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Revenue-driven CMOs think of their 'qualified pipeline' as a core success indicator.
A qualified pipeline comprises sales opportunities that, both, performed high-intent actions and met certain demographics and firmographics criteria.
To bring predictability into their operation, CMOs attempt to forecast pipeline creation – Let’s cover how it's done and how this practice can be improved, sometimes by orders of magnitude.
CROs and sales executives recognize the importance of sales forecasting.
Think of such a forecast as a prediction of how much business a firm can expect to close within a specific period (such as a quarter or year).
A marketing pipeline forecast seeks to estimate the number of qualified sales opportunities that CMOs can reasonably expect to generate from marketing initiatives.
There are various pipeline forecasting methods, but most of them require at least two factors:
A marketing pipeline forecast should accurately predict the size of the pipeline that the marketing department is likely to create.
It also anticipates how long it will take for Sales to validate, verify, and subsequently qualify these pipeline opportunities.
This forward-thinking perspective offers C-level executives, board members, and stakeholders enough confidence to plan ahead and fulfill future corporate booking targets.
With that, how can a CMO successfully forecast marketing pipeline results?
Let’s go from easy to hard.
The least complicated method is to estimate the amount of marketing-sourced opportunities expected to be qualified during a specified period (e.g., fiscal quarter or a year) using forecasting approaches like moving average, linear, and seasonality metrics.
The forecasting model can be extrapolated further by examining past pipeline performance by the marketing channel.
Some marketing executives create more advanced forecasting models based on variables such as planned marketing programs, spending, and SDR productivity.
However, analyzing past marketing pipeline performance is more of a challenge.
Many companies don't have an exact database of historical opportunities organized by source, stage, and period and so cannot reliably use these metrics.
In other words, these methods don't factor in the complete buyer journey and only take into account events and actions from a limited number of data sources.
In other words, these methods don't factor in the complete buyer journey and only take into account events and actions from a limited number of data sources.
Forecasting also necessitates specialized analytical abilities, which are typically uncommon skill sets for marketing professionals.
While many point solutions offer some sort of forecasting solutions, they are quite limited in their ability to factor in all relevant touch points and conversion-impacting intent signals.
Solely relying on them can sabotage your forecasts and result in poor performance.
Instead, a new category of no-code predictive analytics solutions have hit the market in recent years, providing a simple, easy-to-understand, real-time pipeline intelligence forecasting, that considers all relevant data sources, and their respective events, to help you anticipate your marketing pipeline by sector and sales area.
Regardless of your team’s technical skills, any subject matter expert in the GTM organization can easily define a business objective and predict which leads will turn into pipeline, in order to help the GTM organization prioritize and react to leads with confidence.
Leveraging this new no-code predictive analytics paradigm is the most accurate approach to empower a wide variety of stakeholders, from C-level executives and board members, all the way down to the frontline employees that interact with prospects daily.
Let us show you how Forwrd.ai, the no-code operational predictive analytics platform can allow you to predict who will convert and why, so you can prove that marketing efforts align with corporate strategy goals.
