Prashant Pitti, Co-Founder, EaseMyTrip shares his perspective on the entrepreneurial spirit flourishing in India. For more details, visit insight2024.in.

Travel industry is going to flourish in the upcoming years | Prashant Pitti

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The travel industry is known to contribute heavily to world's greenhouse gas emissions especially through transportation. In a bit to tackle the climate disasters looming ahead, the travel industry has been urged to prove its commitment to sustainability before it is too late.

WION Climate Tracker: Tourism generates 8% to 11% of world's greenhouse emissions

The European Union is mulling to issue what is described as the COVID Pass to allow for seamless travel in a bit to boost the tourism industry.

Coronavirus Update: European Union seeks to restart travel with 'COVID Pass

The impact of coronavirus on travel may cost as many as 46 million jobs globally. Watch report.

COVID-19 Pandemic: Travel slump threatens 46 MN jobs

COVID-19 has caused an unprecedented change to the international tourism industry. Pandemic-related lockdowns, flight cancellations, and border closures may be putting a crimp on summer vacation plans.

Coronavirus Pandemic: What is the way forward for global tourism?

The Coronavirus pandemic has brought the world to a grinding halt. Lockdowns around the world have virtually eliminated all but the most essential travel within cities and countries. International travel has been shelved indefinitely. At this juncture when travel is being seen as an activity that endangers individuals and communities, the tourism industry--destinations, tour operators, hotels, and the cross-section of people employed in the sector--has been virtually decimated. In this first episode of The Future Series we focus on what lies ahead for the travel and tourism industry with a special focus on Thailand.

The Future Series - Episode 1: Travel and Tourism

During a recent trip, as I was about to board my aircraft from Terminal 3 of the Delhi Airport, I was surprised when a young girl scanning my boarding pass gave me an extra warm smile. She greeted me with an enthusiastic ‘Good Morning’. &nbsp;

She must have been in her early twenties, but her confidence and poise impressed me. Before I moved on, she told me that she had done her vocational course at an academy, after her graduation. &nbsp;&nbsp;

The warmth of her greetings remained with me on the flight. On my subsequent return, I made inquiries and learnt that she hailed from a small town in western Uttar Pradesh. &nbsp;

She had come to Delhi to pursue her graduation but soon realised that a plain Bachelor’s degree was not of much use to her in the job market. That’s when she took the decision to pursue a professional course alongside her graduation, and the outcome was there in front of me.&nbsp;

This young girl, in my mind, typifies the future of tourism, hospitality, and the aviation sector. Keeping in mind their contribution towards the overall experience of a journey, it is important that these three sectors are viewed holistically, whether while framing policies or while providing training to personnel engaged. &nbsp;

Since these are service-driven sectors, quality training assumes great importance. And as far as training is concerned, like for everything else, the quality of output is dependent on the quality of input and of processing. 
 
Both of these – quality of input, and quality of the training process – are amongst the largest challenges faced by the industry.

The quality of input is a challenge because jobs in the travel, tourism, and hospitality sectors even at the entry level, require a degree of sophistication. &nbsp;

This includes good communication skills, emotional intelligence, decision making power, and above all, ability to work under pressure while continuing to smile. &nbsp;

Such skills are not formally taught in any school, college or training programme. So, while the focus of specialised training is on equipping potential and professionals with specific technical skills required for specific jobs, the possession of requisite soft skills are a matter of chance. People like the young girl I met possess them, or develop them instinctively. &nbsp; 
 
The skill gap in the sector is accentuated by several other factors. In order to leverage the massive potential that travel and tourism sectors offer in a culturally rich and diverse country like ours, we need the right talent at every level. &nbsp;

It is important to cast our net wider than just the metropolitan cities to recruit such talent at the entry level. This means being able to bring quality training to smaller cities and towns. &nbsp;

The government supported programmes like the Pradhan Mantri Kaushal Vikas Yojana (PMKVY) are trying to do that, but for these to be more effective, the industry needs to get involved. The industry engagement is specifically useful in ensuring that the training curriculum is designed to meet the practical needs at the workplace.&nbsp;

Availability of suitable trainers is another area that requires committed efforts from the industry. Ideally, trainers need some practical experience of working in the industry and finding such people is always a challenge, especially in smaller cities and towns. &nbsp;

That’s understandable since they have better opportunities available elsewhere. The industry can step in to fill this gap by a two-fold approach. In the short term, they can depute some experienced employees as temporary trainers, as part of their Corporate Social Responsibility initiatives. &nbsp;

This would be a win-win, as students will benefit from the practical experience of such guest trainers, while the industry will ultimately get better-trained talent. &nbsp;

In the long term, the industry can contribute by helping build capacity of local trainers and by giving them exposure to an actual working environment through short-term familiarisation programmes. &nbsp;&nbsp;

Growth is awaiting us. We have the potential, demand, and demographics. All the pieces for a massive boom in the sector are present, yet we need to find innovative ways to ensure they fit together to make it happen. 
&nbsp;

(This article was originally published on The DNA. Read the <a href="https://www.dnaindia.com/analysis/column-look-for-service-sector-professionals-in-india-s-tier-2-towns-2742274">original article</a>)

(Disclaimer: The opinions expressed above are the personal views of the author and do not reflect the views of ZMCL.)&nbsp; 
&nbsp;

&nbsp;

During a recent trip, as I was about to board my aircraft from Terminal 3 of the Delhi Airport, I was surprised when a young girl scanning my boarding pass gave me an extra warm smile.

During a recent trip, as I was about to board my aircraft from Terminal 3 of the Delhi Airport, I was surprised when a young girl scanning my boarding pass gave me an extra warm smile. She greeted me with an enthusiastic ‘Good Morning’. She must have been in her early twenties, but her confidence and poise impressed me. Before I moved on, she told me that she had done her vocational course at an academy, after her graduation. The warmth of her greetings remained with me on the flight. On my subsequent return, I made inquiries and learnt that she hailed from a small town in western Uttar Pradesh. She had come to Delhi to pursue her graduation but soon realised that a plain Bachelor’s degree was not of much use to her in the job market. That’s when she took the decision to pursue a professional course alongside her graduation, and the outcome was there in front of me. This young girl, in my mind, typifies the future of tourism, hospitality, and the aviation sector. Keeping in mind their contribution towards the overall experience of a journey, it is important that these three sectors are viewed holistically, whether while framing policies or while providing training to personnel engaged. Since these are service-driven sectors, quality training assumes great importance. And as far as training is concerned, like for everything else, the quality of output is dependent on the quality of input and of processing. Both of these – quality of input, and quality of the training process – are amongst the largest challenges faced by the industry. The quality of input is a challenge because jobs in the travel, tourism, and hospitality sectors even at the entry level, require a degree of sophistication. This includes good communication skills, emotional intelligence, decision making power, and above all, ability to work under pressure while continuing to smile. Such skills are not formally taught in any school, college or training programme. So, while the focus of specialised training is on equipping potential and professionals with specific technical skills required for specific jobs, the possession of requisite soft skills are a matter of chance. People like the young girl I met possess them, or develop them instinctively. The skill gap in the sector is accentuated by several other factors. In order to leverage the massive potential that travel and tourism sectors offer in a culturally rich and diverse country like ours, we need the right talent at every level. It is important to cast our net wider than just the metropolitan cities to recruit such talent at the entry level. This means being able to bring quality training to smaller cities and towns. The government supported programmes like the Pradhan Mantri Kaushal Vikas Yojana (PMKVY) are trying to do that, but for these to be more effective, the industry needs to get involved. The industry engagement is specifically useful in ensuring that the training curriculum is designed to meet the practical needs at the workplace. Availability of suitable trainers is another area that requires committed efforts from the industry. Ideally, trainers need some practical experience of working in the industry and finding such people is always a challenge, especially in smaller cities and towns. That’s understandable since they have better opportunities available elsewhere. The industry can step in to fill this gap by a two-fold approach. In the short term, they can depute some experienced employees as temporary trainers, as part of their Corporate Social Responsibility initiatives. This would be a win-win, as students will benefit from the practical experience of such guest trainers, while the industry will ultimately get better-trained talent. In the long term, the industry can contribute by helping build capacity of local trainers and by giving them exposure to an actual working environment through short-term familiarisation programmes. Growth is awaiting us. We have the potential, demand, and demographics. All the pieces for a massive boom in the sector are present, yet we need to find innovative ways to ensure they fit together to make it happen. (This article was originally published on The DNA. Read the original article) (Disclaimer: The opinions expressed above are the personal views of the author and do not reflect the views of ZMCL.)

Jobs in travel, tourism, and hospitality sectors require a degree of sophistication

Indian Railways recently announced the introduction of dynamic pricing (DP) on trains, such as the Rajdhani Express, Duronto and Shatabdi with the fares increasing by 10 per cent after every 10 per cent berths being sold, but a cap of maximum 50 per cent hike has been placed on the original fare. Across segments of the population, DP has been painted as evil and designed to hurt travelers. Dynamic pricing, most famously introduced by the American Airlines (AA) in 1988, is being scrutinised by the whole nation. AA estimated the benefit to be around $1.4 billion over three years (at 1990s prices) and expected a steady state contribution of over $500 million through the implementation. This has given rise to a completely new area of research and application in Revenue Management (RM). The field finds wide usage and adoption in many industries, including airlines, railways and hotels amongst others. &nbsp;

Fundamentals of dynamic pricing 
Humans love certainty. All of our efforts go into trying to convert uncertain situations into certain ones. And we Indians love prices that are not only certain but also low! But in the real world, certainty is a myth. Businesses operate under huge uncertainties.&nbsp;

The cost of inputs, the nature of demand and the cost of servicing the demand are all exposed to uncertainties. Any change in any of these parameters severely affects the cost of satisfying the demand. Especially in hospitality and transportation (or logistical) businesses. This is primarily due to the &ldquo;perishable&rdquo; nature of the products that they sell.&nbsp;

Perishability: In this context, we use the term perishable to refer to its shelf life. An airline cannot sell a seat once the flight takes off. A hotel that has an empty room for a day cannot go back in time and sell the room. The revenue foregone due to this perishable nature is detrimental to the ranking of the firm. It would do well to remember that the additional cost incurred by the firm on selling the seat or selling the room at a low price is way less than the additional revenue it would have earned from it.&nbsp;

So, the logical question comes up. If that&rsquo;s the case, why increase prices? Just sell it at any price more than the cost of providing the service.

Yes, that is what DP is actually meant to achieve. In times of low demand, the system tries to increase occupancy by selling seats or rooms at lower prices (obviously above the floor price) to reduce the losses. The losses are made up by charging a premium in situations of high demand.

The popular assumption is DP is time-based, that is, if a ticket is bought closer to the departure time, the price of the ticket would necessarily be higher. But true DP models are not. They are purely demand based.

How does dynamic pricing work?&nbsp; 
The primary objective of such a model is to maximise load factors or occupancy factors for the trains while maximising revenue.&nbsp; Occupancy or load factors are a widely used metric in the hospitality and logistics industry. DP algorithms work to ensure higher load factors via pricing differentiation. This is done by rewarding customers with certain travel plans and extracting a rent from customers with uncertain travel plans. However, this rent extraction is only possible if there is high demand.

DP categorises the resources available into categories or buckets. For ease of understanding, let us consider just three buckets; low fare (deep discount), regular fare and premium fare. All these three buckets are for service at the same class level, providing the same facilities.&nbsp;

The first bucket sells resources at low fares (40-50 per cent of the actual fare) to achieve high occupancy. But to keep the service viable, a minimum occupancy factor is needed; think of it like the minimum customers that a hotel must have to sustain itself. However, these discounts come with a caveat. These fares are non-refundable because the service provider is selling you at a cheaper fare to ensure occupancy levels. In case you back out, the occupancy level of the service goes down and the firm needs to reduce its losses.&nbsp;

If you don&rsquo;t want to take up this offer from the service provider, you can choose to pay the regular fare with the regular refund restrictions. Once the first bucket is full, no more low fares are on offer. Once the second bucket is full, even the regular fares are exhausted. Only premium fares are available.&nbsp;

Generally, premium fare bucket differs from the other buckets in the sense that there is no fixed fare. For example, given high demand, the 10th person who buys a ticket would be paying more than the 9th person who bought the ticket. The sale of this ticket indicates the existence of heavy demand. I am sure you remember how touts earlier sold train tickets priced originally at Rs. 500 for&nbsp;Rs. 3000? It was informal DP at work.&nbsp;

The really good DP algorithms have a few additional features:

&nbsp;Dynamic bucket sizes: Based on historical data and expected demand, buckets sizes are not static. This means that the same fare bucket would have different number of seats available at different times of the year.

Migration between buckets: Based on factors, such as current booking trends&nbsp;compared to historical data, closeness to departure time, seats can shift between buckets. For example, if the time is close to departure and a large number of seats in the premium bucket are unsold, these seats would be shifted towards regular fare bucket or even the low fare bucket. Hence, it is possible to get low fare seats even minutes before departure due to low demand.

Number of buckets: Based on the levels of price discrimination that the service wants to offer, it can have more buckets. This helps to gain better revenues for the service provider.

&nbsp;Price discrimination within bucket: Seats getting booked quickly within a bucket implies high demand. The same price discrimination system applied in the premium category can be applied within other buckets as well. So the first ticket sold in the low fare bucket would be quite cheap as compared to the last seat in the same bucket.

How is it different in the Indian Railways? 
Firstly, there is no additional advantage for a traveler with a certain plan. Regular fares are same as low fares, the traveler gets no discount. The argument given for&nbsp;this is, regular fares are already below par. But these fares are refundable. So from the perspective of the IR, is this helpful in guaranteeing occupancy?&nbsp; No.&nbsp;

What&rsquo;s the flip side? Rationalisation of demand is very much needed to meet supply-demand mismatch. Because refunds are nearly full and overbooking is rampant. Trains with waitlisted tickets sometimes have occupancy as low as 60 per cent. Revoking the Ticket Deposit Receipt, submitted in case of inability to undertake the journey, to obtain a refund has been a logical&nbsp;step in this direction.&nbsp;

Secondly, the DP model of IR occurs in flat and established discrete steps:&nbsp;10 per cent increment after 10 per cent seats gets full. This happens irrespective of the rate of demand and&nbsp;is totally contrary to the underlying principles of DP.

Thirdly, there is no true dynamism in the prices. Prices can move only up and not down. Here, they make an assumption of demand being greater than supply at all times. While this may be true for regular fares during normal periods, this may not hold true for premium fares. There is a high chance of seats going unoccupied in such cases, defeating the primary purpose of dynamic pricing.&nbsp;

There is no migration between regular and premium fares according to demand. A fixed quota of seats is earmarked. There are several possible reasons behind this policy, such as:

Bureaucratic approach: A mindset entrenched in certainty that believes in&nbsp;fixed quotas mean fixed prices. This is what happens when a business is viewed as a government department. This is highly harmful to businesses operating in an uncertain environment. Fixing important parameters leave seats unsold. Bureaucrats don&rsquo;t answer for unsold seats and unrealised revenue. Additionally, the unsold seats are used to appease the unauthorised &ldquo;stakeholders&rdquo;.

&nbsp;Citizens&#39; approach: We have taken Indian Railways for granted. Anything that departs from the traditional way of operations is taken as taking away some fundamental right.&nbsp;

What the Indian Railways should do? 
The first step is to introduce non-refundable deep discount fares. Announce this move, but do not disclose the number of seats in this bucket. Airlines do not disclose their pricing structure. Even Air India doesn&rsquo;t.

People from the lower-middle class who travel long distances for vacations (infrequent travel) will be hugely benefited. This is because their travel plans are mostly certain and they can take advantage of the deep discount fares. Adding the no refund clause also reduces bogus bookings by touts.&nbsp;

It is necessary to introduce partially refundable discount fares. Refund rules have to be stringent than regular fare class, but lenient when compared to deep discount fares.&nbsp;

IR has to introduce dynamic bucket allocation. They have to adopt the dynamic pricing model in its entirety and not piecemeal. It needs to be introduced across all express trains, in all classes, not for a few months, but for a whole year.&nbsp;

All quota need to be removed. If needed, coupons or vouchers have to be introduced. Every quota is a leakage that needs to be plugged.&nbsp;

The first two steps help in covering up occupancy issues and appeasing the masses. The next two steps help in garnering the revenue needed for future investments.&nbsp;

Yes, of course, we must ask Indian Railways to cut costs and manage itself more efficiently. But that&rsquo;s a different topic, needing&nbsp;a different article.&nbsp;

&nbsp;

&nbsp;

&nbsp;

The demand based dynamic pricing system converts uncertain situations into certain ones

Indian Railways recently announced the introduction of dynamic pricing (DP) on trains, such as the Rajdhani Express, Duronto and Shatabdi with the fares increasing by 10 per cent after every 10 per cent berths being sold, but a cap of maximum 50 per cent hike has been placed on the original fare. Across segments of the population, DP has been painted as evil and designed to hurt travelers. Dynamic pricing, most famously introduced by the American Airlines (AA) in 1988, is being scrutinised by the whole nation. AA estimated the benefit to be around $1.4 billion over three years (at 1990s prices) and expected a steady state contribution of over $500 million through the implementation. This has given rise to a completely new area of research and application in Revenue Management (RM). The field finds wide usage and adoption in many industries, including airlines, railways and hotels amongst others. Fundamentals of dynamic pricing Humans love certainty. All of our efforts go into trying to convert uncertain situations into certain ones. And we Indians love prices that are not only certain but also low! But in the real world, certainty is a myth. Businesses operate under huge uncertainties. The cost of inputs, the nature of demand and the cost of servicing the demand are all exposed to uncertainties. Any change in any of these parameters severely affects the cost of satisfying the demand. Especially in hospitality and transportation (or logistical) businesses. This is primarily due to the “perishable” nature of the products that they sell. Perishability: In this context, we use the term perishable to refer to its shelf life. An airline cannot sell a seat once the flight takes off. A hotel that has an empty room for a day cannot go back in time and sell the room. The revenue foregone due to this perishable nature is detrimental to the ranking of the firm. It would do well to remember that the additional cost incurred by the firm on selling the seat or selling the room at a low price is way less than the additional revenue it would have earned from it. So, the logical question comes up. If that’s the case, why increase prices? Just sell it at any price more than the cost of providing the service. Yes, that is what DP is actually meant to achieve. In times of low demand, the system tries to increase occupancy by selling seats or rooms at lower prices (obviously above the floor price) to reduce the losses. The losses are made up by charging a premium in situations of high demand. The popular assumption is DP is time-based, that is, if a ticket is bought closer to the departure time, the price of the ticket would necessarily be higher. But true DP models are not. They are purely demand based. How does dynamic pricing work? The primary objective of such a model is to maximise load factors or occupancy factors for the trains while maximising revenue. Occupancy or load factors are a widely used metric in the hospitality and logistics industry. DP algorithms work to ensure higher load factors via pricing differentiation. This is done by rewarding customers with certain travel plans and extracting a rent from customers with uncertain travel plans. However, this rent extraction is only possible if there is high demand. DP categorises the resources available into categories or buckets. For ease of understanding, let us consider just three buckets; low fare (deep discount), regular fare and premium fare. All these three buckets are for service at the same class level, providing the same facilities. The first bucket sells resources at low fares (40-50 per cent of the actual fare) to achieve high occupancy. But to keep the service viable, a minimum occupancy factor is needed; think of it like the minimum customers that a hotel must have to sustain itself. However, these discounts come with a caveat. These fares are non-refundable because the service provider is selling you at a cheaper fare to ensure occupancy levels. In case you back out, the occupancy level of the service goes down and the firm needs to reduce its losses. If you don’t want to take up this offer from the service provider, you can choose to pay the regular fare with the regular refund restrictions. Once the first bucket is full, no more low fares are on offer. Once the second bucket is full, even the regular fares are exhausted. Only premium fares are available. Generally, premium fare bucket differs from the other buckets in the sense that there is no fixed fare. For example, given high demand, the 10th person who buys a ticket would be paying more than the 9th person who bought the ticket. The sale of this ticket indicates the existence of heavy demand. I am sure you remember how touts earlier sold train tickets priced originally at Rs. 500 for Rs. 3000? It was informal DP at work. The really good DP algorithms have a few additional features: Dynamic bucket sizes: Based on historical data and expected demand, buckets sizes are not static. This means that the same fare bucket would have different number of seats available at different times of the year. Migration between buckets: Based on factors, such as current booking trends compared to historical data, closeness to departure time, seats can shift between buckets. For example, if the time is close to departure and a large number of seats in the premium bucket are unsold, these seats would be shifted towards regular fare bucket or even the low fare bucket. Hence, it is possible to get low fare seats even minutes before departure due to low demand. Number of buckets: Based on the levels of price discrimination that the service wants to offer, it can have more buckets. This helps to gain better revenues for the service provider. Price discrimination within bucket: Seats getting booked quickly within a bucket implies high demand. The same price discrimination system applied in the premium category can be applied within other buckets as well. So the first ticket sold in the low fare bucket would be quite cheap as compared to the last seat in the same bucket. How is it different in the Indian Railways? Firstly, there is no additional advantage for a traveler with a certain plan. Regular fares are same as low fares, the traveler gets no discount. The argument given for this is, regular fares are already below par. But these fares are refundable. So from the perspective of the IR, is this helpful in guaranteeing occupancy? No. What’s the flip side? Rationalisation of demand is very much needed to meet supply-demand mismatch. Because refunds are nearly full and overbooking is rampant. Trains with waitlisted tickets sometimes have occupancy as low as 60 per cent. Revoking the Ticket Deposit Receipt, submitted in case of inability to undertake the journey, to obtain a refund has been a logical step in this direction. Secondly, the DP model of IR occurs in flat and established discrete steps: 10 per cent increment after 10 per cent seats gets full. This happens irrespective of the rate of demand and is totally contrary to the underlying principles of DP. Thirdly, there is no true dynamism in the prices. Prices can move only up and not down. Here, they make an assumption of demand being greater than supply at all times. While this may be true for regular fares during normal periods, this may not hold true for premium fares. There is a high chance of seats going unoccupied in such cases, defeating the primary purpose of dynamic pricing. There is no migration between regular and premium fares according to demand. A fixed quota of seats is earmarked. There are several possible reasons behind this policy, such as: Bureaucratic approach: A mindset entrenched in certainty that believes in fixed quotas mean fixed prices. This is what happens when a business is viewed as a government department. This is highly harmful to businesses operating in an uncertain environment. Fixing important parameters leave seats unsold. Bureaucrats don’t answer for unsold seats and unrealised revenue. Additionally, the unsold seats are used to appease the unauthorised “stakeholders”. Citizens' approach: We have taken Indian Railways for granted. Anything that departs from the traditional way of operations is taken as taking away some fundamental right. What the Indian Railways should do? The first step is to introduce non-refundable deep discount fares. Announce this move, but do not disclose the number of seats in this bucket. Airlines do not disclose their pricing structure. Even Air India doesn’t. People from the lower-middle class who travel long distances for vacations (infrequent travel) will be hugely benefited. This is because their travel plans are mostly certain and they can take advantage of the deep discount fares. Adding the no refund clause also reduces bogus bookings by touts. It is necessary to introduce partially refundable discount fares. Refund rules have to be stringent than regular fare class, but lenient when compared to deep discount fares. IR has to introduce dynamic bucket allocation. They have to adopt the dynamic pricing model in its entirety and not piecemeal. It needs to be introduced across all express trains, in all classes, not for a few months, but for a whole year. All quota need to be removed. If needed, coupons or vouchers have to be introduced. Every quota is a leakage that needs to be plugged. The first two steps help in covering up occupancy issues and appeasing the masses. The next two steps help in garnering the revenue needed for future investments. Yes, of course, we must ask Indian Railways to cut costs and manage itself more efficiently. But that’s a different topic, needing a different article.

Dynamic pricing: Indian Railways cope with the uncertainties of business

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