We select tariff plans for various focus groups of customers very smartly and targeted.
We cooperate with the California supplier of Big Data in the tourism industry. They provide us with about 120 different parameters daily, so we aggregate more data and determine the probability of the daily rates for a year ahead. Among such parameters is the occupancy of the nearby hotels and similar properties, holidays in the countries of our target audiences, local events, the number of tickets purchased for Bali for this day, weather. We collect statistics on the correspondence of these parameters with the sales history and use machine learning to forecast prices. For example, on September 22, the price for a particular property may be set on 13 dollars more than on the 23rd, because many properties around this place have already been booked, or there is a holiday, or there are other objective reasons.
There is a change and adjustment of the price daily. We usually create 15 tariffs for each object, besides regular seasonal rates.
Customers book with us from all over the world. For each guest who arrives prior 1 day, two weeks, or a month, the price will be different. The geographical location of the client plays an important role. For Europe, Asia, and North America, the rates will be different.
Sometimes it happens that 1-2 days between long bookings are exempted. The probability of booking just these 1-2 nights is low, so we use a special gap price to attract guests. In case of a last-minute discount, which is applied for booking and check-in on the same day, if guests book two nights, then only the first night will be discounted, the second night will be calculated based on the weekly tariff plan. We have a strong revenue management department, so all these tools can increase the level of profitability by 18-24%.