Features | RateGain | Other Data Providers |
---|---|---|
Data Acquisition: How fast do you get airfare travel data and from which all sources? |
||
Real Time Rate Shopping | Data is shopped real time from the websites | Data is shopped in batches and then distributed |
Limitation on data acquisition (supply millions of data) | β | β |
Rate Shopping across multiple channels (OTA/Brand/Mobile Apps) | β | β |
Granular ancillary data | β | β |
POS (Geo based)Shopping | β | β |
Rate Parity Tracking | β | β |
Data Match | Exact Match | Mismatch Issue |
Data Quality: Do you get data accuracy and sufficiency commitment in SLA? | ||
Overall data accuracy | 98% | No Commitment |
Overall sufficiency | 95% | No Commitment |
Usability: Is the migration swift and seamless? Is there enough support provided by your current vendor? | ||
Easy of Migration (Seamless onboarding with report and UI setup within 48-72 hours*) | β | β |
RMS Integration | β | β |
Dedicated Account Manager | β | β |
24*7 customer support | β | β |
No limits on customisable reports | β | Limited customized report Β |
Easy to navigate dashboard | β | β |
User Interface Uptime | 99.50% | Unsure |
UI Perspective: How is your user experience? Do you get actionable insights or still stuck to take decisions? | ||
Intuitive and Responsive UI | β | β |
Availability of Historical data | Last 365 shop dates | Last 3 months shop dates |
Detailed breakdown of RT fare | β | β |
Personalised emailer alert on competitor fare change | β | β |
User Access Management (ease of control on accessing UI) | β | β |
Demand Forecasting : How does the future demand looks like? Impact of upcoming events on demand patterns, booking trends and search volumes? | ||
Price willingness to pay | β | β |
Capacity Market share | β | β |
Trending news and events | β | β |
Trending length of stay (basis passenger profile) | β | β |
Demand Index | β | β |
Search basis passenger type | β | β |
High and low demand routes | β | β |