By 2025, CFOs are not only looking at financials and spreadsheets when preparing their revenue forecasts but also incorporating campaign data into their forecasts, as the information from marketing (campaigns performed, signals from pipelines, consumer behaviours) helps to provide more accurate forecasts of future revenues with fewer surprises.
The transition has occurred for two primary reasons; there is an ongoing push to make forecasts more accurate, and the use of AI tools has dramatically increased the ability of CFOs to merge data from multiple functional departments.
Three practical ways marketing data changes forecasting
1. Faster, signal-driven short-term updates – The gathering of customer engagement and response data (ad click trends, conversion funnel velocity, and lead quality scores) will allow CFOs to continually monitor those metrics. Therefore CFOs can adapt their near-term revenue projection curves quickly and easily as opposed to tracking them conventionally with formalised reporting.
The result is that FP&A departments can easily move away from traditional static forecast scenarios and use a more flexible event-driven approach that allows for adjustments to the revenue curve based on marketing campaign performance results. The foundation of this quicker decision-making process for FP&A departments is based on the combined evaluation of metrics related to marketing campaigns and the metrics related to the revenue earned against bookings and pipelines.
2. Customer-level and cohort analysis for precision – The CFOs use cohort forecasting to create models that enable the prediction of Revenue Based on Retention/Monetization patterns, by grouping customers according to Acquisition Source, Channel or Campaign and through these cohorts they create models of Lifetime Value and Churn, using each marketers spend as a variable.
3. AI-Powered Scenario testing with better assumptions – A new Salesforce research indicates that finance teams today are testing AI Agents and Predictive Models that merge Historical Finance Data with Marketing Inputs to provide “What If” scenarios like “What Happens To ARR If A 20% Increase Is In A Paid Search Spend, And A 5% Decrease Is In Conversion?” By 2025, CFOs are increasingly confident in the potential for AI Agents to have an impact on Revenue – almost all expect that they will assist in increasing Company Revenue – thus making the simulations they provide even more actionable and useful. Using AI in this manner also assists in determining how much Risk exists on the Downside, and How Revenue Will Be Impacted By Trade-Offs Made in Marketing.
The Three Enablers That Make This Work
● Shared metrics and language: Finance and marketing need to have common definitions, such as what constitutes a “qualified lead” or when revenue is recognized for a sale that was produced through a campaign or event. Having common key performance indicators (KPIs) allows both teams to avoid double-counting and have aligned incentives.
● Data infrastructure and governance: Without the ability to create and maintain dependable integrations between marketing platforms (such as Salesforce), Customer Relationship Management (CRM) systems, and the general ledger, financial leaders will not be able to achieve success in 2025. For many financial leaders, this creates the need for a unified data source, which serves as the single point of truth for attribution.
● Upskilled FP&A teams: As the forecasting process continues to require an increased amount of data, FP&A professionals will need additional skills to manage these data sources: basic machine learning proficiency, understanding of marketing metrics, and the ability to convert model outputs into a presentation-ready format for boards. Financial leaders stress that in order to maximise the utilisation of marketing data, FP&A will require an increase in training and development.
A pragmatic roadmap for CFOs
Start Small: Establish agreement on a particular shared use case, such as enhancing the accuracy of monthly revenue forecasts for one particular product; develop appropriate aligned marketing signals to identify market trends and make comparisons between previous forecasts; and lastly conduct forecast analysis over a minimum of 3 months for benchmarking purposes – do this until data supports a reconciliable correlation.
The Bottom Line
As the role of finance becomes increasingly reliant on marketing data as predictive rather than as supporting revenue forecasting becomes less static and more substantive. Therefore, finance’s ability to know when to ask the right questions has improved; finance can move from “what has happened?” to “what will occur if we change direction?” in regard to revenue trends; finance has a distinct competitive edge in an economy where performance measurement accountability is foundational.
(Views are personal)
