Direct-Sales Digital Ad Revenue Forecasting

Helping broadcast and cable networks scale sales forecasting.

Digital advertising and traffic patterns aren’t always in alignment. Sometimes, for reasons that have little to do with the business, digital traffic patterns can be high, but selling low, or vice versa. Companies need data tools that let them  examine their historical data and  track records so they can identify recognizable patterns in how revenue collects on their books. 

Background

The heads of Finance and Sales of these major broadcasters wanted to better forecast revenue from directly sold digital ad campaigns in order to answer key business questions:

  1. How does revenue pace, and are the sales teams on target to achieve revenue goals?
  2. How does that revenue pacing differ by quarter and client?
  3. Is there a way to identify potential problems in time to address them?

To answer these questions, these clients wanted an easy and more accurate way to understand how revenue accumulates on the books, and to compare revenue year on year to assess whether the teams are meeting, overperforming, or underperforming planned goals.

Advertising and traffic patterns aren’t always in alignment. Sometimes, for reasons that have little to do with the business, digital traffic patterns can be high, but selling low, or vice versa. 

The organizations wanted to examine their historical data, or track records, so they could identify recognizable patterns in how revenue collects on their books. In essence, the client wanted to plot booking curves for revenue.

Going deeper, individual brands, such as the sports or entertainment sections of the site, experience their own booking curves, and understanding those variances would enable the sales team to proactively spot issues that need addressing.

Challenge: Messy Painful Process

There were a number of challenges creating hurdles on the path to achieving the clients’ goals.

  • Extracting and normalizing historical data was extremely difficult. At its heart, plotting a booking curve is a data science exercise, which means that all data inputs need to be standard. But the historical data had significant variations in categories over time. There was no authoritative naming of categories or time period.
  • All existing historical analyses were built in Excel workbooks. That meant extracting data was far from straightforward. And the analysts continuously evolved their analysis, which meant there were a lot of natural variations within these Excel workbooks.
  • Booking curves were complex. A booking curve is the intersection of the capabilities of the sales staff, combined with the buying habits of each client. A site with numerous categories -- general news, entertainment, sports, health, etc. -- will have a unique booking curve per section, making them a complex exercise in data science.
  • Data disparity was a big challenge. To get a full picture of the revenue, the head of sales needed to tap into a wide range of data sources, including its Salesforce.com and Operative systems. But sales professionals are experts in the dynamics of the market, not data science.
  • Data capture and data warehouse approaches needed to change. Because this was a new initiative, the data wasn’t stored in a way that would facilitate this type of exercise. The clients needed to change the way dates were stored, but lacked the skills or time to do so.  The project required fluency in more robust databases such as Teradata, Snowflake, as well as BI tools, such Tableau, Microstrategy, or Looker.
  • Real-time data was difficult to achieve. Although the teams used spreadsheets to gain some insights, the process wasn’t scalable, and required significant manual input. That meant it was difficult to get insights on demand updated in real time.

No future or historical views were available. The spreadsheets didn’t allow a level of drilling, either in future or historical views, that the sales team needed to track current performance on the booking curve.

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Solution

The clients asked us to develop a model as quickly as possible, and we began creating an authoritative way to represent categories and time periods. Next, we pulled and normalized the historical data from the Excel workbooks, Salesforce.com, and Operative, and imported it into Python which is well suited for normalizing disparate datasets and crunching data. 

Creating the booking curves was more of a data science exercise. Using linear regression, we plotted how sales accumulated on the books over time. This data exercise revealed that each quarter begins with some portion of its available inventory already sold. As the year progresses, the percentage of pre-sold inventory increases, so that by Q4, up to 90% of the revenue the network would receive for the quarter was already booked. Looking back over several years, we were able to build annual and quarterly booking curves – insight that has implications for both revenue and inventory forecasting. Since the curves change across sales teams, properties, and time periods, we also needed to smooth out agreed upon anomalies or variations in the signal, but still retain the general patterns. 

Our team also built numerous features into the model, such as what-if scenarios to help the head of sales to answer questions about the impact of potential market events on revenue, and alerts that trigger if revenue isn’t tracking as anticipated.

To make it easy for the head of sales to spot trends quickly and easily, we fed the data into Tableau, which created user-friendly dashboards, available from any PC or laptop. The dashboards are continuously updated in real time, so accurate information is always available.

Results More Timely And Accurate, Every Step Of The Way

  • Timely and Accurate Insights. The heads of Sales now have more timely and accurate insights into sales efforts at every step along the way. The booking curves eliminate any surprises by making it easy to see how sales are trending compared to prior quarters.
  • Far More Accurate Forecasting of Revenue Results. With the booking curves in place, sales management is able to forecast revenue results with much higher degrees of accuracy, and with a lot less effort on their part.
  • Early Warning. The solution provides an early warning signal for the heads of sales and finance teams, that earnings may not be what they anticipated. This signal allows them to take corrective actions, and advise the appropriate C-level executives.
  • What-If Scenarios. The heads of sales can easily test what-if scenarios and predict their implications on revenue.
  • More Time to Focus on Sales. The dashboards have eliminated the countless hours spent entering data into spreadsheets, which means sales executives have more time to focus on strategic sales initiatives.

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