Hospitality and Digital Marketing Case Study
A hotel chain marketing department wants to quantify the effect of a digital marketing campaign on bookings. The campaign starts on February 10 and runs for 30 days. The promotion offers a lower rate across a number of properties if the booking is prepaid.
The central booking system of the company is setup to post (via http REST POST) to Kanin.io data records in this format:
“Property”: “Company name*”,
The Kanin.io Data group is configured with these time buckets, aggregates and dimensions:
We were able to configure the Data Group, defining all aggregates and dimensions and load a test data set of 500,000 records in less than 90 minutes. The endpoint was ready to receive data immediately thereafter.
The system starts receiving data on January 1, 2019 and stops on April 1, 2019.
The digital promotion is for 30 days, starting February 10.
In real time, the marketing department is able to observe the following pattern of clear bounce in the number of bookings:
The pattern of increased bookings is clearly identifiable.
Interesting to observe is that the rise in bookings begins before the actual campaign – the marketing analysis is that the special deal has been pre-announced, and people start coming to book, expecting a lower rate in advance of the actual promotion. The lower rates driving the increased bookings are also clear:
The taper off is also obvious after the end of the campaign.
Kanin.io’s ability to easily define and aggregate across many dimensions allows in-depth analysis across multiple criteria, down to the level of individual properties:
Kanin.io enables this type of project with less than 10 hours of setup and at a cost of less than $5,000.
Most importantly, there is minimum time and effort needed from the IT department – setting up a single transaction POST is all that is required. All the rest of the setup is done by the marketing department business analyst.
There is no batch processing or delayed data availability – Kanin.io makes the data available across all dimensions and aggregates within 2-3 seconds of receiving the raw data records.