Why Oyo is parsing playbook one hotel at a time?

If one could paraphrase OYO’S elevator pitch, it would sound something like this: “OYO standardizes a highly fragmented budget hotel industry with consistent and uniform upgrades in infrastructure quality and service levels to ensure a consistently comfortable and reliable stay for the hotel guest and, in turn, enhance the occupancy levels, revenue, and profitability of the hotel owner”.

So, how does this play out on the ground?

OYO has a feet-on-the-street marketing team that stakes out budget hotels to find partners.

Approaching the right person

The hotel owner who spoke to us runs a budget hotel in North India. OYO approached him sometime last year and offered to take it under its wing on the following terms:

Following an audit report, the hotel would invest in upgrading the hotel to OYO’s prescribed standards.
The hotel would bear all the capex expenditure.

  1. OYO’s only upfront investment involved putting up their branding signage and other branding materials and providing a tablet device to be used for managing bookings.
  2. OYO would be responsible for all 35 rooms at the hotel. It offered a minimum guarantee of Rs 14.5 lakh ($20,400) per month (NB: This sum was arrived at after OYO examined the hotel’s legacy booking and occupancy records). This is the assured sum that OYO would pay the hotel irrespective of how much business was actually generated.
  3. Once this minimum guarantee fee was crossed, the hotel would need to pay OYO a commission of 20% of the excess revenue every month (17.5% as revenue share and 2.5% as “platform fee”)
  4. The booking process would work as follows: Customers book rooms through the OYO website or app or any other OTA/aggregator with which OYO has tied up (for instance, MakeMyTrip and GoIbibo). In addition to this, any customer can walk in and book a room at the hotel counter directly (these bookings would also count against the minimum guarantee).
  5. The payment and settlement process would work as follows: Some bookings are prepaid, for which OYO collects the payment upfront; others pay at the hotel itself (both pre-booked rooms and walk-ins). At the end of each month, the hotel owner is liable to pay 20% of the booked revenue to OYO—OYO consolidates the online and offline payments and settles the balance payment to the hotel after deducting its commission.
  6. The hotel is completely responsible for operating the hotel—OYO bears no operating expenses.
    On the face of it, it is a brilliant model with an irresistible win-win value proposition.

OYO gets additional inventory without any capex or opex—the textbook thin-asset business model.

The hotel owner gets a higher revenue through the increased utilization that OYO’s platform provides, as well as the comfort/insurance of a minimum guarantee that he will earn irrespective of what the actual utilization is.

All kosher, right?

On paper, yes.

On the ground, not so much.

How so?

Let’s examine the metrics one at a time (NB: These numbers are drawn from the actual reports and bank statements over a nine-month period shared by the hotel owner).

Occupancy rate

Claim/promise: Sharp increase (Before: 25%, After: 65%)

Actual: Moderate increase (Before: 70%, After: 80%)

OYO promises a nearly three-fold increase in room occupancy rates from 25% to 65%, but in reality, the owner saw a much more modest increase (from 70% to 80%). Of course, this might have been a function of the original occupancy rate which, at 70%, was already high, to begin with. Perhaps OYO’s value proposition is far sharper for hotels with vastly underutilized inventory. Be that as it may, the hotel owner was not particularly happy with the increase in occupancy.

Why so?

According to the owner, there was a marked difference in the profile of hotel guests before and after tying up with OYO.

Previously, a large chunk of them were corporate guests. Hotel owners prefer corporate guests because they are more profitable on multiple counts. Corporate guests are usually long-term partners with a higher level of loyalty and stickiness. Most corporate bookings are for longish periods—a minimum of three to four days.

Servicing corporate customers carries lower overheads, and most of these guests use the rooms primarily as a place to sleep at night and therefore don’t require constant care and attention in terms of housekeeping during the day (when they usually step out for work).


An additional experience to go with

In 2015, China saw a surge of rip-offs of popular electronics being sold on popular e-commerce companies. It was difficult to handle. The big e-commerce companies in China had to make difficult decisions. If they cleansed the database of merchants who sold fakes, the number of sellers on the platform would drop drastically. And no one could figure out where in the value chain the fakes crept in.

“Companies tied up with big brands and asked them to start selling their offline products online via a QR code. It meant the original manufacturer or distributor would be making the sale,” says a former executive of TataCLiQ. CLiQ is Tata’s O2O experiment. He asked not to be identified as his current employer doesn’t allow him to talk to the press.

Getting visitors from all over the world

He explains that during his time at TataCLiQ, the company would get visitors from Tmall and Alibaba primarily to swap notes on TataCLiQ’s tricks in execution. TataCLiQ used a similar model to sell its products but without a QR code. It hasn’t been successful so far.

The number of orders has been dropping and so is the average ticket value. Multiple former executives and those in the e-commerce business say that CLiQ does about 20,000 orders a day with an average ticket size of Rs 1,500 ($22.2). (This means that 20,000 people buy online from Tatacliq.com and about 500 people walk into a Tata store and use the Paytm Mall concept to shop.) Compare this to Flipkart, which does over 100,000 orders a day.

When Tmall went online, there was a specific plan in mind. Minimize the fakes. Customers would walk up, scan the QR code and walk out. The absolute volume of people doing this was high. In China, the O2O market recorded sales of $78 billion in 2017 with projections of close to $100 billion in 2018. A lot of people found it convenient; it gave customers a touch and feel of the product, and retailers got people to upgrade their purchases.

“If a customer came to buy a phone, they would sell them Bluetooth headphones, too, which meant bigger margins,” the executive adds. The logistics were then handled by the retailer. “It was possible because there is so much volume,” he adds. It gave the retailer economies of scale when making these deliveries. “Sometimes, the likes of Tmall would make deliveries, but that meant a larger commission from the retailer,” he says.

Great relief from the problems

This solved two problems for Chinese customers: it took out the risk of being saddled with fake goods and they got to experience things first hand. For the retailers, it increased footfall, which meant they could sell more. The likes of Tmall showed huge GMV and the commissions worked as well.

Now, let’s come back to India. “There is a problem of fakes, but not as acute as China,” says Satish Meena, senior forecast analyst at market research company Forrester. And the reasons for pulling Indians to the store are different. India still doesn’t pull in the same volume. “Alibaba has also been skeptical about logistics in India,” adds Meena. And now starts the tug-of-war between the retailer and Paytm.

Both of them want the other to do deliveries. “At CLiQ, because they were all Tata products, it was the retailer who did the delivery because that’s how it is always done,” says the executive quoted earlier. But retailers don’t want to do deliveries. “It makes sense only if there is volume. Imagine a retailer making deliveries for one phone,” says a senior executive at a phone manufacturer who also has e-commerce experience. He asked not to be identified as he is not allowed to talk to the press.

In this model, there are two ways to make a purchase:

  • The customer makes an immediate purchase, scans the QR code and pays via wallet
  • The customer scans the QR code and buys from the retailer’s online store while standing at the shop
  • Here is where things get a little tricky. Let’s say the retailer sells phone Brand X. Now, Brand X’s corporate headquarters decides that this new series it has developed will retail at Rs 10,000 ($148.07). Typically, within this Rs 10,000 is where the retailer makes her margin.
  • This pays for the rent of the store, the salaries and the commission to a sales agent for making the sale. “All of this is done in really thin margins,” says Meena. Now, within this thin margin, she also has to make space for deliveries.


Thrice bitten, fourth time insight platform?

Having been at death’s doorstep numerous times, in early 2017, Bhise decided to raise venture capital to give the company “a cash flow cushion”. This would allow them to play for the long-term without being curtailed by whatever monthly revenue came in from current clients.

For the year that ended March 2017, Mobisy declared Rs 10.22 crore ($1.5 million) as revenue, up over 150% from a year ago. Its revenue this year is Rs 19.80 crore ($2.9 million), although it is yet to file it online. Bhise expects next year’s revenue to be in the “Rs 30-50 crore ($4.4-7.3 million) range”.

Expanding all over the world

Bizom has also slowly expanded outside India, with Nepal, Bangladesh, Tanzania and Nigeria now accounting for 10% of its users. It’s considering expanding into Vietnam, Indonesia, and the Philippines this year.

Mobisy has also launched Distiman, a separate product targeted at small retailers that allows them to take control of their inventory by being in direct touch with brands and distributors. In contrast, Bizom is targeted strictly at brands. With this Mobisy was trying to bridge both ends of the retail supply chain—brands and retailers. And in the process, turn the data residing in its systems into a “virtual distributor” that could match supply and demand faster than anyone else.

But it’s a crowded space. Dozens of venture-funded startups are chasing every available Kirana store, retailer or brand. Some of the well-known startups in this space are Jumbotail, Shotang, ShopX, and Udaan.

But VCs still wasn’t convinced about Mobisy’s two-product pitch.

“Why are you profitable? Why don’t you invest in growth? Don’t you know growth is more important than either business model or profits?” asked one VC.

Focusing on things

“You are schizophrenic,” said another. “How can you focus on both a SaaS-based model to brands and an e-commerce marketplace for retailers?”

But Bhise says he doesn’t see Bizom and Distiman as separate at all. “They are both part of our stack for insights,” he says.

A partner with a venture capital firm that had examined (and passed on) Mobisy’s funding pitch was still skeptical about its long-term potential.

“They are a vertical play, not a horizontal one. In my analysis, sales force automation has started plateauing. And big brands will always want to do their own analytics. So how will they scale? How many brands can they sell? Perhaps a maximum of 100. There just aren’t enough large retail brands around.,” he said, preferring anonymity.

“I was told Rs 40 lakhs was the limit for MRR (monthly recurring revenue) in India because that’s what Capillary had hit the limit domestically. We crossed that. Then I was told the limit was Rs 1 crore. Meanwhile, HUL gave a $600 million contract to Accenture to migrate its on-premise software to the cloud,” counters Bhise. (Update: Bhise says he was referring to the 2012-13 period when he first encountered these objections.

Making things clear

Furthermore, Capillary’s CEO Aneesh Reddy clarified that its current monthly run rate from India is close to Rs 8 crore. Both these points only validate Bhise’s point of view that VCs tend to underestimate the size of markets at times.)

He says his estimates point to an $800 million market in India for just automating “feet on the street” for consumer packaged goods brands. “India has 10 million retailers, 1 million distributors and 800,000 feet on the street.”

There’s also the threat of your best customers growing so fast that they desire bigger, flashier things. iD Fresh’s Musthafa says he wishes he could have all his company’s sales data within his SAP ERP system. Instead of having to import data from Bizom into SAP every day.

Bhise smilingly alludes to the “Mercedes syndrome” among SMEs. “It’s “Let’s use SAP because we can afford to”. But remember, iD Fresh still pays us more than they SAP.”

The damn journey to a billion dollar company is yet to happen

Reddy’s take away from the current situation is simple. It tallies with the venture capital fund manager I’d spoken to for context. “In India, funds need to have longer lives.” The corollary to this, he explains, is that funds shouldn’t be deploying a lot of the capital in primary cheques in the first two-three years. Instead, this should happen in years four, five and six.

“If you crunch all the investment in the first three years and you return 3X in the twelfth year, even then your IRR (internal rate of return) will be shitty. This is my biggest worry about Fund I. It is not whether I will deliver 3X. With every year that you postpone, 3X is not good enough. The bar only goes up,” he says, adding that IRR is a dangerous metric, to begin with.

Are there any other reasons?

But this isn’t the only reason funds need to have longer lives. Reddy points out that the bulk of the value is added in the second half of the fund, with the last few years usually seeing far more growth than the initial years. “God forbid you to have to sell prematurely just to meet exit requirements,” says Reddy, “That’s how I feel about Grey Orange Robotics (one of Blume’s bigger investments).

After all the struggle of putting together a global robotics company, I can’t look at my clock, saying it’s the eighth year, let’s the exit. The damn journey to a billion-dollar company is yet to happen. You might say, you have made enough but should I leave money on the table?” he asks.

Tick tock. Tick Tock. Tick tock.

Forget about how things should be. Let’s deal with what is. Blume is running against time. And even as all this is happening, the firm must raise capital for Fund III. In the context of Fund I’s performance, the sooner Blume gets to the first close, the better. The first close is when a fund is able to raise 40% of its target. In the next few months, Blume is hoping to raise $80 million from investors in India and outside the country. The firm is targeting a first close by September. This is easier said than done.

Not because there aren’t enough people willing to bet on India. Of course, there are; the India tech story is still alive. Blume’s problem is more peculiar. The last time it went out to raise money, the firm took 18 months to completely raise Fund II. That is, 18 months to raise $60 million. At the time, the firm was selling hope. It had cast its net wide. Spray and pray, if you will. There wasn’t any performance pressure. And still, it took 18 months. This was just 15 months ago. And now, the firm is out to raise again. This part of the story is called, always be raising.

Investments made

“Well I signed up for this, didn’t I?”

Blume’s Fund II was a peculiar fund. Out of the total $60 million, $20 million came from Indian investors. From its experience of selling to Indian investors in Fund I, Blume realized that the whole ordeal can be quite painful. Quite a few aren’t dependable, long-term partners. They are whimsical.

They like bargaining. Their strategy could be as dependable as, sorry, I can’t invest because I just bought a Bentley. So, Blume went looking outside. In this search, the firm raised $20 million from investors, which is called strategically. Simply put, these are foreign corporations who’d like access to deals in India but are afraid of getting their hands dirty.

Losing the products

Or worried about losing their shirt altogether. In Blume, these corporations found a willing strategic partner. Vice-versa. Blume’s other $20 million came from institutions. Pension funds or trusts like Harvard, etc., who are interested in allocating some part of their capital for an India risk exposure.

So far, so good. The problem, though, is that in the last three years, the strategic investors have wisened up.

For instance, Dream Incubator (DI), a consulting firm based out of Japan. DI invested in Blume’s Fund II because it wanted an understanding of the Indian market. It wanted to access it. In the last three years, DI has co-invested in six companies along with Blume.

All follow-on rounds. But they’ve come to understand the Indian market. They have a decent pipeline. Now, DI wants to play India independently. In January this year, DI said it would start a $44 million India-focused fund. DI isn’t alone. Recruit Holdings, another investor in Blume, is also starting out on its own.


Ola’s cab supply conundrum

The company, hoping to attract a wave of drivers to its platform, ran an ad featuring a cabby who credited Ola for an upswing in his fortunes. He narrates how he “turned his life around in 6 months” after leasing a vehicle through Ola’s leasing scheme.

Cab leasing scheme

Ola’s cab-leasing scheme, started that same year under the banner of Ola Fleet Technologies Pvt. Ltd, saw the company stockpile both new and used cars, leasing the same out to drivers for down payments as low as Rs 25,000 ($366). Following this, drivers get to drive around in their chosen vehicles for a daily fee. The objective was simple: increase the number of cabs on its platform to keep up with growing demand.

These upsurge in-cab numbers were something Ola realized was vital very early on. Demand for affordable cabs, in a country riddled with poor public transport infrastructure, rising fuel prices and a burgeoning population, was never going to be the hard part. Even with Uber and its deep pockets entering the fray. However, it would take more than affordability and pricing to win the cab wars. And Ola bet that supply would be the X-factor.

With attractive incentives and the leasing scheme to get them started, Ola managed to attract several youngsters and middle-aged Indians struggling to find stable jobs to come onboard as driver-partners. Traditional taxi and fleet operators with 10-50 cars also started listing on Ola, and they too were interested in leasing cars under the new scheme. For its part, Ola set aside Rs 5,000 crore ($730 million) for its cab-leasing operation when it first launched.

The success of Ola’s strategy is there for all to see—the company has managed to scale from 250,000 vehicles in 2015, to around 900,000 vehicles today.

Great earnings for driver-partners at a nominal entry fee plus huge strategic gains for Ola, it sounds like an outcome that’s too good to be true. Because it is. Behind the big numbers of Ola’s cab fleet is a ticking time bomb.

Three years after Ola launched its leasing unit, the hordes of people who flocked to Ola’s platform, either on a part-time basis or as full-time driver-partners, are an unhappy lot. According to several Ola fleet owners and individual drivers, The Ken spoke to, revenues have reduced drastically over the years. With driver incentives plummeting by at least 50% since Ola’s launch and fuel prices continuing to increase, both individual drivers and fleet owners are left counting the cost.

Roadhouse blues

For drivers, the idea of leasing from cab aggregators made sense when incentives were astronomical. You see, back in 2016, when the leasing program of Ola was kicking into high gear, incentives made up more than half of a driver’s salary.

Sometimes, daily incentives could even be double that of the day’s fares. But over the years, both Ola and Uber have lowered incentives substantially. According to a report by consulting firm RedSeer, the share of incentives as a percentage of gross earnings dropped to 16% in the March quarter of 2017 from as much as 60% in the same period last year.

This has been a huge blow to drivers looking to pay their daily, weekly, or monthly rentals to the cab companies. To make matters worse, drivers who leased from Ola also found that the company offered them markedly lower incentives as compared to those on the platform with their own vehicles. And as the number of drivers on the platforms went up, the fewer rides there were to go around.

Drivers soon realized that they were working as hard for less money, or much harder just to keep their earnings stable. The glory days were over. Disillusionment came creeping in.

This is a far cry from what drivers had been sold. And the repercussions were not far behind.


A Bengaluru-based fleet operator who listed 45 cabs on Ola along with his partner, delisted all their vehicles early in 2017 after the earnings per cab fell below Rs 15,000 ($219) a month (after commissions). According to the operator, anything below Rs 15,000 is not feasible since he has to pay a variable salary to the drivers of those 45 cabs.



Starting by taking the baby steps

Ultimately for Swiggy, though, it will need to find a model that not only draws customers in but is also sustainable across multiple categories. And this is the real challenge. It is faced with two options. Swiggy must choose between simply aggregating local supermarkets and wine stores, or creating a retail model by maintaining its own inventory and supply chain.

A source who was briefed on Swiggy’s grocery bet says that the company will start with aggregating superstores for the supply of groceries. This, though, will remain at an experimental stage as Swiggy is venturing into the segment with trepidation.

On-Demand Grocery Startup

Swiggy’s refusal to go all-in is based on the experiences of businesses such as on-demand grocery startup PepperTap. PepperTap raised some $50 million but shut down within just two years of operations in April 2016. It followed an inventory-less, hyperlocal delivery model. Similarly, Ola burnt money by running a grocery delivery pilot which was shuttered a year after it began. Flipkart very recently relaunched grocery delivery after its initial pilot—Flipkart Nearby—failed to take off.

An investment banker, who requested anonymity, explained this play. In a two-kilometer radius (within any metro city), there could be at least four supermarkets which essentially stock nearly every grocery category, he says. These already have an established base of walk-in customers, but their delivery operations are either non-existent or inefficient. He believes Swiggy can help these supermarkets increase their sales.

However, unlike in the restaurant business where margins are high and Swiggy can charge a 20-25% commission, margins in grocery are low. Accordingly, Swiggy will have to take a hit on commissions. For the venture to remain viable, says the investment banker, Swiggy will need to charge a commission of at least 5-10%. Supermarkets are unlikely to agree to higher commissions than this, he believes.

Swiggy will face similar issues with a hyperlocal alcohol delivery play as well. Alcohol retailers will not part with 20-25% of their earnings since they get their stock from multiple vendors themselves. Additionally, there is also the cost of obtaining and renewing an excise license. When asked if it will indeed employ an aggregation-based model for a groceries and alcohol delivery service, Swiggy chose to skirt the question rather than deny it.

In the short term, Swiggy can eat these losses. But as it gains greater insight into purchase behavior and demand hotspots, etc., Swiggy will have to move to an inventory-led model in order to increase profitability.

Inevitability of inventory

An inventory-led model, as Swiggy has realized with its private labels like Homely, allows Swiggy to earn higher margins. Similarly, as it looks for improved profitability in the consumables delivery space, maintaining its own inventory of products will help Swiggy maximize profitability.

This wisdom is borne out by the experiences of grocery delivery startups Grofers and BigBasket. Grofers, which began in 2013, initially followed a hyperlocal aggregator model—partnering with local stores to source fresh products and ensure speedy delivery. By 2016, in the face of mounting losses, it pivoted to an inventory-led model where it did its own sourcing; stocking inventory in its own warehouses. BigBasket, which began in 2011 and also started in the same vein as Grofers, was already pivoting to an inventory-led model by the time Grofers was still in its infancy. BigBasket currently also offers a hyperlocal model for its express service.

With learnings and insights from its initial aggregation-based grocery and alcohol delivery, Swiggy will be hoping it, too, can plan and execute an inventory-led approach. In this regard, Swiggy will be looking to replicate Amazon’s evolution in the grocery space.

“If you look at Amazon Now, it was earlier doing grocery delivery by aggregating multiple stores such as Big Bazaar and SPAR. Once they gained insights into how grocery ordering works, they have started opening up their own dark stores,” said the person quoted earlier who is aware of Swiggy’s operations.

Swiggy is already making moves to this end, especially when it comes to alcohol delivery. Swiggy is in discussions with alcoholic beverage manufacturer Diageo India for a direct retail partnership, according to a source aware of the development, who requested anonymity. Diageo India owns and produces liquor brands such as Jura, Dalmore, Whyte & Mackay, Black Dog, etc. When The Ken enquired about this, Diageo refused to comment while Swiggy was unwilling to either confirm or deny a potential partnership.


What Is The Tower Of Babel?

Things aren’t, of course, quite so straightforward. The massive datasets required to train Natural Language Processing (NLP) engines—which help voice-driven interfaces execute search queries with greater accuracy—don’t exist in regional languages.

According to the Annual Digital Indian Language Report published by Reverie, an Indian technology firm enabling Indian languages across various devices, regional language content formed a minuscule 0.1% of the total online content as of 2017. As a result, there aren’t enough web pages for the NLP software to crawl through and extract voice output from.

Agentive Design Tech

An artificial intelligence method named ‘Agentive Design Technology’ used in assistants like Siri and Alexa allows voice assistants to learn user habits and behavior to make better suggestions to users

And the problem doesn’t end with maintaining the quality of the response. How the response is delivered by the app, could also pose problems. This is something that Gopal, Reverie’s voice assistant, is facing. Reverie found that just using voice didn’t make for a smooth end-to-end process. The voice assistant also had to understand the assortment, quantity, and product categories.

Take, for instance, buying apples. A simple query like Mujhe seb khareedne hai (order me apples) wouldn’t be enough for Gopal to place an order.

Even if it gets the quantity right by asking Kitna? (how much?), it will still have to choose from a list of apple varieties and price ranges. It might start to list apple varieties of every price. That would considerably slow down the buying process, possibly leaving the user confused as well. It would also require work to get Gopal to identify good deals when a consumer asks for them. “Just quoting the cheapest item won’t do the job,” adds Vivekananda Pani, co-founder, and CEO of Reverie.

To address this challenge, Slang Labs and Reverie are training their engines to identify words or phrases rather than translating entire sentences.

For instance, “order me apples” can be broken down into different groups. “Order” is an action category, “apples”, too, can be classified as a category, and, similarly, there can be other groups such as price range, color, etc. To reduce transaction time further, Reverie sees a combination of voice, text and visual components as the optimal approach, says Pani.

The focus of their efforts—both for Reverie’s Gopal and Slang Labs—has also been narrowed as a result. Both have zeroed in on very specific use cases or internet segments to double down on. “Segments that we are looking at have been (online) travel, fashion, and e-commerce segments, or essentially where there is a transaction to be done,” says Rangarajan.

Taking an example of a service like Swiggy, he points out that Swiggy’s “proprietary data” such as restaurant data, cuisines, dishes and preferences can act as data points to train the NLP engine. “We are coming in as a tech layer by providing all the pieces that are required to recognize a user’s voice for search and discovery in an e-commerce app,” Rangarajan adds.

Walking the talk

Yes, the use cases of vernacular voice tech are not as expansive as one might have hoped. Sarath Naru, managing partner, Ventureast Capital, however, believes their limited focus is the way to go. Ventureast is an investor in Indus OS, a mobile software company that develops operating systems in regional languages, and had backed Rangarajan’s earlier venture Little Eye Labs.

“When it comes to voice assistants in India, I don’t think only one company or stakeholder can make it big. There are plenty of applications to crack, and one single startup or product won’t be able to provide everything. I believe the Indian developers will build [voice recognition] around niche internet use cases, while the big shots like Google and Alexa will build a broader voice assistant for browsing content online,” he says.




Foodtech and the delivery personnel dilemma

Roshan is a college student, with little to no disposable income and no prior job experience. He signed up with UberEats when he was unable to find an internship during his college break. He felt like he’d struck gold.

The job afforded him the chance to make more money than a lot of entry and mid-level degree-holding job seekers—up to Rs 50,000 ($690) a month.

Roshan even gets to pick his own work hours. As a result, even though his classes have started again, he can still do deliveries during his free time. With this alone, he’s able to net a cool Rs 8,000-10,000 ($110-$138) each month.

But beyond the money, Roshan realizes that things don’t quite add up. “Last night, I did a delivery of a Rs 105 ($1.50) meal, but I made Rs 95 ($1.3) on that delivery. I don’t understand what business Uber is doing for Rs 10 ($0.14)?

Another time, the food price was just Rs 70 ($1), but I was paid Rs 125 ($1.72) for that delivery,” he recounts, still trying to wrap his head around the economics. Is this sustainable, he asked. A pertinent question indeed.

As the various players in the food delivery space look to keep up with growing demand, delivery personnel have become a precious commodity. According to an employee of food delivery startup Swiggy, the company, which has already tripled its fleet size to 60,000 since the start of this year, intends to double its current strength in the next six months.

Hiring individuals and handling them

Similarly, Foodpanda, resurrected since Ola’s buyout late last year, plans to hire 50,000 delivery personnel in the same time period, said a Foodpanda employee. Both spoke on condition of anonymity as they are not allowed to publicly discuss company strategy.

But shoring up their delivery fleets is one thing, maintaining them is another. Attrition is high. Loyalty is low. These jobs are seen as stop-gaps or looked down on altogether.

With little else to keep their delivery fleets loyal, companies have begun throwing cash at them in the hopes of reducing the churn. Roshan is one of around 185,000 delivery personnel—from college students and graduates to dropouts from other jobs—who are reaping the benefits of this. Their earnings have soared to levels scarcely imaginable a year ago.

For now, companies are meeting delivery fleet targets, and delivery personnel is a happy lot. But the gravy train is bound to grind to a halt as these salaries simply aren’t sustainable. So, what then for delivery companies? How do they grow their delivery fleets and still manage to stay sustainable?

History repeats

This isn’t the first instance of a gig economy bubble. We saw a similar situation when online cab aggregators Ola and Uber went head-to-head for supremacy, just two years ago. Both lured drive partners in with promises of high earnings coupled with lucrative incentives. It worked like a charm. Drivers flocked and were kept loyal by the incentive programs offered by each company.

But once the incentives were scaled back, loyalty became a thing of the past. At present, according to an ex-Ola employee The Ken spoke to, only about 15-20% of drivers on Ola are exclusive to the platform. The vast majority, he said, was registered with rival cab aggregators as well. Hyperlocal food delivery platforms now risk going down the same path.

This is an intrinsic trait of the gig economy—it changes the underlying relationship between an employee and employer. Riders on platforms such as Swiggy and UberEats currently choose their own work timings. They can log in and out as they wish. And they alone decide where their loyalties lie.


I found the car I wanted to buy on CarWale

This incident dates back to sometime in mid-2017. The car buyer, who requested not to be named because he doesn’t want to be involved in any mess, was looking for a good used car.

After searching for a while across listing companies such as CarTrade, Olx, Quikr, and many others, he found and liked a car on CarWale. In Mumbai. Next, he gets the dealer’s number and calls him to set up a test drive. The car in question is a 2014 Volkswagen Jetta.

As with all such transactions, the buyer did a test drive, liked what was on offer, and started negotiating with the dealer for the best price. The listing was for Rs 15,50,000 ($21,350). After much haggling, both parties settled on a price of Rs 14,50,000 ($19,970).

“At this point, the dealer asked me to make a token payment,” says the buyer. “So I went to his shop and made the payment by cheque. The dealer then offered to arrange a used car loan for me through HDFC Bank at a very good interest rate, to which I agreed. After the loan formalities and approval, payment of the loan amount was made by the bank to the dealer in a few days. Soon after, I went and picked up the car.”

Clear so far? Now, let the fun begin

The buyer doesn’t exactly remember when it happened, whether at the stage of the token payment or when he went to pick up the car, but sometime around then, the dealer told him to expect a call from Droom.

Needless to say, the buyer was suspicious. Droom wasn’t even in the picture. On the contrary, he’d found the car on CarWale. “I remember that I then went to Droom and found the car listed there by the same dealer,” says the buyer.

“Anyway, the dealer asked me to say something to the Droom caller, but I can’t recall what exactly he asked me to say. I think he asked me to tell the Droom caller that the sale is finalized, but the payment hasn’t been made yet, or that the sale is final and payment has been made. One thing is for sure—the dealer was trying to prove to Droom that this sale happened through Droom.”

Except, it didn’t. Droom had nothing to do with this transaction. No value-add whatsoever. But because the dealer, lured by the company’s incentive, wanted to make a cool buck, he had no qualms in showing that the transaction happened in Droom. In return, Droom got GMV worth Rs 14,50,000. In the world of e-commerce and for people who snort GMV, no matter how ridiculous the metric is, Rs 14,50,000 is a lot. In fashion e-commerce terms, that’s a 1,000 dresses worth.

On 14 September, Ken sent a detailed questionnaire to Droom.

Response to the email

In a detailed emailed response, Sandeep Aggarwal replied to all questions, running in at over 2100 words. In no place does Aggarwal categorically deny that any of the incidents or practices cited by The Ken took place, but he makes a few key points.

That, firstly, the “lifetime value” of a car owner or two-wheeler owner in India is $42,000 and $3000 respectively. Customer lifetime value refers to the sum total of all future profits a business can earn over the lifetime of its relationship with a customer.

Thus, “acquiring these buyers and owning them for a lifetime can create significant, sustainable long-term value for Droom and its shareholders,” he wrote. Aggarwal provided no source or corroboration for these figures.

Other considerable facts

Secondly, Aggarwal says that when Droom “acquires” buyers and sellers through offline efforts, there is a “very high likelihood that they would subsequently buy and sell their automobiles on Droom or other services.” Unfortunately, the history of e-commerce around the world and in India is littered with the corpses of VC-funded firms that tried to do just that – offer discounts to buy lifetime value. What they got instead were opportunistic bargain hunters.

Lastly, in the case of this car buyer’s experience, Aggarwal said that there is no way a buyer could say that he did not know about Droom for the transaction.

“The only thing mathematically then is possible is that when two conditions have to be met,” he said. “1) dealer has not told the buyer about Droom and 2) dealer has used his credit card, debit card or net banking to make payment for the 3% token amount.”

For Alphabet’s CapitalG, offline lending is back in vogue

It’s little surprise that CapitalG, the venture capital fund of search giant Google’s parent company Alphabet Inc, makes tech bets. The late-stage growth VC’s first investment in India was in Freshworks in 2015, a company born in the cloud. Then it invested in Practo, an online healthcare platform, CommonFloor, a real estate platform. And then car portal CarDekho. Companies that believed in an online model for the lifecycle of their product or service.

First Investment

And then it made a puzzling call with its first fintech investment in 2018 in India.

It was in a little-known lending company—Aye Finance. Aye is a non-banking financial company that believes in the age-old practice of setting up brick and mortar branches. Aye takes 14 days to disburse loans and does not rely on tech for acquiring users. All this, even as its fintech lending brethren reach out to users, underwrite, disburse, and collect loans online.

For a cutting-edge digital VC with a focus on tech-first businesses—all 34 of its investments globally are tech or tech-enabled—the Aye investment is as counter-intuitive as it gets.

This is the fintech world’s equivalent of e-commerce giant Amazon buying grocery retailer Whole Foods. First, it puzzled people. But it was soon followed by a light bulb moment for the rest of the industry. The idea of on an online retailer buying an offline retailer suddenly seemed like the most natural thing to happen.

Digital Lending

Fintech lending in India has thus far been mostly about digital lending. They are expected to disburse about $2 billion this year, a mere drop in the credit ocean. But companies come up with new business models as if they were creating a Subway sandwich.

Pick the base—a target segment, then choose the loan ticket size, the next layer is the tenure of loans and type of lending product like a term loan, merchant cash advance, payday loans and the like, then choose whether you like your loans secured or unsecured. The sheer variety of what you can build using these ingredients is endless. And given the insatiable hunger for credit—most pronounced among micro-enterprises—you also have the perfect coming together of product and market.

These conditions have seen the mushrooming of over 300 fintechs, which have raised $1 billion in the last five years, according to Tracxn, a startup database monitor. But not all of these lending companies are healthy. Many fintech lenders have built online models that focus on acquiring users digitally, as well as disbursing and collecting loans digitally. This tech-led model has surged in popularity thanks to its scope for enormous scale and pace of growth.

But a consensus is slowly forming that tech-driven pace of growth is overrated. Instead, the quality of credit is the holy grail in fintech lending. Zouk Loans, for instance, which lent to SMEs, shut shop in 2016. Zouk’s CEO Ash Narain lamented to Tech Circle that the credit ecosystem in India is mostly offline, and isn’t given to automation in its current form.

Which brings us back to CapitalG

Though they have been in India since 2014, CapitalG stayed away from the fintech space until three years later. Finally, in 2017, even as the dust of demonetization—when 86% of India’s cash was banned by the government in November 2016—was settling, CapitalG turned its attention to the fintech sector.

It was a good time to survey the scene, as the VC firm had 9-12 months of data on lending firms’ portfolios post-demonetization. According to analysts, bad loans for some financial institutions reached highs of 30% among the SME and MSME sector. Lenders big and small, offline and online saw stress in some sectors like textile manufacturing, dairy farming, all of which were affected by the non-availability of cash.

CapitalG ran the rule over at least 30 lending fintechs and was able to assess how well companies withstood the ravages of demonetization. Finally, after nine months of searching, it led a $21.5 million series-C round and cut a cheque of about $10 million to four-year-old Aye Finance.

“We looked at Aye post-demonetization. We could see how their business model could withstand shocks, and the return on equity they could get in a difficult period. That helped us gain confidence in their business model,” said Kaushik Anand, India head of CapitalG.