FTI Consulting frequently is called in by companies to help improve revenues and profits. In the media and entertainment industry, boosting revenues typically involves enhancing ad sales performance. Common methods of doing so include effective use of sales time, bundled selling, improved compensation incentives, better pipeline management and so on. These techniques can and do increase sales volumes and revenues, but such tactics do not raise the underlying ad rate. This is important because elevating the advertising rate, even slightly, can drive significant improvement in revenue growth and profitability. However, it can be quite difficult to up the rate, particularly if a company is in a competitive market or, like magazines and newspapers, is in a sector facing secular or cyclical decline. Ironically, we have found that giving up on standardized ad pricing is often key to raising rates.
Executives in media companies often try to boost revenues through price hikes in the rate card. The rate card contains the published prices for ad space in magazines, newspapers and TV ad spots. However, in recent years, we’ve found these rate hikes to be totally ineffective. At virtually every company we studied, we found no correlation between management-legislated rate increases and an improvement in negotiated pricing outcomes (Figure 1). In fact, we found no correlation between rate card prices and actual ad prices generally.
This is because both buyer and seller know the rate card does not reflect the marketplace. In the magazine industry, the published rate card has become meaningless, and it’s an industry-wide truism that no one pays the standard rate. Similarly, newspapers that have tried to enforce rate card pricing usually fail because print buyers knows the industry is under siege and recognizes it’s a buyer’s market. Broadcast television does a bit better because it is a market of constrained ad supply, but even in TV, both buyer and seller know that the rate card is wishful thinking and that the real price is up for grabs. Since the rate card is ineffective and there is nothing else to guide salespeople during negotiations, pricing becomes more random.
Killing the Rate Card
A rate card and a standard price list exist in many industries and settings. These pricing guidelines work when a company is in a position to set prices. However, in most B2B markets, there is a process of price discovery around the buyer’s willingness to pay and the seller’s willingness to sell at the buyer’s preferred price. Hence, in the advertising business, the rate card ultimately is meaningless as it pretends to set a fixed price in an environment in which both parties are trying to optimize price and each one has other alternatives.
Recently, we have been working with media companies to abandon the rate card altogether and replace it with a pricing tool that is more useful to salespeople in effectively pricing proposals and in negotiating terms. Though early in its adoption, this tool already is lifting ad rates.
The development of this new pricing tool is due to three key insights we’ve gained in studying ad rate outcomes.
Insight 1: Underpaying is rampant
Our data illustrate that underpricing, especially for small to midsize advertisers, is widespread. Figure 2 shows that many advertisers (yellow dots) receive a much lower ad rate (vertical axis) than others for the same amount or less of spending (horizontal axis). This is an important strategic fact. It means there are buyers in the same market and advertising category that will pay a higher rate than the low payers. In other words, there is an opportunity cost to the seller for allocating ad space to these underpaying advertisers when higher payers exist.
Knowing there are buyers willing to pay a higher ad rate can give an organization the fortitude and actual market data to negotiate better rates (or to extract a larger spending commitment commensurate with the discount the advertiser is seeking).
Insight 2: Ad rate should decline with total spending but is usually random
It stands to reason that customers that spend more should receive better ad pricing. However, our work across a range of media sectors reveals there is little correlation between ad spending and price. Again, as illustrated in Figure 2, many smaller advertisers (yellow dots) get lower prices than do the larger spenders. (In fact, a best-fit correlation between price and spend indicates virtually no correlation.)
Ideally, pricing should decline with spend, as shown by the dotted line in Figure 3. Yet many smaller advertisers that, in theory, have little bargaining power enjoy the best rates.
Raising prices with these smaller advertisers will add up to big improvements in revenues simply because there are so many in this category. Indeed, over numerous engagements, FTI Consulting has found the value of the underpriced rates (below the market best-fit curve) ranges from 18 percent to more than 30 percent of total revenues.
This lack of pricing discipline becomes apparent even when one considers variables other than price and total spend.
Figure 4 is an example from a magazine client. In this sample, the bubbles are advertisers, and bubble size represents the number of issues in which an advertiser’s ads appeared. Typically, magazine publishers offer discounts to advertisers that agree to appear in all issues. However, the small bubbles clustered around the lower spending advertisers reveal they get a better rate while appearing in fewer issues than the larger advertisers that run in all issues. So, here again, there is no rate discipline even when factors other than price and total spend are considered.
Insight 3: Deal size and complexity disguise bad rate setting
It is natural to wonder why poor rate setting has come about when salespeople are heavily incented to drive up revenues. It turns out that the focus on the size of the deal, without regard to the underlying rate, often makes that rate opaque to both buyer and seller, which commonly leads to low inherent pricing. Figure 5, again from the magazine industry, shows this clearly. Some large deals (yellow bars) have a low underlying rate (blue line), while others have a high one. In practice, there should be a balance between these two, and rate integrity should be preserved for large and small deals alike.
Deal complexity also can obscure poor rate setting. Take ad sales in newspapers. Usually, the salesperson and the buyer are goal seeking: The buyer has a certain target audience to reach and an available budget to spend, and the salesperson attempts to maximize the buyer’s return on investment. This is done by determining what products and sections of the paper will provide the buyer’s ad the most exposure, how many column inches (lineage) are possible within the buyer’s budget, what digital advertising should be included in the ad campaign and so on. The focus on the buyer’s audience and return on spend is the right one. It aligns the seller with the buyer’s objectives. However, when a deal becomes complex and multidimensional, the underlying rates are obscured, and a simplistic rate card provides little guidance.
New Pricing Tool
Considering these insights, abandoning the rate card clearly is a good idea. But what should replace it?
Based upon the above, we have sought to solve two problems simultaneously with our clients. The first is to help clients understand what their revenue-maximizing curve should be. This is needed since, as we have shown, many companies achieve random pricing and are rudderless when it comes to knowing what ad price at what level of buyer spending will maximize revenues.
Second, it is important to simplify the deal-pricing process for salespeople while making the underlying rates overt. This is mandated by Insight 3, as most advertising deals these days are multifaceted, and complexity obscures pricing. Today, few companies have a sales tool that makes it possible for ad reps to calculate various deal scenarios quickly, let alone allow them to know whether they are selling the ad space at a desirable underlying rate.
In working with our clients, we realized that providing such a pricing tool for salespeople would not only expedite deal making but would permit organizations to embed rate discipline (i.e., a rigorous pricing curve) into the tool itself.
Using client historical data, we first formulated a methodology for understanding a revenue-maximizing ad pricing curve. A newspaper example is shown in the two charts in Figure 6. The first chart is our client’s historical pricing outcomes. The second one, which is logarithmic in nature, is the pricing curve that captures the most value for the newspaper. The yellow dots pinpoint the rate along the vertical axis that should be charged for the various spending levels along the horizontal axis. In practice, each category of advertiser, such as autos or healthcare, may require a different price-maximizing curve since their willingness to pay differs. Equally, digital inventory, shopping inserts, home pages, etc. all have dissimilar optimal price curves. (No wonder salespeople have trouble pricing effectively!)
Once a pricing curve is developed, it can be implemented within the pricing tool (see Figure 7). Continuing with our newspaper example, the pricing tool we are rolling out for clients lets salespeople immediately enter a customer’s spending commitment. This total spend establishes the customer’s price discount along the pricing curve. Then the salesperson can choose the products, sections, ad sizes, placement and lineage that are possible within the customer’s budget. The tool recommends the rate and quantity for each possible ad type, and the tool tracks the remaining available spend as these selections are made. When complete, the tool automatically generates a detailed sales quote for the customer.
The tool is web based so sales reps can use it when they are on the road. Of course, the tool enforces the ad pricing curve discussed earlier. (The salesperson can override the tool’s rate, but overrides are recorded and reported.) To improve pricing even further, many of our clients award salespeople an added commission or bonus if they finalize a deal whose rate is higher than the advertiser’s historic price.
Another benefit of the tool is that it can enable a shift to audience-based selling. The tool has been designed to calculate overall reach and the effective CPM (cost per thousand) rate, and to print out a demographic profile of the audience reach achieved through the ad buy.
While it’s early in the rollout of this tool, our clients already are reporting ad pricing improvements. One client, for example, has reported an average pricing increase of 9 percent across three-fourths of its salesforce. In addition, the tool is allowing salespeople to revisit dormant accounts that had been overpaying relative to the new pricing and offer them better terms and a reason to become a customer again.
It is widely acknowledged today that deal making in media buying continues to become increasingly complex. Buyers are far more sophisticated, with a greater ability to compare prices. Rates for all media are under pressure as new forms of media compete for advertising dollars. In this context, the rate card provides no real guidance for salespeople. A pricing tool such as this one that can dramatically simplify the pricing process — while enforcing rate discipline and elevating average rates — is a timely and necessary solution.