3 skills to enable more efficient forecasting

Unlocking efficient SaaS forecasting by going beyond models and data

So many topics to focus on when it comes to SaaS forecasting; complex models, AI tools, crystal balls, bad data quality, to name a few. While all valid and important topics, they miss an important element when it comes to efficient forecasting: skill.

My experience is that there are diminishing returns when focusing on modeling or data quality for forecasting. I’ve gone through numerous iterations myself, including at LinkedIn, refining forecasting models and focusing on data quality for increasingly accurate predictions, but there’s a limit to the efficacy of focusing on just the mathematics or the implementation thereof.

On the contrary, focusing on a few key skills can get you much further in enabling efficient forecasting:

• Building stakeholder trust

• Inspiring adoption

• Managing ambiguity

Let’s dive deeper into each of those elements and explore why they’re powerful enablers.

Building stakeholder trust

• Why it’s a big deal? Without trust, forecasting isn’t just challenging, it’s a castle built on sand. Trust is foundational in any business context but if stakeholders do not trust the forecasting process or the individuals involved, the forecasting process is fundamentally set up for failure.

• Lack of stakeholder trust can manifest in a few ways like lacking consistency in execution, covering up mistakes in reporting or fake promises on deliverables.

• Investing in building stakeholder trust as part of forecasting is crucial. Here are a few trust elements to consider:

  • Being direct and truthful

  • Maintaining high commitment and consistency between words and action

  • Building relationships both on formal and informal basis

  • Valuing the exchange of ideas and thought

• Stakeholder trust also drives adoption and ensures better communication. More on that in the next 2 skills!

• An example that underscores the importance of stakeholder trust, involved an analyst that was tasked to send a forecast update to key stakeholders at a specific hour on a given day. The analyst failed to consistently send updates at the promised time and day, and thereby, over time, eroding trust and disrupting the key stakeholder’s next steps. Reliable consistency is, I would argue, one of the best ways to build trust.

Inspiring adoption

• If forecasting isn’t widely adopted across the relevant teams, the forecast results are likely not representative, accurate or impactful.

• Roadblocks to adoption often include complexity in models and processes and, time consuming tasks with little utility or relevance.

• Quite on the contrary, the benefits of high adoption include:

  • Improved decision making as more informed people, means more informed decision making

  • Increased alignment because people spend less time discussing, they’re already aligned(-ish)

  • Higher accuracy as high adoption yields process benefits that arise beyond what just models and data can provide

• Key elements to look for in the skill to inspire adoption:

  • Understanding the tradeoff between modeling limitations and process benefits

  • Having the ability to instill and sustain organization-wide energy for what is possible

  • Navigating through change requirements and facilitating transitions toward adoption

• Let me share a personal forecasting example. A complex spreadsheet, intended to collect extensive forecast information from sales and stakeholders, saw very low adoption due it’s complexity and density of information requested. The low adoption became a fundamental issue for accuracy. After engaging stakeholders and refocusing the spreadsheet on driving higher adoption, adoption increased and resulted in much more efficient forecasting operations.

Managing ambiguity

• Forecasting inherently involves ambiguity and volatility, and embracing it is vital to avoid being blindsided by it.

• Volatility is often managed through increasingly complex models. However, building a model that can accurately cover 100% of the probability spectrum is likely impossible. While there are tactical ways to manage the ambiguity, such as what I’ve highlighted in 2 other recent articles, on managing forecast uncertainty and inspecting big deals, it ultimately just comes down the skill of managing ambiguity.

• Analysis paralysis is typically a result of failing to manage ambiguity effectively. So is the result of analysis paralysis, the inability to make progress despite uncertainty.

• Key competencies for managing ambiguity include being able to:

  • Deal comfortably with the uncertainty of change

  • Decide and act without the total picture

  • Effectively handle risk

• A good example that illustrates managing ambiguity is when forecast hinges on one big deal. I’ll defer to my other post on how I’ve seen great sales leader manage the ambiguity as part of forecasting.

Efficient forecasting is not just about navigating numbers but also skillfully managing stakeholder trust, driving adoption and managing ambiguity. Focusing on these skills in addition to the numbers and the process will enable more efficient forecasting, especially in times of high volatility.