For most linear TV sellers, the process for updating rate cards is fundamentally unchanged from the 1950s, as I explained in a previous post. Instead of using a pencil and a ledger as they did in the past, today’s analysts use Excel, but they’re still applying across-the-board percentage increases instead of analyzing actual sales data to make decisions on pricing.

An automation strategy can help to reduce price variability and achieve sustainable increases in total revenue, but most sellers haven’t introduced one. The obvious question is how—and where—automation should be introduced.

Here are the five key processes and workflows to consider:

1. Delivery of more frequent, granular rate cards

Remember that the volume of historical transactional sales data that most broadcasters and programmers have available is too much for analysts using Excel to handle and process. As a result, pricing analysts often settle for averages and aggregates to inform pricing decisions, which means rates aren’t optimal, particularly for high-value inventory. This leads to money being left on the table.

Automation of data ingestion and processing means that detailed actual results (the best measure of current market forces) can be used to inform the calculation of optimal rates—and rates can be provided for inventory at a granular level to ensure yield is maximized through the optimal packaging of inventory. For example, selling by program instead of by daypart or network can ensure that a seller’s 8 p.m. and 9 p.m. Thursday primetime programming yields every dollar it should.

More frequent publication of rate cards requires automation of the ingestion and processing of data, as well as the use of data science to calculate rates. By taking steps today to increase the frequency of rate card updates, sellers are preparing for a future transition to dynamic rate cards, which rising adoption of addressable TV will eventually push into the mainstream.

2. Forecasts to inform optimal rate calculation

With transactional data scrubbed and cleansed (and with tools in place to automate rate cards), sellers should also ensure that rates are informed by future forecasts of supply and/or demand. Remember that TV advertising is perishable, which means that as demand pressure builds up prior to the airing of a program, its value increases. Rate cards informed by forecasts typically deliver higher yield for sellers because they reflect market demand and raise prices when inventory becomes constrained.

3. Governance processes to monitor adherence to rate cards and manage risk

Automated rate cards will only deliver more revenue for sellers if they are used when doing business. Today, determination of price is often delegated to sales teams and driven more by gut than by data, which—again—risks leaving money on the table. As a result, tools to monitor rate card adherence and ensure processes are in place to change rates when market or competitive intelligence warrants it are urgently needed in the TV advertising industry.

4. More frequent, detailed reporting

Also remember that “what gets measured gets managed.” You can’t know what you don’t see, and sales organizations that rely on Excel for reporting are effectively flying blind with only aggregate data to guide them. Granular, transaction-level reporting with actual prices recorded by client, program and daypart is essential and should be readily available to members of sales, sales operations, and pricing and planning teams. The availability of detailed reporting will build confidence and increase adherence to recommended rates over time, resulting in sustainable yield increases.

5. Different methodologies for different programming types

Finally, because a manual Excel-led process can’t handle high volumes of data to calculate rates, it typically results in only one methodology being used across all programming types. (That methodology is often historical AUR, or average unit rate, with or without an increase over last year’s prices.) Automation enables sellers to use different methodologies for different types of programming to ensure that pricing is optimized. For example, the methodology for calculating core, daypart rates should be very different from the one used for high-value sports, given the unique role that league type and geography play in determining the value of sports inventory. Thus, the value of an NFL spot in Cleveland is bound to be very different from an NBA spot in Phoenix.

To learn about how to plan, implement and operate a system for automating rate cards and pricing, download our playbook, “How Automating Rate Cards and Pricing Helps Linear TV Sellers Increase Revenue."