REVENUE REPORTING DASHBOARD FOR A HOTEL GROUP

Transcription

1 REVENUE REPORTING DASHBOARD FOR A HOTEL GROUP

2 THE CLIENT PROBLEM Our client, an international hotel chain, wanted to create a completely automated performance evaluation engine for ancillary products. The existing manual reports which tracked revenue contained multiple data sources and were fragmented. Slice and dice options, cross-program impacts were unavailable in the reports. THE AQ SOLUTION We collated data from four primary data sources. ENTERPRISE DATA WAREHOUSE (TERADATA) Contained data from booking and check-out systems with a delay of one day. DATA COLLECTION PEOPLESOFT DATA This revenue data source was used for enterprise wide data. The updates were made available by the tenth of every month for the previous fiscal month. PARTNERS DATA Credit card partners shared their customer information in flat files. Their data was updated on a fortnightly basis. PROMOTIONS DATA Product owners maintained their own promotion information on Google sites and spread sheets.

3 DATA CLEANING Based on business rules, we conducted an outlier treatment on important metrics like revenue, duration and points. CREDIT CARD CIF/PROMOTIONS Since partners provided the data in different formats we created one SAS SCD table across partners. The entire ETL processing was done using SAS. EXTRACTION INTO SAS DATA PROCESSING (ETL) SAS was connected to different databases and the data was extracted into a SAS dataset. DATA TRANSFORMATION Data transformation was done according to the design of the reporting layer and stored in temporary SAS tables. AGGREGATION Transformed data was aggregated in such a way that it could be accessed for all required metrics and levels of granularity in the dashboard. LOADING Aggregated data was loaded to a reporting layer. High level data flow diagram DATA SOURCES ETL PROCESS DATA FEEDS EDW SAS SCRIPTS DASHBOARD TERADATA FINANCE DATA TRANSFORMATION Data management LOADING Finance reporting Product performance Product scorecards Campaign reporting Campaign evaluation PEOPLE SOFT PARTNERS DATA FLAT FILES SAS Data transformation Scrubbing process SAS reporting layer Teradata DM Data mart AD HOC REQUESTS Supported ad hoc request of product owners Data needs for quick analysis PROMOTIONS DATA GOOGLE SITES Special exclusions Outlier treatment Aggregation FUTURE ANALYTICS Readily available data for future analytics needs Provided granularity with major data elements

4 Star schema approach for reporting layer design MEMBER DIMENSION Membership ID Ambassador indicator Treat/control Partner Cardholder indicator Member region CAMPAIGN DIMENSION Mailing ID Cell ID New/lapsed Unique members FACT TABLE Mailing ID Membership ID Mnemonic code Time period Rate category Room revenue Nights Booking Stays Booking window Redemptions HOTEL DIMENSION Mnemonic code Region Sub region Chain code Country TIME DIMENSION Time period Week Month Quarter Year DEVELOPMENT: The star schema approach reduced time and effort for the development team. QUERY PERFORMANCE: Enforced accurate and consistent query results. GRANULARITY: Provided better granularity to cater to the needs of different business users. KPIs: Provided flexibility to accommodate metrics like unique member count at any given hierarchy level. EASY TO MAINTAIN AND ENHANCE: Metrics and levels could be added easily at any given point in time.

5 The performance snapshot provided a birds-eye view of the business in a visual manner. Clickable widgets displaying key metrics of each section Global filter and time-period selection panel Thank You For any queries, get in touch with us. connect@aqinsights.com