Pricing in a Social World: How consumers use ratings, reviews and price when choosing a hotel

With the advent of the Online Travel Agents (OTA’s), prices became transparent, and hotels were forced to pay close attention to how they were priced relative to the competition in the market.  Now with the growing popularity of online ratings and reviews, consumers have additional information to use to evaluate the value of your hotel relative to your competitors’ properties.  In order to continue to make profit-maximizing price and positioning decisions, hotel managers must understand how consumers use this new information with price to make a purchase decision.

As a follow up to our first study, when we found that there is a strong relationship between user-generated content (UGC), or ratings and reviews, and quality and value perceptions of hotel room purchases, Breffni Noone, Associate Professor, The Pennsylvania State University and I wanted to explore a bit further how consumers trade-off these attributes with price.  We designed a choice modeling experiment where we asked consumers to select the hotel they would buy from among a choice of three, with varying levels of attributes.  By following the participant’s choice patterns, the value they place on each attribute, and each level of each attribute can be statistically derived.  As well, the likelihood that they would pick a hotel with a specific combination of attributes can be identified.

The study design

This was a scenario-based, online study.  We recruited a representative sample of the U.S. population via an online survey recruitment company, ensuring that participants had traveled for leisure in the past, and that they had made the booking themselves, online.

We told our participants that they were taking a vacation with a friend, and were looking for a four star hotel in a city center. We provided a selection of three hotels that met their quality and location criteria, and they were asked to indicate which they would buy.  They repeated this exercise three times, and then we asked them to tell us what they were thinking about when they were making their choices.

We varied the price, the name of the hotel, the aggregate rating, the TripAdvisor Rank, the sentiment of the review, the content of the review and the language of the review for each hotel.  Table 1 shows the attributes and levels that we tested.

Table 1: Attributes and Levels tested

Our first study demonstrated the power of the reviews in consumers’ assessment of the quality and value of the hotel purchase, so we wanted to take the opportunity to learn more about how elements of the reviews influenced consumer decision making.  In addition to the valence of the reviews (positive or negative), we tested whether what the reviewers talked about (content) and how they talked about it (language) had an influence on our participants’ choice behavior.

The results

Results showed that the review valence (positive or negative) had the most significant impact on choice behavior, followed by price, then aggregate rating, then TripAdvisor rank.  Known versus unknown brand was marginally significant, with consumers showing a slight preference for the known brand.  The content and language was not a significant influencer of choice.

We think the reason that review content and language were not significant is probably because consumers equally value both the service and physical property of the hotel (this was validated in the open-ended responses we collected).  Further, whether the review was positive or negative appeared so important that it is likely that the impact of the language style was overshadowed.

Figure 1 shows the utility value of each attribute level in the study.  The utility is the relative value the consumers place on each change in level of the attribute.  In this type of analysis, the value of the utilities themselves are less important than the direction of the impact and the magnitude relative to the other metrics.

The bars with an asterisk represent significant utilities.  The red bars represent the negative impact of negative reviews and of raising the price from low to mid, and then from mid to high.  The blue bars represent the positive impact on choice of raising ratings and TripAdvisor rank, as well as the positive impact of a known brand over an unknown brand.

Figure 1: Utility value of each attribute level

There are two findings to note in particular on this chart.  First, you can clearly see the strength of the impact of those negative reviews.  It is the largest bar on the chart, even greater than raising price. Further, the relative positive impact of ratings, rank and brand is small as compared to those negative reviews.   Note that the TripAdvisor rankings have a smaller impact than the ratings.  Secondly, when you break out the individual impacts of the levels of ratings and rankings on choice, you will notice that consumers only notice a difference when comparing hotels with mid-range to those with high-range values.  They do not value a mid-range as compared with a low-range value.  This finding adds a nuance to the recent study from the Cornell Center for Hospitality Research, which found an 11.2% increase in pricing power for each point increase in a ratings metric.  Our study suggests that hotels will only see this benefit if they raise their ratings from a mid-level score to a high score.  There will be no benefit from a lower score to mid-level score movement.

Impact of negative reviews

Choice modeling allows for a calculation of the overall value consumers place on a combination of attributes, which means we can evaluate the relative impacts of changing attribute levels on the whole picture of the consumers’ likelihood to choose.  Once again, the actual value of the number is less important than the values relative to each other.

Not surprisingly the combination of attributes that maximize a consumer’s likelihood to choose is positive reviews, low price, high TA rank, high rating and known brand.  This results in a baseline overall utility of 1.95.

Positive + $195 + High Rank + 4.8 + Known Brand = 1.95

Notice the drop in overall utility when you change only the price to the highest price level.

Positive + $295 + High Rank + 4.8 + Known Brand = 0.46

Raising the price has a relatively large impact on overall utility, even when everything else is held equal. Clearly consumers prefer to pay the lowest price they can.  Now observe what happens when you hold all of the values equal, but change the reviews from positive to negative.  The utility value drops to practically zero.

 Negative + $195 + High Rank + 4.8 + Known Brand = 0.01

Even the positive impact of a lower price does not outweigh the negative impact of the negative reviews.

Key Takeaways

This study was designed to evaluate how consumers make tradeoffs between price and non-price attributes of a hotel when making a purchase decision.  There are four major takeaways from this study for managers:

  1. Reviews and price are the most important influencers of choice. While consumers did pay attention to aggregate ratings, TripAdvisor rank and to a lesser extent, brand, positive reviews contributed the most to consumer choice behavior followed by lower price.
  2. Negative reviews remove you from the choice set. Period.  Lower price or higher ratings do not overcome the impact of negative reviews.  Consumers simply will not choose a hotel with negative reviews.  Hotels that are in this unfortunate situation should focus energies on improving their reputation.
  3. Consumers prefer to pay a lower price. While consumers would go for a higher-priced hotel when the reviews and ratings were better than the alternatives, all things being equal, they will look for the lowest price.  Hotels need to understand their position relative to their competition both on reputation and on price in order to take advantage of any pricing power associated with positive UGC.
  4. Consumers only notice high ratings and rankings.  Our results showed that consumers only notice ratings and rankings when they are high as compared to other choices.  Consumers do not place any value on the comparison between low and mid-level ratings and rankings.

The bottom line is that driving revenue and share in the hospitality industry is no longer just about competing on price.  Consumers are clearly turning to user-generated content to inform their purchase decisions, in particular, reviews. This means that hoteliers must not only keep an eye on how they are priced relative to the market, but also on how they are positioned in terms of their reputation.

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Delivering on integrated hospitality analytics: The real world perspective

Recently the Analytic Hospitality Executive has been exploring the possibilities of integrating departmental analytics. We have examined how the marketing, revenue management and service operations departments of hospitality companies can make use of the analytic outputs of other departments to improve their results. Much of this has been based on our vision for the future of analytics within the hospitality industry, however, I am also happy to say that we see plenty of evidence of the closer integration of analytics out in the industry.

Recent market trends indicate that hospitality and gaming companies are profiting from tightened integration between departments when it comes to analytics. Marketing and revenue management are a natural place to start, because they are usually the most analytically sophisticated departments. In a recent interview with Eye for Travel, Eoin Furlong, Senior Director Revenue Management Analysis and Support at Hilton Hotels talks about how useful some traditional marketing analytics are to the revenue manager. Eoin highlights just some of the analytics that revenue managers are starting to use from marketing, such as propensity to cross sell and upsell.

Most predictions for the future of revenue management identify a closer alignment with marketing. In an article on total revenue management in Hospitality Upgrade, Bonnie Buckheister paints an integrated future for revenue management and marketing by the year 2020. Bonnie predicts that the new revenue manager or what she calls a “demand/profit optimization manager” will be fully versed in analytics that have been traditionally under the oversight of marketing, such web analytics, social media metrics and digital marketing performance measurements. If you have attended any hospitality revenue management or marketing conference lately you will have heard similar themes, and even attended sessions around integrated revenue management and marketing.

Craig Eister, Senior Vice President, Global Revenue Management and Systems at InterContinental Hotels Group elaborated on the how integrating analytics across departments can drive better strategic results in his discussion with Lee Ann Dietz on strategic pricing in the real world. Craig explained that “…we know now that revenue management can be a lot more about how to influence strategic decision-making at your company, whether it be marketing, or sales, or brand-based decisions.”

Social media is another area where departmental analytics can happily and productively collide. In her post giving an update on social media analytics for hospitality, Kelly highlighted that it has become very clear that social media data has value beyond the marketing department and customer engagement strategies. She explains that insight from user generated content can inform operational initiatives, guide new product or service development, assist in competitive positioning and, even influence pricing strategies.  Adding to the amount of data and the complexity of the analysis, is the challenge of providing access to the right data in the right format to any user that could benefit anywhere within a hospitality organization.

Speaking of social media as an influence on decision marking – next week Kelly McGuire will share with you the results of research that she and Breffni Noone, Associate Professor, The Pennsylvania State University conducted into how consumers trade-off attributes including user-generated content (UGC), or ratings and reviews, and quality and value perceptions of hotel room purchases with price. Check back in with the Analytic Hospitality Executive next week to read the results.

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Integrating departmental analytics: Hospitality service operations

Over the last few weeks here at the Analytic Hospitality Executive we have been discussing how to integrate the analytic outputs from different departments. Integrating departmental analytics is one of the key milestones on the journey to a strategic analytic culture for hospitality organizations. So far we have explored how to integrate revenue management analytics into marketing decisions and how to include marketing outputs into revenue management decisions. But what about our service operations? Wouldn’t they benefit from the analytic outputs of other departments too?

It‘s often difficult for service operations departments to take full advantage of analytics because of the nature of well, the operations. Whether in a hotel or casino, many service operations departments cover 24 hours, leaving little time for thinking about analytic results. However, there are several focus areas within service operations that could benefit by using the analytic results of other departments.

The first focus area for service operations is in analyzing customer feedback. Social media data gives service operations executives the opportunity to efficiently and effectively listen to what customers are saying about a particular area of their operations. Whichever department owns responsibility for social media data in a hospitality organization, if they are using good analytic tools, it is possible to automatically classify what customers, guests and patrons are saying. This can be turned into essential information for services operations teams. If you can understand what your guests and patrons like and dislike about your service offerings, then you can respond to improve what is lacking or further enhance what is working.

Good text analytics can take advantage of the fact that hotel review data is linguistically rich and easy to process. It is possible to derive a lot of information from review data. For example, if a customer writes “in the hotel” in their review you can determine that they have stayed in a hotel. Once you know this, the information becomes more compartmentalized. If the customer writes “TV”, they are speaking about the TV in the hotel room; if they mention food, it is either room service or the restaurant; if they mention a game, they mean gaming such as blackjack at a casino. With text analytics, the data becomes very granular and accurate which increases the ability of service operations to use it to determine actions.

We learned from Kelly’s post on her research into the effects of price and user generated content on consumer’s pre-purchase evaluations that online hotel reviews are the most powerful value indicator for consumers. Kelly also found that lowering the price of a “badly rated, and negatively reviewed property” drives no additional value in the minds of the consumers. This means service operations team are (and should be!) under extreme pressure to improve the perceptions of their guests and patrons to ensure that hotel reviews remain positive.

The second area of operations that can benefit from the analytics of other departments is in labor planning. Every hospitality operations manager has experienced the challenge of being caught short-staffed, and dealt with the wrath of guests who had to wait, whether it was waiting at check-in, waiting for their rooms to be ready, or waiting for a seat at a gaming table or service in a restaurant. However, hospitality operations managers are also under significant pressure to maintain costs. Labor is one of the highest variable costs on the P&L, and excesses in this area can negatively impact profitability, bonuses and salaries.

The most important input into an accurate labor plan is an accurate demand forecast. This is where service operations can use the analytic outputs of the revenue management department. Having access to accurate forecasts for arrivals, stay-overs and departures is essential for planning housekeeping, front desk, bell and restaurant staff. If your revenue management department activities also extend to forecasting other entities, such as demand for gaming, spa appointments, or guests in your restaurants then these forecasts can be used as a direct input to the labor planning process, as outlined in the Analytic Hospitality Executive post on labor planning.

The last area where analytics can enhance the efforts of service operations is in providing personalized service. Service operations teams see first-hand the impact they create with providing personalized service. The challenge is, how can this personalized service be amplified across the many customers of hotels or casinos that a hospitality organization may operate? What’s more, personalized service is not just limited to on-property interactions but should extend to ever customer touch-point with your organization. Master data management is the glue that brings together master information for guests and patrons and provides real-time access to this key information at the point of contact.

Master data can as simple as a guest profile including information such as name, address, phone number and birthday, or it can contain more analytically driven information such as eligible offers, customer lifetime value, likelihood to respond and theoretical gaming value. Master data management allows service operations teams to access the exact information needed to make the right decisions about how to treat guests and patrons. These interactions can be enhanced by the same analytic results that marketing using to make marketing communications more personalized. Master Data Management is the real-time delivery mechanism that gets this information into the hands of service operations personnel who can use it in their daily interactions with customers.

Generally, analytics use in hospitality is most widespread throughout the revenue management and marketing departments. However, hospitality service operations can benefit considerably from harnessing some of these analytic outputs to improve customer experience, from ensuring that the causes of negative sentiment are actioned promptly to accurate planning of labor and delivering on the promise of personalized service.

How are you using analytics in your service operations departments?

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Integrating departmental analytics: Hospitality revenue management

In her post last week, Natalie introduced the topic of integrating analytic approaches across departmental boundaries, and elaborated on the potential benefits to marketing from integrating analytic outputs from the revenue management department.  This week, I’m going to take a look at the other side of that coin – considering the value of analytics originating in marketing to support improved and expanded revenue management capabilities.

The function of the revenue manager in today’s hotel environment is primarily to drive improved profitability by managing pricing, overbooking, and rate availability through a variety of available distribution channels.  To accomplish this Revenue managers must coordinate with a number of other functions (including marketing and sales), and consider both methods to increase revenues (including room and ancillary products and services), and control distribution charges.

In supporting these decisions, revenue managers are often limited to making decisions at a market segment level.  Today’s marketers, though, can take advantage of analytics that consider the willingness to pay of the individual – the result of careful targeting and tracking of promotions.  What can revenue management do with this information?  By combining analytics from marketing and revenue management, revenue management can have access to each customer’s purchase history, preferences, etc. – in addition to remaining available room inventory, forecast demand by segment and channel, and so on.  This is a powerful set of tools for making a broad set of decisions!

One of the richest opportunities that revenue managers have taken to using this combined set of information is in the booking site.  As noted above, revenue managers are increasingly interested in both improving revenues and controlling distribution costs – and the right kind of improvements to the hotel booking site can produce both of these benefits, as it is generally the lowest-cost booking source.  An important criteria for measuring the productivity of a booking site is its propensity to turn visitors into bookers (the “look-to-book” ratio).  The easiest and fastest way to improve “look-to-book” ratios is to ,make discounts available on the hotel booking site, however offering discounts indiscriminately runs the risk of eroding revenue, and should not be the preferred method for revenue managers.

From their normal forecasting processes, revenue managers knows when there are excess rooms to sell, and when there aren’t.  By using that information, along with customer-specific information noted above, revenue managers can tailor the response from a customer’s booking query.  Booking sites typically return multiple booking options – they are designed to provide several alternatives, which is a natural extension of turning “lookers” into “bookers”.  By looking closer at individual customer preferences and purchase history, and combining this information with forecasting information from revenue management, it is possible to provide a responses and real-time offers that consider both the real value of a booking (on a particular date, and by room type), and each customer’s preferences.  The result is a more efficient booking engine that maximizes both the value of the booking to the property, and the probability of their acceptance – all within the context of a low-cost distribution source.

By combining these analytics from revenue management and marketing, the best overall assortment to show the customer can be determined: an assortment that maximizes the expected value each visit to the site by each customer – considering their preferences, current booking levels, rates and discounts, and remaining demand.  When customer purchase history information includes information regarding ancillary spends, then this approach can augmented by giving preferred availability on full or shoulder days to customers with significant ancillary spend history, or adjusting the assortment display to include packages that match the customer’s preferences and history.

Integrating both sets of analytics in the booking process helps to improve look-to-book by anticipating the responses (dates, room types, amenities, etc.) most likely to be accepted by a given customer, and then combining this information with revenue management forecasts to help identify which responses are most valuable– to help drive up revenue – and including both in the alternative set shown to the customer.  However, integrating revenue management and marketing analytics to support these approaches can be challenging.  This level of integration cannot be undertaken until the departmental systems that provide the basis of the information are available, and the information from them is “clean” and can be readily integrated into a real-time process.  A flexible booking engine that can manage intelligent assortments, sorting, and offers is also required.

Before I close out our discussion on integrating analytics, I want to refer back to Natalie’s piece from last week.  In her piece, Natalie referred to marketing’s dual objectives of nurturing and stimulating demand, and how revenue management analytic output can be used to guide stimulation activities.  Accordingly, it is important to recognize revenue management’s role in this process to not only identify weak booking periods, but to also identify and communicate the underlying reason for the weakness: is this a normal, seasonal pattern?  Or is this due to some outside factor?  Is this expected to be a short-time impact, or are some longer-term affect (economy, competitive changes, etc.) in play? Accurate diagnosis and communication between revenue management and marketing will ensure that the appropriate actions are taken to improve the demand situation.

When revenue management and marketing share analytic approaches and coordinate activities, analytics can help an organization reach new levels of effectiveness – and drive impacts to the bottom line in ways unavailable in single-department approaches.

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Integrating departmental analytics: Hospitality marketing

As Kelly mentioned in her last post, integrating departmental analytics is one of the key milestones on the journey to a strategic analytic culture for hospitality organizations. When departmental activities are synchronized at the data and analytics level, not only are department decisions made considering the good of the enterprise, but you have the opportunity to apply analytics at a more strategic level. Over the next few posts, we will be exploring the benefits when departments join forces from both an information-sharing and analytics perspective.

In recent years, the revenue management and marketing department of hospitality organizations have been working much more collaboratively. The rise of internet distribution has created an increasingly competitive marketplace, and the appearance of new channels for marketing has not only provided opportunities for new methods of distributing promotions, but also provided access to more readily available customer data. By sharing of information and integration of departmental analytics, marketing and revenue management can improve how they manage, stimulate and control demand. For the purpose of this post, let’s first focus on the revenue management information and analytic outputs that could be useful for marketing. In the next post we will review the marketing information that can help inform revenue management decisions.

Marketers are responsible for the dual objectives of nurturing demand through brand image and identity, and for stimulating demand through more targeted actions. Promotions and offers can be used for both objectives, depending on how targeted they need to be. However, a marketing approach that leans too heavily on offers can have the impact of encouraging customers who make up the long-term demand to buy at a discount, otherwise known as “cannibalizing” demand. What if marketers could include information from revenue management such as demand forecasts, prevailing price, and occupancy forecasts? Would that help marketers to better balance these objectives?

We have seen that promotions placement can be much more effective when decisions are supplemented with information from revenue management. When marketers have ready access to demand and occupancy forecasts, they can place promotions and offers more productively. If marketers knew how many rooms were forecasted to be left unsold in advance, they can use marketing optimization techniques to make the right selection of customer segments and campaigns to stimulate the right amount of demand to sell the unfulfilled rooms at the right time and place.

Additionally, if marketers have access to the prevailing price and price sensitivity information from revenue management, they can also more effectively manage promotional pricing. Understanding price response information from revenue management in partnership with information on what has worked in the past can help marketers evaluate the response of the overall customer base to a general offer. When stimulating demand for specific periods, including price information from revenue management could also ensure that offers are not at a level that will cannibalize demand or that may be rejected later for availability by the revenue management system.

Incorporating revenue management information into the marketing process is not without its challenges. One of the biggest hurdles to overcome is the mismatch in segmentation between marketing and revenue management. This mismatch is natural, since the needs of each department are different, and the segmentation method that is best for one department’s purposes will be a poor fit for the other. But, to move beyond this difference, an effective “translation” needs to be created so that information from one department can be effectively consumed by the other.

Another challenge relates to the tradeoff between highly targeted offers and offer complexity.  From a revenue management point of view, under-targeted offers run a greater risk of cannibalizing existing demand.  On the other hand, marketing takes the position that overly-targeted offers make them harder to understand, more difficult to promote, and less attractive to the consumer. The art is finding an appropriate balance that produces offers that are well-targeted, attractive, and easy for guests to understand and purchase.

When marketing incorporates data and analytics from other departments, like revenue management, decisions can be made at a higher level than just marketing.  As the analytic approaches for both departments continue to evolve, it is critical that hospitality enterprises take advantage of the opportunities for a more integrated approach and avoid the pitfalls of decision making in siloes.

Are you incorporating revenue management information into your marketing efforts? What other departments analytic outputs have you included in your promotions placement and pricing decision process?

 

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The journey to a strategic analytic culture: Integrating departmental analytics

For the next month or so, the Analytic Hospitality Executive will be talking about a milestone on the journey to a Strategic Analytic Culture: integrating departmental data and analytics.  As we’ve discussed this year, hospitality organizations that want to survive and thrive in today’s economy need to make the strategic use of analytics part of their corporate DNA.  We talked earlier this year about the anatomy of a strategic analytic culture, the importance of an information management strategy (and a plan for managing big data), and provided some examples of how established analytics can contribute to business strategy with Lee Ann’s recent series on Pricing as a Strategic Tool and my discussion about how revenue management can use social media to support competitive positioning strategy.

The process of becoming a strategic analytic culture is a journey.  The diagram below describes the phases of that journey.  Taking this process on in this way ensures not only a successful analytic technology implementation but also organizational and cultural adoption.   If you try to move too fast, or skip steps you risk failure – or at a minimum, you fail to achieve the organizational buy-in that will turn your departmental analytic effort into the foundation of an enterprise commitment to analytics.

The process of attaining a strategic analytic culture is a journey with the phases Establish, INtegrate, Optimize and Innovate.

The phases of the journey to a strategic analytic culture.

The Establish phase is where you have identified the analytic technologies, information management processes and/or reporting infrastructure that you will need to get your individual departments started on the journey towards using analytics.  It may be that you have this technology up and running in some places and want to establish it in others, or it may be that you will begin working with departments that are relatively mature in their within-department use of data and analytics and want to take things to the next level.  Either way, you need some sort of foundation of data and analytics thinking within target departments to move through the next phases of the journey.

In the Integrate phase you allow the departments to start working with each other’s data manually. They sit down together and develop a common business language so their data definitions and key metrics match up with each other.  Do you have different ways of thinking about market segments?  How do you define occupancy?  Do you think about the geographical break down of your properties or your customers the same way?  (You will definitely be surprised at how often different departments disagree on these common metrics!)  Each department has “right time” access to the data so they can begin to utilize it in routine decision making (like marketing using RM demand data to decide whether they should add blackout dates to a promotion), or collaborate together on special projects (like operations determining whether the restaurant needs new operating hours or a new menu using social media data from marketing).  In both cases, they begin to understand how their decisions change in the presence of new information.

The optimize phase is where inter-departmental data is automatically incorporated into the analytics.  This could be a brand-new analytics solution built on top of a combined data set, like a labor forecasting application for the restaurant that includes rooms demand forecast from revenue management. It could be new data feed incorporated into an existing analytics application – like including promotion plans into the revenue management forecast.  Routine analyses are then automated, and users trust results because they know where the data comes from and what it means.

Once you are working on a solid foundation of integrated data and analytics, you have an opportunity to Innovate by adding new data sources (unstructured text data), new departments (operations or finance) or new delivery methods for analytic results (real time).  Often, the innovate phase takes you all the way back to the establish phase, but you have the opportunity once again to establish a solid foundation to ensure success.

This month, we’ll focus on the integrate and optimize phases of the journey.  We’ll describe the advantages of departments sharing data and collaborating on decision making. We’ll provide examples where different data sources can improve analytic results, and we’ll discuss case studies of companies that have seen preliminary successes on this milestone of their journey to a strategic analytic culture.

We plan to start with an area that has been talked about a great deal recently, especially after the downturn of 2008: bringing marketing and revenue management closer together through shared data and analytics.  Stay tuned for that next week, and as always, we hope you will share your experiences with us!

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Pricing as a strategic tool: Part Two

We have come to the end of our series on strategic pricing and revenue management.  I really hope you have enjoyed the two interviews: first, with Craig Eister, Senior Vice President, Global Revenue Management and Systems at InterContinental Hotels Group (IHG); and second with Cathy Enz, PhD, a full professor in strategy and The Lewis G. Schaeneman Jr. Professor of Innovation and Dynamic Management at the School of Hotel Administration at Cornell University.

Since then, Alex has written an excellent post about another example of strategic pricing:  Sports ticket pricing. In every case, what we are finding is that there are trade-offs to be made between maximizing revenue today (or overnight) and some other strategic value.  If you price too low – by reducing prices at the last minute to gain quick occupancy improvements – the customer “learns” to wait for deals and the RevPAR decreases over the long term.  If you offer the wrong price – by not adequately understanding who the target customer segment is –you risk revenue dilution of the most price-inelastic customers.  If you allow your revenue management system to operate without any intervention – by “maximizing” revenue based on the information currently available in the system – perhaps you end up sacrificing long term business viability for short-term revenue gains.  How can you handle these conflicting goals?

I realize I sound like a broken record when I recommend using analytics to help manage these different priorities, but today I’m going to offer an analytical framework for supporting these tradeoffs.  This framework is based on a set of discussions I have been having with Maarten Oosten, Senior Manager in the Advanced Analytics & Optimization Services team at SAS. Maarten recently wrote a white paper about how to use pricing to solve these problems. Maarten agrees that, although analytics play an important role, the first order of business is to clearly articulate the business vision.  Once the business vision is complete, you can perform a trade-off analysis between the traditional revenue-maximized decisions and the strategic decisions that are also important (for example, supporting a loyal customer by making available a reduced price or promoting a bundled package which offers a lower ADR for the hotel but a higher profitability across the entire bundle of services).

As Maarten’s white paper illustrates, the concept of Pareto Optimality can be used to review decision alternatives across the “efficient frontier” of possible solutions.  By doing this, you are no longer simply maximizing short-term revenue; you are evaluating the trade-off between short-term revenue maximization and any other strategic goal you have identified.  Specifically, you are identifying the difference between your current strategic decision and the revenue-optimized decision.  That difference is not optimal, but Pareto-optimal, in that no better decision, or allocation of scarce resources, can be made without negatively affecting any of the individual goals.  Yes, you could improve short-term revenue, but at the risk of alienating your most loyal customers, thereby impacting long-term revenue.  Yes, you could offer a promotion to more customers, but at the risk of not making enough money on a nightly basis to continue operating – and, so forth, across any and all of the other alternatives.

Operationalizing this trade-off analysis into your revenue management system is the next step and requires significant changes to the revenue management optimization process.  Without getting too technical, I’ll summarize Maarten’s approach.  The trade-off analysis can be used to create a new resource constraint that asks the question: how much would it cost if you had one more unit of market share by retaining your loyal customers (or whatever strategic direction you are trying to achieve)?  The answer is then translated into a displacement cost representing the cost of that strategic constraint and needs to be included in your optimization processing.

Since most revenue management systems do not have a way of easily integrating strategic constraints, with their associated costs, Maarten suggests that this integration should take place outside of the revenue management system through some sort of Real-Time Decision management system that takes the decision outputs from the revenue management system, applies the new displacement cost (which acts like a market subsidy to the preferred option) and then re-compares the costs of all of the resources with the prices in the reservation systems.

Clearly, this idea, and specifically the operational implementation of this idea, is at the leading edge of revenue management science. However, just because you aren’t ready to execute a Real-Time Decision Manager solution doesn’t mean that you aren’t making these kinds of trade-off decisions every day.  Each time your revenue manager overrides a price or availability decision, he or she is implicitly or explicitly moving away from system-generated expected revenue maximization toward some other objective.  As a hospitality executive, the question is whether you are aware of these overrides and whether they are actually moving your business in the strategic direction you have identified.

Pricing is a strategic tool – perhaps the most effective tool in your strategic arsenal – because it can have a major effect on your profitability with very little organizational disruption. Like any tool (strategic or otherwise), it has the most benefit when the tool fits the job – don’t use a hammer, when you need a screw driver.  Similarly, make use of price changes that don’t erode your brand integrity and which are targeted to the appropriate customer segments.

So, to reinforce Craig Eister’s point from earlier in the series: “it starts with a clear (and better) understanding of the guest by identifying your brand’s value proposition and which guests you are targeting. If you can understand this, then you can understand what creates value for them and then create strong rate and inventory offers that match their needs.”

I hope you have appreciated my investigation into strategic pricing: the linkage between business strategy and pricing and the operationalizing of pricing into revenue management.  As always, we at the Analytic Hospitality Executive are interested in your perspectives, so please feel free to comment below.

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Sports ticket pricing – an example of strategic pricing

Over the last few posts, Lee Ann has been exploring how to marry business strategy with day to day pricing decisions.  This week, I’m going to take this discussion in another direction by describing how pricing decisions are made, and the importance of strategic pricing in another industry – sports.  Specifically, I’ll be exploring sports event ticket pricing.  Sports ticketing is a complex subject, of course, and I’ll only be touching the surface of it.

Professional sports event ticket pricing is a complex business because the make-up of the teams change constantly, and therefore, so does the “competitiveness” of the product.  An “off” year can have a significant impact on ticket sales – but so can a championship run.  On top of this, individual game characteristics such as opponent, day of week (for sports that play more than once per week), and weather (for sports that play outdoors) can impact the value of an individual game.

Buying Sports Tickets – the “Rules of the Game”

Sports tickets are generally purchased in one of three ways:

Season Tickets – Season tickets are offered for sale from the team or league prior to the beginning of a season.  In fact, it is not unusual for season tickets to go on sale during playoff season, and before the schedule of actual games is known.  Teams often have a close relationship with their season ticket holder base (I’ve met more than one sports executive who claimed that he knew the face and name of nearly every season ticket holder) – and appreciate the high level of “engagement” held by the season ticket holding fan.  Because of the high level of commitment that they make to a team, season ticket holders expect to get the highest discount on tickets – and become vocal when they feel that better offers are made available in the wider market.

Individual Tickets – Individual tickets are offered for sale by the team or league after season tickets.  The “face value” of a ticket is normally based on the individual ticket sale price, which is set prior to the season, and is typically the same for all games.  However, because (as noted above) the real value of any individual game can vary, this creates inefficiency – some games become very high demand (and can sell out), while others sell poorly.  The team can deal with the “distressed inventory” of tickets for these low-demand games through the use of promotions (typically in conjunction with corporate sponsors or local charities) – but run the risk of raising the ire of the season ticket base if promotional deals become so attractive that the season ticket holders feel that the promotional offer starts to devalue their season ticket purchase.

The Secondary Market – The “secondary market” is the general term for the marketplace in which sports tickets are resold.  These are tickets that may have been purchased originally as season tickets or as individual tickets, but in either case, are now being resold to a second owner.  Now, there are legal implications regarding the resale of tickets, and certain practices may be prohibited locally.  For example, on-site “scalping” is illegal in many states in the U.S., and selling tickets above face value is prohibited in some countries, as well.  In the UK the resale of football tickets is illegal unless the resale is authorized by the match organizer.  In general, though, the internet has led to greater liberalization on this front, and today a simple search on “sports tickets” will show you many, many options for purchasing tickets from the secondary market.

The Strategic Decision

What makes sports ticket pricing so interesting is the dynamic that exists between the strategic decision (how to price season tickets), and the more tactical decision of how to price individual tickets, and what in-season promotions can be run.  Price the season tickets too high, and the team’s flexibility with promotions can be greatly restricted – impacting the ability of the team to generate revenue on “off peak” games.  Price the season tickets too low, and the team will soon find itself with significant competition from the secondary market (due to speculative buying and resale of season tickets), and inhibiting the team’s ability to generate revenue from individual tickets.

It is also important to note that most season ticket holders become speculators at one time or another – either they have tickets that they cannot use or the value for their tickets become so high that they would prefer to resell the ticket for a tidy profit rather than attend the game.  The ability to do either of these things is considered sacred by many season ticket holders that would not be able to afford the season package without them.  In addition, outside speculation (by brokers and professional ticket resellers) will occur on the secondary market whenever significant inefficiencies in ticket pricing (i.e. significant difference between purchase price and market value) are perceived to exist.  So, it is not unusual for speculators to buy and hold individual tickets for teams best games (against conference rivals or last season’s champions, for example), and sell them at a tidy profit later.

Interestingly, most teams prefer to err on the side of selling more season tickets – even at the potential cost of overall revenue.  The reason for this is twofold:

  1. The Bird in the Hand: There is a lot of uncertainty at play in sports: injuries, bad luck, or a bad economy can impact or even ruin profitability.  Therefore, many owners prefer the certainty of a season-ticket-holding base to the uncertainty of a (potentially higher-revenue) set of individual ticket sales.
  2. Customer Lifetime Value: It is a widely-held belief that the more a fan attends games, the more emotionally “involved” the fan becomes.  And, as this Penn State study illustrates, more avid fans spend more money on tickets, merchandise, concessions and so on.

Of course, the degree to which a particular team can “go high” on pricing or should “stay low” is also impacted by a variety of other factors, including:

  • Performance in the most recent season (a rise in season ticket pricing following a strong playoff run is almost expected)
  • Player movement (retirements, drafts, and free agency): All of these factors influence team makeup – which directly influences expected team performance… and winning sells tickets
  • Coaching: Similar to new players, a team bringing in a well-known coach is often expected to produce more wins

Changing the Rules of the Game

Recently, some leagues and teams have experimented with a couple of methods for dealing with the tension that exists between season and individual ticket pricing, and capturing a greater share of the value from their tickets.  These approaches include:

Variable Face Value – With this approach, the team varies the face value of the tickets based on what the team believes to be their relative market value.  So, very desirable games have a higher face value, and less desirable games have lower face values.  This approach gives the team more flexibility in dealing with “distressed inventory”, while protecting the value of high-value games, and capturing value that is otherwise lost to speculators.

Dynamic Individual Ticket Pricing – With this approach, the team varies the price of individual tickets based on market demand – and can change the price from the initial face value.  Dynamic ticket pricing again gives the team flexibility in dealing with “distressed inventory”, while protecting the value of high-value games – but requires more complex systems to determine optimal pricing and manage ticket prices.

Participating in the Secondary Market – Given that they have the most information, it should surprise no one that teams themselves would consider buying and reselling tickets on the open market.  Some teams and leagues have even created their own ticket exchanges.

Applying Analytics to Season Ticket Sales – Here in the U.S., season ticket sales typically make up the majority of ticket sales for major league teams – so approaches that focus on individual ticket pricing can have limited (or even no) value.  However, as illustrated by the Orlando Magic, by applying analytics to season ticket sales, teams can identify “at risk” season ticket holders, and even model the impact that price changes may have on season ticket sales.

From an analytic perspective, the approaches that one would use to deal with the first three approaches above are similar to the methods used by typical revenue management solutions in hospitality.  The fourth approach, though is quite different – and maybe that is part of the reason that I find that line of work particularly interesting.  Analytically speaking, segmenting season ticket holders and anticipating their propensity to renew at an individual ticket holder level has more in common with typical marketing analytics than it does with revenue management analytics.  And, for teams whose revenue streams depend heavily on season ticket holders this type of analytics can have a lot of “bang for the buck”.

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Strategic Pricing - Learnings from the latest academic research

Today we move to part three of my series on strategic pricing as we interview Cathy Enz, PhD., a full professor in strategy and The Lewis G. Schaeneman Jr. Professor of Innovation and Dynamic Management at the School of Hotel Administration at Cornell University.  We discussed her latest research examining whether stable competitive pricing positions yield better average annual RevPAR growth than do price shifts either upward or downward, as compared to competitors’ positions.  Her studies seem to be a good confirmation of our earlier posts about maintaining pricing integrity.

Lee Ann Dietz (LAD): From your earliest studies, immediately after 2011, to your latest research, you have been evaluating whether changes in pricing, including price discounts, improve revenue performance.

Cathy Enz (CE): Exactly. The downturn after 9/11 predicated our interest and we looked at the bad times. We then looked at the good times because, after a period of several years, we started to see some real performance improvements in 2004 up through 2007. We examined Asian and European performance. Right now we’re doing a study where we’re looking at Brazil. So fundamentally we began to ask this question: if you price higher or lower relative to your competitive set, what’s the net effect on occupancy and ultimately on Rev Par?

And what we continue to find over and over – in the US, in Europe, in Asia; across all customer segments; across different hotel sizes, whether they were or weren’t chain affiliated – was that if you price lower than your competitive set, you will get a boost in occupancies – much more so in the US and Europe than in Asia – but ultimately you will see lower relative RevPar.

We hypothesized that demand may be a lot less elastic than some believe, and that it might be more difficult to stimulate new demand.  And, although there’s a possibility of stealing share, we found that often the competition follows suit by dropping their price. And then you end up with a vicious cycle, a downward spiral of potentially detrimental price wars. So our studies continue to say, if you want to maximize revenue, the best strategy is to maintain rate integrity, to price above the competitive set, because although you’ll take a hit in occupancy, it will be more than offset by a higher relative RevPar.

LAD: What about the long term consequence of discounting below your competitive set?

CE: My belief is that you create real confusion about the product itself. You erode the brand’s equity and the notion of what the brand is, so you have a lot of price-positioning confusion. And you train the customer to accept these lower prices.

It’s really tricky in the lodging industry because – given the fact that hotels can change prices so dynamically and your competition can easily follow suit – your anchor gets shifted downward and your competition shifts it downward, which ultimately leads a consumer to what we call the deal effect. They’re just going to wait. They’re going to wait until the last minute, and we see this with booking patterns over the last ten years that have gotten closer and closer to the arrival date. In fact, customers have begun to realize that if they wait long enough, the price will go down.

And, you compound this with the fact that there are so many players looking over the shoulder of our revenue managers, each with slightly different and frequently conflicting strategies for how to maximize revenue. You might have your ownership group and asset manager pushing on occupancies even though you’re trying to maintain rate integrity.  As a revenue manager, you are caught in the middle where you must do everything simultaneously. And you are going to have to make choices. And the choice to drop price, or the choice to maintain rate is something that gets sabotaged by a whole lot of people with their fingers in the pie all the time.

LAD: We had a similar discussion with Craig Eister last week about the silos of marketing, sales and revenue management and how to align the strategies so we had an integrated pricing strategy. It must be a very challenging environment for revenue managers and hotel executives to balance these objectives.

CE: You know, I think you’re right. I certainly think we’re making progress, in learning that demand is only modestly elastic, and frequently inelastic. Which means that you and I, if we were going to go on a holiday together to Hawaii, are not suddenly going to shift our holiday to the Caribbean because the prices are 30 dollars lower in the Caribbean. We’re going to Hawaii. What we might do, is we might shift from one brand to another brand if we find a great deal, or additional credits, or a package that meets more of our needs. So we might be able to steal a little bit of share, but we won’t necessarily generate new demand for the Hawaii market. And I believe that is frequently confusing to hotels: when they discount, they are really only stealing modest share from their competition. Their competition responds too fast, dynamically, for it to be a sustained benefit, and the net effect is everybody loses money.

LAD: Your point that a customer might shift from one brand to another brand reminds me of how some of these global international brands are operating.  They have a brand value proposition “spectrum,” stretching from a brand that might meet business travel needs to another brand that is more suitable for a leisure occasion.

CE: Some hoteliers are becoming quite savvy at understanding customer spend, differentiating customer segments and offering targeted products to targeted customers. We need to provide targeted products to targeted customers and then increase the value proposition for that customer by unique forms of differentiation. And that’s the big gain. And then you price with confidence because you have created the value.

One problem is the OTA’s, and they’re not just the bad guys here, but they do make value transparent. And so, if you are not differentiated from your competition and you don’t have a good selling proposition for a targeted customer, then it all rolls back to price: the monetization of the industry.

LAD: If you go with that premise, and I agree with it entirely, then I must step on my soap box: the only way you’re going to do that targeting of customer segments and creating value-based products that match your price offering is through analytics – you can’t do it based on gut feel. You have to use some strong analytics, you have to get data on your customer, you have to get inventory history, and you’ve got to get price history. You’ve got to get all the things revenue managers look at on a daily basis through their systems, and run it through some more strategic analytics. Because you’re not looking just maximizing this price, or this customer segment for this day, like a revenue management system does. You’re looking at the overall issue of how do I stimulate demand in a very focused way. But you still need that data that a revenue manager would feed into their systems.

CE: I think you’ve absolutely nailed it. For me, there’s a big difference between data and knowledge. Somebody has to take all that information, and translate that information into something that becomes a story to be told. And I think you’ve got some really interesting blogs (link here?) on data visualization and I just think that is really fascinating because strategic decisions have to be made where you can see the big picture but you can drill down. Data analytics has to inform strategic price positioning and inform the day to day tactical execution on that price positioning. And it can’t be intuitive anymore. There’s simply too much going on for it to be simply an intuitive or gut feeling. The problem I think we have is we gather more information than we have the capacity to siphon through, synthesize, and understand. And that’s the challenge for future leaders, having the ability to synthesize a lot of what data analytics does, to guide them in an overarching decision choice.

LAD: Thanks, Cathy, for your informative and insightful perspective on strategic pricing.

CE: You’re welcome.  I look forward to speaking with you and your readers again.

Next week, we’ll have the last post in our summer series on strategic pricing and revenue management, focusing on what to do when your strategic direction isn’t about short term revenue maximization and how to use your revenue management system to support these objectives.  Stay tuned!

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Strategic Pricing in the Real World

As part of my series about strategic pricing and revenue management, today’s post covers my discussion with Craig Eister, Senior Vice President, Global Revenue Management and Systems at InterContinental Hotels Group (IHG®).  We chatted about how IHG balances strategy and revenue management operations and how he thinks about loyalty programs in the context of pricing and revenue management.

Our discussion echoed a theme from last week’s post: specifically, Craig used the phrase “pricing integrity” as an essential element of pricing strategy.  That mirrored the Retail Systems Research report regarding pricing image being an important contributing factor toward profitability.

I asked Craig how IHG defines strategic revenue management and he replied that it is “the ability to influence a company’s bottom line, driving profitability using basic revenue management principles but integrating them into a broader business context.”  Previously, RM was silo-ed (just a “group of PhDs doing forecasting” in one part of the company); however, we know now that revenue management can be a lot more about how to influence strategic decision-making at your company, whether it be marketing, or sales, or brand-based decisions.

There are consequences of not integrating revenue management with a company’s other strategic decisions.  Revenue management principles are about understanding future forecasts for demand, including which rates and/or inventory you are going to put on the shelf, and how to yield them.  “That’s the core of the hotel business,” Craig emphasized.  For example, if marketing is planning a program to generate demand, and they are out-of-sync with how the revenue management department is forecasting demand, or they think differently about who are the relevant customer segments, then there’s a big disconnect between how a company generates demand and how it manages demand.  Another example is in Sales: if sales goals are based on different ways of looking at customers or demand, and that’s out of line with the established forecasting and pricing philosophy, then performance will be suboptimal. This works counter to having well-defined strategic objectives for the company.

To summarize, Craig believes that you need an integrated view across the company in terms of how you think about – and segment – your customer and how you think about – and capture – demand.  In determining appropriate pricing and yielding recommendations, you need to look holistically at a variety of elements.

We transitioned our discussion to specific events from the last few years to underscore the theme of pricing integrity.  Craig said that any time that there’s a downturn in the economy, people start to panic; people believe there is a need to lower rates, put a promotion out there, offer discounts, etc.  IHG believes that you need a sense of pricing integrity, continually ensuring that you are driving the value and integrity of the brand.  If you just have the mentality that you’ll drop the prices whenever the economy falters, then your customer will come to expect it and you will lose that sense of pricing integrity. When there is a need to stimulate demand, Revenue Management can work closely with Marketing to ensure that a company is thoughtful and strategic about where the largest performance gaps are.  For example, if there is a problem with an upscale segment in North America, then one should target demand for that specific segment; if a similar problem exists within the mid-scale segment in Germany, then target that. You shouldn’t put holistic changes in place that could damage your overall business.

Craig and I then turned our attention to what may be the highest strategic value opportunity in the revenue management world – the idea of customer-centric revenue management.  I wanted to understand how IHG viewed this opportunity and how it identified its RM goals in this area.  He acknowledged that neither IHG, nor anyone in the industry, has achieved this full integration of customer information into the revenue management world.  However, he believes that it starts with a clear (and better) understanding of the guest by identifying your brand’s value proposition and which guests you are targeting.  If you can understand this, then you can understand what creates value for them and then create strong rate and inventory offers that match their needs.

According to Craig, it’s possible to be short-sighted in this respect. We know that people travel for different reasons at different times.  Even if someone is a heavy business traveler and your normal interaction is in this respect, it doesn’t mean that he or she doesn’t take family vacations or romantic weekends. IHG tries to focus on all of a customer’s travel experiences with the goal of increasing the number of times that a guest will choose IHG brands to meet his or her travel needs. One brand might meet business travel needs but another brand will be more suitable for a leisure occasion.  Being able to target different offerings (brands, products, price offering) depending on the specific travel need is very important.  By understanding a customer’s travel intentions, you can build loyalty, target offers and have effective revenue management that makes sense for that customer.  IHG has done a lot of work looking at that whole lifecycle, understanding the guest first and matching the product to the guest to see what makes sense. Craig believes that growing IHG brands, growing their understanding and offerings around loyalty, will enable IHG to grow their revenue management practice as well. There is still a lot that can be done in terms of customer loyalty as you work your way toward customer-centric revenue management.  As part of this endeavor, recently, IHG introduced IHG Rewards Club (formerly Priority Club Rewards), helping to make customers aware that IHG has a multi-portfolio of brands which can satisfy multiple stay experiences for them.

I came away from our discussion very much impressed with the IHG approach to pricing and revenue management.  Specifically, Craig’s perspective on ensuring that IHG maintains pricing integrity even in a down economy corresponds with much of the research that I’ve been reviewing. And, there’s no doubt in my mind that integrating revenue management decision making with other strategic and tactical activities in your hotel can produce value at the bottom line.  Finally, I appreciated that taking some steps toward “customer-centric revenue management” by better understanding your guests and their wide variety of needs is critical.

I’m interested in how others in the industry are approaching the idea of pricing image and/or integrity in an economy that keeps changing.  Now that the economy is gaining momentum, have you changed your pricing philosophy or are you taking a more opportunistic approach?  With respect to strategic planning, how are you integrating changes in your company’s strategic direction with your pricing and revenue management operations?

Next week, we’ll spend some time exploring the current academic research on strategic revenue management and will be talking to Cathy Enz, PhD, a full professor in strategy and The Lewis G. Schaeneman Jr. Professorship of Innovation and Dynamic Management at the School of Hotel Administration at Cornell University. Cathy has written extensively about strategic revenue management. I look forward to seeing your comments throughout this series.

 

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