How Will The RMS Evolve to Move Revenue Management Forward

Jul 09, 2020, 04:00 PM
Today we are joined by 3 Guests

Michael McCartan - founder of mccartan tech consulting

Neville Isaac - Chief Customer Officer at BeOnPrice

Marko Lukicic - Co-Founder and Director at Acquaint 

This discussion continues our series looking at the evolving role of the revenue manager

Today we explore how the RMS is evolving and what additional data sets are being integrated to enhance the pricing element - looking at optimal pricing and fair pricing.

We then talk about potential, non traditional, indicative data sets and consider some constraints around this in terms of cost and privacy legislation

If you would like to watch the interview you can see it on our YouTube channel by clicking this link:

Here is the timelinechapter breakdown of topics discussed:

0:00        General welcome
1:00        Welcome guests
2:00        Guest introductions
7:15        Current RMS solutions - Is it providing sufficiently accurate predictions
9:17        Does the current RMS only really automate what we did manually
11:13      From a data science perspective reliance on historical data Is very limiting
14:50     RMS 2.0: How is the RMS evolving
16:20     Market Intelligence and the influence on a hotel - marrying internal and external data to improve price recommendation
20:50     Fair pricing
23:15     Revenue professionals restricted to location because of market knowledge
25:05     Willingness to pay and how individual property features could play a greater part
33:02     How are those without an RMS doing revenue management. Do they really understand the concept
36:04     Is most revenue pricing forecast still just adding a multiplier to last years rate
40:15     Where do we go next above and beyond historical in-hotel data
41:14     How the Finance sector moved beyond traditional historical data
45:50     RMS 3.0: What untraditional data sets could we be exploring to enrich forecasting
49:35     Macro data and micro data
52:50     How trust can be an issue as more data sets get added
58:00     Can the current one size fits all RMS become more bespoke
1:01:41  Wrap up summary