“Placer gives you some of the most transparent insights into what's going on in your city. It gives us higher precision in tracking tourist traffic, and helps in turning that into predictive power for our overall budgeting process, helping us confidently plan on an extra ~$2M. And we've only just started to tap the applications of what it can do.”

~ Assistant Budget Director

The Challenge

A Tourist Town Dependent on Tourist-Driven Sales Tax Revenues

Like any city dependent on tourism as a key contributor to its economy, a popular winter destination is keenly focused on predicting the sales tax revenue generated by visitors, which can then be used to provide services for residents. The assistant budget director for the city built the statistical model used to predict yearly sales tax revenue.

With the unexpected rise of COVID-19 and its impacts on tourism, having a reliable way to forecast sales tax changes became even more crucial.

Early Sales Tax Models Relied on Set Increases and imprecise Flight Data

The city’s model for predicting sales tax revenue was an inexact approach common to many cities: they used the revenue from the prior year as a base then increased it by a set amount. That method had a variance of 9.4%. With a projected sales tax revenue of approximately $40M / year, a variance of 9.4% meant the $40M budget was accurate to plus or minus ~$3.8 million. Under-predicting by $3.8M could mean missing out funding potential services or personnel, while over-predicting could lead to the cancellation of services and personnel previously planned for the year.

The assistant budget director first added two new factors to the model: historical sales tax revenue and Department of Transportation (DOT) data of flights into their airport from major markets that drive traffic to their city. That improved the variance to 8%. While flight information is useful, it is too broad, as many people visit their state without going to the city. The budget director knew she could improve the model with more localized data.

The Solution

Adding Placer Foot Traffic Data Improved Model Accuracy by ~$2M

Placer’s near real-time data gave the increased accuracy needed to lower the model’s variance. To get accurate counts of visitors to the city (who then spend and bring sales tax revenue), the budget director geo-fenced the two main areas that drive sales tax revenue: Main Street and a collection of nearby resorts.

Learn more about Geofences

To ensure the model is as robust as possible, she added Placer’s daily local visit history from Jan 1, 2017, using all 5+ years of visit history, including time before COVID-19.

With Placer foot traffic data as a new input to her sales tax model, she saw a significant improvement in the accuracy of her forecasts, as the variance in their predicted sales tax model decreased from 8% to 5% (a difference of 37%), meaning an extra $1.8-$2M in sales tax could be predicted, with high confidence, for the upcoming year’s budget.

Learn more about Visits Trend‍

The Outcome

SUCCESS: ~$2M in Budgeted Funds For Fiscal Year 2023 New Personnel Hires

The real benefits from the more exact model came with the city’s budgeting process. With an extra $2M of sales tax revenue predicted with such a high degree of confidence, the city was able to plan for new personnel hires across multiple departments, including water, transportation, and parking, services that greatly benefit the local residents and tourists.

The benefits don’t end there; the team began incorporating Placer data into other key decisions, including monitoring park utilization, signage considerations, and traffic routing.

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