In a recent post, I described how every media seller should be focused on maximizing yield (i.e., the total revenue available from their pool of inventory), which represents an evolved way of doing business. Managing overall yield calls for continuously assessing the relative contribution of each ad product category to total revenue and rebalancing available inventory allocation to ensure that the greatest total revenue is achieved.  

In some ways, this approach is counter-intuitive. It steers the industry away from selling as much sought-after inventory as possible for as high a rate as it can fetch, since that can lead to cannibalization of other inventory and diminish the value of a portfolio overall—if the impact on other products and channels isn’t considered. It’s also a departure from relying on last year’s pricing and market feedback to inform current rates. 

While portfolio optimization via Excel is theoretically possible, it’s wildly inefficient; there is simply too much data for a human to process. Furious’ platform, PROPHET, was designed and built to solve portfolio optimization. PROPHET uses sophisticated data science, software and algorithms developed over many years, as well as machine learning to scale the processes of forecasting, pricing and inventory allocation. Customized business logic gets smarter over time as PROPHET ingests and learns from your organization’s data, including historical sold inventory, rate cards and program/daypart performance.   

Here are three ways PROPHET ensures that revenue and inventory performance are optimized across all of a seller’s channels and ad products: 

Inventory Allocation:

Maximizing revenue and sell-through requires proper inventory allocation to all the ad products and sales channels in your portfolio. But due to the complexity of multiple currencies, ad products and sales partners, optimizing allocations is a lot of work and too much math for media sellers to handle manually and frequently. While accounting for all known constraints, PROPHET does the heavy lifting to fluidly allocate and optimize inventory usage by client and across entire advertising portfolios. It also gives stakeholders clear visibility into whether they’re tracking to hit their forecasts and sales goals.

Dynamic Pricing:

When working to close a deal, salespeople commonly offer price reductions to get a signature. In the absence of data insights to show the ripple effects of these decisions across the portfolio, this was a logical and effective way to conduct business. But now we know that uniform pricing and rate-card recommendations invariably improve a seller’s yield and that variability is a sign of sub-optimal pricing. 

To that end, PROPHET arms salespeople with pricing recommendations on a per-deal basis as well as portfolio-level guidelines to ensure revenue is protected and pricing is managed strategically.  

Packaging:

When large swaths of inventory are removed to create packages, which are often priced at a discount, chunks of time that would otherwise have been available for rotators or general daypart sales are also stripped out. Siphoning off time for packages can impact the rates of other inventory.  

In an ideal world, you would see rate increases as a result of total inventory getting compressed. But in practice, rates tend to remain exactly as they were prior to package inventory being pulled out. That’s because rate cards aren’t updated frequently enough to reflect changes to available inventory, and salespeople negotiating prices typically don’t have access to all the information needed to make an informed choice. 

The bottom line is that revenue is often left on the table because of how packaging works today. PROPHET helps sellers package inventory by various parameters, such as network, zone, program and daypart, so that inventory is allocated to each plan in a way that optimizes the yield of each package. Using data science to evaluate a large volume of variables, PROPHET ensures that inventory is more efficiently utilized across a portfolio and that underutilized inventory is considered and included more often in packages served to advertisers.  

To start using data science to make price and inventory decisions and to enable portfolio-level TV advertising yield optimization, click here.