The RocketRez Customer Journey

After Investment

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Analysis of Cost Benefits

Here are the quantified cost savings applied to our composite organization.

Discount Rate: 15%

This rate was calculated based on current rates applied by similar companies with publicly available data.
I thought I would need a week to learn the whole system. It’s just so user-friendly and intuitive. It takes a somewhat savvy person ten minutes to learn it and figure it out. Lots of things that would take a lot of time previously, take no time now. It’s incredible.

Assumptions

Time Savings

Automation:

Front line employees using the RocketRez platform save an average of 35 hours per week saved at average $19/hour.

  • Updating pricing – 6 hours
  • Collecting guest feedback – 5 hours
  • Data entry – 5 hours
  • Routine customer service tasks – 4 hours
  • Generating reports – 15 hours

Employee Training:

Platform training on legacy ticketing systems reported at 142 hours per year vs. RocketRez Training Time 62 hours per year

  • Ticket office employees – 24 hours/employee
  • Call center employees – 30 hours/employee
  • Retail service employees – 8 hours/employee

Cost Savings

  • Software Consolidation: Attractions moving to RocketRez reduce an average for 4 external software programs
  • Transaction fees: Industry standard 6% of online sales vs. RocketRez platform subscription + variable per ticket fee
  • Industry average 18% OTA sales vs. RocketRez composite organization 4%
  • Headcount: Automated chat bot customer service + 1 self-serve kiosk reduces the need for 3 full-time employees at $19/hour
  • Ticket cancellations: With ticket cancellation assurance, RocketRez customers save on the industry average 8% of cancelled tickets and 0.65% chargeback fees for refunds.

* Figures are for illustrative purposes only and will vary depending on individual business factors including revenue, channel mix, employee salaries and discount rate applied. Interested operators are encouraged to reach out to RocketRez directly for consultation and custom analysis.

Key Findings

Key Findings

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