by ad sales executives to project anticipated revenues in a turbulent market:
- Bottom Up — In this scenario, account execs work with their ad sales managers on anticipating how much revenue they expect to book from each of their clients. Locke finds this approach can be effective in forecasting ad revenues for up to 45 days.
- Percent of Finish — This model can be more effective for forecasting ad revenues for the next 60 days, according to Locke. It begins by looking at the percent of the money booked on a given date compared to the total ad revenues of that year. From there, the TV sales executive compares the percent of money booked so far in the current year to his or her revenue forecast.
“If your historical averages show you should have 85% of your money in, and this year’s calculation to your forecast is 75%, your forecast is probably too high,” Locke pointed out. The method also takes into consideration how the money came in on a particular month compared to their historical averages. This allows the ad sales executive to compare both the patterns for the current month over several years as well as the recent pattern for previous months for anticipating how the year will finish.Brand Connections
- Month-to-Month — The Martingale Effect — an economic forecasting model that contends that the recent past is the best predictor for the near future — is at the heart of the month-to-month model. Locke has found it can be an effective forecasting tool for up to six months out. It allows forecasters to factor such elements as the impact of political advertising on revenues during even and odd years as a means for anticipating ad revenues in the current year.
Local-pacing patterns also are also a helpful forecasting tool. This analysis relies on charts that compare a station’s month-to-month revenue growth (or losses) with other stations in one given company. By aligning themselves with media-tracking companies like ShareBuilders, stations can get pattern-recognition information about the total universe of stations covered by the service, or compare information specific to their own market, such as results for other stations or ad trends within their geographic area.
This approach also allows stations to apply the Martingale Effect for tracking their revenue share within each of these groups and graph the marketplace to a total index.
Using this type of analysis, Locke says, stations can build their yearly budgets within hours, and keep their forecasting at a macro level.This may be preferable to the bottom-up method, which can be affected by account reps, who are tempted to engage in some sandbagging with their estimates.
Paul Scott, general sales manager of Meredith’s WSMV Nashville, and Charlie Izzo, VP and general sales manager of rep firm HRP, agree with Locke on the risks associated with using the bottom-up method. Izzo says, “Local account execs can’t tell you with certainty what the client is going to do, because the client’s agency often doesn’t know.”
Anticipating the effect of political advertising is key to the forecasting process, Locke notes. In key election years, his clients have experienced “crowd out” levels of 2% to 3% in the third quarter, and up to 6% in the fourth quarter when the overall advertiser demand is greater than the inventory.
WSMV’s Scott emphasized the importance of services like ShareBuilders, especially over the last year, when the ad sales broke later than usual. “Good forecasting relies on good data, which means market intelligence is key,” he says.
Scott finds the “bottom up” approach to be the least helpful in revenue forecasting during a late-breaking market. In contrast, analysis that incorporates ad-spending patterns across all markets over two or more years has been very effective for him.
Izzo, whose team manages national ad sales for the Meredith and McGraw-Hill Broadcasting station groups, says that station forecasters can benefit from working with a station group’s national ad sales team to accurately forecast — and optimize — their outlet’s ad revenues.
In Izzo’s experience, forecasting is affected by three factors: percent-to-final data; ratings and rates; and the amount of available inventory.
HRP reviews market data for a four-, five- or six-year period and compares local sales with industry-wide results for local and national spot sales. The analysis allows HRP and its client stations to assess the health of a particular market and apply the indices to revenue forecasting, according to Izzo.
Izzo finds that pacing reports aren’t as effective as a percentage to final, since the pacing analysis can be thrown-off by one advertiser’s shift in spending. In addition, the percentage-to-final analysis can be updated, such as looking at results for the first week of the prior quarter when updating the forecast during the first week of the current quarter. HRP cross-references its reports with industry data, such as reports from the Television Bureau of Advertising, as well as data tracking the station group.
It’s important to factor new, anticipated business into the forecast model, says Izzo. In his experience, ShareBuilders’ tools may be used to guide the station in deciding how much of its inventory to allocate for new business as well as its pricing activities.
In addition to taking into account programming considerations, such as competing with networks affiliates carrying the Super Bowl or March Madness basketball, stations need to factor local-market conditions into their forecast. Izzo suggests evaluating the station’s market conditions versus the industry.
“Local can be up 20% while the national market is up only 8%, which is when you need to take advantage of that strong local demand and sell more [of your inventory] to the highest bidders. This is a great example of why local and national [ad sales] need to work together,” he explains.
Additional examples of trends to watch include the impact major adverting sectors can have on inventory. For example, the auto industry went from representing 32% of the TV ad market to just 18% in 2009.
Fortunately, 2010’s revenue forecasting challenges are a lot more fun. With the heavier upfront buying for the fall TV season and greater inventory demand from political advertisers, Izzo predicts that inventory sell-outs in the third quarter could be at an all-time high.
I am hopeful that these suggestions from our Media Finance Focus 2010 experts will help you to make the most of that opportunity.
Mary M. Collins is president-CEO of the Media Financial Management Association. Her column appears in TVNewsCheck every other Friday.