By Neville Isaac, Chief Customer Officer

Tips to improve competitiveness by designing a forecast adapted to your product

In a competitive market it is essential to find reliable formulas that allow us to establish a pattern that will simplify the task of achieving our revenue goals. Market competition and the emerging Big Data phenomenon provide the ideal scenario in which to understand what influences the consumer’s decision-making processes and how a business can use this to its advantage. This does not mean exerting some kind of control over the customer, but understanding the market better in order to optimize processes and adapt products to the potential needs of an increasingly demanding customer.

Developing a forecast is conceived as a way of “providing information” to the revenue management department to forecast the income and variables that influence an establishment’s growth. The procedure for making an estimate of expected income for future periods involves studying growth in previous years, analyzing current booking processes, as well as parameters and future availability so as to transfer to the accommodation what should be the most competitive market.

Price variables such as competitor set, demand, brand focus, target, sociopolitical changes, past events that have affected positively or negatively and future events related to the sector, directly influence the reservation processes of consumers and, therefore, affect competitiveness.

Importance of forecasting

By forecast, we mean the prediction of demand during a certain time period. As specialists in hospitality, we look at elaborating a correct forecast to anticipate future performance and reduce uncertainty about what will happen in the coming months. By analyzing historical and current results, as well as market trends and consumer and competitor behavior, the hotelier can make projections using the main result indicators: occupation, average price, revenue per room, income, etc.

The goal of forecasting is to better understand where the business is going and make the necessary strategic adjustments to obtain greater profitability. In Revenue Management, we can predict demand through forecasting, which will simplify the task of making strategic decisions to optimize inventory and pricing in the aim of maximizing income and operating profit.

A forecast, broken down by consumer market segments, will make it easier to identify the most profitable customers, so that we can take specific action aimed at providing the best possible customer experience, and thus win the loyalty of the most interesting target audience. By focusing on demand prediction, we can establish pricing customization strategies, working from the point of view of Customer-Centric Revenue Management.

Developing a forecast is a job for the Revenue Manager: many use basic spreadsheets to tackle this task. Bear in mind that an inadequate forecast can lead to poor decision-making, and thus impact negatively on revenue and profit margins, therefore investing in technology is highly profitable in optimizing the process of collecting data, synthesizing information and making decisions.

Since the forecast provides medium and long term insights, the work of the Revenue Manager can be challenging especially since it needs to be done on a daily or weekly basis, especially if broken down by segments or by booking channels, in which case gathering information can be an arduous task.

The importance of forecasting has a direct impact on the market intelligence that we can apply to the day-to-day running of an establishment.

Preparing an adequate forecast

Given the high expectations regarding the importance of forecasting, it is important for us to lay down the elements that affect this model. Both data collection, and the human and technological ability to interpret them will be decisive when developing the forecast. True business forecasts consider the following data:

Historical Data

Collecting and analyzing operational data is essential for a correct estimation of demand, our understanding of demand being the number of potential customers who are willing and able to acquire our product at a certain price.

When talking about data, we are not only referring to the historical results, for example in a hotel, we may consider occupation, average price and income, but also to many other indicators provide information about the behavior of our customers in the past, and any situation or event that has impacted demand.

Breaking down data by segments, markets and marketing channels, will allow us to access a much more accurate analysis, so that we can identify which are the most profitable customers that can provide a sustainable improvement in hotel profit.

Current Data

It is essential to relate results from past periods to what is happening now. In this respect, it is necessary at this point to collect data regarding previous indicators for reservations that we already have confirmed and compare it with the same time for the previous year. This allows us to check whether demand behavior is similar to that in the previous year, and to detect whether there have been variations in trends.

If we know the existing offer for a destination, this will inform us about alternative products, their level of quality and their price-performance ratio. By using this information, we can identify our competitive positioning and the reasons why clients choose our hotel against those of our competitors.

Future data

Forecasting tries to predict the demand in a future period of time. We must therefore consider all the demand generators that we can predict and those we know will have an impact on our bottomline, either because they have already occurred in the past, which we expect to be repeated with a similar impact, or because they are new events which we know will generate demand peaks.

Access to information sources

The goal of forecasting is to estimate future demand as precisely as possible. Thus, we must make a comprehensive analysis of data and information from both inside and outside the hotel. The technological challenge is to access reliable and precise data sources that we will need at all times. Technology helps in this respect, allowing us to use different tools that will enable us to collect data and organize information.

Types of forecast

In order to reduce costs associated with excess expenditure and opportunity costs, it is crucial to establish a cross-sectional revenue system for different departments. To ensure that the forecast is suited to our predictions, it is important for to know the two types of forecasts available:

• Demand forecast. This is the most important of the two, especially for perishable products like a hotel room.  We estimate demand by the type of room, market and segment, regardless of hotel capacity. It may so happen that we end up with an estimate of demand that exceeds the number of rooms in the hotel, because a demand generator at the destination triggers booking requests around that date or period (for example, a football match, a concert, or public holidays). In this case we talk about unconstrained demand or unlimited demand.

It is essential to know dates which we can predict an excess of demand, as this will affect the strategic decisions to optimize the hotel’s results. The opposite case is that of demand deficit: dates on which the forecasts tell us that there is not enough demand to fill the hotel. Identifying these dates is of equal importance when making decisions.

• Capacity Forecast. The capacity forecast will allow us to estimate future income. According to the demand forecast, we can predict what part of that demand we will be able to accommodate and what income bookings will generate. If we have total expenditure statistics by segment, such as breakfast catch rate or average expenditure on food and beverages, we can also estimate income in other departments, while also predicting how resources and raw material needs will be allocated.

The capacity offered by these types of forecasts will determine the ability to react to different market situations. An information flow that feeds into decision-making in each of our departments will improve performance and satisfaction in a hotel that is connected to the needs of your market.

Forecasting phases

Developing an adequate forecast means implementing a well-structured and well-informed methodology by which to make decisions. Traditionally, this process has been developed manually. Currently, technology provides tools that aids in the capture, generation, calculation and implementation of conclusions regarding forecasts.

We propose a forecasting process that is designed in five well-defined phases:.

1. Collect historical data:

Data about what happened in the past will help us to understand market movements. Traditionally, these data have been the cornerstone of any forecasting process, although this is currently getting more complicated, as decision-making processes can be enriched with new sources through which we can interpret the market.

2. Identify causes:

Patterns for more complex market situations will allow us to estimate what associated problems may arise and estimate what approximate percentage the accommodation should assume. These kinds of eventualities are more pronounced when the distribution is more decentralized from our own booking channels, and that is where we will have to control these booking specifications.

3. Demand behavior:

Identifying and developing strategies in the main market segments will make it easier to work on the needs of each of them with policies and totally differentiated products. This type of approach will help to be more precise and consistent with the peculiarities and perceptions of the different types of accommodation traveler:

4. Compare:

Once historical and current data regarding demand behavior and our establishment’s performance indicators have been identified and analyzed, we must compare current trends with those of the previous year. Thus, we will be able to identify variations in trends and look for the causes, in order to make decisions about how to deal with the variations.

5. Adjust forecasts

Compare your policies with the rest of the market. What had traditionally been done first becomes the final part of a process of study and analysis. Determining a competitive set for both products and destinations based on our hotel’s objectives will be crucial to see the position that we occupy in the market.

Objective quality vs online reputation

Nowadays, travelers can use multiple information tools to reserve a room in a market saturated with accommodation and information. With this is mind, and in the interests of competitiveness, all hoteliers and accommodation managers must understand the numerous factors that condition the booking/purchase processes, analyzing everything from the qualities of the hotel and the configuration of available services, to overall experience throughout the booking process: a full set of factors that influence the price the customer is willing to pay at all times.

Online reputation, which is based on post-stay opinions, is a way of measuring the degree to which the customer’s expectations of quality during the purchase process has been met. Fulfillment of expectations will be measured according to the extent to which these conform to the reality of the hotel, that is to say, its objective quality and how it communicates this, and the way in which this provides the consumer with a realistic perception of this quality.

Objective quality and online reputation are two factors that should be constantly fed back into the organisation, and then to what extent that the hotel has understood the needs of the market, levels of satisfaction of the traveler will be improved.

Beonprice has created the Hotel Quality Index (HQI®): the only index of the hotel market that measures the integral quality of a hotel to know the competitive positioning and the price elasticity in the market, depending on each of the traveler segments.

The HQI® takes into account more than 350 objective parameters such as location, hotel services, catering, room size, etc., as well as online reputation. This index summarizes customer-booking behavior taking into account the quality expectation of the establishment before and after the reservation. The HQI®, as an innovator in the market, will continue its evolution on the calculation of the price elasticity that the market accepts for each accommodation. That is, the key information so that the recommendation is much more precise in order to increase the net RevPAR.

The HQI® takes into account the different perception of quality in each customer segment, which will enable a hotel to identify the most profitable segments, and work on a day-to-day basis from the standpoint of a customer-centric revenue management. Thus, what we seek is greater customization of price, so that we can achieve loyalty through optimizing prices and reducing acquisition costs in the future.

Probability of sale

Identifying competitive positioning allows the accommodation to obtain a new indicator that will directly impact the forecast: the probability of sale.

Given that we have information about the price the customer is willing to pay for a certain standard of quality, and we know our level of quality plus competitor quality and price details, we can determine the probability that a customer will decide to make a reservation. This increases precision when preparing our forecast.

Considering the probability of sale constitutes a new approach to forecasting that was, until now, impossible, since there was no access to technology that would allow this analysis. It is a new way of working that provides greater precision when calculating the demand forecast.

We believe that the key to success lies in using artificial intelligence to manage Big Data and know the behavior of demand. Having the perception of the integral quality of each establishment by each segment will allow the business to define its competitive positioning at all times.

This very precise analysis of demand will enable us to know what price the customer is willing to pay to meet their expectation in order to calculate a probability of sale. From here, businesses can establish strategies to optimize results, based on live data obtained.