Five Mistakes to Avoid When Forecasting Hotel Performance

Written by: Nate Nasralla

It’s been said only one percent of businesses are able to achieve 90 percent forecasting accuracy 30 days out.

That is pretty poor — and despite access to unprecedented amounts of guest and operations data, and the aid of previously unavailable technologies, things aren’t getting any better. While many pieces to the hospitality experience, from reservations to revenue management to the guest experience, have embraced technology, forecasting remains an antiquated exercise dominated by Excel spreadsheets, heuristics and gut feel.

Why is that a big deal? Well, in the words of a hospitality group COO my team sat down with last week:

Knowing the future makes all the difference. It’s our competitive advantage. If we know our revenues, we know what we can and can’t spend. For example, if we under-forecast bookings, we might under-staff and comprise the guest experience. But if we over-staff, labor is a major driver of our expenses, which cuts into profit and the value we can deliver as a management organization.

On a more personal level, as a finance and/or technology professional, the less time you spend building a monthly forecast (without comprising its accuracy), the more time you can spend on the truly strategic and value-added parts of your week. It is the path to promotion within your hospitality organization: less time crunching numbers and untangling webs of spreadsheets, more time guiding and advising to ensure your team(s) deliver on their financial outcomes.

So then, if accurate and efficient forecasting is both corporately-enriching and career-enhancing, what are the obstacles that block effective forecasting, and what can you do about it?

This is the first of a two-part blog post, in which we will break down the most common mistakes that may prevent you from effectively predicting your future financial performance, and we will discuss how technology and a driver-based methodology can help you overcome them.

First, let us start with the shortlist of common mistakes and missteps. How many of these is your organization making? Are there any you’ve observed that are not included here?

  • Lack of a systematic and process-driven approach.
    Most forecasting mistakes could be solved by taking an unbiased, replicable, process-driven approach. However, most teams over-rely on one-off conversations, past experiences and other manual methods that cannot be audited and continuously improved.
  • Peanut butter spreading.
    Most times, we default to evenly distributing a single high-level forecast across different departments for sake of speed and ease, when the true story and rich insights are found at a more granular level.
  • Hoping to aggregate poor forecasts into one good one. 
    Many forecasting approaches ask each property to build their own, independent forecasts, so they can then be merged by an area, regional or national manager, but this doesn’t always equate to higher-quality forecasts (see below).
  • Massaging data to fit a preferred story line.  
    Forecasting is meant to provide a view of what your performance will be in reality, and it should ruthlessly matter-of-fact. This is important even though it is in conflict with the natural incentives staff have when forecasting the metrics they’re also responsible for attaining. 
  • Inability to efficiently procure and structure data.
    Statistical models and seasonal analysis typically yield better results than an over-reliance on simple methods like trending or spreading, but preparing clean and structured data that can be used to train a statistical model is often easier said than done.

If some (hopefully not all) of these mistakes fit your experience, what’s next? Where you should look to improve your forecasting process?

Stay tuned for part two of this blog series to find out.

Nate Nasralla is director, data analytics at He is responsible for building and executing market strategies to transform the nonprofit and private sectors through data-driven approaches to revenue generation and organizational scalability.

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