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Abstract

Daily deals platforms (DDPs) are perceived as an attractive means for hospitality merchants to acquire and retain new customers and generate ancillary revenues. However, these potential benefits come at the cost of steeply discounted prices and high commissions. How could hospitality merchants decide if working with DDPs is beneficial to their firm’s financial performance? This longitudinal study aims to investigate the impact of DDP on merchants’ performance by comparing DDP and non-DDP customers on key performance indicators, analyzing the benefit of product bundling, and potential cannibalization effects. The results show that DDP guests contributed lower revenues, had shorter lengths of stays, longer lead times, lower retention, and less positive reviews than their non-DDP counterparts. In deciding to work with DDPs, hoteliers must consider the opportunity cost of their choice because the expected vital benefits are more likely to be attained by non-DDP customers. Unexpectedly, DDP and non-DDP guests do not switch channels, and cannibalization is negligible. At the macro level, channel migration indicates that DDPs have contributed more bookings over the study period and become a more important channel in the channel mix.

Keywords

Daily deals platform multiple-sided platform cannibalization channel transparency channel migration

Introduction

Multiple-sided platforms bring together merchants and customers in high-value exchanges (Hagiu and Wright, 2021). The advancement in information technology enhances the scalability of these platforms, creates the network effect (Van Alstyne, Parker and Choudary, 2016), and disrupts many industries, including the hospitality and tourism industries. Daily deals platforms such as Groupon are one type of multiple-sided platform. Where previous researchers used “flash sales” (Aday et al., 2018; Berezina, Semrad, Stepchenkova and Cobanoglu, 2016a; Geerts and Masset, 2022; Piccoli and Dev, 2012), “online coupons” (Sigala, 2013), “social coupons” (Cassia et al., 2015; Cox, 2015, 2017; Kumar & Rajan, 2012), “daily deals” (Budler et al., 2020; Pentina and Taylor, 2013), this research uses the term “daily deals platforms (DDPs)” to reflect the elements of the multiple-sided platforms. In other words, these daily deals or flash sales are sold or bought through multiple-sided platforms, not directly from the merchants. In this research, the terms “daily deals” and “flash sales” are interchangeable.

Daily deals platforms (DDPs) work with merchants to reach customers, present steeply discounted offerings, and process transactions and logistics. As distribution channels, DDPs create a market of customers and demand high commissions (as high as 50%) from merchants to access these customers (Berezina et al., 2016a; Kumar & Rajan, 2012; Piccoli and Dev, 2012). DDPs attract customers because their offerings are between 20% and 70% off the regular prices (Ong, 2015). DDP customers enjoy the treasure hunt experience due to the limited promotion duration (Meyrowitz, 2014) and hedonic benefits (Sigala, 2013), while merchants benefit because a time-restricted offer can induce clickthrough intention (Wang et al., 2021).

The steep discounts and the high commissions demanded by DDPs make it almost impossible for merchants to profit from their DDP sales. However, DDPs can help small and medium enterprises reach three aims: acquiring new customers, earning ancillary revenues above the face value of the deal, and converting these DDP customers into full-price paying customers (Sigala, 2013). Hence, merchants should not see these as financial losses but as marketing investments and aim to attract and retain as many new customers as possible (Cassia et al., 2015). Nevertheless, researchers found that some hoteliers consider DDP customers to be the “wrong” customers (Geerts and Masset, 2022) and saw existing customers becoming DDP customers (cannibalization) (Cassia et al., 2015; Edelman et al., 2016; Kumar & Rajan, 2012). In the restaurant industry, managers had difficulties identifying returning customers, tracking ancillary spending, and assessing the DDP campaign performance (Aday et al., 2018).

Furthermore, researchers state that DDPs create short-term referrals and long-term e-Word-of-Mouth (eWOM) (Sigala, 2013). However, review ratings from DDP guests are lower than other customers (Cassia et al., 2015). These conflicting findings made it difficult for researchers and practitioners to conclude if working with DDPs can contribute to their business objectives.

Recently, Budler et al. (2020) compared the business models and value propositions between online travel agencies (OTAs), DDPs (e.g., Groupon) and niche DDPs (e.g., SecretEscaptes.com, Voyage-Prive.co.uk, or Verychic.com). Budler et al. (2020) stated that niche DDPs are DDPs targeting the travel industry, and they differ from OTAs because of their registration requirement and periodic, time-limited promotions. Budler et al. (2020) further reported that to compete against OTAs and generic DDPs, niche DDPs demand smaller discount percentages and lower commission percentages from hoteliers, bringing in customers who are more willing to spend. Given these changes, it is vital to re-evaluate working with DDPs.

Researchers have developed frameworks to help managers decide and leverage DDPs (i.e., Berezina et al., 2016a; Sigala, 2013; Lee and Lee, 2012), proposed performance indicators (Berezina et al., 2016a; Sigala, 2013), and advocated more research investigating the performance outcomes of working with DDPs (Kumar & Rajan, 2012; Ong, 2015; Sigala, 2013; Wu et al., 2012). Nevertheless, DDP research is scarce, and none has addressed the performance indicators of DDP customers. Furthermore, many DDP offerings include rooms and ancillary services to respect the rate parity and justify the high commission percentage. This product bundling approach may reduce DDP customers' ancillary spending opportunities and should be considered when assessing DDP customers' performance. Lastly, to assess DDP customers' retention rates, the study period must be more extended than a few weeks to reflect that staying in a hotel is not a routine purchase.

Nevertheless, no research has compared DDP and non-DDP customers on key performance indicators over an extended period of time. This research aims to fill these gaps by an empirical study of an independent upmarket hotel in Switzerland that ran a DDP deal in January 2018, 2019, and 2020. The research objectives are to compare DDP and non-DDP customers on performance indicators, including average revenues, lengths of stay, lead time, retention rate, and review sentiment between 2018 and 2020.

This research makes three contributions. For academia, this research addressed the call from researchers to conduct more research investigating the outcomes of working with DDPs (Kumar & Rajan, 2012; Ong, 2015; Sigala, 2013; Wu et al., 2012). This research used longitudinal data and compared the performance indicators for DDP and non-DDP customers, which can be considered methodology improvements. Previous researchers advocated the short-term and long-term benefits of working with DDP. A longitudinal data set provides opportunities to measure the long-term impact of DDPs on merchants. Furthermore, comparing DDP and non-DDP customers (the control group) represents a better measure of the impact related to the presence of DDP. The second contribution is to expand the DDP discussions by incorporating the breakeven contribution margin and the opportunity cost concepts. Most research focused more on the marketing contributions of DDPs but did not address the outcomes of working with DDPs concretely. By expanding the discussions to the breakeven contribution margin and the opportunity costs, this research helps academia and practitioners to have a more holistic picture of the factors to be considered when evaluating the working relationship with DDPs. The third contribution addresses cannibalization and channel migration and broadens the debate on hotel distribution options. This research can assist practitioners in decision-making, demonstrate methods to compare performance, and provide suggestions when working with DDPs.

Literature review

Daily deals platforms

Daily deals platforms (DDPs) are multisided platforms attracting merchants with the potential to reach new customers they cannot reach on their own and attracting customers with deeply discounted deals they cannot find anywhere else. Merchants pay DDPs massive commissions on the revenues to access these potential customers. In exchange for great deals, customers opt-in to receive e-mails. Most DDPs differ from traditional online travel agencies (OTAs) because they do not focus on the travel industry. However, DDPs have niche players targeting the travel industry (e.g., SecretEscapes.com, Voyage-Prive.co.uk, or Verychic.com) or concentrating on a specific local market (e.g., QoQa.ch for the Swiss market).

Daily deals platform customers

Researchers have mixed findings of DDP customers. Some customers were highly price-sensitive and transaction-oriented (Boon, 2013; Dholakia, 2010, 2011), while others showed hedonistic shopping tendencies (Boon, 2013), were quality-conscious, variety-seeking, and preferred to be viewed as “smart shoppers” and share their knowledge with a broad community (Berezina et al., 2016a; Boon, 2013; Ong, 2015; Sigala, 2013). More recently, Geerts and Masset (2022) found that depending on the luxury motivation (personal or social) and the importance of price as a prestige signal, luxury DDP customers can be segmented into four categories. Except for customers seeing price as a prestige signal, other customer segments do not change their perception of luxury hotels presented on DDPs. Dsilva and Elangovan (2021) found that the limited promotion duration influences customers’ attitudes and the enjoyment they receive from the purchase. In the hotel industry, DDP customers are not more price-conscious (Berezina et al., 2016b). DDP customers' satisfaction with purchased deals influences their perceptions and attitudes (Ong, 2015). Consumers with a negative attitude toward DDPs worry about negative social consequences or receiving poor service or believe redeeming these vouchers makes them look cheap (Ashworth et al., 2005; Boon, 2013). Before their hotel stays, some DDP customers worry they would be seen as B-class customers (Cox, 2015). After their stays, although most customers (60%) indicate their willingness to return, some will return only if a similar DDP deal is available (Cox, 2015). Furthermore, male consumers are more likely to be influenced by emotional gratification, such as finding a good deal, while female consumers are influenced by their cognitive evaluation (Chen et al., 2016). Scheinbaum et al. (2020) found consumers have different reasons for purchasing and not-redeeming daily deals. Consumers purchase daily deals because of price consciousness, buying impulsiveness, and susceptibility to interpersonal normative influence; but fail to redeem the deals because of offer distinctiveness, the number of daily deals sold, restrictions on using the deal, and low discount size (Scheinbaum et al., 2020). In other words, consumers buy for personal reasons but do not redeem for contextual reasons (Scheinbaum et al., 2020).

Aims, objectives, benefits and drawbacks of working with daily deals platforms

Hotels and restaurants working with DDPs have three main aims, including customer acquisition and retention (Berezina et al., 2016a; Cassia et al., 2015; Krasnova et al., 2013; Piccoli and Dev, 2012; Sigala, 2013) and increasing revenues beyond the face value of the deal (Berezina et al., 2016a; Sigala, 2013). Beyond these aims, merchants have additional objectives including occupancy, profits, the desperation to sell a perishable product inventory (Berezina et al., 2016a; Piccoli and Dev, 2012), branding, increasing brand awareness, enhancing visibility and exposure, advertising (Aday et al., 2018; Berezina et al., 2016a; Geerts and Masset, 2022; Pentina and Taylor, 2013; Sigala, 2013); balancing seasonality and stimulating demand from existing customers (Cassia et al., 2015). Tomat et al. (2019) pointed out that specialized DDPs are a marketplace that acts as a semi-permanent distribution channel for quality hotels. Some hotel managers work with DDPs for innovation and leadership because their competitors do not work with DDPs (Geerts and Masset, 2022).

Benefits of working with DDPs include offering first-time customers trials, improving management of a perishable inventory, creating opportunities to generate incremental revenues for other operating departments, improving brand marketing through the exposure and advertising of the property (Berezina et al., 2016), reaching customers on new media or on mobile devices which the merchants are not able to offer, and using DDP platform as forecasting and feedback tools (Aday et al., 2018).

Hoteliers work with DDP to attract customers who are easier to acquire and retain (Berezina et al., 2016a). However, Kumar and Rajan (2012) state that this approach stops businesses from developing a long-term vision and balancing acquisition and retention efforts. The objective of any hotel participating in this type of promotion should be to retain as many of these new guests as possible (Kumar & Rajan, 2012). Nevertheless, whether this objective has been achieved or not has never been verified in academic research.

Shortcomings of working with DDPs include failure to retain or convert these guests into full-price paying customers (Cassia et al., 2015; Cox, 2015, 2017; Edelman et al., 2016; Kumar & Rajan, 2012); cannibalization by converting existing customers into DDP customers (Aday et al., 2018; Dholakia, 2010; Pentina and Taylor, 2013; Sigala, 2013), attracting the wrong customers (Aday et al., 2018; Bratec et al., 2023), and the acquisition costs outweighing the benefits (Edelman et al., 2016; Sigala, 2013). Furthermore, serving these customers may cause employee frustration, poor service quality, and lower review ratings (Bratec et al., 2023; Pentina and Taylor, 2013). Moreover, some hotel managers can’t work with DDPs because of contract requirements from tour operators or chains (Geerts and Masset, 2022).

Daily deals platform frameworks

Researchers have developed frameworks to support practitioners in evaluating the potential to work with DDPs and identifying indicators for practitioners to benchmark their performance. Based on repeat purchase potential and margin, Piccoli and Dev (2012) developed a matrix for hotel managers to evaluate if and how to structure their offering on DDPs. Lee and Lee (2012) developed a lifetime value model, including assessing the DDP, consumers, operations, social network, performance, post-sale, and feedback. Sigala (2013) created a four-stage framework that identified corresponding factors that may impact consumers' readiness, companies' aims, and performance indicators. The performance indicators include website-related metrics, deal-related metrics, and customer-related metrics (Sigala, 2013). Berezina et al. (2016a) propose a framework that starts with a need assessment, evaluates and selects the DDP, identifies the main factors that can contribute to or damage the DDP experience, and finishes with performance indicator suggestions. Berezina et al. (2016a) suggest promotional phase indicators that echo the website-related and deal-related metrics proposed by Sigala (2013) and post-promotional phase indicators similar to Sigala’s (2013) customer-related metrics.

We want to draw our readers' attention to the framework definition. Budler and Trkman (2023) defined a management framework as “a combination of interlinked items that supports a particular approach to a specific objective or a set of articulated memes”. Budler and Trkman (2023) further stated that a sufficient number of users contribute to the success of a framework and even create the network effect, which further contributes to the framework’s success. This research addressed frameworks (Sigala, 2013; Berezina et al., 2016) at their initial stage when they were first published. However, this research did not investigate whether these frameworks have attracted many users or created the network effect.

Daily deals platform performance indicators

Researchers encourage merchants to use quantitative and qualitative indicators to evaluate DDPs’ short- and long-term performance (Berezina et al., 2016a; Sigala, 2013). Quantitative performance indicators or metrics include the number of new customers, spending beyond the face value, retention rate, redemptions, occupancy, average daily rate (ADR), revenue per available room (RevPAR), profit per occupied room (PPOR), and return on investment (ROI) (Berezina et al., 2016a; Sigala, 2013). Qualitative indicators include brand image, awareness, price sensitivity, customer co-marketing behavior, and reviews (Cassia et al., 2015; Sigala, 2013). In the short run, merchants monitor customer acquisition, revenues, ancillary revenues, occupancy, ADR, RevPAR, and referral behaviors (Berezina et al., 2016a; Sigala, 2013). In the long run, merchants can monitor the retention rate and eWOM from DDP customers (Berezina et al., 2016a; Cassia et al., 2015; Sigala, 2013).

Existing research proposed frameworks, analyzed the pros and cons of working with DDPs, compared differences between DDP subscribers and purchasers, examined reviews posted by DDP customers, or compared reviews posted by DDP customers and other customers. However, there is scarcely any empirical DDP-related research addressing DDP’s effects on the hotel industry. Researchers have advocated more research is needed (Berezina et al., 2016a; Ong, 2015; Sigala, 2013; Wu et al., 2012). Furthermore, research should compare indicators between DDP and non-DDP customers to identify the benefits of working with DDPs. For example, some travelers prefer visiting different places (Cassia et al., 2015), so their retention rates will be lower, irrespective of their purchasing channels. Some DDP deals group hotel room and other services to avoid breaking the rate parity. Consequently, the ancillary spending will be low, as the deal includes most ancillary services. Because of traveler preference of visiting different places and ancillary spending included in the deals, monitoring and comparing the performance indicators between DDP and non-DDP guests who had purchased a similar package offering is critical.

This research compares critical performance indicators between DDP and non-DDP customers to provide a more accurate assessment. Furthermore, previous empirical DDP research investigated the activities during a short period of time and did not explore the retention rate or cannibalization issues. This research will expand the research period to address these issues and provide further insights.

Hoteliers work with DDPs to acquire new customers, earn ancillary revenues, and retain these customers as full-price paying customers (Sigala, 2013). The overarching research aims to assess if a DDP can help a hotel achieve these objectives through the following five research questions.

(1) Is there a difference in daily revenues between DDP and non-DDP customers?

(2) Is there a difference in the length of stay between DDP and non-DDP customers?

(3) Is there a difference in the lead time between DDP and non-DDP customers?

(4) Is there a difference in the retention rate between DDP and non-DDP customers?

(5) Is there a difference in review sentiment between DDP and non-DDP customers?

The first two research questions are related to new customers and investigate if these customers provide similar revenues and lengths of stay. The lead time refers to the number of days between the day of booking a hotel and the day checking in at the hotel. Revenue management typically encourages advance booking by offering discounts and charges last-minute travelers the full price. While hotel revenue managers use the lead time to set up their rate plans, hotel marketing departments use it to plan marketing campaigns. Hotel managers can work with DDPs to fill rooms; otherwise, they will be empty (Berezina et al., 2016a). Hence, the lead time can indicate if DDP customers complicate revenue management practices.

The first three research questions address the short-term impact of working with DDPs. The last two research questions evaluate the long-term impact. The retention rate question is related to hotel managers' objective of retaining these DDP customers, while the review question intends to monitor the eWOM of DDP customers.

Research method

The subject hotel and the daily deals

The subject hotel is an independent upmarket hotel (57 rooms) in a famous Swiss resort destination. The hotel opens for the summer season between June and October and the winter season between December and March; it closes in April, May and November.

The hotel has been selling vouchers through a Swiss DDP in January 2018, 2019, and 2020.

Guests buy a voucher on the DDP and contact the hotel or use the hotel’s website to book their stays and redeem their vouchers. The hotel has no exclusion dates or redemption conditions and rules. DDP and non-DDP guests have the same room availability and are treated on a first come, first served basis.

The voucher, priced at CHF 555, includes an overnight stay in a standard room, a four-course dinner and breakfast at the hotel. The voucher incorporates additional benefits such as complimentary transfers to and from the train station, a welcome drink, complimentary minibar and Wi-Fi, and unlimited spa access. The voucher has a validity date of 1 year.

For each voucher, the platform deducts a commission of CHF 95 and pays the hotel a net rate of CHF 460. Altogether, the hotel has sold 2,312 vouchers since 2018.

The daily deals platform

The Swiss company QoQa is the daily deals platform used for this study. Like Groupon and Living Social, QoQa has been in the business since 2005. In addition to a range of consumer goods, QoQa sells accommodation vouchers for different types of hotels, ranging from budget to luxury hotels. A typical voucher sale lasts 7 days and is open to registered customers and the general public.

Quantitative data collection and analysis

Data collection

The authors set the study period from January 2018 to March 2020 and downloaded all reservations. From the initial data set of 16,503 reservations, the authors removed cancellations, no-shows, travel agent reservations, and OTA reservations. The authors identified non-DDP package guests who bought similar packages (room, breakfast, and dinner) directly from the hotel or its website through the reservation records. These package prices reflected the seasonality and were higher than CHF 460. The final sample sizes were 623 DDP reservations and 742 non-DDP reservations. Among 623 DDP reservations, 139 guests paid an extra charge to upgrade their rooms. The authors added these revenues to these guests.

Data analysis

The authors conducted t-tests to compare DDP and non-DDP reservations to address the first three research questions (daily revenues, length of stay and lead time). To address retention, the authors identified returning guests. When multiple rooms were booked under the same name for the same stay period, these rooms were recognized as only one guest to calculate the guest retention rate. After the data processing, 1,190 unique stays were from DDP guests, while 1,450 unique stays were from non-DDP guests during the study period. Furthermore, 54 unique DDP guests and 171 non-DDP guests had returned during the same period. To address Hypothesis 4, the authors conducted a chi-square test.

Qualitative data collection and analysis

Data collection and cleaning

To address the last research question, the authors downloaded DDP reviews from QoQa. The initial data set included 555 comments, with 503 written in French and 52 in German. An initial screening removed emoji-only comments, comments responding to other users or comments related to the DDP. The final number of reviews was 43, all written in French.

The authors also downloaded TripAdvisor reviews representing non-DDP guests. The authors downloaded the 50 most recent reviews in English (25) and French (25) to avoid a potential language bias. During the initial screening, the authors removed reviews written by voucher guests from other deal platforms or reviews focusing on the F&B offering.

Data analysis

Previous researchers statistically analyzed TripAdvisor’s numerical ratings between DDP and non-DDP guests (Cox, 2015). Since the DDP does not offer numerical ratings but only reviews texts, the authors analyzed them in two steps. In the first step, the authors structured the analysis with seven categories: pre-stay experience booking processes, room experience, food and beverage experience, spa experience, staff and service comments, value for money, and return or recommendation (Cassia et al., 2015). Each review was analyzed to identify the categories and the associated positive or negative sentiments. For example, a review has statements of “amazing buffet breakfast” and “room service menu is underwhelming,” which count as one positive and one negative point within the Food and Beverage category. Once analyzed, the authors added the total positive and negative points and used the final points to determine the review sentiment (positive or negative). The authors conducted a chi-square test to assess the difference in reviews between DDP and non-DDP customers.

Results

The means and standard deviations for daily revenues, lengths of stay, and lead time are presented in Table 1. To test the first three research questions, the authors conducted three t-tests and presented the results in Table 1. In terms of daily revenues, DDP guests (M = 482.15, SD = 45.52) compared to non-DDP guests (M = 856.79, SD = 647.22) demonstrated significantly lower revenues (t (1393) = (14.4), p = 0.000). Lengths of stay (LOS) of DDP guests (M = 1.05, SD = 0.22) compared to non-DDP guests (M = 3.20, SD = 2.89) were significantly lower (t (1393) = (18.5), p = 0.000). Finally, regarding the lead time, DDP guests (M = 49.54, SD = 46.57) compared to non-DDP guests (M = 39.23, SD = 53.95) demonstrated significant differences (t (1393) = 3.77, p = 0.000). These findings address the first three research questions. There are significant differences in average daily revenues, lengths of stay, and lead time between DDP and non-DDP guests.

Table 1.

t-test results comparing DDP and Non-DDP guests.

	DDP guests		Non-DDP guests
	Mean	S.D.	Mean	S.D.	t stat	p value
Average daily revenues	482.15	42.52	856.79	647.22	(14.4)	0.000
Lengths of stay	1.05	0.22	3.2	2.89	(18.5)	0.000
Lead time	49.54	46.57	39.23	53.95	3.77	0.000

The authors conducted two chi-square tests for the last two research questions and presented the results in Table 2. In terms of retention, there was a significant relationship between the types of guests and the rebooking behavior (X² = 44.129, p = .000). Similarly, there was a significant relationship between the types of guests and the review sentiment (X² = 26.174, p = .000). Furthermore, because the number of negative review from non-DDP guest was less than five and violated the Chi-Square test requirement, Fisher Exact Test was conducted and the result was p = .000. These results address the last two research questions, and there are significant differences in the retention rate and review sentiment between DDP and non-DDP guests.

Table 2.

Chi-square results comparing DDP and Non-DDP guests.

	DDP guests		Non-DDP guests
	Yes	No	Yes	No	X² statistic value	p value
Rebook	54	1136	171	1279	44.129	0.000
	Positive	Negative	Positive	Negative	X² statistic value	p value
Reviews	21	22	42	1	26.174	0.000

Discussions

The verdict

Previous researchers propose that working with DDPs may help hoteliers acquire and retain new customers and earn revenues above the face value of the deal. (Berezina et al., 2016a; Cassia et al., 2015; Krasnova et al., 2013; Piccoli and Dev, 2012; Sigala, 2013). This research used 3 years of data to test if DDP customers help hoteliers achieve the above three aims. Furthermore, this research identified non-DDP guests who purchased a similar package to establish a more equivalent comparison. The comparisons between the two groups show that the DDP guests contributed lower revenues, shorter lengths of stays, longer lead times, lower retention, and less positive reviews. All results are statistically significant. This research addressed the call from previous researchers to empirically investigate the impact of DDPs on merchants' performance and contribute to academia by filling a research gap (Berezina et al., 2016a; Kumar & Rajan, 2012; Sigala, 2013; Wu et al., 2012).

Because of the heavy discounts and DDP commissions, DDP guests are less profitable than non-DDP guests. Researchers state that merchants should see DDP deals as a loss leader with the goal of generating trial among consumers (Cassia et al., 2015; Pentina and Taylor, 2013; Sigala, 2013) and earn profit through ancillary sales or repeat purchases.

In terms of ancillary sales, Jang and Moutinho (2019) found that when economy travelers booked luxury hotels on price promotions, they were more willing to spend money on hotel amenities. Alternatively, Berezina et al. (2016b) found that DDP guests want to get the best discounts and conduct ancillary spending outside the hotel. Murphy et al. (2013) found that the discounting of room rates did not significantly influence hotel’s restaurant revenues during the low season. In this research, the DDP package includes the room, a four-course dinner, breakfast, transfer between the train station and the hotel, and spa access. The almost full board nature of this DDP package may reflect the hotel’s intention to justify the commission of CHF 95 (or 17.1%). For example, a simple bed and breakfast package can be priced much lower and offer more ancillary sales opportunities, but the commission will take away most of the package revenues.

Furthermore, by including dinner and breakfast in the package, the hotel guarantees ancillary revenues instead of facing the risk of guests not spending at the hotel. Previous researchers have identified factors contributing to successful DDP deals, including the face value, the discount depth, the redemption duration, the redemption conditions and rules, the maximum and minimum numbers of deals to be sold, the duration of the daily deals, the number of deals can be bought per customer, brand image, and customer loyalty (Drossos et al., 2015; Sigala, 2013). The only research addressing the DDP deal design is Piccoli and Dev (2012), which encouraged bundling services to increase the perceived value and compensate for the room discounts. This research agrees with Piccoli and Dev (2012) and encourages bundling meals and other benefits in the DDP deals to earn more revenues per transaction, reduce the commission cost percentage per transaction and guarantee revenues for ancillary services.

Previous research reported a retention rate of 3% for small and medium businesses working with Groupon (Aday and Phelan, 2015) and 35% for the restaurant industry (Wu et al., 2012). Some travelers prefer new experiences and may not return to the same destination or hotel (Cassia et al., 2015), resulting in the hotel industry’s low retention rate. By benchmarking the retention rate of the DDP guests with the other guests, this research demonstrates that DDP guests have a significantly lower retention rate (4.5%) than non-DDP guests (11.8%). This empirical data helped academia and practitioners to benchmark their research findings and operations. Given the retention rate, using DDP deals to attract new customers and earn profit through repeat purchases is extremely challenging.

Financial factors

Lee et al. (2015) stated that the DDP business model benefits most merchants with high fixed costs and low variable costs, which fits the characteristics of the hotel industry. Theoretically, as long as the revenue can cover the variable costs, there is a contribution margin to cover the fixed costs and profits (Cassia et al., 2015; Gallo, 2017). In this research, the variable costs include the amenity costs and the food costs, but not salaries. Salaries are considered fixed costs because most staff receive compensation in monthly salaries, not variable wages. Hence, these DDP revenues can cover the variable costs and provide a positive contribution margin.

Nevertheless, only considering the contribution margin and looking at the contribution margin in silos is dangerous (Gallo, 2017). A positive contribution margin only indicates the portion of revenues available to pay for the fixed costs after paying the variable costs. However, a positive contribution margin does not guarantee all fixed costs can be covered. Hoteliers should estimate and use the breakeven contribution margin to benchmark different products or distribution channels. Due to the lack of information, this research cannot calculate the breakeven contribution margin. However, by assuming similar variable costs and benchmarking with the revenues from non-DDP guests, this research concludes that the DDP’s contribution margin is inferior, and working with DDP is not the most optimized decision.

In addition to the breakeven contribution margin, hoteliers should consider the opportunity cost of selling a room or package on the DDP. In this study, the daily revenue for DDP and non-DDP customers were CHF 482 and CHF 856, respectively. Every room sold on the DDP has an opportunity cost of CHF 856, representing the loss of the potential gain from selling on a different channel. Given that the hotel has only 57 rooms and is open 9 months a year, it is crucial to use every room night to support the property’s financial health. Managers may better understand the economic impact of working with DDPs by considering the opportunity cost concept.

Long-term marketing implications

Hotels work with DDPs to increase brand awareness and use DDPs as free advertising and marketing channels (Aday et al., 2018; Krasnova et al., 2013; Pentina and Taylor, 2013; Sigala, 2013). The long-term impact includes converting these guests into relational customers and spreading positive eWOM and recommendations (Sigala, 2013). Guests' reviews influence future potential customers' purchase decisions and hotel revenues (Anderson, 2012; Kim et al., 2015). DDP customers sequentially use customer ratings and the number of purchases as risk relievers when making their purchase decisions (Nakhata and Kuo, 2014). Jang and Moutinho (2019) found that price promotion negatively impacts luxury hotel customer spending, and its negative effect is strengthened when the review valence is high. Researchers cannot agree on whether DDP guests give better or worse review ratings (Pentina and Taylor, 2013; Zhu et al., 2019).

In this research, there is a significant relationship between the guest types (DDP or non-DDP) and review ratings (positive or negative), and DDP guests have less positive reviews than non-DDP guests. The subject hotel in this research is among TripAdvisor’s top three leaderboard positions and has the policy to provide the same service quality to every guest. Hence, the lower rating from the DDP guests may indicate that they have higher expectations than other guests, have biases due to their concern about being treated like second-class guests, or lack experience in staying at upmarket hotels. Further research is needed to understand the cause of the difference in reviews.

Cannibalization and channel migration

This research investigated the existence of cannibalization between DDP and non-DDP with two analyses. The first analysis found that between January 1, 2018, and March 31, 2020, three DDP guests later booked the hotel directly, while four non-DDP guests became DDP guests. The cannibalization seems negligible and much lower than the 7.9% cannibalization reported by Cassia et al. (2015). The second analysis identified that the hotel had 417, 820, and 1075 DDP transactions in 2018, 2019, and 2020, representing 3%, 6%, and 7% of available room nights. Based on these two analyses, cannibalization does not happen between customers, as warned by researchers (Cassia et al., 2015; Edelman et al., 2016; Kumar & Rajan, 2012), but between channels. In other words, most returning customers did not switch between DDP and non-DDP channels; hence, there was no cannibalization. However, the hotel’s distribution channel mix has changed, and more rooms are sold through the DDP. The hotel may set the maximum number of rooms available for the DDP to avoid leaving money on the table.

Luxury hotels attract consumers with a high need for status, and these guests have negative evaluations of luxury hotels working with discount websites for price promotion (Yang et al., 2016). Luxury hotels may assume their target customers will not visit discount websites because they are less price-sensitive but care about hedonic benefits and experiences (Kimes and Ho, 2018). Our discovery of DDP guest reviews on TripAdvisor signals that no channel boundary exists, and what happens in one channel may be reviewed in another channel. Hotels should consider channel transparency when deploying their distribution channel strategy.

When competing with other websites for online audience’s attention, hotels have lower visibility than distribution channels such as OTAs, meta-search sites, and DDPs (Xiang and Law, 2013). Therefore, hoteliers should maximize their share of the shelf and build on interdependencies and network effects (Beritelli and Schegg, 2016). Given channel migration and transparency, reviewing the financial costs of different channels, including OTAs, generic DDPs (Groupon), and niche DDPs is crucial. The DDP commission percentage of this research is calculated as 95 / 555 = 17.1%. Budler et al. (2020) reported that niche DDP (e.g., SecretEscapte.com, Voyage-Prive.co.uk, or Verychic.com) demand a lower commission percentage (20% negotiable plus EURO 10-20 transaction fee) to compete against generic DDP (e.g., Groupon, 15% - 25% commission) or OTAs (25% commission). Furthermore, niche DDPs also demand a smaller discount percentage (20%) compared to generic DDPs (40%–60%) but higher than the Booking.com Genius rate (10%–15%) (Budler et al., 2020). Another financial consideration is the cash position. Hotels receive the cash payment immediately after the DDP campaign but receive the OTA or GDS revenues after guest check-out. DDP’s speedy payment can improve hotels' cash positions.

Earlier researchers stated that DDPs require no marketing budget from merchants and provide free advertising opportunities. However, OTAs offer free advertising, need no marketing budget from hoteliers, and become threats to niche DDPs (Budler et al., 2020). Additionally, the real-time inventory and reservations can be communicated simultaneously between OTAs and hotels, a benefit most DDPs cannot provide.

Hotels working with generic DDPs leverage the spillover effect, present their offerings along with promotions from other industries, and may have less direct competition. On the contrary, the niche DDP offer hotels opportunities to target potential travelers but face competition from the same industry or even destination. Working with niche DDPs is similar to working with OTAs, except for the limited duration of the niche DDP campaign. Budler et al. (2020) stated that hoteliers believe these niche DDP guests spend above the deal value, and their lack of technology integration and real-time inventory control hinders their intention to work with these sites.

Although hoteliers may welcome the more competitive commission and discount percentages demanded by niche DDPs, hoteliers should remember the opportunity cost, breakeven contribution margin, channel migration, channel transparency, lack of technology integration and inventory control challenges when evaluating the feasibility of working with niche DDPs.

Conclusions, implications, and research limitations

Optimizing the distribution channel mix will always be a challenge for hospitality professionals. DDPs offer an attractive alternative due to their ability to reach new customers, help manage a highly perishable inventory, and create greater visibility and exposure. Although prices are highly discounted and the commissions can be considerable, using DDP deals as a loss leader is motivated by the goal of ultimately retaining those customers, getting them to create ancillary revenues in other operated departments, and making an increased positive eWOM.

This research shows that using DDPs does not actually help attain these objectives. Compared to non-DDP customers, DDP customers contributed lower revenues, had shorter lengths of stays, lower retention, and less positive reviews. Moreover, the longitudinal study determined that although the cannibalization effects were negligible, the number of DDP transactions increased from 3% in 2018 to 7% in 2020, leading to a shift in the distribution channel mix of the hotel. In sum, the hotel sells more frequently to customers who generate less revenue, are more challenging to retain, and tend to leave less positive reviews.

The choice of a niche DDP does carry certain advantages, including lower commissions compared to OTAs and generic DDPs, and lower discount percentages than the generic DDPs. Niche DDPs also provide the benefit of receiving payments after the transaction rather than after check-out. However, the lack of real-time inventory control of niche DDPs can be a supplementary challenge that makes managing them as a channel more complex (Bratec et al., 2023).

Research has already warned practitioners working with DDPs about considering factors contributing to successful DDP deals, such as the face value, the discount depth, the redemption duration, redemption conditions and rules, the maximum and minimum numbers of deals to be sold, the duration of the daily deals, the number of deals that can be bought per customer, brand image, and customer loyalty (Drossos et al., 2015; Minor, 2017; Sigala, 2013). In this research, the lack of restrictions on the number of deals sold on the DDP and the lack of redemption conditions and rules has led to changes in the distribution channel mix of the property, which is clearly not the desired outcome. Previous research warned about cannibalization, but channel migration has proven to be a more serious issue.

Another important point made by previous research concerns bundling services to increase the perceived value, compensate for the room discounts, and lock in the ancillary spending to improve the financial contribution (Piccoli and Dev, 2012) when working with DDPs. This research clearly demonstrates how bunding reduces transactions cost percentage (down to roughly 17%). It is evident that bundling keeps guests within the property to consume those bundled services. However, it would be essential for hoteliers to monitor exactly how much ancillary revenue can be generated from a DDP versus a non-DDP guest.

This research has several limitations. Firstly, the longitudinal data is from an independent upmarket hotel in Switzerland and may limit the generalizability of this research. Secondly, TripAdvisor data was used to represent non-DDP guests. However, some DDP guests may not reveal their purchase channels and be included as non-DDP guests. Thirdly, as longitudinal research, more TripAdvisor reviews could be included in the analysis. The authors downloaded 555 DDP comments, removed irrelevant comments and emojis, and finished with 43 usable DDP reviews. The authors decided to analyze a similar amount of TripAdvisor reviews (50). The authors manually coded these reviews to estimate review sentiment and limit how many reviews could be analyzed. Fourthly, the data collected was from 2018 to 2020, and the potential impact of the COVID-19 pandemic on both travelers and hoteliers was excluded (Bratec et al., 2023). Lastly, this research did not consider external factors such as government regulations, seasonality, destination typology, tourism demand, and the intensity of competition in the hotel industry, which could have an impact on hoteliers’ strategic decisions in using DDP in their distribution mix (Minor et al., 2024).

This research evaluated the financial and marketing performances of DDP and non-DDP customers and discovered research questions for future research. One of the critical factors in optimizing the distribution channel mix is to consider the contribution margin of each sale and determine the degree to which each of those sales covers the fixed costs. Exploring to what degree the discounted DDP transactions, less their commissions, contribute to covering fixed costs would be essential for future research. Moreover, although product bundling guarantees certain ancillary revenues and reduces the commission cost percentage per transaction, it would be important to measure this more precisely. Comparing the ancillary revenues, especially profits, generated from unbundled and bundled products would be another avenue for future research. Studying both of these essential aspects of DDP transactions would allow hoteliers to have an actionable way to measure the overall financial impact of using DDPs, thereby improving their ability to make better-informed and more financially accurate decisions with regards to their distribution channel mix.

Another research topic is related to the cause of different reviews between DDP and non-DDP guests. Whether DDP guests’ inferior reviews were due to their higher expectations, their biases, lack of experience in staying at upmarket hotels, or other reasons should be investigated.

Yet another research topic is to investigate the impact of the macro environment factors such as the destination tourism demand, destination typology, and the local competition among the hotel industry on hoteliers’ adoption of DDPs as a distribution channel.

Footnotes

Author contributions

Meng-Mei Chen: Methodology,Formal Analysis,Investigation,Writing - Original Draft

Claudia Beaufort: Conceptualization,Investigation,Project Administration,Writing - Original Draft

Karen Earl-Erpelding: Formal Analysis,Investigation,Writing - Original Draft,Writing - Review & Editing

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Meng-Mei Chen

Author Biographies

Meng-Mei Chen is an Assistant Professor of Marketing at EHL Hospitality Business School,HES-SO University of Sciences and Arts Western Switzerland. She is a Ph.D. from the University of Surrey,the UK. Her research interests are hotel distribution channel marketing,customer decision-making process,marketing strategy,and destination marketing. She has published in leading tourism and hospitality journals. She actively engages in various tourism sectors by delivering executive education,serving as a keynote speaker at international conferences,and publishing in trade journals.

Claudia Beaufort is the Chief commercial officer of Matterhorn Group. She received her MBA from EHL Hospitality Business School,HES-SO University of Sciences and Arts,Western Switzerland.

Karen Earl-Erpelding is a Senior Lecturer at EHL Hospitality Business School,HES-SO University of Sciences and Arts,Western Switzerland. She holds an MBA from the University of Strathclyde,Scotland. Her industry experience ranges from restaurants,hotels,and cruise liners to event communication and marketing. She has also been involved in consulting and training missions in Switzerland and overseas.

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Should independent upmarket hotels work with daily deals platforms? An empirical case study from Switzerland

Abstract

Keywords

Introduction

Literature review

Daily deals platforms

Daily deals platform customers

Aims, objectives, benefits and drawbacks of working with daily deals platforms

Daily deals platform frameworks

Daily deals platform performance indicators

Research method

The subject hotel and the daily deals

The daily deals platform

Quantitative data collection and analysis

Data collection

Data analysis

Qualitative data collection and analysis

Data collection and cleaning

Data analysis

Results

Discussions

The verdict

Financial factors

Long-term marketing implications

Cannibalization and channel migration

Conclusions, implications, and research limitations

Footnotes

Author contributions

Declaration of conflicting interests

Funding

ORCID iD

Author Biographies

References