The revenue outlook for media and entertainment organisations is in flux. For the moment, CFOs are optimistic; Deloitte reported an optimism index of 64% in Q1 2021. But just a year ago, that number fell to an all-time low of -54%. Streaming subscriptions massively increased in 2020, reaching the 1 billion milestone… but so did subscriber churn.

No matter how these trends take shape, or abruptly reverse course, media finance executives are responsible for mitigating their bottom-line impact. It’s no surprise, then, that data analytics and forecasting are the skills CFOs are most looking to improve; anticipating and adjusting to rapidly changing conditions is a best practice during the best of times, and a survival skill during the most turbulent.

However, lack of tooling is putting finance teams at a critical disadvantage. The antiquated desktop solutions that 90% of businesses rely on for forecasting are no match for the speed and complexity of the media and entertainment market today.

The first challenge: real-time, comprehensive data ingest. Direct-to-consumer platforms offer a wealth of real-time data. But continuously uploading, formatting, and processing every possible source of audience insight takes more human and technological firepower than most organisations have. For licensed revenue, distributors self-report data retrospectively. Content owners won’t know what’s happening this month until data arrives next month. How can they forecast with any agility?

On the opposite end of the workflow, packaging and visualising forecast reports in an actionable format are costly, time-intensive tasks. Along the way, employee bandwidth constrains the volume and speed of output. Human errors come at a high cost for teams that rely on spreadsheets alone. Or, worse yet, errors go undetected. It’s no wonder that 50% of media and entertainment CFOs want to enhance analytical, forecasting, and modeling capabilities.

To better understand tomorrow’s business risk and opportunities, finance leaders are turning to automation and artificial intelligence (AI). AI is assisting media finance teams of all sizes. More powerful data collection and analysis provide the quickest and most accurate future outlook. The applications of AI technology range from predicting new subscriptions and churn rates to compiling data-driven insights that serve as leverage in licensing contract negotiations.

For CFOs considering whether to make the shift to AI-powered forecasting, there are five questions that can help determine where it can add the greatest value.

1. Is my current forecasting model valid?
The first step to improving a forecasting model is asking whether and how the current one is working. This requires automated modeling to measure the accuracy of existing projections. But even if today’s model proves valid, new data could arrive tomorrow that renders it obsolete. Companies can’t afford to revise their models manually every time a variable changes. AI can constantly update and refine projections on the fly, without the need to constantly measure the validity of each model.

2. How is my revenue tracking against the forecast?
To know whether your revenue is in line with projections, stakeholders need granular insight into distributors, time periods and market moves – and actionable intelligence on the root causes of any gaps that emerge. Data obtained from underachieving distribution channels, for example, can be leveraged for negotiations and strategic decisions concerning licensing fees and contract terms. This shouldn’t require technical expertise; AI can not only uncover the cause of such anomalies, but provide visualisations and explanations customised to stakeholder concerns so they can understand what they’re seeing.

3. Is my budget on track with expected revenues?
Strong forecasting capabilities enable better resource planning and capital allocation. Executives depend on the accuracy of financial forecasts to make investment decisions, from production budgets to promotional strategies. How much cash is available to fund operations? Are there sufficient reserves in the event of erratic or unanticipated subscriber changes that reduce revenue? AI tools can apply predictive, trend, and variance analysis to determine operational liquidity needs and create forward stability.

4. Are my revenue objectives achievable?
Revenue projections have significant implications, from shareholder guidance to operational planning. Consistently unrealistic forecasts erode investor confidence, compounding the problem of revenue shortfalls. If forecasts rely on human input alone, bias is inevitable. AI algorithms can combine historic trend analysis with machine learning to correctly weight each variable within a model – for example, distinguishing between a recurring seasonal anomaly and a true upward trend.

5. What are we missing?
Errors, risks, and opportunities are not always visible to the human eye. As mentioned earlier, a single miscalculation can invalidate an entire model. Even if a human-generated forecast model reflects historic revenue with 100% accuracy, it doesn’t necessarily capture every business risk. Undetected distributor noncompliance, for instance, may be contributing to a slow revenue leak. AI can evaluate whether historic revenue was fully recognised before building it into a forecast model. Finally, AI forecasting can reveal opportunity – such as the revenue impact of a 5% rate increase against a 50% promotional discount to attract new subscribers.

The future may be uncertain, but the proper forecasting technology provides greater control of the seemingly unpredictable. CFOs who embrace AI innovation can deliver timely, accurate, and actionable insight. They’ll also have the greatest likelihood of success in a disruption-heavy market.

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