Ideally, before producing any program content, a media enterprise would be able to gather the data necessary to create a financial model that provides insight as to the likely profitability and financial risk of producing that content. In this scenario, producers would have the information needed to create overall production budgets and, in turn, could avoid spending more on the production than could be easily recouped from its initial release. This same model could also be used to help test the viability of a syndication deal at the price offered by a syndicator or for a content buyer in a given market. A robust model could even be used by financial staff to better forecast the potential revenue stream available across a variety of different business and scheduling models for content to be produced and sold.

Such a comprehensive predictive revenue model needs to be driven by accurate information, but without a solid base of accurate data drawn from a number of different sources, attempts at such models are most often theoretical and no more helpful than an exercise in spreadsheet-generated numbers. The inability to aggregate and analyze accurate data from across the enterprise, as well as from the media industry itself, has hobbled the practicality of content-driven revenue models and has constrained businesses in making the most of their content libraries.

Aggregating Data Across the Enterprise

Media corporations invest heavily in transactional systems. Traffic systems manage ad revenue. Program management systems create schedules and manage contracts. CRM systems manage pending business. Production management systems manage production costs. Proposal systems streamline negotiations. General ledger systems manage costs and revenues. Rights systems ensure ownership, and research systems display performance.

However, while the model needs information from each of these systems, not one by itself can deliver or access all of the data needed to generate the comprehensive content profitability model discussed here. In lieu of investing in a complete solution, many enterprises are forced to rely on ad hoc systems in their attempt to collect, consolidate, and build the data relationships required to make sense of disparate transactional systems and to create reasonable profitability forecasts. These efforts are most often focused on only one or two of the transactional systems involved and so inevitably, these approaches are limited in scope, clarity, and utility.

For example, experienced media executives will combine traffic revenue with program management cost information to get a picture of the profitability of current programs. However, this approach fails to illuminate content purchase scenarios or to track future rights and projected audience information. Others may enter sales assumptions into complex manually generated models to project future profit over cost, but this type of approach typically addresses only the concerns of the single department where the model originated, and these models typically fail to account for all of the run scenarios in which content will be used. They also do not adequately incorporate future sales information to ensure accurate pricing and audience information.

Another issue with ad hoc, manually created revenue models is that they are often narrowly focused on one type of media outlet, such as a straight broadcast or network model. However, revenue for a given movie title or television series is generated through many media outlets. Each outlet is governed by different release windows driven by different business rules. Equally important, many of these outlets are managed outside of the enterprise’s transactional systems noted above.

Understanding the impact of these different release windows requires additional information including media outlet type, date range, and business model, such as per use, per subscriber, ad-supported, revenue share, or contract. In today’s rapidly changing marketplace, these factors are complex and variable, yet they all generate substantial revenue and each is critical to total potential profitability of any given piece of content.

In spite of all of this complexity, comprehensive analytics models can successfully leverage key data that drives potential revenue calculations across the different release windows and can incorporate data from each of the aforementioned transactional systems.

By using a platform-based approach, one can combine data from multiple disparate transactional systems so that the data can be analyzed in aggregate, with sufficient granularity to generate the insights required. Such a solution can also include integrated master data management capabilities, which are essential to ensure that the data produced by these different departmental systems has been properly mapped to corporate standard values and categories that work across the enterprise for meaningful company-wide analysis.

This allows such a model to collect market spot and CPM rates from the traffic system for linear and nonlinear sales models. Production cost information, as well as proposed amounts for purchased contracts, can be tied to scheduled content and pulled in from production management systems. Broadcaster and network buyers of content can develop representative future schedules, based on accurate sales assumptions, to avoid overly optimistic buying recommendations. Schedulers of content can explore all feasible scheduling and revenue earning scenarios available to the media organization.

Accounting for External Sources of Revenue

A comprehensive predictive revenue model would also support the tracking of revenue from other business models operating outside of the transactional systems. “Ticket” prices for pay-per-view business models can be safely estimated. Syndication revenue is critical as well, and historical pricing information for similar content sold in different markets can be used to reasonably project prices for specific markets. Syndicators can identify reasonably priced product for each specific market sales situation.

A model that takes all of this information into account would generate a wealth of data enabling accurate valuation of content even in the midst of evolving consumption preferences and patterns. The critical factor is that this content profitability model would work across each of the business systems involved while taking into account revenue from the different release windows — and include the tools required to take each of the business models into consideration.

Optimizing Opportunity and Revenues

Because this approach relies on comprehensive business model support, it sustains both current and future release window cases generated to meet new market realities. Taking advantage of business intelligence precepts, this approach consolidates all the data from different transactional systems to ensure that the models match cost, contract, and sales realities. With this insight into the value of content, media enterprises are positioned to recognize valuable opportunities and to fully leverage their media assets to drive business success.

The content profitability model driven by a business intelligence platform described here offers significant economic benefits to media organizations. Going into production, content creators can use it to guide creation of a realistic production budgets. Both buyers and sellers of content can use this model to both control costs and target credible revenue streams. Content owners can use this model to properly value their largest asset, their content.