Data Analytics: Changing The Face of Hospitality
By Anurag Gaggar January 30, 2017
Hospitality industry has always focused on delivering a better guest experience through the warmth and care of its people. The favourite stories are of hotels where the staff remembers the names of all guests staying with them and greet them with their names or remember food and other preferences of their loyal guests and deliver superlative experiences. Technology and data analytics plays a supporting role in enabling some of these experiences, but many of them are delivered through extensive staff training and coaching.
However, there are many more functions that directly or indirectly impact the guests and hotels themselves where data analytics is playing a crucial role. Having been closely associated with India's travel tech sector for over half a decade now, I have had the privilege of seeing some of these trends unfold from close proximity.
Hotel room inventory (supply) doesn't fluctuate much. Physical infrastructure takes time to build and lasts for years. New rooms may come up or some existing rooms may go under renovation/maintenance, but the supply of rooms in an area shows little variation over a short time frame. However, the demand for hotel rooms can vary significantly over days of the week, seasons of the year and witnesses spikes and troughs because of external events. A concert or conference leads to sharp spike in demand for a small number of days, wedding season sees demand increase spread over a larger number of days. Some of these are known well in advance, but some of them take hoteliers by surprise. This is where data analytics becomes extremely useful.
Using data on consumer searches for different cities and areas, we are able to identify changes in demand patterns sooner than each small hotel in the network is able to establish looking at their advance purchase pattern. By conducting A/B experiments (where you show two different experiences to two different but identical set of users and determine which one performs better against a measurable goal) on pricing, we are able to ascertain price elasticity of demand. This enables us not only to determine instances where price needs to be adjusted upwards or downwards, but also to establish the quantum of the change.
At each point in the customer's lifecycle, it's possible to predict the next-best action for each user to improve their experience. A 360-degree view of customers can be created by generating intelligence about their behavioural patterns based upon interactions with the network. Using this specific data, the network can pitch most relevant offers to customers at the ideal time to drive conversations and raise engagement levels. Using supervised and unsupervised machine learning techniques, we have generated more than 50 markers for user behaviour. Patterns are then identified and recommended systems used to address the next-best action for each user.
Based on customer behaviour on the app and localised trends, the experience each user gets on the network's app can be personalised. The big data system analyses interactions real-time and combines this with the user as well as local inclinations picked up via machine learning models. It is thereby possible to generate real time recommendations on offers, city recommendations as per the user's present location, previous searches and bookings. We have seen more than 10% conversion lift just by using these personalization techniques.
Improving Guest Experience
The markers we create for user behaviour are also quite helpful in improving guest experience. By understanding our users better and using data from their previous feedbacks as well as users of similar cohorts, we are able to recommend properties higher up the search funnel that not only convert better but are also likely to provide a better guest experience. Some users care more about a faster Wi-Fi while some may be more particular about the size of the room. When you are able to match the right user with the right properties, it becomes a win-win situation for all.
We also incentivize the hotels on our network that deliver a better customer experience to get higher visibility and more business from us, ensuring that guest experience remains a priority for everyone in the network.
We believe we have just begun our journey of using data analytics to improve all facets of our business. We are investing in building the right platform for data collection, processing and analysis that enables us to do this at scale and in real time. We have no doubt in our mind that this will change the face of Indian hospitality in the times to come.
The author is Vice President - Product Management, OYO