The difference between a Revenue Management System and Barebones Data Feed is similar to the difference between using a Windows laptop and a Linux laptop. While the first one is easier to use and graphical in nature, thereby allowing people with minimum computing experience to operate it, the second one has the advantage of allowing you to change raw computations, view and understand the inner workings of your systems and more.
RMS software allows you to get a dashboard-like view. This view does not contain raw data but instead, a presentable view after the data is consumed by custom algorithms. On the other hand, barebones data feeds would give you only one thing – clean and structured data, customized into the desired format. This data can then be converted into graphs, or be used to build a prediction model, or basically be plugged into any part of your business workflow as you see fit.
Most companies, especially those in the hospitality sector have been using revenue management systems for quite some time now. These RMS are proprietary programs built by big software companies as per the generic needs of these businesses. However, once the system is set, maintenance and support are mostly related to system bugs or issues while using it. Even if there are new enhancements, mostly it is not in the control of companies. In no way whatsoever do companies using RMS have any clue about the real raw data that is being crunched to make the beautiful charts and figures. Earlier non-tech-savvy business managers preferred this since you would not be getting your graphs and figures directly from the system without putting in any effort, and hence lead time to insights was quick. Hospitality companies focused on the business and did not have technical employees, due to which having an RMS was the only way companies could keep track of several metrics.
However things have changed, and every type of business today is analyzing raw data for several reasons-
But having a proprietary RMS would mean that many of the above points would not be explored using all the data at hand. Using a proprietary RMS means you do not ever get to see the raw data. That directly means that you cannot identify new data points or suggest better ways to use the data. In case the algorithm being used is not the most optimal, you cannot go on and ask for a different algorithm to be used. At the same time, if the data points captured by the system are not enough, you can’t even ask for more data streams to be used with existing data.
And all of the above definitely means leaving data on the table for your competition to use and falling back in the race.
We have spoken a lot about RMS systems being used by hotels, rentals, airlines, travel agencies and more. What happens when you switch to barebones data feeds? Well, for one, “poof” – your good looking dashboard is gone. Instead, you have to first decide which data streams you want to tap into, to gather your data points. You could most definitely use web scraping to gather most of the data and also scrape from multiple places for data redundancy.
Once you have decided which places to scrape and gather data from, you will have to build a scraping engine or take the help of DaaS providers like PromptCloud, to get your data in your desired formats. Note that in many of the websites that you will be scraping to get your data will be unstructured and their user interfaces may change with time. So putting a lot of focus on the scraping engine is a must, in case you are doing the task yourself. If you are taking the help of a team like ours, you can leave the headache of changing UI and unstructured data to us since you will only need to specify the data you want, the format you want it in, and how you want it to be delivered to you.
Although the data gathering part is the most complex, it is worth it, since you can now go through the raw data and run algorithms on it to build predictive models. The data can also help you spot market changes, user sentiments, change in buying trends and more. How the business team decides to use the data is completely up to them-be it using an off-the-shelf BI tool for fancy visualizations or a proprietary analytics engine. Another aspect that you have under control is how often you want to refresh the data so as to keep your models updated.
Building your custom decision engines to monitor your competition, your customers and the market itself is a tough job, but if you want to make the most of data, you have to give up on age-old RMS-systems and use intelligent models built on regularly updated fresh data streams. The reason behind that is simple. You can change any part of the model you build at any time. And you would also have complete autonomy over this system. This way you would also know how well the predictions and models are actually working.
For most non-tech companies, it is recommended that you outsource the data gathering part once you have decided on the data sources and the data points that you will be needing. After that, you will need a tech team of machine learning engineers and data scientists to crunch the data and see which data streams are best for providing important insights.
One of the biggest airlines from India reached us with a requirement of crawling different airlines and online travel agents websites for flights details and airfare related data. One of our sales consultant executives explained them with an overall idea of how we can help them with our customized web crawling services in their desired frequency and format. The airline company found our service as the right match shared its requirements in detail for feasibility check and the quotation. After a few levels of discussions and negotiations, we both agreed to the deal.
Data is the new oil and gathering and crunching raw data is the only way your business can survive in today’s dynamic market, where even big companies with hundreds of stores all over the US are failing. While it might look difficult at first, setup is a one-time affair and all you need to do is add to it, maintain it, and keep analyzing the data streams in the long run.