The season is over for many sports franchises across the different leagues so now what do we do!
• For many team’s tickets sales and attendance is low!
• Concessions sales are down 30%
• Viewers are staying home and watching the game.
• Revenue is down another 20%
Now what do we do to increase revenue and engagement?
Some of the many common questions team owners and executives are trying to answer without any insight into the fan in how they purchase tickets, who is the fan coming to the game, what will my tickets sales be next year, how to increase engagement. The questions can continue and it becomes throwing a dart and hopefully trying to hit a bull eyes when it comes to increasing fan engagement.
Big data and predictive analytics takes the guess work out of the equation with real time information on the business systems and fan information. Using big data you can cleanse the data sources with the most accurate and relevant information. The majority of teams are trying to make business decisions on old and inaccurate information.
Once the data is cleansed applying analytics to be informed of real-time game day and business information is the goal. Everything from sales, tickets, concessions, merchandising, parking, gate entry, CRM, finance, and Human resources are integrated with the data warehouse and powerful analytics take out the guess work.
Predictive analytics is the next most powerful step to increasing fan engagement and revenue not just during the season. This will allow teams to engage the fans year round, help to keep the fan in the stadium for the entire game, and provide insight into the business.
Predictive analytics brings together advanced analytics, predictive modeling, data mining, text analytics, stadium and parking analytics, ticket life cycle analytics, optimization, real-time scoring, fan behavior, enhanced marketing experience, and more.
FanPaaS puts these capabilities into the hands of business users, owners, sales, tickets, marketing, and with our mobile platform in the hands of the fan
