Digital disruption: the hyperlocal delivery and cloud kitchen driven future of food services in post-COVID India

Kishore Thomas John (Department of Management Studies, Indian Institute of Technology Madras, Chennai, India)

International Hospitality Review

ISSN: 2516-8142

Article publication date: 5 August 2021




The pervasive impact of the COVID-19 virus on the food services sector in India has created conditions for fundamentally altering the structure of the industry. This paper offers a nuanced evaluation of the transfiguration of the market, explaining descriptive views supported by numerous secondary data sources.


This is a self-driven study grounded in secondary data. Qualitative and quantitative assessments are assimilated from credible market research reports of multiple agencies in the Indian context, as well as news developments during the pandemic period.


Digitally pivoted platforms such as cloud kitchens and delivery aggregators will eclipse all other formats due to the potential long-term prevalence of the COVID-19 virus. These formats would rise to a dominant position in the Indian food services sector in the coming decade.

Research limitations/implications

This study is entirely driven by secondary data due to the inherent difficulties of collecting sizeable and good quality primary data as a result of the lengthy and stringent lockdowns imposed across India. Future studies should consider collecting consumer responses to get a better picture of changing dining habits in the post-pandemic scenario.

Practical implications

The dynamic and evolving food services in India, catalyzed by the Internet and digital technologies will help academicians study the long-term implications of this change, and how it would impact society at large. The paper provides a rich body of contemporary data and analysis in the food services sphere.

Social implications

The COVID-19 pandemic and its long-term persistence would dramatically alter food service consumption across India. This will not only change how the industry is structured, but will reshape how food is consumed into the future.


The study is a holistic examination of the relationship between the coronavirus pandemic and the food services industry in India. The macro perspectives aided by news coverage and industry research would help generate potential research questions on its own merits.



John, K.T. (2021), "Digital disruption: the hyperlocal delivery and cloud kitchen driven future of food services in post-COVID India", International Hospitality Review, Vol. ahead-of-print No. ahead-of-print.



Emerald Publishing Limited

Copyright © 2021, Kishore Thomas John


Published in International Hospitality Review. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at

1. Introduction

In the wake of the global spread of COVID-19 virus in March 2020, India imposed one of the world's longest and strictest lockdowns (Purohit and Parmar, 2021). The impact of this action was devastating on the economy, with millions driven into poverty, and the economy grinding to a near halt (Agarwal and Bellman, 2020). The Indian restaurant sector, valued at over $50 billion was forecasted to cut about 1.5 million jobs and suffer $9 billion in losses, being one of the worst affected sectors in the economy (Ahmed and Roy, 2020). Reports by market research companies and food-tech firms suggested that 40% of the restaurants in the country were permanently closed due to the pandemic with dine-in revenues cut by half (CRISIL Research, 2020a; Zomato, 2020). However, the sector started witnessing the green shoots of recovery following a relaxation of restrictions and the waning of the first wave. Between the months of September 2020 to February 2021, the sector recovered slowly but steadily, with the growth and resumption of food services driven mostly by delivery apps and food aggregators (Ishwar, 2020; Bhalla, 2020). The recovery was aided by the long festive and sports season that extended from September till the year's end. By New Year's Eve, food delivery apps were taking record number of orders as customers increasingly embraced the delivery format (ETTech, 2021a; Ahmed, 2021). The dine-in business saw recovery in numbers only by February 2021 when the effects of the first wave were on the decline (Tandon, 2021a). In March 2021, the Union Health Minister had declared that the worst of the pandemic was over (Biswas, 2021).

The impact of the unexpected and devastating second wave changed all that. With infection rates in India breaking new records every day, the country was once again preparing for the long haul, with fresh lockdowns imposed across states and cities by local authorities (Slater, 2021; Singh, 2021). The government's principal scientific advisor had warned of an imminent third wave, exacerbated by mutant variant strains of the virus (Chirtavanshi, 2021). The ravages of the second wave were borne adversely by the battered food service industry, with restaurants closing down once again amidst new lockdowns in major cities and states (Tandon, 2021b; Ganguly, 2021a; Das, 2021). This development was ruinous considering that restaurants and bars were clamoring for the government to ease restrictions for diners only two months earlier (Bengaluru Bureau, 2021; Sadhu, 2021).

The re-imposition of stringent lockdowns on the food service sector which was making a slow and arduous recovery had put many restaurants in a “ventilator mode”, essentially surviving solely on takeaways and food-delivery apps (PTI, 2021; Roy, 2021; Ganguly, 2021b). Published studies by the American CDC had pointed to the risk of spread in restaurants (Explained Desk, 2021;, 2020). This suggests that the dine-in sector would survive only in a much diminished mode for as long as the pandemic posed a threat. The coronavirus continues to be an undeniable and persistent problem for India. Its tenacity is expected to bring in disruptive changes in the dining and restaurant industry. This perspective paper examines the likely scenario of how the sector would survive and progress into the foreseeable period and its impact on the industry as well as consumers in India. Specifically, the paper would examine the disruptive effects of cloud kitchens, food-tech apps and the delivery segment, and how they rose from relative obscurity to command a dominant position in India's food services industry.

2. Food services in India: a sector feeling the pangs of disruption

India's food services sector saw its first major growth impetus following the liberalization and opening up of the economy in 1991. Its growth since then has been marked by acceleration and an internationalization of tastes and preferences. This is reflected by the increasing number of international food chains setting up their businesses across the country. Technopak's study titled Indian Food Services Market cited in Barbeque Nation Hospitality Limited (2021) had divided the Food Sector's growth story into 3 distinct phases, each spanning a decade beginning in 1991. The specificities of each stage are highlighted in Table 1. The sector witnessed pronounced acceleration and growth toward the second decade of the new century, characterized by the rapid growth and expansion of the organized sector.

The growth and expansion of the third phase is accentuated by the exponential rise of Internet access and adoption of mobile telephony. In 2007, only 4% of the entire population had access to the Internet (Keelery, 2021). However, the period from 2010 onwards saw a sharp increase in Internet adoption. As of 2020, close to 50% of the population could access the Internet (Table 2). Simultaneously, there has been a significant increase in the number of smartphone users in the country (Table 3). Reports by market intelligence agencies have suggested that India had 468 million smartphones in 2017, expected to reach 859 million in 2022 (PwC-ASSOCHAM, 2018). By end of 2019, it was reported that India had crossed half a billion smartphone users (IANS, 2020). Interestingly a year later, another study reported that 97% of all Internet users in India access it through their smartphones (Mumbai Bureau, 2019). By mid-2020, the number of rural Internet users had overtaken urban users, signaling a phase shift in India's digital revolution (Mishra and Chanchani, 2020).

Reports from 2014 had suggested that India's food service sector was poised for a major transformation (CII-Grant Thornton, 2014). The rise of digital technologies was seen as a catalyst for transitioning marketing expenditures of food service segments into the online sphere. In 2016–17, marketing expenditure was approximately 4–6% of the total revenues of the industry (FICCI-Technopak, 2017). However, the spend pattern indicated a favorable tilt to digital media in comparison to traditional formats. Figure 1 shows the breakup among different formats. The QSR and FD/IC spending on Digital videos account for more than half of their entire budget. The Café and PBCL segments spend most on third party aggregators in order to make it easier for customers to identify outlets on their devices. The casual dining segments spend most on E-mail marketing and search engine optimization. Fine dining spend was the highest on social media, as was PBCL.

Cost-based analysis in 2018 (Table 4) showed that most of the segments were operating with low margins, aggravated by a regulatory system that requires up to 15 different licenses for a restaurant. This was in stark comparison to other Asian counties where licensing regimes are less cumbersome and more business friendly (FICCI-Technopak, 2017). The extent of regulation coupled with low margins were a cause of much distress in the sector, which is typically cash-strapped- having only around 16 cash buffer days (ETCIO, 2020). In effect, the abrupt first lockdown in March 2020 had sounded the death knell for a significant number of restaurants, being unable to resume operations after the restrictions were eased 75 days later. Anurag Katriar, the president of National Restaurant Association of India (NRAI), representing around 500,000 restaurants reflected “Knowing the high-risk nature of our current business model and the fragility of our profitability were perhaps the biggest lessons learned from this pandemic…reducing the fixed expenses or converting some of them to a variable is a key requirement for survival under the circumstances” (Bhatia, 2021).

3. The evolving Indian diner

India has a predominantly younger population. It is also the youngest among the BRICS nations with a median age of 28.7 years. Estimates by the CIA in 2020 put 43.82% of the population below 24 years, numbering at over 581 million. A substantial chunk of the population is of the working age. This is enumerated in Table 5.

India's population and family structure also underwent a structural transformation in the post-millennial period, characterized by rising urbanization, growing number of nuclear families, changing consumer tastes and preferences, higher experimentation in foods, awareness and access to market offerings and a larger proportion of women in the workforce. These changes have been cited by numerous industry analysts (FICCI-Grant Thornton, 2015; FICCI-KPMG, 2016; MoFPI, EY and CII, 2017; FICCI-Technopak, 2017).

To better understand the dining-out profiles by age group, a study by FICCI-Grant Thornton in 2015 showed that the 21–30 age group comprised the largest segment of diners. Importantly, consumption of food services was driven predominantly by younger age groups, with those above the age of 40 constituting a small fraction. These figures were largely upheld by another study conducted by Nielsen in 2017 which specifically concentrated on India's middle and affluent classes. The findings showed that food services consumption was more prevalent among the affluent classes-where a higher percentage dine out and spend more and also among the millennial age groups of both categories, who spend a greater share of their income on food as well as higher amounts per visit. Notably, the study highlighted that the bottom 80% of India's middle class typically spend less than $ 10 during a visit, demonstrating the limited purchasing power among Indian households for food services (see Figure 2).

Juxtaposing Table 6 with Table 4 allows shows that 80% of India's middle class can only afford to eat as Café’s, QSR and CDRs, based on budgetary considerations. Furthermore, the culture of dining-out is most prevalent among the millennial age groups. A study by CBRE in 2018 showed that millennial Indians dine out around 5 days a month with 60% of millennials eating out more than thrice a month. In 2019, data published by the NRAI reported that the dining-out culture is fast picking up in India, led by major metro cities (Sanand, 2019). The report also said that Café’s and coffee shops were the most popular formats, frequented by college students, office-goers, couples and families. Table 7 shows the eating out frequency and spending pattern among different age groups as well as cities.

What the data make evident is that prior to the COVID-19 outbreak, eating out was picking up pace in India, while ordering-in was much behind, both among consumers as well as in major cities. A schema of how an urban couple spends their year showed that dining-out was the third most popular recreational activity after movies and get-togethers (Table 8). However, what is also evident is that while dining-out in India is above the global average, it is below several other regions such as Hong Kong, Taiwan, Malaysia, Thailand, Vietnam, Singapore, Morocco, Saudi Arabia and Brazil (Nielsen, 2016).

4. The shifting shape of India's income distribution

The national census of 2021 was stalled due to the second wave of the pandemic. As a result, estimates of India's wealth distribution are available only from various market research agencies, which have used their own metrics and segmentation to show the different socio-economic classes of Indian households. Figures 3 and 4 and Table 9 show estimates from three leading research companies. What can be gleaned from these insights is that India has a substantially larger middle class and highly tapered upper and elite classes, suggesting a wide disparity in income among households.

The data consistently shows that India's struggling households at the bottom of the pyramid would rise out of impoverishment as the country progresses into the third decade. This however, was not to be. By 2020, however, India had plunged into an economic recession before the onset of the pandemic (Kochhar, 2021). The lockdown and the economic fallout that followed severely impacted India's population. Data published by the Pew Research Center indicated the pervasive effect of the pandemic in India, with 75 million people driven back into poverty and a substantial reduction in the low and middle income groups. The pandemic had more than doubled India's poor and reduced India's middle class by 33 million people (Refer Table 10).

5. How COVID-19 affected India's food services sector

The onset of the pandemic had severely disrupted India's food services sector, which was forecasted to bring in revenues of US $ 20 billion in the Oct–Dec 2020 quarter alone. Data from Figure 5 show the severity of the contraction following the imposition of the first lockdown. The period April–June 2020 showed near total loss of revenues. Recovery was slow for most of 2020. Projections for 2021 were based on assumptions of a weakening pandemic, relaxation of restrictions and a resumption of normal life. This unfortunately has been reversed by the state-level lockdowns that have begun since April and are at full force in most of the states by May 2021. Reports have suggested that close to 98% of India is again under partial or complete lockdown, with night curfews, as well as restrictions and curbs on movement and normal conduct of business (Sharma, 2021; Sukumar, 2021).

To get a better understanding of how the whole industry has been affected, data published by Technopak are examined in Figures 68. Projections for the period from April 2021 suggest a steady recovery, with almost all segments recovering and exceeding the revenues reported prior to the pandemic outbreak. This may perhaps be very optimistic. The outbreak of the second wave was not foreseen at the time of publishing the data. The ominous projections of a third wave, with the continuing threat of mutating strains that are less responsive to existing vaccines suggest that estimations of recovery from April 2021 are too early and unlikely to be realized in the near future.

Figure 6 shows that the unorganized segment would grow at a much slower pace in the post-pandemic period. The sector which is comprised mostly of dhabas (highway joints), roadside eateries, street food and hawkers is the segment for which the least unit-level data are available. In the pre-COVID era, it constituted a sizeable chunk of the total market. However, its overall share has been steadily falling.

The aggregated data presented in Figure 6 do not consider the impact of prolonged and repeated lockdowns on this segment. A possible reason for this is the lack of data, since such outlets do not come within the tax net and also do not require real-estate unlike that of the organized food sector. The organized standalone and chain market register seemingly higher growth rates. Calculating CAGR from Figure 7 shows that CDR would register the sharpest growth in the standalone format, while from Figure 8, it is clear that QSR would be the fastest growing segment among chain formats.

The fixed cost implications are different between the unorganized and organized segments. Rentals and fixed costs would be high in the organized segment, especially in major cities where space is in high demand. Malls and F&B clusters typically charge high rentals, including maintenance costs, while high streets are typically congested in India, making it inconvenient for vehicular mobility and parking spots (see Table 11).

Even after the first lockdown was lifted, restrictions on restaurant operations, specifically in matters of social distancing, hygiene norms, seating capacity, working hours and sale of alcohol affected dine-in and revenues (Tandon, 2020). In the days leading up to the second lockdown in 2021, restrictions were imposed again, leading to much anxiety and concern in the industry (Das, 2021; Bhushan and Verma, 2021). With infection rates continuing in high numbers into May, the lifting of lockdowns and restrictions seemed to be uncertain (Sinha, 2021). There was open speculation that the restaurant sector would have to survive only on a takeaway/delivery mode for the foreseeable period (Roy, 2021).

6. Enter the disruptors: food aggregators and cloud kitchens

Food-tech and cloud kitchens are emerging phenomena in India that have found rapid growth and ascendancy. Until a decade ago, these segments were virtually unheard of. Their popularity was facilitated in large part by the growth of Internet and smartphones. The rapid rise of these formats points to an imminent disruption in the industry. Market research reports as late as 2015 on food services made no mention of these segments (FICCI-Grant Thornton, 2015). However, by 2017, their emergence was observed; aided in-part by increasing discretionary spends of younger consumers (FICCI-Technopak, 2017). India's food-tech industry has witnessed a steady rise in investments with the figures increasing even after the pandemic. Table 12 indicates the Year-on-Year trends in investment across the 3 major categories of technology-driven food services in India.

6.1 Cloud kitchens

Cloud kitchens are variously known as dark kitchens, ghost kitchens, commissary kitchens, virtual kitchens or cyber kitchens (Oracle, 2020). Oracle had classified the cloud kitchen concept into three main types (shared space, dedicated space and virtual brand) based on usage of space, real-estate and equipment, as well as branding. Unlike typical restaurants which offer seating for diners, these outlets cater exclusively to a delivery or takeaway market, situated in spaces where rentals are low to keep costs down (Sanwaria et al.., 2021).

Cloud kitchens are almost entirely dependent on food-tech and aggregators for sustaining their business model. In 2020, a report by Euromonitor International stated that India has around 3,500 cloud kitchens; far ahead of developed western economies like the USA (1,500) and the UK (750) (Schaefer, 2020). The sector was also poised for steep growth after the onset of the pandemic, forecasted to grow five-fold from $ 400 million in 2019 to $ 2 billion by 2024 (Biswas, 2020). However, these projections were revised by the end of 2020, with new estimates suggesting that cloud kitchens would take 30% of the total market share in India by 2021, up from only 13% the year before (Inresto, 2020). Table 13 lists out the some of the publicized players in this space in India. Besides these, there are hundreds of fragmented cloud kitchen entities spread all across the country. A significant portion of the players are regionally focused and do not enjoy pan-India presence, apart from Rebel Foods which owns the maximum number of brands and outlets. The typology of Indian cloud kitchens is indicated in Table 14.

Analysts have estimated that if an Indian cloud kitchen was set up with an initial investment of INR 1 million (Approx. $ 13,333) with an average-order-value of $4 and gets 750–800 number of orders daily, it could recover its entire investment within a year (Sanwaria et al., 2021). This volume is possible if multiple brands operate from the same kitchen in a shared space. The market researcher Datalabs by Inc42 calculated the cost of setting up a cloud kitchen in India to be about a third of that of a conventional restaurant (Cheema, 2019). A detailed split-up of costs is shown in Table 15. Cloud kitchens are specifically suitable for the delivery business. To get a better perspective, Table 16 compares the breakup of costs of a cloud kitchen vs. a typical restaurant in the delivery format. A sizeable cost component (almost 25%) is incurred as commissions to the delivery partner. This cuts into margins deeply, leaving traditional restaurants with practically no profit. In contrast, cloud kitchens offer economic viability due to lower cost of setting-up, running and better margins.

Cloud kitchens encapsulate the delivery meal experience in an affordable, value-oriented offering. The low-cost operating structure allows the format to pass on the benefit to the consumer. An expert at PwC in 2019 had described India as a “highly price-sensitive market” (BBC, 2019). Consumer studies in retail have shown that Indians are more demanding; seek value and are conservative with their spending when compared with other countries (BCG-CII, 2016). The economic impact of the pandemic, the threat of loss of livelihood and the uncertainty of further disease spreads might amplify this behavior, leading to less discretionary spends. This has indeed been reflected in market studies which have shown an increasing trend to saving and thrift (Beniwal, 2021; KPMG, 2021). The president of NRAI was quoted as saying that “F&B business is largely fuelled by discretionary expenses” (Bhatia, 2021). It is implied that eating out at restaurants will be affected. However, the cloud kitchen segment might be able to buck this trend by altering the cost structure vis-à-vis a traditional restaurant, allowing more value to be realized by end consumers.

6.2 Delivery apps

India's online food-delivery market has evolved into a duopoly in recent times, with major international players marking their exit (Goel and Conger, 2020). Within the food service sector, these are called platform-to-consumer (P2C) segments in contrast to restaurant-to-consumer (R2C) segments, where the order is typically received and serviced online by the restaurant itself (Poluliakh, 2020). The online platform serves as an intermediary between the consumer and the food service outlet, providing detailed information about different restaurants, menus and offers (prices and discounts). The logistics of the collection and delivery are also handled by the platform's own agents. The platform collects a commission from the restaurants and/or consumer (Barbeque Nation Hospitality Limited, 2021).

Comparative data from Table 12 show that close to 83% of all funding received in the food-tech space has been in the delivery segment. Collectively, the two largest players in the arena each are billion dollar plus in valuation, although they are yet to become profitable. Even in the midst of the pandemic, when investor sentiment was affected, these majors continued to attract funds, allowing their businesses to grow and race closer to profitability. Table 17 lists out the major players in the past and the present time. The growth of the two dominant food-delivery platforms in India has been phenomenal, rising from near obscurity and confinement in major cities in 2015 to nationwide presence by the end of 2020. Interestingly, it can be seen from Table 13 that both Swiggy and Zomato had launched their own cloud platforms only to wind it down later. Clearly, the delivery business was more profitable. To lend a perspective on the growth of the two players, Table 18 provides a comparative view on the city-wise presence and daily orders across time.

The COVID-pandemic and the associated lockdowns, while affecting the restaurant industry as a whole had provided some relief for online delivery apps. Despite curfews and restrictions for dine-in at restaurants, these segments were given sufficient leeway to operate under the consideration that food delivery was an “essential item” (Arun, 2021). Reports in October 2020 suggested that the two biggest food delivery apps were able to recover their business volume to pre-COVID levels (Abrar, 2020). By November 2020 Goldman Sachs had forecasted both Swiggy and Zomato to become profitable entities in 2022, revising earlier estimates that had suggested late 2023 (Dash, 2020). In April 2021, Zomato filed for an IPO with India's regulator and was expected to raise $ 1.1 billion in funds (Toh, 2021).

HSBC in a 2021 report claimed that Swiggy and Zomato would become profitable in the COVID-era due to increase in the average-order-value coupled (AoV) with reduction in discounting (Salman, 2021). The cost-wise breakup is shown in Table 19. A sustained pandemic period extended over multiple lockdowns and repeated waves of infection would allow the delivery players to dominate traditional formats, permitting losses to be recovered.

A Bank of Baroda Capital Market study claimed that approximately 63% of the online food service customers in 2019 comprised of millennials (Burde and Naik, 2021). Projections show the P2C segment moving past R2C by 2025. The differences of the pre-pandemic and post-pandemic estimates suggest that the market would grow by an additional $ 1.7 billion due to the pandemic alone to reach a cumulative $ 18.1 billion by 2025 (Figure 9).

The projections are based on the underlying assumption that the pandemic is a singular, unusual occurrence rather than a prolonged and persistent social disruption. The figures therefore might be much higher in the online delivery space, as latitude for regular restaurant operations are curtailed and/or restricted. The longer the pandemic continues to persist; the steeper would be the rise of online deliveries. However, the rise of these online aggregators to dominant positions in the industry has not been without contention. The NRAI had called these apps “Digital Landlords” for their exorbitant commissions and punishing terms (Sharma, 2020). There have also been cases of labor unrest among gig workers, protesting unfair employment practices (Vaidyanathan, 2020). A report by the Oxford Internet Institute claimed that the work cultures in Swiggy and Zomato were the worst in India, with unfair policies and practices prevalent in wages, working conditions, contractual terms, management policies and worker representation (Fairwork, 2021).

A particularly sore issue for restaurants was the lack of access to customer data, which aggregators refuse to share (Narayanan and Ambwani, 2021). The NRAI in 2020 had mulled the creation of a rival aggregator online delivery portal to offer better terms to restaurants (Sharma, 2020). Until 2021, the NRAI had not succeeded in developing a viable alternative that can match the scale and scope of these aggregators. The entry of a dominant e-tailer like Amazon is predicted to shake the market hold of the exiting players, due to lower commissions and better terms offered to restaurants (Motilal Oswal, 2021). However, Amazon at the present time is operating only in a single city in India (Kelkar, 2021). Its large scale expansion might have long-standing implications for the delivery segment.

6.3 The future is hyperlocal

Food services are by default hyperlocal. Unlike conventional e-tailers that transport merchandize over long distances, hyperlocal businesses work on satisfying demand within circumscribed geographies using other businesses based within these regions (Team Inc42, 2019). The augmentation of logistics capabilities supported by technological advances allows such businesses to respond to customer needs rapidly, usually in less than an hour. India is attracting investment to hyperlocal startups in a diverse range of portfolios such as groceries, pharmacy, logistics, etc. (Singh, 2020). The COVID-19 pandemic would accelerate the growth of such services due to the inherent restrictions it imposes on mobility and physical access.

7. The dynamics of disruption

The effects of the predicted disruption in the food services sector are two-fold:

  1. The manner in which the consumer purchases food services.

  2. The existing structure of the industry

A prolonged COVID-era punctuated with multiple waves of the pandemic and mutating virus strains will continue the legal confinement of citizens, leaving them more dependent on hyperlocal businesses. This may be embraced by the younger generation given their inclination toward food services and online aggregators. However, there will be a significant impact on discretionary spends due to the economic fallout of the pandemic. This will affect spending on food services (CRISIL Research, 2020b). Cloud kitchens can circumvent this problem by value pricing and food-portioning to position their offerings as home meal replacements.

The indicators seem to suggest that the future of food services will be sustained by the delivery segment due to the COVID-19 virus. This will fundamentally shift the control over the service experience from the restaurant toward the online aggregator app (Figure 10). The landscape of the industry will be altered due to the predicted capitulation of the unorganized segment. This may be attributed to continuing concerns and fears of safety and hygiene within this segment. It is unlikely that the unorganized segment will be able to weather the ravages of the pandemic over prolonged periods due to lesser working capital. The projections which have forecasted a significant share of the unorganized market even in 2025 may not hold true in the face of unfolding crises. The future may point toward low-cost business models which are supported by higher digitization and marketing. The cloud kitchen model sustained by aggregators might be a viable format for the coming era (Figure 11).

8. Concluding observations: the way forward

Consumer food habits have fundamentally changed in the COVID-era. A study by IPSOS in the Indian context has revealed altered dining behavior, with increased focus on healthy and immunity boosting foods (Gangwani, 2020). Notably, the annual food trends report by Godrej (2021) which compiles expert opinions of food writers, consultants, chefs, hoteliers, restaurateurs and industry leaders from across India suggested the post-pandemic period to be driven by safety and health concerns and the rising dominance of cloud kitchens.

Cloud kitchens have inherent advantages in operating during the COVID-era due to a scalable architecture and lowered costs of labor and rental. However as a downside, these segments do not enjoy the same visibility or brand presence of full-service operators and are entirely dependent on online aggregators for their sustenance. The absence of client-facing-staff would also make service recovery difficult in the event of a failure. Furthermore, the problem of commoditization is increasingly ominous as the segment becomes crowded.

The inherent challenge of cloud kitchens would be to balance the pre-COVID consumer drivers of discounts, convenience and variety with the post-COVID drivers of downsized spending, safety and hygiene in a transparent setting to build consumer confidence. These may be achieved through various digital features like live kitchens and streaming.

Food-tech aggregators on the other hand, face more daunting challenges. Their increasing prominence and growth will invite the attention of regulators and the government as they become “too big to ignore”. Already, complaints to the competition commission of India have flagged anti-competitive practices of these aggregators and their potential abuse of dominant position (CCI, 2020). There are also fears from industry watchers about a digital monopoly imposed by these players, who after reaching market dominance may overcharge customers and restaurants (Shahane, 2020). Furthermore, as the gig economy expands in India, it will attract the attention of lawmakers to protect the interests of such workers (ETTech, 2021). Food aggregators already have hundreds of thousands of delivery agents on their payroll. The Indian government is deliberating labor codes for this emerging workforce (Bala, 2021). There is also a genuine possibility of unionization of gig workers for collective bargaining and representation. These factors may ultimately extract additional cost expenditures from the end-consumer.

Notwithstanding the above risks, the present market conditions are favorable for the growth of these segments. India had more than 200 million online shoppers in 2020 (BCG-Google, 2020), and this is slated to increase further. The vast amounts of unit-level user data collected by aggregator apps would allow them to leverage on technologies like artificial intelligence, machine learning and data mining for targeted marketing, advertising, demand management, route planning and optimization as well as horizontal integration into other hyperlocal segments. For consumers, the long honeymoon with aggregators may be coming to an end, but for the aggregators the party is just beginning.


Spending patterns among different dining format in India by percentage in 2016–17

Figure 1

Spending patterns among different dining format in India by percentage in 2016–17

Age profile categorization of Indian diners in 2015

Figure 2

Age profile categorization of Indian diners in 2015

Changes to India's socio-economic groups from 2008 to 2030

Figure 3

Changes to India's socio-economic groups from 2008 to 2030

Changes to India's socio-economic groups from 2005 to 2025 (estimate)

Figure 4

Changes to India's socio-economic groups from 2005 to 2025 (estimate)

Revenue changes in food services (US $ Billions) with COVID-19 impact estimates

Figure 5

Revenue changes in food services (US $ Billions) with COVID-19 impact estimates

Fiscal-year wise estimates for India's food services industry (US $ Billions)

Figure 6

Fiscal-year wise estimates for India's food services industry (US $ Billions)

Fiscal-year wise estimates for organized standalone market (US $ Billions)

Figure 7

Fiscal-year wise estimates for organized standalone market (US $ Billions)

Fiscal-year wise estimates for organized chain market (US $ Billions)

Figure 8

Fiscal-year wise estimates for organized chain market (US $ Billions)

Online P2C and R2C market in India with estimates (US $ billions)

Figure 9

Online P2C and R2C market in India with estimates (US $ billions)

Comparing different food service consumption formats

Figure 10

Comparing different food service consumption formats

The rise of lower-cost and highly-digitized cloud kitchens in the post-COVID era

Figure 11

The rise of lower-cost and highly-digitized cloud kitchens in the post-COVID era

Phase-wise expansion of India's food services sector from 1991–2020

Concentration areasBusiness typesBusiness modelKey driverKey challenge
Transforming India (Introduction) 1991–2000Mega metros (2 cities) and mini-metros (6 cities)International (mainly US) brands and home-grown brandsComplete ownership, or franchising with self-fundingOpening up of Indian economy and end of license-raj era. Accelerated economic growth and rise of the services sector. Growth of the middle class and rapid expansion of metro cities with cosmopolitan culturesLimited infrastructure and connectivity. Issues of logistics and lack of skilled manpower. Limited market research and lack of availability of consumer data. Culture shock for international brands to Indian food preferences (McDonalds beef controversy)
Ambitious India (Growth) 2001–2010Tier-I cities (20 cities in total)Diversification of sector and customer clusters, new brand entriesFranchise and emergence of joint ventures partnerships and public equity, angel investorsGrowth of regional cities in state capitals, satellite cities and industrial, commercial and IT hubs. Attraction of foreign investment and international players into India's growth story (India Shining Campaign)Limited market research and availability of consumer data. Growing but nascent Internet landscape. Adoption of digital technologies. Insularity of India's growing middle class toward new lifestyles. Building the food services sector as a lifestyle choice
Digital India (Expansion) 2011–2020Emerging lower Tier cities and townsExpansion of multiple American brands across Indian subcontinentJoint ventures driven by IPOs and angel investmentsIncreasing urbanization, accelerated economic growth and an enabling regulatory environment with lifting of restrictions for international players. Rapid proliferation of Internet and mobile telephonyDeveloping loyal customers in an increasingly crowded market. Digital proliferation. Leveraging CRM initiatives. Market segmentation and diversification. Disruptive effects of food-technology firms
Transfiguring India (Disruption) 2021–2030Pan-India expansion and presenceFood-tech aggregator and cloud kitchen drivenHyperlocal, with partnerships and tie up with local businessesExpansion of smartphone usage. Growth of cash-less and contact-less payment methods. AI, Machine learning and data miningMonopolization of digital market. Commoditization of food service. Decline of dine-in formats. Government intervention

Source(s): Adapted by the author from Barbeque Nation Hospitality Limited (2021)

Internet users in India and penetration

Users (In millions)226.3332321.8446560688747.41
Penetration (%)18.3273435384850

Source(s): Compiled from multiple reports from IBEF (2021) and Statista in Keelery (2021)

Smartphone users in India

Users (In millions)123.3250.66304.51394.82479.34634.58696.07

Approximate store economics for various food service segments in 2018

CafésQSRCDRFine diningPBCL
Store economics table by food service type
APC (US $) (approx value)2.00–3.005.50–8.508.50–12.00>15>15
Table turnover1.8–2.21.4–1.61.7–2.51.2–1.81.7–2.0
Avg. store size (Sq. Ft.)1,000–1,5001,200–1,5003,000–3,2503,250–3,5003,250–3,500
Avg. CAPEX per store (US $)97K–139K139K–208K416K–486K486K–556K416K–486K
Avg. sales/day/store (US $)208–277902–972NANANA
Store-wise breakup of costs
Employee costs12%22%18%18%25%
Rent costs12%11%13%15%15%
Outlet expenses16%15%12%8%10%
Outlet EBITDA20%22%25%27%20%
Total revenue100%

Note(s): Costs calculated in constant 2018 dollars 1 US $ = approx. 72 INR

APC: Average per Cover, CoGS: Cost of Goods Sold. CAPEX: Capital Expenditure

EBITDA: Earnings before Interest, Tax, Depreciation and Amortization

QSR: Quick Service Restaurants. CDR: Casual Dining Restaurants. PBCL: Pubs, Bars, Café's and Lounges

Source(s): Compiled and tabulated by the author from FICCI-PwC (2018)

Age structure of India's population

Age groupPercentageMale populationFemale population
0–14 Years26.31185,017,089163,844,572
15–24 Years17.51123,423,531108,739,780
25–54 Years41.56285,275,667265,842,319
55–64 Years7.9152,444,81752,447,038
65 Years and above6.7242,054,45947,003,975

Source(s): CIA (2021)

Spending pattern on food services by Indian households (economic class and generation wise)

DescriptionMiddle income class ($ 4,166 to $ 13,888)Affluent class (>$ 13,888)
Percentage of households that eat-out5%9%
Average annual spend on eat-out$ 76.38138.88
Percentage of total food expense spent on eating out among millennials (ages 18–34)10%13%
Average expenditure on eating out among millennials (ages 18–34)$ 109.91$ 159.18
Percentage of total food expense spent on eating out among Gen X (ages 35–50)3%7%
Average expenditure on eating out among Gen X (ages 35–50)$ 61.9$ 130.38
Split of classes into sub-categoriesBottom 20%2nd Quintile3rd Quintile4th QuintileTop 20%Top 10%Top 1%
Average spend per visit$ 1.34$ 2.70$ 4.87$ 8.51$ 28.3$ 37.5$ 88.8

Source(s): Nielsen (2017)

Spending pattern among consumers based on age groups and cities (2020)

DescriptionEating-out frequency/monthAverage spend/outing ($)Ordering-in frequency/monthAverage spend/ordering-in ($)
15–24 Years2.
25–34 Years1.
>35 Years1.
Mega metros6.313.852.16.6
Mini metros5.511.481.95.77
Tier-I and II cities4.89.411.14.21

Source(s): Technopak, compiled by author from Edelweiss (2021) and Barbeque Nation Hospitality Limited (2021)

Day-wise split of activities of an urban Indian couple in 2017

ActivityDays spent
Commuting and parking28
Watching TV20
Get-together with family and friends14
Homework and children activities13
Eating out9
Household chores4
Birthday parties4

Household distribution by income slabs (in millions)

Households2013201420152016201720182019 (PCE)2020 (PCE)2021 (PCE)2022 (PCE)
Household numbers236239243247251255259262266270
$ 5,000–$ 9,999809096108126138149173188206
$ 10,000–$ 49,99917192124313641546479
More than $ 50,0000000001111

Note(s): PCE- Pre-COVID-19 Estimates

Income Distribution is calculated in constant 2018 dollars. 1 US $ = 70 INR

Gross population distribution by income slabs (in millions)

Socio-economic groupDaily income (US $)Pre-COVID (millions)Post-COVID (millions)DifferenceImpact
High income> $ 50321Decrease
Upper-income$ 20.01–$ 5022167Decrease
Middle-income$ 10.1–$ 20996632Decrease
Low income$ 2.01–$ 101,1971,16235Decrease
Poor< $ 25913475Increase

Note(s): Income Distribution is calculated in constant 2021 dollars. 1 US $ = 75 INR

Source(s): Pew Research Center in Kochhar (2021)

Distribution of organized segment among real-estate formats at 3 Indian metros

DescriptionF&B cluster%High street%Commercial space%Mall%
Overall market distribution760429
International restaurants826462
Domestic restaurants768322

Source(s): CBRE (2018)

Y-O-Y investment trends in Indian food-tech segments in US $ (Millions)

Food-tech type201420152016201720182019
Food delivery6012981891,672102
Cloud kitchen1266281022165
Table reservation04312340
Year total721991121111,728267

Note(s): Investment figures are calculated respective years in millions of US Dollars

Source(s): Compiled by author from BCG-Google (2020)

Publicized cloud kitchen players in India

Name of operatorYear of foundingMarket valueLocation/ HQRemarks
Zomato2008$ 5.43 Billion (2021)GurgaonLaunched Zomato Infrastructure Services (ZIS) in 2016 to help build cloud kitchens for use by multiple restaurants. This venture was closed in 2020. It is now investing in other cloud kitchen start-ups like Loyal Hospitality of Bengaluru. Also tied-up with major star hotels and fine dine restaurants across India during pandemic for food delivery
Rebel foods2011Approx. $ 800 million (2020)MumbaiCommenced operations as a restaurant in 2011. Converted into a cloud-only model by 2016. 350 kitchens across 35 cities in India as on Mar 2021. 11 brands in total including Behrouz Biryani, Oven story, Mandarin Oak, Lunch Box, the good Bowl, Sweet Truth, Firangi Bake, the 500 Calorie Project, Navarasam, Slay. Planning to offer IPO by end of 2022 and targeting Billion dollar valuation. Largest player in India by size, market value, brands and reach. Franchisee model launched for expansion. Deal signed with American Giant Wendy's in 2020 to open 250+ cloud kitchens across India
Box 82012$ 55.5 million (2020)MumbaiOperates in 4 major cities: Mumbai, Pune, Bangalore and Gurgaon. Delivers close to 660K meals from 100 outlets monthly. Also owns the Pizza brand Mojo Pizza
FreshMenu2014$ 53 million (2020)BengaluruInternet-first restaurant mainly focused on Bengaluru's cosmopolitan market. Last major round of funding was 2019. 5 kitchens closed in 2020. Company reported a slow-down in 2021 with fresh investments drying up
Swiggy2014$ 4.46 Billion (2021)BengaluruCloud kitchen business started in 2017 as Swiggy access in limited format. Hundreds of outlets opened and 200+ cloud kitchen brands created. Many shut down due to pandemic. Tied-up with most fine-dine restaurants and five-star hotels across India for delivery during pandemic. Unicorn company
Biryani-by-Kilo2015$ 20 million (2020)GurgaonSells biryani by weight, including chicken and mutton. Last major funding in August 2020. Operates 48 outlets from 25 cities
Inner chef2015$ 30 million (2020)GurgaonProvides food services in Gurgaon, Noida, Delhi, Mumbai, Bengaluru and Hyderabad
Petoo2015$ 1.69 million (2019)BengaluruStruggling with seeding in recent years. Last major funding was in 2016. Had 31 franchisees with presence in 12 cities across 7 states at a time. Now, it is mainly based out of Karnataka and has an outlet in Chennai
Cure-Fit2016$ 399 million (2020)BengaluruLast major funding received in 2019. Offers entire gamut of fitness, diet, training and nutrition. Food is offered under its sub-brand EatFit. Outlets in 6 cities
Ghost kitchens2019$ 3 million (2019)MumbaiCloud kitchen incubator. Owns over 18 brands spread across desserts, biryani, south Indian food, snacks, Chinese, ethnic Indian and pizza
Ola foods2019No data available. Subsidiary of OlaBengaluruNetwork of 40 cloud kitchens across 6 cities. Major brands include: The Biryani Experiment, Khichdi Experiment, Paratha Experiment, the daily diner, Bowl some, that Pizza Place, Nashta Express and Glupp

Source(s): Compiled by author from multiple sources and Tracxn (2021)

Cost components of cloud kitchens in India

Cost centerDescriptionCost (US $)Description
Operating expensesRentals$ 357–$ 571Area of 800 Sq. ft. Monthly rentals
Licenses$ 214–$ 257Multiple licenses required annually
Kitchen and Equipment$ 7,142–$ 14,285Cost for area- one time investment
Staff$ 571–$ 1,000Monthly, including chefs and commie
Marketing and selling expensesCustomer acquisition$ 571–$ 2,000Monthly, including CRM, website, etc
Social media$ 285–$ 571Monthly
Branding and Packaging$ 714–$ 857Package design, containers, materials
POS and inventory$ 57–$ 71Monthly

Note(s): Costs is calculated in constant 2019 dollars. 1 US $ = 70 INR

Source(s): Compiled by author from Limetray (2019)

Typology of cloud kitchens in the Indian context

TypeFloor areaDescriptionSpecifics
Independent cloud kitchen model500–600 Sq. ftThe oldest and original modelOrders are online with delivery only model. Exclusive kitchen space. Highly specialized cuisine. Aggregator dependent. Found all across India
The brand house model2000+ Sq. ftMulti-brand kitchen for multiple cuisines. Single kitchen. 1 mother kitchen, many child brandsAdopted by Rebel foods. Online orders to single kitchen with multiple brands. Different cuisine variety. Aggregator dependent, but with own platform also
The Storefront Franchise model1,200–2000 Sq. ftSingle brand in single kitchen, but multiple outlets and a visible storefrontHas the traits of a typical cloud kitchen with the option of takeaway. Allows physical access to end customers. Used by FreshMenu in Delhi
Aggregator Owned (Shell type) modelMultiple kitchens of 100–500 Sq. ft in a larger kitchenMulti-brand offering owned by an aggregator with rented kitchens. A shell model with only space and basic utilities. Clients bring all equipment, staff, raw materials and menu etc.Parent aggregator is source of orders. Provides space for partner brands in same kitchen. Higher vertical integration. Used by Swiggy in Swiggy access
Aggregator Owned (Filled-shell type) modelMultiple partners, each operating a kitchen space of 250–500 Sq. ftRented kitchens, but equipment provided by aggregator. Technical details shared, including recipes. Has a visible storefrontFuller than the shell type model, with more support from aggregator. Customers can walk into the store for takeaway. Patronized by Zomato Infrastructure services
Fully Outsourced modeln.aEverything is outsourced. Even major kitchen work is outsourced. Chefs only do final touches and finishingCalled Kitopi model after the call-center. Own online platform. Raw materials purchased and stored. Pre-prepared in central kitchen. Sent to satellite kitchen for final touches

Source(s): Compiled by author from Limetray (2018)

Comparison of costs between a cloud kitchen and restaurant in delivery format

AttributeParticularCloud kitchen%Restaurant%
Fixed costsRent2524
Energy (Electricity + Gas)
Raw materialMeat/vegetables/oil etc.4018
PackagingPreparation for deliveryNA5
Ads and discountsPromotion of outlet25
CommissionsPaid to the delivery partner2525
Profit marginWhat is retained by outlet103

Source(s): Cheema (2019)

Publicized food-tech delivery players in India

Name of operatorYear of foundingMarket valueLocation/ HQRemarks
Zomato2008$ 5.43 Billion (2021)GurgaonStarted off as a restaurant review portal. Commenced its delivery business in 2015. $ 977 million funding raised in 2020. $ 252 million raised in Feb 2021. Approximately 230,000 gig workers in 2019. Offers a Zomato Gold loyalty programme
Amazon India2012No dataBengaluruCommenced in May 2020. Operations solely in Bengaluru in 62 pin-code areas as of March 2021. Offers free delivery for prime members and charges nominal fees for non-prime customers
Food Panda2012$ 31.7 million (2017)DelhiEarly entrant in market with operations in 5cities. Sold off to Ola India in December 2017. Ola shut down the delivery business 18 months after acquisitions to concentrate only on the cloud kitchen business
Dunzo2014$ 267 million (2021)BengaluruNiche player concentrated only in 7 cities. Offers a drop-off facility also involving food among a “Curd to condoms” delivery model. Will pick up item of choice from store/restaurants and deliver to specified location
Swiggy2014$ 4.46 Billion (2021)Bengaluru$ 157.88 million funding in 2020. $ 800 million funding until April 2021. Over 250,000 gig workers as of 2019. Offers a Swiggy Super loyalty programme
Uber eats2017$ 350 million (2020)GurgaonRide sharing platform entered food delivery business in May 2017. Started business in Mumbai and expanded to other cities. Sold off business to Zomato in Jan 2020. Had 10 million users at time of sale. Took 10% stake in Zomato

Source(s): Compiled by author from multiple sources and Tracxn (2021)

Growth of the major food-tech players (City count and daily order count)

Description of food-tech app20162017201820192020
Swiggy: Number of cities by
Swiggy: Order counts per day40,00080,000700,0001,400,000n.a
Zomato: Number of cities by
Zomato: Order counts per day30,000100,000650,0001,300,000n.a

Note(s): n.a- Data not available

Source(s): Compiled from multiple sources and BOBCAPS in (Burde and Naik, 2021)

Pre-COVID and Post-COVID breakup of cost components for Online Aggregators

Cost-wise breakup per order on online aggregatorsPre-COVIDPost-COVID
Average value of an order in (US $)45.33
(Minus) amount taken by restaurant (US $)3.0664.13
(Equals) net revenue of aggregator App (US $)0.931.20
(Minus) delivery cost (US $)0.600.53
(Minus) support cost (US $)0.0660.106
(Minus) discount (US $)0.2660.133
(Minus) restaurant refund (US $)0.0660.066
(Equals) contribution margin (US $)(0.066)0.360
(Minus) marketing + fixed cost (US $)0.3330.266
(Equals) EBITDA for the delivery food-tech aggregator (US $)(0.40)0.0933

Note(s): Revenue distribution is calculated in constant 2021 dollars. 1 US $ = 75 INR

Source(s): HSBC in S.H. (2021)


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The author is a fully funded research scholar of IIT-Madras and is supported financially under the Half Time Research Allowance (HTRA) scholarship scheme of the institute.

Corresponding author

Kishore Thomas John can be contacted at:

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