Providing the right rate at the right time for the right spot requires the assistance of science. In striving for ad spot pricing optimization, broadcast companies have options in pricing linear inventory. Some still rely on analog means and intuition. Others have realized that the key to optimization is data, with broadcast TV sellers leveraging automation or depending on data scientists.
Both automation and data scientists can provide positive outcomes, including higher top-line revenue, more sellouts and less over-discounting. So, which is right for your organization? Let’s look at how each strategy works and what you can expect.
Ad Spot Pricing Optimization via Automation
The basis of optimizing ad spots resides in data. That data includes real-time avails, timelines, historical rates, discount tolerances, ceilings and floors. It’s a lot of information, but every media company generates this. A significant challenge you may face is exporting or integrating this data into a single platform for managing rate card optimization.
However, with a revenue management platform, you can consolidate this data and let it guide how you price. In deploying automation to crunch the numbers, the tool to use is one for dynamic pricing, or yield management. These pricing policies change the spot cost based on the available data.
How Automation Works in a Revenue Management Platform
Here’s a high-level overview.
A dynamic pricing tool engine ingests data as described above. It then works within prebuilt rate curves that you select. A rate curve is based on fill, floor and ceiling rates, and timing. You can set this up in many ways, with increments as granular as every 30-minute time block. Tailoring rate curves could include scenarios like:
- A flat line for low-demand segments like overnight
- No-ceiling curves for high-demand spots (e.g., live sports, local news)
- Curves that “price drop” the closer it gets to airtime
These rate curves are algorithms that automate what the most optimized rate should be. They will fluctuate as demand and timing change. Another key aspect in automating rate optimization is including ratings data, which also impacts what you charge.
Other benefits of using automation for ad spot pricing:
- Keep sales out of complicated traffic platforms, and easily manage front-end order entry.
- Save time with an optimizer feature that automatically places spots based on customizable cost efficiency or placement settings.
- Increase productivity by converting proposals and creating orders in minutes with just a few clicks.
- Deploy the solution quickly to sellers with minimal training and no new tech skills required.
- Achieve rate optimization with a small investment in software that delivers ROI fast.
The alternative to automation is using data scientists, which we’ll cover next.
Ad Spot Pricing Optimization via Data Scientists
Data scientists are highly skilled and analytical data experts. They hone their skills to be proficient at extracting meaning from and interpreting data. Their analysis is typically accurate because they spend lots of effort cleaning, collecting and aggregating data.
According to industry reports, these roles are in high demand. One report revealed that data scientist-related tasks increased 295% in 2021. This desire for organizations to employ more of them equates to rising salaries. According to an assessment, the median salary was $164,500 in 2020, up 8% from 2019.
It’s easy to discern why this is such a hot job market. Data is the new currency for companies, and they realize the many advantages of analyzing it for actionable insights. That’s the same thing you want to do for ad spot pricing optimization.
While media companies don’t typically have on-staff data scientists, some work with rate optimization solutions providers. In such a deployment, you’d still use a platform. The rate “science” would be based more on human analysis than automation, although automation would play a part in the workflows. As a result, you’d likely increase top-line revenue. You’ll need to invest significantly in the software and the experts.
Deploying such a solution may also take months of setup, testing and iterations to get it “right.” It also may be slower to respond to disruptions or unplanned shifts (e.g., another pandemic, market forces).
Most broadcast media companies may not be able to afford this solution. It may be overly complex for small and midsize TV stations. As with any product, it’s not a one-size-fits-all solution.
Ad Spot Pricing Optimization: Data-Driven Rates Are Critical for Media Companies
No matter what optimization path you take, you should be prioritizing data usage for pricing. Making the shift and putting your trust in data will serve you well. You can increase revenue and eliminate the chaos and inefficiencies of current processes.
