Our Smart Data scoring model contains proprietary methods and algorithms which took a great deal of time to develop and which we are not willing to fully reveal – for obvious reasons. However, the following general overview can help you better understand how demand is estimated.

• All venues are categorized based on type, with more than 60 unique venue types recognized (e.g. community center, stadium, concert hall, arena, etc.). The larger the venue the more likely it will drive demand. Larger venues are carefully categorized into types by assessing factors such as number of seats, amount of floor space, and other factors.


• Each event is assigned a frequency variable based on its recurrence. The more commonplace an event, the less likely it is to have demand impact. However, the seasonal impacts of frequently recurring events are taken consideration.


• Each city is assigned a variable depending on the historic volume of events that take place within that city. Certain cities don’t often get big events and when they do the impact is greater than it would be in another city with significant “daily” demand drivers.


• The demand differential impact of holidays on certain cities is factored (e.g. Christmas in NYC, Memorial Day in Lake Havasu City).


• Various additional factors are inserted into the model to give due consideration to sizeable recurring events at fixed locations (e.g. the Kentucky Derby in Louisville, the Indy 500 in Indianapolis), traveling “national” events (e.g. the Super Bowl), events involving multiple venues (e.g. a city wide convention), series of events, and many other specific impact determinants.


• A logit model is applied across all variables. This scoring process looks at correlation across multiple entries and applies the relevance down to specific variables (e.g. when multiple events are scheduled to occur on the same day the relative demand of each event will be impacted).


• Expected demand is scored on a fixed scale. The higher the score the higher the impact (e.g. small, medium and large local attractions are assigned correspondingly larger scores; multi-day sporting events score higher than single-day sporting events, infrequent major sporting events score higher still; a festival at a medium sized venue scores lower than a festival at a large venue; etc.).

Your city has a local minor league baseball team that plays at a 5,000 seat stadium. In home game series, the demand for those games is primarily local. Our scoring model would assign these events with a category of "Daily Recurring Local Sporting Event". However, if the same stadium were to host a state-wide high school baseball championship in May, that event would be categorized as an "Annual Playoff Sporting Event". The venue remains the same and both are baseball games, but the effects on demand are considerably different. Our “Smart Data” subscription can help you differentiate this demand variance.

Our "Smarter Data" subscription can predict how your business trends will likely be affected. You may learn that over the past 12 months your property / business has seen improved sales because of these “Daily Recurring Local Sporting Events” and, when the stadium has hosted events similar to the “Annual Playoff Sporting Event”, your sales were much higher and at an increased average price. You can now, by reference to our prospective event data, adjust your pricing, sales and/or marketing accordingly.