Type of Project
Caesars, Entertainment, Company’s, Success, Paper
One of the great things about the hospitality business is that you can take care of the guest the way you know they should be taken care of. That is the most noble of all callings.
— Gary Loveman, CEO, Caesars Entertainment
Gary Loveman, CEO Caesars Entertainment, knew that the customer experience was absolutely crucial to the company’s success. An economist and former academic, Loveman also believed that using analytical tools was the key to measuring, understanding, and driving the customer experience. “What you want is to imbue in your organization a decision-making culture that is based on data and information and not on intuition or hunch,” he said. Loveman continued,
We use analytics to solve problems that have meaningful financial results. Some think of analytics as an indulgence of egg-headed people, but there is tremendous value to using analytics to solve problems. In our industry we have a lot of customer data and much of it we collect in real time.
With these data we can treat different customers differently. The fact that the business is high margin gives us a lot to work with, meaning we can get our customer to do things that she may not have planned, things that are appealing to us from a profitability standpoint but importantly, also appealing to the customer because it is something that we think she would enjoy based on our data.
Recent events had significantly challenged the gaming industry’s ability to maintain profitability. Loveman noted, “Since the financial crisis in 2008, we have faced an unfortunate combination of events.” He continued,
On the one hand, demand has been relatively weak. Consumers still visit us, but they spend less. So unlike a lot of businesses, you can come to Las Vegas and have a great time and bet $25.00 a hand rather than $50.00. And we’ll still treat you well and you’ll be in the same building, under the same general circumstances, but my revenues come down like crazy.
What has exacerbated this situation is that a lot of capacity that was conceived in the peak came on-line in the trough. In Vegas, in particular, a massive amount of new product, rooms, casinos, showrooms, and retail space came on-line just as we were in the worst of circumstances. So, we’ve been working our way through weak demand and gigantic, quite luxurious new supply at the same time. It’s been tough.
In this context, improving firm profitability was challenging. Were there ways to increase the efficiency of Caesars’ operating model without sacrificing the customer experience? Loveman described the challenge:
When the analysts who follow the casino business hear us talk about efficiencies, they hear that we’re going to operate with lower costs than we had before—and they immediately assume that that’s going to lead to service degradation. That’s not a crazy notion. Think about the way we used to teach production functions to economic students: If the production function is fixed and you put less labor in it, then you get a less beneficial result. But it’s the job of people like me to make sure that that historic trade-off does not occur. We have to write a new production function.
In the period since 2008, the world has been all about writing new production functions. How do we get better performance with lower inputs, or at least the same performance with lower inputs?
Loveman knew that Caesars spent around $2 billion annually on staffing and staffing-related expenses across all of its properties. Could this be an area where Caesars could “write a new production function”? He asked Ruben Sigala (HBS MBA 2003), chief analytics officer, senior vice president of Enterprise Analytics at Caesars Entertainment, to look at staffing models in Las Vegas.
Sigala undertook a project to forecast demand and determine staffing levels for nearly 500 different job classes across Caesars’ U.S. hotels, restaurants, casinos, and convention and entertainment venues. A particularly vexing challenge was how to create a staffing model for the front desk at Caesars’ hotels, particularly in Las Vegas, where revenue and profit growth had slowed considerably since 2008.
Ensuring sufficient front-desk staff was critical to the customer experience. Sigala collected data showing that customers who had a bad check-in experience spent less at Caesars’ restaurants, casinos, stores, and shows than those who had a good check-in experience. However, the ability to staff the front desks so that all customers had a good check-in experience (i.e., little wait time) was problematic due to the variability and volatility of guest arrival times.
Sigala and his analytics team had been partnering with Caesars’ central scheduling group to develop a model to forecast when guests would arrive; with a forecast in hand, he could then build a model to staff the front desks accordingly. The team’s initial results had shown promise, but were not where they needed to be. Sigala needed to decide how to improve forecasting accuracy for the firm’s properties.
Caesars, a U.S.-based casino-entertainment company, began in 1937 in Reno, Nevada, as a bingo parlor. By 2012, the company operated 53 casino-resorts in 13 U.S. states and 7 countries.
The company had over 40,000 hotel rooms and operated some 300 restaurants.1 In addition, Caesars offered convention space, retail shopping, and entertainment (e.g., music shows).
The company owned a range of resort brands: Caesars, Planet Hollywood Resort & Casino, Harrah’s, Horseshoe, London Club International (which operated 14 casinos in Egypt, the U.K., and South Africa), Bally’s, Harvey’s, Rio, Paris, Tunica Roadhouse, Showboat, The Grand Biloxi, and Flamingo. In 2012, the company had revenues of $8.6 billion with a net loss of $1.5 billion (see Exhibit 1 for financials).
Caesars employed about 68,000 (most working in the casinos) with approximately 28,000 employees covered by collective bargaining agreements, most of those working in Las Vegas and Atlantic City, New Jersey.2 The company was opening new urban casinos without hotel accommodations in the U.S. to grow revenues. Caesars operated 10 properties in Las Vegas, employing about 25,000 (see Exhibit 2 for a list of properties). Las Vegas contributed 35% of the corporation’s total net revenues (see Exhibits 3 and 4 for data).
Loveman left academia in 1998 to become chief operating officer of Harrah’s Entertainment Inc., and was appointed president of Harrah’s in April 2001 and chief executive officer in January 2003. In 2004, Harrah’s Entertainment Inc. bought Caesars Entertainment for a reported $5.2 billion in cash and stock, creating the world’s largest casino operator. Harrah’s was rebranded as Caesars Entertainment in 2010, Loveman said, “in recognition of Caesars’ status as the world’s preeminent and most respected casino brand.”3 He continued,
The name change reflects our evolution as the industry’s leading provider of branded casino entertainment. While our name is changing, our dedication to who we are as a company will remain the same. Our employees will remain dedicated to the innovation, customer-service excellence and corporate citizenship that have been hallmarks of our company for decades. This rebranding of the corporate name can open exciting new opportunities for us in the future.4
Total Rewards Program
Caesars was known throughout the casino industry as a pioneer of data collection and analysis. The backbone of Caesars’ analytics was its Total Rewards Loyalty Program, which it launched in 1999. Total Rewards provided members with single-card access to nearly 40 casino locations across the U.S. and Canada.
In addition, members received invitations to special events, including tournaments (e.g., the World Series of Poker), gift giveaway promotions, shopping sprees, concerts, and festivals. Members were offered multiple ways to redeem the Total Rewards credits they earned, including purchasing items at participating property outlets and at the TR Marketplace (totalrewards.com), which featured a range of retailers. In 2013, Total Rewards had 45 million members.5
In return, Caesars gathered data on Total Reward members, learning about customer habits, needs, and desires by analyzing the frequency of visits and amounts spent on gambling, lodging, food, and entertainment. Loveman said, “You have to get people to show you what their interests are and what they’re doing. And to get them to do that consistently, you need to give them something for it. In exchange for the information we want, we give our customers immediate credits, which can be redeemed quickly, and without the limitations or conditional requirements other industries impose.” Loveman continued:
We want to get to know who you are. To do that, we offer you participation in our loyalty program. In exchange for that, we give you things you tell us you like. So, you gamble, you stay with us, you get dinner tonight, you get a limo tonight, you sit in front of Celine Dion tonight, you stay an extra night at the hotel. Tonight. Not after 37 visits and you have to call in at midnight on a full moon and beg, like most loyalty programs. We want to give you something. We want to give it to you today.
And you get to choose what you want. One person prefers a buffet. One person prefers Celine Dion. One person prefers a room upgrade. Somebody else wants a limousine. Somebody else wants golf green fees. We have the currency—we have lots of toys. You can pick your toy, and have it today. And through that cycle, we will learn more and more about you and your loyalty to us will grow.
Although the data obtained via Total Rewards was the foundation of Caesars’ analytics, the company had turned more and more to social media to augment its data collection. “Between social-media data and our transaction data, we are always looking for some type of incremental information that we can use to move our customers to have a better experience and improve our profitability,” Loveman explained.
The company built an advanced analytical toolkit to influence guest visitation and optimize its pricing. “For example, data tells us that customers that stay with us and go to one of our shows—Celine Dion or Elton John—will have a better Caesars’ experience than those that do not attend a show. So, we can devise ways to improve the non-gambling entertainment products we offer.”
The casino-entertainment business was unique in that competitors were often clustered in close geographic proximity. Services included lodging, gambling, entertainment activities, restaurants, and shopping. Casino-resorts not only competed against one another but also competed against non-gaming vacation and entertainment options. To a smaller extent, the casinos also competed against lotteries and online gaming.
In the U.S., casino-resorts were once the domain of Las Vegas and later Atlantic City, New Jersey. However, in 2012, casinos were operating in 23 U.S. states, and more were in the planning stages. In fact, more than 70% of U.S. gambling revenue was generated outside Nevada.6
The Las Vegas Market
Despite the growth in casino-resorts outside of Las Vegas, “Vegas” still drew throngs of visitors. Annual visits reached 39.2 million in 2007. The numbers dropped after the financial crisis, but rebounded to 39.7 million in 2012 (see Exhibit 5). According to Caesars’ data, visitors visited, on average, six to seven properties (i.e., casinos, restaurants, etc.) during their stay.
Las Vegas was also the top tradeshow destination in the U.S., with 10.5 million square feet of convention and meeting space.7 It was home to the country’s largest trade show, the International Consumer Electronics Show (CES), which brought over 150,000 visitors in January 2012.8
Since the financial crisis, however, visitors’ stays had shortened and what they spent per visit had declined. Furthermore, increasingly, visitors’ spending went to non-casino-entertainment options (see Exhibit 6 for revenue by product segment).
From 2007 to 2012, gaming revenue in Las Vegas dropped 10%.9 Meanwhile, the total cost of running casinos rose 32% in the same period.10 A report on the Las Vegas market said, “The segment of tourist dollars spent on gambling, now less than 50% of the total, has been declining for years as the town continues to open more celebrity-chef restaurants, Cirque du Soleil shows, high-rise amusement rides and nightclubs. Diversification has long been part of the city’s strategic plan, and that has helped Las Vegas retain its status as one of the country’s top tourist and meeting destinations.”11
“Right now, the gambling industry has a serious health problem,” explained one CEO in the summer of 2013.12 The CEO added, “For the first time in my career we experienced negative operating leverage. When variable costs can’t be cut any further, your losses multiply the longer you are open.
”13 One article said, “Table games such as baccarat, blackjack and craps used to represent 80% to 90% of the casinos’ revenue and earnings. Since the [financial] meltdown, the casinos have become ever more reliant on slots, where the ‘hold’ (the percentage of bets the house keeps) has been dwindling. Slots now represent 50% to 60% of revenue and 75% of gross profit.”14
Just before the financial crisis, about $15 billion of new casino-resort facilities came online or were in progress in Las Vegas, which added 13% more hotel rooms during the recession.15 The average daily room rate in 2012 was $108, 18% lower than five years earlier.16 For casino-resort operators, hotel occupancy rate percentages typically were in the mid- to high-90s year-round.
And operators were not opposed to offering free rooms to known gamers to bolster occupancy if weak. “It is never a question of whether our hotels will be full,” Loveman said, “but who will be in them. We devote a lot to science and math to get the right person in our hotels.”
The influx of new facilities and product offerings and the resultant drop in gaming revenues combined for a $5.2 billion economic swing for the Las Vegas casino-resort industry, from a profit of $3.6 billion in 2007 to a $1.6 billion aggregate loss
The Staffing Problem
Each year in Las Vegas, Caesars checked in 3 million guests, at a cost of between $20 million and $25 million. The cost of staffing the front desks in Las Vegas averaged about $3 per occupied room. Caesars Palace and the Flamingo, two of the largest hotels in the world, might have as many as 16 to 20 front-desk personnel working at any given time. The check-in process involved a series of steps. (See Exhibit 7 for a description of steps used by front-desk staff to check in guests.) Sigala added, “Overall, our goal is to have all guests checked in within roughly five minutes.”
There was no single check-in process; it differed by property, and staff were trained according to the expectations at each. Furthermore, the check-in process differed depending on the customer type. Caesars picked up VIPs–high-end gamers who visited frequently and spent a lot of money gambling—at the airport in a limousine and drove them to a private entrance, where there was a reception area and guests were treated to champagne and other delights. Some areas in Caesars Palace, such as the Nobu Hotel, also had separate check-in areas. A special line, staffed by senior employees, was cordoned off at the front desk for Caesars’ “Diamond” and “Seven Star” customers.
All other guests, both Total Rewards members and non-Rewards members, formed the main check-in queue. However, the check-in process for Reward members was quicker than for non-members once they reached the front-desk agent. “We want non-members to have a favorable check-in experience too,” Loveman said, “but they will see our loyalty customers checking in quicker.”
For all guests, the earliest check-in time was at 4 p.m., although many wanted to check in earlier. An efficient front-desk employee checked in around 14 guests per hour, whereas an inefficient employee might process half that amount. Caesars had a fixed number of front-desk staff at each hotel, and scheduling and staffing were limited by union contracts. “In Las Vegas there are a number of union-contracted restrictions that we have to work through,” Sigala explained, “such as the number of part-time (vs. full-time) employees we may have worked, and restrictions on the amount of overtime.
In addition, work schedules need to be in place at least two weeks before the scheduled work date.” Even if union rules had been more flexible, Loveman was sensitive to the needs of those employees who worked in shifts. “Many of our employees have to worry about things such as child-care coverage,” he said. “You can’t give them a schedule and then change it at the last minute.”
Check-outs were also the responsibility of the front-desk staff. Check-out was 11:00 a.m.; many guests checked out either online or via programming available on the televisions in their rooms.
Economic Implications of a Bad Check-in Experience
Sigala and his Enterprise Analytics team found that with an unhappy hotel experience, particularly at check-in, guests tended to spend 5% to 10% less at Caesars’ properties, often taking their gambling, meals, and entertainment activities to a nearby competitor. Furthermore, with the prevalence of social media, disgruntled customers could vent their frustration to a large number of people instantly. Loveman said,
Usually, the host casino will get around 60% of the customer’s gaming dollars. You could lose all of it if you make guests wait too long for check-in or the process is unsatisfactory in any other way. We worry about the second stop, third stop, fourth stop, after the check-in. Why? Because the customer can walk out the door of the Flamingo and on either side or across the street, 50
yards in any direction, there are competitor casinos, which an unhappy customer is free to go to. And they will if they are angry with us.
In fact, we did a text mining analysis of all of the negative reviews in Las Vegas. [See Exhibit 8 for comments.] Some commonly used phrases were related to waiting—wait line, long line, took forever, etc.
Caesars had experimented with placing check-in kiosks in some hotel lobbies. Guests preferred not to use them, even when check-in lines were long. Loveman explained, “The check-in process in the casino-resort industry is significantly more complex than, for example, simply choosing a seat on an airplane, so kiosks are not a robust solution.” Sigala added, “The airlines clear out the entirety of their occupancy or their seats before anybody gets on board, so there is a lot more certainty on what’s available and what’s not. Not only that, our customers want to interact with the front desk because they think that there is the possibility that they can talk their way into a room upgrade.”
At the same time, because the company was continuing to seek to identify operating expense efficiencies in the face of an uncertain revenue environment, adding more employees to staff the front desk was not a viable option. Further, due to union agreements, staffing could not be augmented by employees from other departments who were outside of the job classification.
“Staffing is a big, big deal . . . the volume of check-ins is tremendous,” Loveman said. “For example, we have 3,600 rooms at our Flamingo property and most visitors stay three nights. If something goes amiss, a check-in can take an hour or more. The check-in is a leading indicator of a customer’s overall experience.”
Solving the front-desk staffing problem relied heavily on being able to accurately forecast guest arrivals on a daily basis and then break that down by staffing shifts. But there were many variables, outlier events, and uncertainties that made forecasting difficult. “Variability in arrivals is very problematic in forecasting,” Loveman explained.
The number of customers depended on the season of the year, prices (hotel, airfare, etc.), the number of large group functions and events (e.g., conventions), as well as marketing programs and promotions. Gamers, those who came to gamble, often reserved their rooms with a short lead time—in some cases, just a few hours before arrival. “More people are just showing up—same-day bookings,” Sigala said, “perhaps as much as 10% of bookings, which might be 30% of all check-ins.” Low room prices tended to draw spontaneous customers from California who could reach Las Vegas by car. Sigala said,
“Before the recession, more visitors came to Las Vegas by air and they stayed, on average, 3.1 days. Now more are driving, which does make their arrival more predictable, but the average stay is between 2.5 and 2.7 days, meaning more room turnover. Customers are younger and booking their rooms less in advance. The traffic patterns also differ if a large number of our loyalty program customers have reservations rather than a large number of non-members.”
Outside the control of Caesars and its customers were weather-related incidents (e.g., storms) and transport issues (e.g., flight delays due to mechanical problems or weather in other parts of the country). “You wouldn’t necessarily think about delayed flights here in Las Vegas, in the desert,” Loveman said, “but we have quite a few windstorms that impact guest arrivals.” Sigala added, “There are a number of key variables that help us predict guest arrival rates. Those include the customer mix, seasonal patterns, days of the week, and one-offs, such as weather disruptions, or first-time large events occurring in Las Vegas.”
Sigala was hired in 2005 into a department focused on pricing in Caesars’ Las Vegas hotels. He hired a number of employees to build the capacity to test prices through all of Caesars’ channels. He was then asked to launch a business intelligence group, which was charged with building and managing the data architecture and flow, as well as reporting, for the pricing organization and other groups.
The business intelligence group was also asked to build capabilities to address more advanced analytics problems such as simulation problems, optimization problems, and other areas that required analytical competencies typically not found within other areas of the organization.
In 2011, the Analytics function across Caesars Entertainment was consolidated into a group called Enterprise Analytics. The charter of this organization was to provide analytical support to all aspects of the operations across Caesars Entertainment and across all jurisdictions. Sigala was asked to develop and lead this group from its inception, and he was named Chief Analytics Officer in 2013. Typically, between 150 and 200 employees worked in Caesars’ Analytics organization.
Before using analytics to staff front-desk operations, scheduling was done by the front-desk managers at each individual property, with varying degrees of success. “Not all variables were identified, and volatility was rarely taken into account,” Sigala explained. In 2012, scheduling resort operations began to transition to a centralized scheduling group, which was also responsible for reviewing productivity levels at all properties and introducing best-practice processes.
During this period, Sigala’s team began to test different analytical approaches to increase the forecasting predictability of staffing several departments, including the front desk. The analytics team ran models and provided the outcomes to the central scheduling group.
Data came from lodging management systems, casino calendars, other operating reports, and outliers such as bad weather. “Las Vegas arrival patterns have a sort of lumpiness,” Sigala said. “Checkout is 11:00 a.m., but some may ask to check out a little later, which means that housekeeping cannot clean the room for the next guest on its schedule.
So, when a person checks in—and often they arrive before 4:00 p.m., the room may or may not be ready, which can make the check-in a little tricky. The traffic patterns will differ if we are having a large number of our loyalty program customers coming in rather than a large number of non-members. So you need to know the patterns at a discrete customer level.”
The data on front-desk check-ins was an example of a time series—a series of data points measured over time at regular intervals, such as hourly, daily, weekly, monthly, quarterly, or annually. Caesars’ analytics group had gathered the number of check-ins per day for the previous three years. The analytics group divided the check-in data into two sets: a “train” data set that was used to build each forecasting model and a “validate” data set that was used to determine how large the forecast error would have been if the forecasting model had been applied to the “validate” data set.
In this case, they used data from January 1, 2010, through early August 2013 as the “train” data set, and used data from the four weeks of data in August 2013 as a validation data set. Gene Lee, vice president, Advanced Analytics, was asked to lead the modeling process for the hotel check-in forecasting model. Sigala explained:
Many of the capabilities that are supported through the Analytics organization are developed through disciplines focused on specific business verticals such as Gaming, Revenue Management, etc. However, with a problem that is as broad and far reaching as labor forecasting, we tend to leverage our Advanced Analytics team, as their view is generally agnostic to the
business concern and is instead focused on exploratory analytics processes. This team works in tandem with the operations and other Analytics professionals to develop a base understanding of the business concern at hand, but we look to this group to develop the most efficient—and, if necessary, quite sophisticated—analytics techniques to create long-term applications and solutions. They’ll often begin with very simple statistical techniques to develop a fundamental diagnostic of the behavior observed, and will iterate through more advanced processes until an appropriate solution is derived.
Initially, the analytics team applied a moving average methodology to forecast guest arrivals. Moving average was one of the most basic approaches used for time series forecasting; the forecast for the next period was the average of a set number of data points. Moving average was often used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles.
To capture the effect of day of week, Caesars implemented the moving average forecast based on the day of week. For example, the forecast for next Monday’s check-ins was the average of the number of check-ins on each of the three previous Mondays (see Exhibit 9 for predicted values).
Applying the model to the validation data set resulted in a Mean Absolute Error (MAE) of 225 and a Mean Absolute Percentage Error (MAPE) of 25% (see Exhibit 10 for moving averages). Sigala commented on these results, “Overall seasonal movement to inform forecasting can be quite useful if the data set is stable. Moreover, the moving average approach is intuitive and users can adopt it easily. But because we have a lot of volatility, the moving average, while helpful, would not be accurate over the long term.”
As a result, the analytics team next turned to linear regression modeling. Linear regression models (also known as ordinary least squares regression, or OLS) were used to find the linear relationship between a dependent variable Y and a set of explanatory variables (X1, X2, X3, . . . , Xn) that minimized the sum of squared vertical distances between the actual values of Y and the values of Y predicted by the regression line. “We like that this approach isolates the impact of specific variables on arrival patterns,” Sigala said. He continued,
The first step was to speak to the front-desk managers to ask them what drives front-desk activity. Then we reviewed the data set, what was available, to see how usable it was, how much confidence we have in it. We started with our dependent variables, daily check-in and check-out volumes, to develop an equation to forecast the number of total check-ins per day. From there we get more granular, and the equations more complex. Our model is specific to each property because data availability is not consistent across all of our properties, and we need to understand any property-specific nuances.
For example, at one property, the Operations people tell us that the number of VIP arrivals really impacts the front-desk check-in process. The central scheduling group then goes and looks to see if there is an indicator for this and whether there is sufficient data for us to be able to predict the number of VIP arrivals on any given day for that property—remember we are doing this several weeks in advance—and while some VIPs certainly have reservations, many do not.
It is a combination, therefore, of what the operators tell us is important and the broader data set where we have identified a variable not from the operators but one which we have observed within the data to consistently be statistically significant. If we do find a variable, we go back to the operators and ask them if at least intuitively this makes sense. The partnership between Operations and Analytics is fundamental to anything our team is trying to do.
The Analytics team iterated with the Operations people to develop a set of explanatory variables, eventually agreeing to use those described in Exhibit 11. A sample of the database is shown in Exhibit 12.
An important consideration, Sigala explained, was that “the data set has to be available to us for the period which we will be forecasting because we need to have the schedule in place at least two weeks in advance.” He admitted there were some variables that they did not have, but would like to, such as cancellations per customer segment, or front-desk sick days. Gene Lee elaborated on how they addressed this problem.
We know that the mix of guests in the hotel significantly affects check-ins. If there’s a big group in town, they may check in together and/or if there are lots of VIPs, they will use a separate front-desk operation. Unfortunately, we don’t know the exact mix three weeks in advance. Thus, we use a “working forecast” that is a combination of two elements: advance bookings and our forecasts of bookings. Specifically, we use:
As we get closer to the stay date, the “working forecast” becomes more accurate because we have more OTB data. Similarly, we have a working forecast of Average Daily Rate (ADR) for Free and Independent Travelers (travelers who did not belong to the casino, group, or wholesale categories) three weeks out using similar methodology (blending actual OTB and expected TBB).
Depending on the property and time of year, actual reservation data may only represent about 60% of final booking outcomes; the volatility of the remaining 40% is driven by last-minute demand, walk-ups, and cancellations.
The results of the linear regression model are shown in Exhibit 13. Applying the model to the validation data set resulted in an MAE of 271 and a MAPE of 34% (see Exhibit 14).
Determining Front-Desk Staff
To translate the daily check-in forecast into the number of staff members needed at the front desk, Lee divided the check-in forecast by 12 (to find how many total labor hours would be needed, assuming each front-desk employee could process 12 check-ins per hour), and then divided by 8 (to find out how many full-time employees (FTEs) would be needed, assuming each FTE worked eight hours per day). He then rounded that number to the closest integer to find the number of staff members per shift.
If this number was less than nine, he increased it to nine, since Caesars Palace had a policy to have at least nine front-desk staff. He then computed the MAE and the MAPE for each of the two forecasting models (see Exhibit 15 for a model comparison). Most check-ins took place during the 4:00 p.m.–to-midnight shift.
Sigala and Lee expressed disappointment with the fit of the first two models. “Success for us is to have a forecast that allows us to be able to staff our properties within one FTE of what they need to process check-ins in about five minutes,” Sigala said. Using linear regression, we had one of our eight Las Vegas properties meet this goal. That result is okay, but not what we need.” He continued,
Should we consider more advanced models to try to forecast check-ins? We know that forecasting time series can be tricky. When we do regressions using time series variables, it is common for the errors (residuals) themselves to have a time series structure. This violates the
usual assumption of independent errors made in ordinary least squares regression. The consequence is that the estimates of coefficients and their standard errors will be wrong if thetime seriesstructure of the errors is ignored.18
We have to ask if we wouldexpect the check-in regression’s error term fromone day to be independent of the error the next day.For example, if we have 1,000 check-ins on a Mondayabovewhat we expected, will thishave a significantimpact on the number of check-ins observedon the Tuesday and Wednesday?
If the errors dohave a time series structure, we would have to use a model likeregressionwith autoregressiveerrors, or ARIMA, a specialized time series model that has parametersreferring tothe AR (autoregressive structure of the errors), I(integration),and MA(the movingaverage parts of the model).How many FTEswould we be off if we were toapply the ARIMA approach?
What could Sigala and his team do to improve the staffing accuracy?
|Exhibit 1Caesars Entertainment Financials (millions except for per share data) 2012||2011||2010|
|Food and beverage||1510||1,508||1,482|
|Less: casino promo allowance||(1,253)||(1,233)||(1,326)|
|Food and beverage||660||659||614|
|Property, general, admin||2,104||2,087||2,030|
|Depreciation and amortization||716||678||699|
|Intangible/tangible asset impairment charges||1,068||33||184|
|Total operating expenses||8,900||7,757||8,070|
|Loss(income) from operations||(313)||816||483|
|Gains on early extinguishment of debt||136||48||116|
|(Loss)income from continuing operations, net of income taxes||(1,383)||(698)||(849)|
|(Loss)income from discontinued operations, net of income taxes||(110)||31||26|
|Net (loss)/income per common share of stock||($11.95)||($5.50)||($8.37)|
|Exhibit 3Las Vegas Region Financials (millions) Percent Favorable (Unfavorable)|
|2012||2011||2010||2012 vs. 2011||2011 vs. 2010|
|Income from operations||429||496||350||(13.5)%||41.6%|
|Operating margin||14.1%||16.4%||12.3%||(2.3) pts.||4.1 pts.|
|Exhibit 5Las Vegas Annual Visitors (millions) Year||Number of Visitors|
Guest Services Agents (GSA’s) will greet every guest immediately with an appropriate greeting as they approach the check-in area at the Front Desk. Suggested greetings are “Welcome to Harrah’s. How may I assist you?” or “Good afternoon, my name is ___. How may I assist you?” Always greet guests with a smile. Please refer to the 5-10 rule (see Portal document for service standards).
Reservations can be located in LMS [Lodging Management System] several different ways:
° Name (First Three Letters of Last Name and First Initial)
° Confirmation Number
° Additional Name Option/Non Registered Option
° Credit Card Swipe
° Date of Arrival
° Group Code
° Total Rewards Number
GSA’s will personalize all conversations by addressing guests by their names as soon as they are known. GSA’s should attempt to use the guest’s name at least three times during check-in.
Picture ID is required from each guest checking into the Hotel for security measures. GSA’s will never check-in a guest to a reservation that is listed under another name.
Acceptable forms of ID are:
° Driver’s license or Federal / State-issued ID
° Military ID
GSA’s will confirm the following items with guests to avoid any discrepancies during their stay:
° Arrival and Departure Dates (Verify Travel with if the guest has a back-to-back offer.)
° Number of Guests and Children
° Room Type Request
° Complimentary Status (If Applicable)
° Any Comments in Guest Services Screen
° Address and Phone Number
° Total Reward Number
° Method of Payment (Credit Card, Cash, Etc.)
° Verify if the Guest Would Like to Apply their Reward Credits Upon Check-Out
° Additional Names or Share-Withs (Include Total Reward Number if Applicable)
A guest’s request for a particular type of room will be honored if that type of room is available. ADA-equipped rooms will be blocked prior to arrival and guaranteed when reservations are made. Some examples of Special Requests are:
° Bed Type
° Connecting or Adjoining Rooms
° Lower or Upper Floors
° Room Near an Elevator
° Smoking or Non-Smoking Room
° Special Views
° Upgraded Accommodations (At a Higher Rate)
If an assigned room number is changed during the check-in process, the status pop-up box from LMS must be updated appropriately by the GSA.
GSA’s will collect payment for the duration of the stay at check-in time. Acceptable methods of payment include the following:
° Cash (Valid ID should be documented on reservation.)
° Credit Card (Credit card should be swiped and an imprint made (if applicable).
° Gift card
° Traveler’s checks
° Money Order
Note: If an advance deposit is applied on the guest folio, the guest is responsible for the remaining balance.
Guests will be advised of any deposit requirements for incidental charges, such as phone calls, in-room services, and in-room vending to avoid inconveniences during their stay. Incidental deposit amounts may vary depending on the property.
All complimentary rooms must have the correct Billing Profile on the reservation.
GSA’s should check each reservation to ensure that accurate codes are assigned. Items to review are as follows:
° Billing profile
° Group code (If Applicable)
° Rate plan
° Offer Code
° Status Code
Each guest must sign a registration card as a record of their stay. In addition to signing the card, the guest must also initial the rate, departure date and Reward Credit authorization to ensure accuracy and agreement. All registration cards are filed in a designated “bucket” until departure date.
Registration cards must contain the following standard text:
“I agree that I will be personally liable for this bill, including any charges to my room for any services, movies, or telephone usage and/or in the event that any other party or entity fails to pay the full amount of the charges and/or in the event of any damage to hotel property. I authorize any and all charges be made to my presented credit card, if available.
The Hotel will not be responsible for any money, jewelry, or other valuables left in a room. Individual safe-deposit boxes are provided by the Hotel at no charge to the guest. The Hotel will not be liable for any items not secured in this manner.” Refer to Risk Management.
Keys and Directions:
Professional Plagiarism Free Paper in APA/MLA/Harvard/Turabian Format, Instant Delivery, High Quality Submissions, 100% Unique, Turnitin Report Attached
The context and relevance of the issue, as well as a clear description of the study aim, are presented. The history of searches is discussed.
The context and relevance of the issue, as well as a clear description of the study aim, are presented. The history of searches is discussed.
With titles for each slide as well as bulleted sections to group relevant information as required, the content is well-organized. Excellent use of typeface, color, images, effects, and so on to improve readability and presenting content. The minimum length criterion of 10 slides/pages is reached.
More depth/information is required for the context and importance, otherwise the study detail will be unclear. There is no search history information supplied.
There is a review of important theoretical literature, however there is limited integration of research into problem-related ideas. The review is just partly focused and arranged. There is research that both supports and opposes. A summary of the material given is provided. The conclusion may or may not include a biblical integration.
The content is somewhat ordered, but there is no discernible organization. The use of typeface, color, graphics, effects, and so on may sometimes distract from the presenting substance. It is possible that the length criteria will not be reached.
The context and/or importance are lacking. There is no search history information supplied.
There has been an examination of relevant theoretical literature, but still no research concerning problem-related concepts has been synthesized. The review is just somewhat focused and organized. The provided overview of content does not include any supporting or opposing research. The conclusion has no scriptural references.
There is no logical or apparent organizational structure. There is no discernible logical sequence. The use of typeface, color, graphics, effects, and so on often detracts from the presenting substance. It is possible that the length criteria will not be reached.