The World-wide Web (www) has been a phenomenal business platform for quite a few years now. It has provided a new channel between Providers and Customers and promises to evolve business models into ones running on cheaper operational and financial expenses. The services sector has struck it big with www. The platform of www has spread its utility across many industries ranging from Retail, Financial, Healthcare, Education, Communications, Entertainment etc. In particular, let’s consider the Online IT services industry.
As Web 2.0 keeps gaining ground, the adoption of Software-as-a-Service (SaaS) appears inevitable. Gartner reports that by 2013, 40% of e-commerce deployments will use complete SaaS e-commerce solutions and 90% of e-commerce sites will subscribe to at least one SaaS-based service, such as product reviews, product recommendations or social sales capabilities.
SaaS programs exist in a web-based format and allow users to essentially rent an Application for a monthly or annual charge. So, for instance, instead of purchasing an accounting system, installing it on their servers, maintaining and updating it, a business can instead use the program over their high-speed data connection hosted on a remote third-party location. The programs run either through a secure web browser or custom interface, providing the user with anywhere/anytime access to a full strength program. SaaS and hosted programs have been offered for around a decade, but only in the past few years have they really matured into the fully-featured solutions that people can rely on for their computing needs, that small business owners can rely on to run their businesses, and that professionals can rely on to provide accurate and dependable client service.
The SaaS concept led to the evolution of Electronic Cloud Computing. One definition of Cloud Computing by academia at University of Berkeley, California is “both the applications delivered as services over the internet (SaaS), and the hardware and systems software in the datacenters that provide those services”. Instead of users simply having subscription-based access to programs (SaaS), organizations, corporations and other entities can also buy access to virtual machines, storage space and even datacenters, allowing them to rapidly expand their own IT capabilities in very little time, but without the infrastructure and maintenance investment.
So, put in simple words, Cloud Computing offers rental ‘virtual space’ for businesses to host and run their IT Infrastructure. Although the model is getting very popular among businesses, especially Small to Medium Businesses, it is still in very germinal stages. Before it can be pitched as a perfect product, it has to overcome many issues around online security, data privacy and others. While substantial investments are being made in making the model more robust and in marketing the current model, perhaps the Cloud Computing Service Providers may be overlooking a very simple technique to increase their revenue from their current models itself! If the earlier reference to the term ‘rental’ drew your attention momentarily to Car rentals then you’re not digressing at all! As a matter of fact, we’ll see how Yield Management (YM), a revenue optimizing technique, can be used by the Web service providers, offering Cloud Computing Services, to maximize their own revenue.
Yield management is the practice of maximizing profits from the sale of perishable assets by controlling the price and inventory. In other words, it allows capturing a larger portion of the available revenue by sensing the price elasticity of different customer segments and manipulating prices accordingly.
Figure 1
Pioneers and successful adopters have been from industries in Airlines, Hotel, Tourism, Car Rentals, Golf Courses etc. and they have been witness to significant revenue increases. Let’s take a very simplistic view of how Yield Management works and then draw a parallel to how it can work for Web service Providers.
Some Key concepts of Yield Management in the Airline Industry are:
1. Consumer Behavior: The consumer segment is most often composed of the leisure traveler and business traveler. The distinction is that the leisure traveler books ahead of time to avail cheaper tickets while a business traveler only manages to show demand close to the last few days or hours of travel. Business logic suggests that the latter be charged a premium due to the inelastic nature of his demand but in order to do so the trade-off would be to reserve some seats ahead of time at the risk of having them unsold at the time of consumption.
2. Booking Limit: The maximum number of rooms that may be sold at discount price. Assume that leisure customers arrive before business customers so the booking limit constraints the number of rooms that these customers get: once the booking limit is reached, all future customers will be offered the full price.
3. Protection Limit: The number of rooms we will not sell to leisure customers because of the possibility that business customers might book later in time.
4. Business characteristics exploited for effective Yield Management:
1. Capital intensive and limited fixed capacity
2. When the resource/ product/ inventory is perishable and is wasted if not used at a particular time.
3. When the product/ inventory is identical or at least very similar but which can be catered to and used very differently by different customer segments.
4. Well defined segmentation of ‘leisure’ vs. Business/ Premium customer who is ‘willing’ to pay a higher price for the inventory if purchased very close to the time of consumption. This is not to be confused with Price Discrimination as the higher price is put in place not forcefully but because there is a market ready to pay such prices.
Case: Say an airline carrier has 210 seats. The Discount Price is $105 and Full fare id $159. To simplify the scenario assume there is just one service class. Given is the demand forecast provided by the Operations/ Business Intelligence team for this season:
Figure 2 from Reference 1
How will you optimize for the right number of reservations? Assume your historical average for the rooms sold is around 70. Now as you keep incrementing the number of reserved rooms, the probability that the incremental room might not be sold also keeps increasing. Thus, as Q* increases, F(Q) also increases. Conversely, the probability that the incremental room might get sold (1-F(Q)) reduces. The business problem can be depicted as:
Figure 3 from Reference 1
To compute the optimum protection limit:
a. Start with the maximum of the range available for reservation (as determined by demand forecast), in this case 87.
b. Reduce the protection limit by 1. If the expected revenue generated by protecting a seat is less than the expected revenue by not protecting the seat then it is better to not reserve. i.e.
From the forecast table (Figure 2) that value would be 0.341 i.e. 79 seats. Thus, 79 seats may be reserved for the business class. Any further reservation will increase the probability of the seat not being sold and hence loss of expected revenue. An additional concept of ‘Overbooking’ is also used by many to account for no-shows/ cancellations. The logic used in doing so is similar to the one described above where you compare the expected revenue of overbooking an additional seat with that of not overbooking.
All of the above is very simplistic though. Yield Management Systems are often highly customized to specific industries as they account for varying business policies, customer segments/ behavior etc.
Recent adopters of Yield Management are within the high-tech industry. Take the case of Telecommunications. Investments in capacity are intensive, capacity is limited and the product (Data Transmission Speed/ Bandwidth) is perishable. The Telecomm industry has been exploiting the market share per customer and their price elasticity to develop enhanced offerings around type of transmission (always on/dialup), access priorities (waiting time when capacity is in demand/ priority access), office solutions (e.g. calendaring, e-mail, directory SAP access), location-based services, m-commerce, and news functions (e.g. MMS, video conferencing), to name just a few, so as to cater to the different customer segments of individual consumer to corporate accounts.
Project analyses by Roland Berger Strategy Consultants have found a 3-8% increase in Revenue for YM adopters in the Telecomm industry.
Other industries to have jumped the YM bandwagon are Internet Service Providers (ISPs) and Media Advertising (Mass media). I see no reason why Web service providers offering Online Advertisements and Cloud Computing Services like Google, Yahoo, Microsoft, Amazon.com etc. cannot use this technique effectively. Online Advertisement inventory and Virtual storage/ infrastructure if unused at any time will perish. Well defined customer segments exist who are willing to pay premium if serviced on Real-time or with additional features. Yield management in this case can be used to set advertisement slots or virtual space allocations (server use, storage and bandwidth) at multiple price points to different customer segments depending on resource availability and market demand.
There are however, substantial differences too when compared to traditional businesses using YM. An airline seat is occupied for the fixed duration of the flight and also the number of seats available is known in advance. An On-demand Web service job on the other hand depends upon the type of servers, number of servers, and combination of servers etc. i.e. both capacity and duration of the job cannot be pre-determined and has to be discovered on real-time. To an extent, time variability also features in Hotel industry, Golf course industry etc., but the capacity in question is still fixed for them. The Telecomm & Internet Service Providers have been researching and creating algorithms based on mathematical models of Queuing Theory to account for better resource usage. The success of this technique however has not been very clearly documented. Even if the issue of Variable Capacity Usage was addressed, another vital difference between the Telecomm/ ISPs and Web service vendors, which is Real-time On-demand, would still pose a challenge.
IBM Research Center has been working on developing a framework that considers the Real-time On-demand factor. In the framework they have integrated components like:
- Demand Change Detector
- Customer Behavior Detector
- Yield Management Engine
- Price & Service Level Offering Engine
The framework can be interpreted to work as depicted below:
Figure 5
Refer to the article A Real-Time Yield Management Framework for E-Services (Reference 4) for the detailed numerical models. The gist is that the two essential differences between Traditional businesses using YM and Web Service Providers,
- Real-time On-demand
- Variable capacity usage
can be theoretically accommodated using the ‘Yield Management Engine’ to enhance the fundamental concept of Yield Management and achieve Revenue Optimization. The Online Web services industry is yet very dynamic and exponentially evolving. It is unlikely then that any Standard Yield Management Software may end up getting a large market share. It does however present an opportunity for software vendors to build high margin customized IT solutions and for Business Process Consultants to cater improved operational/ financial processes to strengthen the Web service industry’s top line.
--
Shabbir Ghadiali.
References:
As Web 2.0 keeps gaining ground, the adoption of Software-as-a-Service (SaaS) appears inevitable. Gartner reports that by 2013, 40% of e-commerce deployments will use complete SaaS e-commerce solutions and 90% of e-commerce sites will subscribe to at least one SaaS-based service, such as product reviews, product recommendations or social sales capabilities.
SaaS programs exist in a web-based format and allow users to essentially rent an Application for a monthly or annual charge. So, for instance, instead of purchasing an accounting system, installing it on their servers, maintaining and updating it, a business can instead use the program over their high-speed data connection hosted on a remote third-party location. The programs run either through a secure web browser or custom interface, providing the user with anywhere/anytime access to a full strength program. SaaS and hosted programs have been offered for around a decade, but only in the past few years have they really matured into the fully-featured solutions that people can rely on for their computing needs, that small business owners can rely on to run their businesses, and that professionals can rely on to provide accurate and dependable client service.
The SaaS concept led to the evolution of Electronic Cloud Computing. One definition of Cloud Computing by academia at University of Berkeley, California is “both the applications delivered as services over the internet (SaaS), and the hardware and systems software in the datacenters that provide those services”. Instead of users simply having subscription-based access to programs (SaaS), organizations, corporations and other entities can also buy access to virtual machines, storage space and even datacenters, allowing them to rapidly expand their own IT capabilities in very little time, but without the infrastructure and maintenance investment.
So, put in simple words, Cloud Computing offers rental ‘virtual space’ for businesses to host and run their IT Infrastructure. Although the model is getting very popular among businesses, especially Small to Medium Businesses, it is still in very germinal stages. Before it can be pitched as a perfect product, it has to overcome many issues around online security, data privacy and others. While substantial investments are being made in making the model more robust and in marketing the current model, perhaps the Cloud Computing Service Providers may be overlooking a very simple technique to increase their revenue from their current models itself! If the earlier reference to the term ‘rental’ drew your attention momentarily to Car rentals then you’re not digressing at all! As a matter of fact, we’ll see how Yield Management (YM), a revenue optimizing technique, can be used by the Web service providers, offering Cloud Computing Services, to maximize their own revenue.
Yield management is the practice of maximizing profits from the sale of perishable assets by controlling the price and inventory. In other words, it allows capturing a larger portion of the available revenue by sensing the price elasticity of different customer segments and manipulating prices accordingly.
Figure 1Pioneers and successful adopters have been from industries in Airlines, Hotel, Tourism, Car Rentals, Golf Courses etc. and they have been witness to significant revenue increases. Let’s take a very simplistic view of how Yield Management works and then draw a parallel to how it can work for Web service Providers.
Some Key concepts of Yield Management in the Airline Industry are:
1. Consumer Behavior: The consumer segment is most often composed of the leisure traveler and business traveler. The distinction is that the leisure traveler books ahead of time to avail cheaper tickets while a business traveler only manages to show demand close to the last few days or hours of travel. Business logic suggests that the latter be charged a premium due to the inelastic nature of his demand but in order to do so the trade-off would be to reserve some seats ahead of time at the risk of having them unsold at the time of consumption.
2. Booking Limit: The maximum number of rooms that may be sold at discount price. Assume that leisure customers arrive before business customers so the booking limit constraints the number of rooms that these customers get: once the booking limit is reached, all future customers will be offered the full price.
3. Protection Limit: The number of rooms we will not sell to leisure customers because of the possibility that business customers might book later in time.
4. Business characteristics exploited for effective Yield Management:
1. Capital intensive and limited fixed capacity
2. When the resource/ product/ inventory is perishable and is wasted if not used at a particular time.
3. When the product/ inventory is identical or at least very similar but which can be catered to and used very differently by different customer segments.
4. Well defined segmentation of ‘leisure’ vs. Business/ Premium customer who is ‘willing’ to pay a higher price for the inventory if purchased very close to the time of consumption. This is not to be confused with Price Discrimination as the higher price is put in place not forcefully but because there is a market ready to pay such prices.
Case: Say an airline carrier has 210 seats. The Discount Price is $105 and Full fare id $159. To simplify the scenario assume there is just one service class. Given is the demand forecast provided by the Operations/ Business Intelligence team for this season:
Figure 2 from Reference 1How will you optimize for the right number of reservations? Assume your historical average for the rooms sold is around 70. Now as you keep incrementing the number of reserved rooms, the probability that the incremental room might not be sold also keeps increasing. Thus, as Q* increases, F(Q) also increases. Conversely, the probability that the incremental room might get sold (1-F(Q)) reduces. The business problem can be depicted as:
Figure 3 from Reference 1To compute the optimum protection limit:
a. Start with the maximum of the range available for reservation (as determined by demand forecast), in this case 87.
b. Reduce the protection limit by 1. If the expected revenue generated by protecting a seat is less than the expected revenue by not protecting the seat then it is better to not reserve. i.e.
From the forecast table (Figure 2) that value would be 0.341 i.e. 79 seats. Thus, 79 seats may be reserved for the business class. Any further reservation will increase the probability of the seat not being sold and hence loss of expected revenue. An additional concept of ‘Overbooking’ is also used by many to account for no-shows/ cancellations. The logic used in doing so is similar to the one described above where you compare the expected revenue of overbooking an additional seat with that of not overbooking.All of the above is very simplistic though. Yield Management Systems are often highly customized to specific industries as they account for varying business policies, customer segments/ behavior etc.
Recent adopters of Yield Management are within the high-tech industry. Take the case of Telecommunications. Investments in capacity are intensive, capacity is limited and the product (Data Transmission Speed/ Bandwidth) is perishable. The Telecomm industry has been exploiting the market share per customer and their price elasticity to develop enhanced offerings around type of transmission (always on/dialup), access priorities (waiting time when capacity is in demand/ priority access), office solutions (e.g. calendaring, e-mail, directory SAP access), location-based services, m-commerce, and news functions (e.g. MMS, video conferencing), to name just a few, so as to cater to the different customer segments of individual consumer to corporate accounts.
Figure 4 from Reference 3
Project analyses by Roland Berger Strategy Consultants have found a 3-8% increase in Revenue for YM adopters in the Telecomm industry.
Other industries to have jumped the YM bandwagon are Internet Service Providers (ISPs) and Media Advertising (Mass media). I see no reason why Web service providers offering Online Advertisements and Cloud Computing Services like Google, Yahoo, Microsoft, Amazon.com etc. cannot use this technique effectively. Online Advertisement inventory and Virtual storage/ infrastructure if unused at any time will perish. Well defined customer segments exist who are willing to pay premium if serviced on Real-time or with additional features. Yield management in this case can be used to set advertisement slots or virtual space allocations (server use, storage and bandwidth) at multiple price points to different customer segments depending on resource availability and market demand.
There are however, substantial differences too when compared to traditional businesses using YM. An airline seat is occupied for the fixed duration of the flight and also the number of seats available is known in advance. An On-demand Web service job on the other hand depends upon the type of servers, number of servers, and combination of servers etc. i.e. both capacity and duration of the job cannot be pre-determined and has to be discovered on real-time. To an extent, time variability also features in Hotel industry, Golf course industry etc., but the capacity in question is still fixed for them. The Telecomm & Internet Service Providers have been researching and creating algorithms based on mathematical models of Queuing Theory to account for better resource usage. The success of this technique however has not been very clearly documented. Even if the issue of Variable Capacity Usage was addressed, another vital difference between the Telecomm/ ISPs and Web service vendors, which is Real-time On-demand, would still pose a challenge.
IBM Research Center has been working on developing a framework that considers the Real-time On-demand factor. In the framework they have integrated components like:
- Demand Change Detector
- Customer Behavior Detector
- Yield Management Engine
- Price & Service Level Offering Engine
The framework can be interpreted to work as depicted below:
Figure 5Refer to the article A Real-Time Yield Management Framework for E-Services (Reference 4) for the detailed numerical models. The gist is that the two essential differences between Traditional businesses using YM and Web Service Providers,
- Real-time On-demand
- Variable capacity usage
can be theoretically accommodated using the ‘Yield Management Engine’ to enhance the fundamental concept of Yield Management and achieve Revenue Optimization. The Online Web services industry is yet very dynamic and exponentially evolving. It is unlikely then that any Standard Yield Management Software may end up getting a large market share. It does however present an opportunity for software vendors to build high margin customized IT solutions and for Business Process Consultants to cater improved operational/ financial processes to strengthen the Web service industry’s top line.
--
Shabbir Ghadiali.
References:
- Yield Management (Serguei Netessine, Robert Shumsky): http://mba.tuck.dartmouth.edu/pages/faculty/robert.shumsky/Yield_management_note.PDF
- Yield Management: http://en.wikipedia.org/wiki/Yield_management
- Yield Management and Mobile communications – lessons from the airline industry (Roland Berger Strategy Consultants): http://www.rolandberger.com/media/pdf/rb_press/RB_Yield_management_20041201.pdf
- A Real-Time Yield Management Framework for E-Services (Parijat Dube, Yezekael Hayel): http://www.irisa.fr/armor/lesmembres/Hayel/papers/10004364.pdf
- Gartner Newsroom: http://www.gartner.com/it/page.jsp?id=734612
- SaaS Metrics – SaaSoNomics 101 (Michael Dunham): http://blog.sciodev.com/2009/02/10/saas-metrics-saasonomics-101/
- www.swlearning.com/quant/metters/first_edition/.../ch09.ppt
