Leverage Media And Advertising Disruption: Reimagine the Rate Card Game

By Jon Higbie

Aug 12, 2020

This article first appeared in Toolbox. 

Jon Higbie, chief scientist, Zilliant, discusses how an intelligent pricing model and automated negotiation technology can help advertisers navigate market fluctuations and drive profitable growth.  

With upfront costs in disarray and ad spending in free fall (U.S. advertising revenue fell 31% in May, according to Variety), it’s time for media and advertising companies to reimagine the traditional rate card game.

Streaming services like Netflix, Disney+ and Hulu have already siphoned off audience and ad spend from the traditional media players, and the widespread cancellation of sporting events due to COVID-19 has only made matters worse. On the buy side, ad agency layoffs are also trending upward.

The intense disruption has executives on both sides looking for signs of hope, whether that’s with sporting events coming back, or weathering the pandemic and hoping for a swift recovery. However, the industry is overdue for critical improvements that can help media and advertising companies ensure the ad buying revenue they do have is more profitable and efficient.

In this article, I will share my perspective from building data science models for large media and advertising brands, including why the status quo of rate card setting is due for an overhaul and what a reimagined approach entails.

Learn More: What Is CPM (Cost Per Mille, Cost Per Thousand Impressions)?

Ad Buying and Selling Today: Stuck in a Bygone Era of Suboptimal Practices

Long gone are the days of Don Draper’s smoke-filled rooms, yet many aspects of the ad selling and buying process remain stuck in the past, in some cases as old as the “Mad Men” era itself. Upon closer examination, this traditional price-setting approach has proven suboptimal. Ad sellers try to navigate one-off negotiations with each client without the benefit of data-driven rate guidance without which, media platforms leave winnable deals and revenue on the table year after year.

The issues start with a lack of readily accessible data but are compounded by a dearth of modern tools to optimize complex ad rates, schedules and sales processes. Additionally, rates are commonly based on the previous years’ revenue, which will certainly be problematic for the 2021 upfront season and beyond, meaning when the landscape has been decimated to the extent that we’ve seen this year, the baseline on which ad rates are set has been completely upended.

Consider the typical proposal process from the perspective of an account executive at a large media platform. The incoming request might come in as a fax or email from the agency requesting a proposal. Typically, just one page with some scribbles on it, reading, “Same spend as last year, no news, equal flighting (weeks the campaign will run).”

Based on that scanty information, the salesperson must build a proposal, then start the pricing process to negotiate a deal. However, with just a rate card based largely on last year’s figures and home-grown tools to work from, quickly and accurately responding with a proposal is impossible. Critical customer data and marketplace changes cannot easily be included in the rate decision; nor can important information about new channels this agency might advertise in that will provide a similar audience with a more efficient ad spend. The reality is that for most in the business, the current data processes and technology infrastructure are woefully inadequate to address a market where buyers know more than sellers.  Couple that with substantial reductions in sales planning staff and you have a process that at last is totally broken. There are just enough people to do this manual analysis and maintain the process as it stands today, meaning less oversight on an already suboptimal process when staff is reduced. This one-two punch is precipitating a dire need for more automation and intelligent guidance.

Learn More: What Is a Demand-Side Platform (DSP)? Key Features, Architecture, and Examples

Using Data Science and Software to Reimagine the Rate Card Game

Savvy media and advertising executives likely have a hunch that there is room for improvement in the rate card negotiation game, of course, knowing where to start is not straightforward. At its heart, ad buying and selling is a business-to-business price negotiation process.

B2B companies start with a list price, then offer matrix pricing for price breaks to defined segments, offer sales reps guided price negotiation guidance when customers request a price exception, and reserve the best pricing for customers that do the most business with them. There are strong parallels to the media and advertising industry, where the rate card acts as the high-level list price, however most companies don’t have the same control and oversight to the advertising rates that are charged through the push and pull of rate negotiations.

The technology and applications to solve price negotiations in the business world are easily transferable to media and advertising. Below are a few examples on the sell side and on the buy side of how data science and software is poised to help this heavily disrupted industry reimagine the rate card game and reclaim more profit and revenue.

On the sell side, imagine a media company receives a proposal request from an agency and it is quite sparse in terms of details. The agency wants a programming mix like last year: heavy on sitcoms, no news programming. On top of that, this agency claims they have only been given a 3% rate decrease, while the broader market has been granted a 5% decrease and are demanding further decreases on their cost per thousand impressions (CPM). Normally it would be impossible for the media company to figure out if the agency’s claim is even true, at least in a reasonable amount of time and resources!

With AI, data science and software in play, this media company could do more than fulfill the request and move on. With comprehensive data at hand, they could have full visibility to programming options to easily see that there’s a news magazine that performs like a prime-time program, and very efficient in terms of ad spend in their key demographic. In short, they could offer the agency programming ad space that has a better reach for less money. To address the CPM increase, they could see spend, agency by agency, factoring in differences in flighting and programming mix, with the click of a button. With full visibility of the entire customer base, the rep evaluating the agency’s proposal has the data and the confidence to reply accurately to the agency’s claim of rate increase.

The buy side of the rate card negotiation game can also benefit immensely by embracing AI, data science and smarter software. Large agencies must manage many clients with varying needs at once, and due to COVID, with less staff. Successfully spreading out each client’s ad budget to achieve maximum reach, given the dizzying array of platform options available, is no small task. It’s certainly not one that can be performed well manually.

There is a wide array of options available, for example, leveraging data science to determine which media channels, times of day, geographies, and demographics to target. By doing so, agencies can optimize their client’s ad mix through an intelligent analysis of budget price options by platform. When negotiating with media companies, they can get more bang for their buck in CPM negotiations with predictive guidance to determine the precise CPM to request.


Companies in the media and advertising industry are no strangers to seismic changes. To hold the line on rates and encourage efficient ad spend on the sell side and the buy side, they’ll need to take a play from the world of B2B price negotiations, that means, embracing AI, data science, and technology to change the rate card game.

Are you ready to learn how Zilliant can help you overcome your pricing challenges?

Reach out to us today to learn how we can help!