Spillover from Complimentary Item-Based Promotions

Abstract

Studies to date on the effectiveness of compliment-based promotion by U.S. casinos have frequently found insignificant effect of such promotion on casino revenues. Yet complimentary offers remain the main marketing strategy practiced by the industry. The authors suspect that demand spillover exists among casinos in a market, which causes a spurious relationship. Thus, the current study examines the effects of compliment expenditures on casino demand after accounting for spatial spillover effects. Results of the spatial panel Durbin model analysis suggest that popular complimentary offers, such as rooms, food, and beverages, lead to fierce competition that can be harmful to the entire market. Contrarily, less-used travel reimbursement, bus programs, and free parking help attract new players to the market. Implications for the industry and suggestions for future research are provided with the findings of the study.

Keywords

casinos compliments promotion spatial panel Durbin demand spillover

The casino industry is one of the major economic drivers of the United States. According to the American Gaming Association’s (AGA) State of the States Survey 2010, in 2009 there were 443 land-based or riverboat casinos operating in 13 states. The casinos earned $30.74 billion in gaming revenues and were responsible for employing 328,377 people, paying $13.1 billion in wages, and contributing $5.59 billion in tax revenues. The growth of the industry has indeed been formidable throughout that last three decades, after the long-standing monopoly of Nevada was annulled in the United States with the emergence of Atlantic City (Gazel, 1998).

The economic significance of casinos is not, however, limited to direct gaming revenues, employment, or tax contributions. AGA’s survey reports that 75% of respondents ate at a fine dining restaurant; 60% attended a show, concert, or other form of live entertainment; and 48% visited a bar or a night club during their casino visit. Furthermore, more than two thirds of sampled residents living near casinos responded that casinos had a favorable effect on the local tourism industry. Wiley and Walker (2011) report yet another benefit from casino operations, the appreciation of real estate property values in the vicinity of casinos.

Proportionate to the considerable economic importance of casinos and the dependence of local and national economies on their performance, a number of studies have offered pragmatic implications for casino operators and managers on aspects such as the impact of entertainment (Christiansen & Brinkerhoff-Jacobs, 1995; Suh & Lucas, 2011; Suh & West, 2010), restaurant volume (Lucas & Santos, 2003), micro-location (Lucas & Dunn, 2005), location and slot machine characteristics (Lucas & Roehl, 1999), amenities mix (Roehl, 1996), or hotel occupancy (Lowenhar & Russell, 1984). These studies have provided meaningful insights for casino operators in terms of guiding their short- and long-term tactics to increase gaming demand and boost casino receipts.

On the other hand, the literature has frequently confronted the rather counterintuitive results of when it came to the effects of complimentary offerings by the casino industry. It is a common practice in the U.S. casino industry to give away free goods such as rooms, food, beverages, travel reimbursements, or gifts in hopes of boosting gaming revenues (Hill, 2008). Often known as the “compliments,” this form of product promotion is unique to the casino industry in that no other industry bears as much cost relative to sales as a part of its marketing strategy (Marfels, 2010).

Although compliment-based promotions continue to constitute the major bulk of U.S. casinos’ marketing efforts (see Table 1), there are criticisms on what is believed to be a somewhat dissatisfactory outcome. As such, the effectiveness of this strategy has often come under scrutiny from researchers. For example, in their market studies, O’Donnel, Lee, and Roehl (2009) and Gu (2001) failed to find a positive correlation between promotional allowances and casino revenues. Similar results using property-level data have been reported by Lucas, Dunn, and Singh (2005), Lucas (2004), and Lucas and Bowen (2002). Following these results, Gu (2007) noted that the use of compliment-based promotion strategies generally deteriorate the casinos’ profitability. This paradox seems particularly disappointing for casino operators and practitioners who still rely heavily on compliment-based marketing tactics.

Table 1

Sample Profile (N = 11, T = 36, NT = 396)

Casino	Sort	Handle	Revenue	Allowance	Expense	Table Win (%)	Slot Win (%)
AC Hilton	Mean	36,492.8	16,730.7	6,223.9	727.4	12.7	8.5
	SD	8,271.8	3,972.7	1,653.1	489.7	1.9	0.3
Bally’s AC	Mean	77,622.8	40,427.4	12,482.0	592.4	16.6	9.2
	SD	11,451.0	6,906.3	1,460.0	163.5	1.2	0.3
Borgata	Mean	136,803.2	55,472.5	17,736.7	2,534.2	14.2	8.1
	SD	16,902.1	6,439.0	2,568.2	423.3	1.5	0.4
Caesars	Mean	94,884.7	38,653.7	11,055.5	1,900.9	15.5	9.3
	SD	12,879.1	7,095.9	1,528.3	640.9	2.7	0.3
Harrah’s AC	Mean	49,630.8	40,724.6	13,851.4	1,276.5	15.7	8.7
	SD	6,928.8	5,942.1	1,905.7	324.0	1.6	0.2
Resorts	Mean	28,986.4	16,084.0	5,452.2	1,141.0	14.0	8.6
	SD	6,189.6	3,637.5	968.3	547.9	1.7	0.2
Showboat	Mean	30,360.8	26,439.1	10,169.3	251.6	15.1	10.1
	SD	5,797.9	4,392.5	1,459.7	57.4	2.1	0.3
Tropicana	Mean	47,394.0	26,393.8	9,443.8	880.9	15.6	8.9
	SD	7,130.6	3,653.0	1,202.3	346.9	1.9	0.3
Trump Marina	Mean	22,819.3	14,267.5	4,486.7	791.1	15.8	8.8
	SD	9,220.9	2,711.5	766.0	323.9	2.6	0.3
Trump Plaza	Mean	37,499.0	17,760.9	5,171.2	1,196.9	14.1	8.6
	SD	9,028.7	3,875.8	897.7	318.3	1.9	0.2
Trump Taj Mahal	Mean	80,445.3	35,528.8	11,410.1	1,921.9	15.2	8.5
	SD	15,553.0	5,950.8	1,725.7	461.7	3.4	0.2

Note: SD = standard deviation. All items in thousands of U.S. dollars, except for Table win and Slot Win in percentages.

Meanwhile, some researchers note the possibility of cross-dependence in demand among casinos in a market. As Dowling and Uncles (1997) explained, coining the term polygamous loyalty, a player may decide to visit a casino destination but may not exclusively game in one casino. Rather, it is more likely that the player will visit multiple casinos and drop bets at several of them. In such a scenario, demand for one casino is likely to be influenced by demand for its neighboring casinos and vice versa. Consequently, spatial interaction among casinos is likely to take place, as recently noted by Suh and Lucas (2011). This phenomenon is referred to as spillover effect.

Thus, there is a gap in theory and a need for empirical analysis. If the demand for casinos in a market is dependent on one another, it is reasonable to expect that the compliment-based promotional efforts of one casino affect the market as a whole. However, studies to date have mainly concentrated on assessing the impact of compliment-based promotions on the rewarding casino itself and largely excluded possible effects on neighboring casinos. When spillover effect is present, examining the impact of promotions without accounting for the cross-dependence of casino demand may lead to inaccurate findings. For practitioners, it is imperative to understand the true effects of compliment-based promotional strategies. Compliment-based promotions are still the most widely used marketing tactic of the U.S. casino industry. Yet research often casts doubts on its effects. As a result, casino operators are left with no clear direction while the advent of Internet gaming (McGowan & Brown, 2009) and the Macau market (Gu, 2007) are posing a threat to the industry.

Therefore, the current study purports to examine the effects of compliment-based promotion on casino demand, while accounting for demand spillover effects in the market using a spatial panel Durbin model (Elhorst & Fréret, 2009). To accomplish this goal, we assessed the impact of compliment-based promotions on casino demand using monthly data from the Atlantic City casino market from 2008 to 2010.

Literature Review

Studies on Casino Promotions

In general, the limited empirical studies on compliment-based casino promotions argue against the effectiveness of current promotional practices. Lucas and Brewer (2001) initially observed the effect of buy-in incentives, measured as the daily dollar amount of redeemed mail coupons, on slot machine demand. The authors found a statistically significant increase in slot demand; however, when the proportion of total wagers retained by the casino was considered, the net increase in revenue was approximately comparable to the amount of redeemed incentives.

In a subsequent study, Lucas and Bowen (2002) found a significant increase in slot machine demand from cash prize offers awarded through drawings. Yet the results were consistent with Lucas and Brewer (2001) in that after accounting for the casino’s win percentage (percentage of wagers retained by the casino), the enhanced demand was not sufficient to compensate for the total cost of cash prizes awarded. Contradictory results were also reported. Examining a riverboat casino located in the Midwest, Lucas and Santos (2003) found an economically feasible, positive effect on casino demand from direct mail coupon offers on slot player premiums.

The study by Lucas (2004) examined the effect of match-play offers, which gives extra rewards to casino players when they have a winning hand on blackjack cash drop. The authors did not find a significant positive effect from match-play coupons on blackjack cash drop. Furthermore, Lucas et al. (2005) tested the effect of free play offers on slot demand. Consistent with the prior studies, the authors could not find a significant relationship between the two, regardless of the amount of cash offered. Such results are also backed by studies by Marfels (2010), O’Donnel et al. (2009), and Gu (2001), who using market data found no significant relationship between promotional expenditures and casino revenues.

In an attempt to comprehend these somewhat confusing results, many have offered intuitive explanations of this phenomenon. Macomber and Karoul (2000) asserted that casino promotions can be viewed as a discount on the price of gaming. Such discounts effectively reduce the price that players pay to visit the casino, and thus casinos have an incentive to raise the frequency and magnitude of these offers. However, such practices may increase loyalty toward the offers provided by the casinos rather than the casinos themselves (Kendrick, 1998). In this light, Dowling and Uncles (1997) use the term polygamous loyalty, a situation in which players are loyal to a group of casinos but not to one specific casino. The notion that gamblers are typically variety-seekers also supports the presence of this phenomenon (Lucas & Kilby, 2008).

An attractive incentive can induce a gambler to participate in a loyalty program and visit a casino, but the gambler may still join another loyalty program or spend money elsewhere when a competitor’s offer is lucrative (Wirtz, Mattila, & Lwin, 2008). Accordingly, Dowling and Uncles (1997) argued that use of loyalty programs only alter the short-term behavior of players and do not ensure long-term profitability. This idea that promotional offerings deteriorate long-term profitability is also shared by Ong, Ho, and Tripp (1997) and Kendrick (1998).

Theoretical Framework

Jointly considering these arguments, we theorized that compliments will increase the utility of individual players by discounting the price of the gaming trip. Such a discount-based strategy will be successful on the casinos’ part if, and only if, (a) a player who would not have visited the casino otherwise makes the trip as a result of the discount and (b) shows loyalty by dropping bets greater than the cost of discount at the casino that rewarded them. Studies exploring players’ perceptions in making the decision to visit a casino acknowledge the importance of compliments and support the first condition (Pfaffenberg & Costello, 2001; Shoemaker & Zemke, 2005). However, empirical evidence testing the second condition has yielded disappointing results, as outlined in the previous section.

There is a reason the latter statement may not hold. Although promotions often have compulsory play requirements attached, this restriction does not ensure that players spend money to the extent that casinos see revenue growth. For example, if a player receives a $100 free-play offer, the player is only required to make a minimum wager of $100 before cashing out the credit (Lucas et al., 2005). Even though the casino’s intention was to induce players to spend money out of their own pockets (after wagering the $100), any remaining balance after making the minimum bet could be dropped at another casino. There is no mechanism in place to ensure that the rewarded player will spend her/his entire gaming budget at the casino that offered them compliments (Lucas & Kilby, 2008).

More specifically, when a player decides to visit a casino, with or without a promotional offer, the player sees a potential utility gain from the trip. The player is likely to have a budget, which may include allocations for rooms, food and beverages, travel costs, gaming, and other entertainment activities. It is important to note that the total budget is an aggregate sum of the allocations in each category. Therefore, for the purpose of the bringing the player on site, the type of compliment is irrelevant. As long as the player sees a utility gain from the trip, the player will visit the casino.

However, questions arise regarding the grounds on which casinos offer compliments. First, gaming demand can be based on variety-seeking and players may visit multiple casinos in a single gaming trip (Lucas & Kilby, 2008). Once a player arrives at a casino, the trip costs to other adjacent casinos are significantly lower than the initial travel from home. Put differently, casinos can always anticipate demand spillover (Suh & Lucas, 2011).

Second, if the player intended to visit the casino regardless of the offer, the player would see a savings on the initial travel budget. However to the disappointment of casino operators, it is difficult to assume that the player will spend all of his or her “savings” on gaming, especially at that particular casino (Lucas et al., 2005). Even if the player decided to visit the casino as a result of the promotional offer, the measures used by casinos do not require the player to spend his or her entire gaming budget at the rewarding casino. In another situation, if the player is planning a trip to a destination rather than a specific casino, the player may choose the casino that offers the best package or discount. To attract such players, casinos are likely to engage in competition, in which whether a player stays at a casino hotel is determined by the type of compliments offered to them (Hendler & Latour, 2008). As a result, there is the possibility that a neighbor’s promotion could directly affect another casino’s demand, which we can theorize as the neighbor effect of promotion.

Figure 1 illustrates such a situation. Demand for casinos is likely to be interdependent as the cost of traveling to and gaming at different casinos is significantly low for a player once he or she arrives at one casino, signaling demand spillover. If Casino A offers compliments to attract a player into the casino, the player is likely to patronize Casinos B and C after the initial visit to Casino A, initiating promotion spillover. If Casino C offers a better compliment than A and attracts the player away, neighbor effect of promotion takes place. While the types of compliments offered are not of interest in simple demand spillover, promotion spillover and neighbor effects can vary based on the type of compliments offered. Players may be subject to different gaming options or increased gaming budgets based on the type and restrictiveness of the compliments offered. This is explained further in the following section.

Figure 1

Theoretical Framework of Demand Spillover Among Casinos

Illuminating the various spatial interactions among casinos in a spatial system requires analysis of spatial data over an extended period of time, as the number of casinos is fairly small and do not allow for meaningful statistical testing. Accordingly, the use of a spatial panel model is motivated by these concerns, as explained in the following section.

Data And Method

Sample and Data

For the purpose of the study, monthly data on Atlantic City casinos between the years 2008 and 2010 were used. Atlantic City is currently the second largest gaming market in the United States and the fifth largest in the world. According to AGA, in 2009 Atlantic City casinos jointly accounted for employment of 36,377 people, employee wages of $1.06 billion including tips, gaming revenues of $3.943 billion, and gaming tax revenue of $347.62 million. They also induced 30.38 million visitors into the region, boasting considerable travel and tourism receipts. The New Jersey Casino Control Commission provides monthly data for respective casinos on casino handle (total wagers made), win percentages (the percentage of bets retained by casinos: casino handle multiplied by the win percentage is equal to the casino revenue), revenues, and promotional allowances and expenses by category on their website. There are currently 11 casinos in operation in Atlantic City: Atlantic City Hilton, Bally’s Atlantic City, Borgata, Caesars, Harrah’s Marina, Resorts Atlantic City, Showboat, Tropicana, Trump Marina, Trump Plaza, and Trump Taj Mahal. Figure 2 illustrates the spatial distribution of the 11 casinos located in Atlantic City.

Figure 2

Spatial Distribution of Atlantic City Casinos

Figure 3

Moran Scatterplot

Table 1 shows the operating profile of these casinos. The operating statistics differ greatly by size. The larger casinos, such as Borgata and Caesars, handle about five times the wagers as smaller casinos such as Resorts and Trump Marina and Resorts. Their gaming revenue is also more than twice the smaller casinos. Promotional allowances and expenses are categorized simply by the choice of accounting method (Greenlees, 2008) and therefore are aggregated. Allowances typically include rooms, food, beverages, and entertainment, whereas coupons, cash-back, and travel reimbursements are commonly categorized as expenses (Marfels, 2010). The significance of promotional allowances and expenses is pronounced; on average these casinos spend approximately 21% of their total handle and 37% of their total revenues on compliment-based promotional expenditures.

The New Jersey Casino Control Commission requires monthly reports on casino promotional allowances and expenditures based on 11 categories: room, food, beverages, travel, bus programs, promotional gaming credits, cash gifts, entertainment, retail and noncash gifts, parking, and other. Using all these categories to model the effect would require estimating too many parameters and sacrifice considerable degrees of freedom. A review of the individual categories suggests that there is a meaningful way of classifying them into groups.

Rooms, food, and beverages (RFB) constitute a major complimentary package known in the casino industry as RFB giveaways (Doyle, 2009; Hill, 2008). These giveaways can be perceived as a discount on the price of the total trip budget, as players who were not offered these compliments would have been likely to purchase them on their own. At the same time, consumption of RFB at a specific hotel can be seen as somewhat binding, as the players will be exposed to the gaming area of the compliment-offering casinos for a greater duration of time (Lucas & Santos, 2003).

Travel cost reimbursement, bus programs, and complimentary parking (TBP) are discounts on the travel costs of players. They encourage travel to the casino but are less binding considering that casinos in Atlantic City are clustered together. The major bulk of the cost of the trip is incurred when players travel to the initial casino that offered TBP compliments not when players decide to travel to different casinos in the area. Gaming credit and cash gifts (CASH) can be seen by players as gains in their gaming budget. However, these offers usually come with more binding measures, such as compulsory play or match-play mechanisms (Lucas et al., 2005). Other types of complimentary offerings (OTHER) such as entertainment, retail and noncash gifts, and spa compliments can be seen as adding hedonic utility to the trip and were therefore aggregated into a single category.

Table 2 shows the expenditure of respective casinos on promotional allowances and expenses, sorted by the categories outlined above, while the percentage of individual categories to total expenditures are shown in parentheses. Although there is some variation among the casinos, RFB is undoubtedly the most heavily used compliment in the Atlantic City market, accounting for roughly 47% of total promotional expenditures during the sample period. The second-most used compliment is CASH, accounting for approximately 36% of total expenditures. These two categories can be considered as binding comps that restrict players to specific hotels and are responsible for some 83% of the total promotional expenditure. OTHER is ranked third with 9.5% and TBP is the smallest expenditure category with 7.5% of the promotional budget.

Table 2

Promotional Allowance and Expenditure by Category

Casino	Room, Food, and Beverage	Travel, Bus, and Parking	Game Credit and Cash	Entertainment, Retail, and Other	Total
AC Hilton	2,838 (40.83%)	871 (12.53%)	2,535 (36.47%)	707 (10.17%)	6,951 (100%)
Bally’s AC	6,291 (48.11%)	766 (5.86%)	5,155 (39.43%)	863 (6.60%)	13,075 (100%)
Borgata	11,951 (58.95%)	474 (2.34%)	5,667 (27.95%)	2,180 (10.75%)	20,272 (100%)
Caesars	5,006 (38.64%)	943 (7.28%)	5,194 (40.09%)	1,814 (14.00%)	12,957 (100%)
Harrah’s Marina	7,831 (51.77%)	1,295 (8.56%)	5,371 (35.51%)	630 (4.16%)	15,127 (100%)
Resorts	2,586 (39.22%)	364 (5.52%)	2,753 (41.76%)	890 (13.50%)	6,593 (100%)
Showboat	4,724 (45.33%)	736 (7.06%)	4,546 (43.62%)	415 (3.98%)	10,421 (100%)
Tropicana	5,519 (53.46%)	931 (9.02%)	3,372 (32.66%)	502 (4.86%)	10,324 (100%)
Trump Marina	1,977 (37.46%)	396 (7.50%)	2,223 (42.13%)	681 (12.91%)	5,277 (100%)
Trump Plaza	2,501 (39.28%)	774 (12.16%)	1,941 (30.49%)	1,151 (18.08%)	6,367 (100%)
Trump Taj Mahal	5,168 (38.76%)	1,480 (11.10%)	5,108 (38.31%)	1,576 (11.82%)	13,332 (100%)
Average	5,127 (46.72%)	821 (7.48%)	3,988 (36.34%)	1,038 (9.46%)	10,974 (100%)

Spatial Panel Durbin Model

The spatial panel Durbin model (Elhorst & Fréret, 2009) is used to simultaneously estimate demand spillover and neighbor effects on Atlantic City casinos. The spatial Durbin model is unrestricted in terms of the spatial effects that may take place and allows isolation of direct and indirect spatial effects from neighbors. Using the spatial Durbin model as a departure has recently been advocated, as it is the most flexible form that allows nested-type tests (Elhorst, 2010). The spatial panel Durbin model can be written as

y = ρ W y + X β + W X θ + α + μ_{i} + λ_{t} + ε,

where y is the vector of the total handle (wager), which is used to measure the demand for gaming (Thalheimer & Ali, 2003, 2008), that has a length of N (number of casinos) times T (number of months). W is the NT-by-NT block-diagonal matrix that has a cross-sectional weighting matrix in the diagonals repeating to the order of T, so that interpretation of Wy is the average demand of neighbors and WX is the average value of the neighbors’ independent variables. Specifically, the W matrix can be represented as

W (396 \times 396) [\begin{matrix} W_{N} & 0_{N} & … & 0_{N} \\ 0_{N} & W_{N} & 0_{N} & ⋮ \\ ⋮ & 0_{N} & ⋱ & 0_{N} \\ 0_{N} & \dots & 0_{N} & W_{N} \end{matrix}],

W_{N} (11 \times 11) = [\begin{matrix} 0 & w_{2, 1} & \dots & w_{11, 1} \\ w_{1, 2} & 0 \\ ⋮ & ⋱ & ⋮ \\ w_{1, 11} & \dots & 0 \end{matrix}]

where weights, w_i,j, are proportional to the inverse of distance between two neighbors i and j, and row-standardized so that each row sums to unity. Neighbors are defined as casinos closer than 2 kilometers (≈1.24 miles).

X is the 6-by-NT matrix of independent variables that include the four categories of promotional expenditures as outlined in Table 2 (RFB, TBP, CASH, and OTHER) and two win percentage variables for table games and slot machines (TABLEWIN and SLOTWIN) that proxy for the cost of wagering (Thalheimer & Ali, 2003, 1995a, 1995b). α, µ, and λ are the time-invariant, space, and time fixed effects that are removed prior to estimation through the demeaning procedure. All variables are log-transformed before estimation so that estimated coefficients are elasticities and allow ease of interpretation.

The estimation strategy involves removing fixed effects through the demeaning procedure prescribed by Baltagi (2005) so that the model is estimated without intercepts. Denoting the demeaned y vector as $\tilde{y}$ and the demeaned [X WX] matrix as $\tilde{X}$ , the log-likelihood function of model (1) to maximize can be written as follows (Elhorst & Fréret, 2009):

\ln L = \ln | I - ρ W | - \frac{N T}{2} \ln (2 π) - \frac{N T}{2} \ln (σ^{2}) - {(\tilde{y} - ρ W \tilde{y} - \tilde{X} β - W \tilde{X} θ)}^{T} (\tilde{y} - ρ W \tilde{y} - \tilde{X} β - W \tilde{X} θ) / 2 σ^{2} .

Elhorst and Freret (2009) shows how a concentrated likelihood function can be obtained since the maximum likelihood estimates of β, θ, and σ² can be expressed as a function of ρ:

{\hat{β}}_{M L} = ({\tilde{X}}^{T} \tilde{X}) - 1 {\tilde{X}}^{T} (\tilde{y} - ρ W \tilde{y} - \tilde{X} β - W \tilde{X} θ)

and

{\hat{σ}}_{M L}^{2} = {(\tilde{y} - ρ W \tilde{y} - \tilde{X} β - W \tilde{X} θ)}^{T} (\tilde{y} - ρ W \tilde{y} - \tilde{X} β - W \tilde{X} θ) / N T

is a parameter vector created by binding the row parameter vectors of β^T and θ^T so that Substituting out β, θ, and σ² from the likelihood function (2) yields the concentrated likelihood function that depends on ρ only (Anselin & Hudak, 1992), which takes the form

\ln L_{C} = C + \ln | I - ρ W | - \frac{N T}{2} \ln [{(e_{0} - ρ e)}^{T} (e_{0} - ρ e)],

where C is a constant that does not depend on ρ and e₀ and ρe are obtained as the ordinary least-squares residuals by successively regressing $\tilde{y}$ and W $\tilde{y}$ on $\tilde{X}$ The maximization problem can only be solved numerically, since closed-form solution does not exist (Elhorst & Fréret, 2009). After obtaining the numerical estimate of ρ, and can be recovered as

{\hat{β}}^{T} = b_{0} - ρ b_{1},

{\hat{σ}}^{2} = \frac{1}{N T} [{(e_{0} - ρ e_{1})}^{T} (e_{0} - ρ e_{1})],

where b₀ and b₁ are the least-squares estimators and e₀ and e₁ the least-squares residuals from regression of $\tilde{y}$ and W $\tilde{y}$ on $\tilde{X}$ .

Results and Discussion

Table 3 shows the results of estimation using the spatial panel Durbin model. As observed, the spatial correlation parameter ρ is highly significant, confirming the presence of spatial demand spillover consistent with the prediction of Suh and Lucas (2011). Straightforward interpretation of the parameter implies that a 1% increase in the average demand for neighboring casinos will result in a 0.162% increase in the demand for own casino.

Table 3

Estimation Results

Variable	Coefficient	SE	t Statistics	p Value
ρ	0.162***	0.056	2.888	.004
RFB	0.220***	0.079	2.793	.005
TBP	0.119***	0.016	7.328	.000
CASH	0.191***	0.038	5.014	.000
OTHER	0.024*	0.013	1.814	.070
TABLEWIN	0.003	0.003	0.829	.407
SLOTWIN	–0.059*	0.032	–1.830	.067
W × RFB	–0.708***	0.179	–3.945	.000
W × TBP	0.141***	0.033	4.331	.000
W × CASH	0.121	0.076	1.586	.113
W × OTHER	0.062	0.038	1.642	.101
W × TABLEWIN	0.012**	0.006	2.073	.038
W × SLOTWIN	0.075	0.071	1.058	.290

Note: N = 11; T = 36; log-likelihood = 241.4105; R2 = 0.9513.

***

p < .01. **p < .05. *p < .10.

Looking at the effects of compliment-based promotions on the rewarding casino, all four categories had a significant effect on casino demand. RFB had the largest coefficient, stimulating gaming demand by 0.220% with a 1% increase in expenditures. CASH followed with 0.191%. The results are consistent with the reasoning that these two types of complimentary offers are more binding and therefore require the players to stay on site for a longer time. TBP also had considerable positive effects of 0.119%, whereas the effect of OTHER was significant at the p < .1 level but rather small in magnitude at 0.024%. TABLEWIN was found to be insignificant, perhaps due to the importance of other aspects in table games such as individual dealers’ skills (Austrin & West, 2005). SLOTWIN had a negative effect on gaming demand, consistent with the observations of Thalheimer and Ali (2003, 2008). In addition, all promotions created positive spillover for neighboring casinos, generated by the spatial autoregressive parameter ρ.

Of particular interest was the effect of neighboring casinos’ promotions on own casino demand. We found that the neighbors’ average RFB had a sizable and negative coefficient of −0.708%, suggesting that RFB compliments do not help much in bringing new casino demand into the market. Rather, they initiate a cutthroat competition in which casinos bear burdensome losses to attract players away from other casinos. This result is intuitive, as the average number of neighbors in the data set is 4.73. If the casinos increased RFB compliments by 1% on average, they would each gain 0.220% of gaming demand through direct effect. However, the coefficient of −0.712% on W × RFB is just slightly smaller than the direct effect times the average number of neighbors: 0.220% × 4.73 = 1.04%. This implies that most of the increase in demand through compliments is produced by taking away players from neighboring casinos, while little new demand is brought into the market.

Average CASH of neighbors did not have a significant effect on gaming volume, as they are the most binding compliments and restrict players to place minimum bets at the rewarding casinos. The average TBP of neighbors generated a stimulating effect of 0.141%, which is notable as it is greater than the direct effect itself. OTHER had a positive coefficient, but was insignificant at p < .1. TABLEWIN of neighboring casinos helped increase the volume at other casinos, as players may see lower cost of gambling at neighboring casinos. SLOTWIN had a positive coefficient, but was statistically insignificant even at p < .1.

Since the model is a log-linear model, for ease of interpretation we calculated the effects of each promotional expenditure category measured in dollars, caused by per dollar of promotional expenditure using sample data. The figures are presented in Table 4. When considering the direct effects from a casino’s own expenditures only, promotion through RFB and CASH categories seem to be viable strategies to pursue. Nevertheless, when the neighboring casinos’ expenditures and their effects are considered, RFB is no longer desirable for the market as the net effect on demand is questionable. The results suggest that during the sample period, casinos have been in a fierce “competition” for gaming demand by giving away RFB.

Table 4

Direct and Neighborhood Effects (in Dollars)

RFB	TBP	CASH	OTHER
Demand change per dollar of own expenditure (effect of X)
$2.51	$8.45	$2.80	$1.36
Demand change per dollar of average neighbor expenditure (effect of WX)
−$9.46	$11.19	—	—

The TBP category deserves much attention. Since it creates favorable neighbor and spillover effects that are greater than the direct effects on its own property, it tends to benefit the market as a whole. As a result, the neighbor effect of TBP on the market is greater than the direct effect. Considering the relatively low allocations to this category, the absolute effect seems even more lucrative.

In this light, the effect of CASH is not very impressive. CASH does generate significant gaming demand through direct effects, as these offers are usually combined with compulsory requirements (Lucas et al., 2005). However, the net effect on revenue is questionable, which agrees with results from prior studies (Lucas & Bowen, 2002; Lucas & Brewer, 2001) that cited increase in business is not sufficient to compensate for cost. Considering that win percentages of tables and slots were around 15% and under 10%, respectively, the results support past findings that the increase in demand does not compensate for promotional costs. Moreover, the spillover effect is minimal. OTHER shows favorable direct effects on gaming demand, but the magnitude of the effect is rather small relative to the marketing expenses incurred, while there is no favorable effect for neighbors.

Conclusions

Studies to date have reported mixed results on the effects of compliment-based casino promotion strategies. As a result, the effects of complimentary offerings, which continue to be the main market strategy for the U.S. casino industry, have remained in question. Suspecting that the spatial spillover of demand and promotions creates a distortion in estimation, the current study used a spatial panel Durbin model with two-way fixed effects to find evidence of demand spillover and neighbor effects of compliments in the Atlantic City casino market during the period 2008-2010.

From our analysis, we found that popular RFB and CASH compliments are indeed effective in attracting gaming demand. However, at the same time the effect from spatial lags should be noted. The effects of RFB may be the result of completion and pulling gamblers away from other casinos rather than tapping potential demand. CASH also failed to create significant positive externalities, while the increase in demand was found insufficient to compensate for cost, which is consistent with past studies.

The estimated effects of TBP deserve much attention. TBP had the only economically feasible and positive direct and neighbor effects on gaming demand. Moreover, the neighbor effect was greater than the direct effect. Considering current expenditure levels, the casinos could significantly benefit by increasing promotional expenditure on travel reimbursements, bus programs, and free parking.

However, as the locations of casinos are legally bound, spillover seems inevitable. With such a spillover problem, casinos will be reluctant to increase promotional allowances and expenses, which would benefit the system as a whole rather than a particular casino exclusively. This may well be the reason behind sluggish promotional programs by respective casinos (Marfels, 2010). The problem becomes more severe when neighboring casinos are investing more on restrictive promotions such as RFB or CASH. Without an implicit agreement in the market, independently acting casinos have an incentive to promote through binding measures when others choose not to.

In this regard, some feasible approaches can be contemplated for casino operators. Having a local administration or an unbiased third-party coordinate joint-promotion programs among casinos in the region may help to more efficiently allocate resources to compliment-based promotions. When a joint marketing program is not feasible, partialling-out and cooperating in terms of TBP promotions only could be considered. This would ensure optimal expenditure levels and that collective interests are reflected. To mitigate competition effects and maximize favorable spillover, differentiating with respect to service quality and other intangible product attributes, such as security, while jointly marketing the location and accessibility are all viable means.

This study is limited in its generalizability and applicability. Due to the availability of data, the analysis relied on only one of the major casino markets in the world within a relatively short time span. The unique nature of each spatial system dictates that the effects of spillover and competition will differ by location and time. Also, the data span reflects a recessionary period in the United States and therefore requires caution when interpreting the coefficients.

Nevertheless, the current study considerably contributes to the understanding of compliment-based promotional strategies by casinos and their effects, which has been a puzzling phenomenon to many researchers in this field. Most notably, the current study provides a theoretical framework for isolating out the direct and indirect effects and finds evidence that there is significant cross-dependence among casinos in a spatially defined market. By examining respective compliment categories, the strategic implications of using each type of compliment were also derived. These will also benefit casino marketing practitioners by offering pragmatic guidance on promotional practices.

In this light, the field’s understanding of this topic could be deepened in several ways. Examining different casino markets in the world using spatial models, accumulating additional data and replicating the model, and investigating other types of promotions and compliments will extend the reliability of the results and the generalizability of findings. Utilization of micro surveys to further investigate player perception and behavior associated with promotions are also warranted. Following researches on this topic are of great need by academia and industry alike, given the importance of the U.S. casino industry in the current economy.

Footnotes

Seul Ki Lee, PhD (seul.ki.lee.80@gmail.com), is an assistant professor in the School of Tourism and Hospitality Management, Temple University, Philadelphia, Pennsylvania.

SooCheong (Shawn) Jang, PhD (jang12@purdue.edu), is an associate professor in the School of Hospitality and Tourism Management, Purdue University, West Lafayette, Indiana.

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