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Putting your Best Numbers Forward with Sales Forecasting: Part 1

Jennifer Sowinski Nemeth

Senior Consultant & Analyst

Jennifer enjoys working with arts and culture clients to help them increase revenue and grow audience through data-driven strategies including pricing studies, venue re-scaling, customer behavior analyses, and segmentation.
February 10, 2020

It’s that time of year again, when many of us are thinking about forecasting sales for the upcoming season. Each new season brings unique challenges, so how can you make sure that you’re creating the most effective forecasts possible?

Your most important asset for forecasting is the data you already have at your disposal from past performances. Using past data, you can predict how your audience will behave, what they’re most interested in seeing, and how much they’re willing to pay for it. The trick to successful forecasting is recognizing which past performances were most like your upcoming performances, then using that data to inform your predictions. But, choosing the right performances for comparison can be tricky!

One of the biggest pitfalls in forecasting is making assumptions about which criteria to use when comparing performances. We want to avoid assumptions, so always let your data be your guide! To make sure you’re using the right comparison criteria, let’s examine a few common ones, and how to determine if they’re right for your organization.

PART 1 – TIME OF YEAR

When it comes to time of year, we often make assumptions about what is relevant to forecasting. The memory of one low-selling performance on a holiday weekend might lead us to believe that all holiday weekend shows will sell poorly. That unfortunate performance may have left a lasting impact in our minds, but it’s important to take a step back and make sure that our assumption is backed up with data. Do all performances on holiday weekends actually sell poorly? Or is it one holiday in particular? Or, is this just a bias that I’ve developed from a particularly memorable experience?

Other criteria related to time of year include the month, the slot in the season (i.e. opening production, Holiday show), or the specific weekend that a performance is taking place. Length of sales cycle (time between ticket on sale and performance date) also falls into this category, as it’s often (though not always) related to the time of year. So, if you’re using any of these as forecasting criteria for your organization, how can you make sure that it’s actually a valid consideration?

To determine if sales are actually impacted by time of year for your organization, gather data about your past performances and do some analysis. Some questions you might ask of your data include:

  • Do performances in the spring sell measurably differently than those in the fall?
  • What are the average sales per performance by month?
  • Does a performance with an 8 week sales cycle sell differently than one that’s on sale for 30 weeks?
  • Does being on a holiday weekend make a difference in sales?

For many organizations, the answers to some of these questions will be a resounding “yes!” and therefore, these considerations should absolutely be used for forecasting. But, you may find that the differences are negligible, and there are more important factors to consider.

Read Part 2 of our 3-Part Series on Sales Forecasting, where we discuss how Programming can impact forecasts.

Want to make better, faster forecasts?
We’re here to help. Our JCA Arts Marketing consultants can leverage our advanced tools (such as the recently released Revenue Management Application v6) to help you easily create accurate, data-driven sales projections. Contact us to learn more.


JCA Arts Marketing collaborates with cultural organizations to increase revenue, boost attendance and membership, and grow patron loyalty. We provide consulting and software services to hundreds of cultural institutions across multiple genres, including dance, museums, opera, performing arts centers, symphony, and theatre. We can help you achieve your marketing goals.