About 10 days back I decided that the number of covid-19 positive cases in Bangalore was high enough to recalibrate my risk levels. So I decided I’m not going to go to “indoor shops” (where you have to step inside the shop) any more.
Instead, as much as possible I would buy from “over the counter” shops (where you don’t have to step inside). This way, I would avoid being indoors, and as long as I’m outdoors (and wearing a mask) when I’m out of homeI should be reasonably safe.
However, over the years we have come to need a lot of things that at least in an Indian context can be classified as “long tail”. Over the last three months I’ve been buying them from the large format Namdhari store close to home. Now, that’s a large airconditioned shop which my new risk levels don’t allow me to go to. So I decided to order from their website.
Now, Namdhari is a classic “omnichannel retail” (the phrase was told to me by one of the guys who helped set it up). There is no warehouse – all customer orders are fulfilled from stores. You could think of it like calling your local shop and asking for delivery.
As you can imagine, this can lead to insane inventory issues, especially for a shop like Namdhari’s that specialises in long tail stuff. It is pretty impossible for a store to reconcile how much stock is there in the store with the website (even with perfect technology, you’ll miss out on what is there in people’s (physical) charts).
There is also the issue of prioritisation of customers that they are kept in the dark about. If the shop has a limited inventory of any item (and with long tail stuff, even a small spike in demand can make inventory very limited), how does it allocate it between people who have trudged all the way to the store and those who have prepaid for it on the website?
I wasn’t that surprised, I guess, when half the items that I had ordered failed to arrive. The delivery guy told me that the rest of my money would get refunded.
I wondered why they wouldn’t try to fulfil my order the next day instead. This brings me to my next grouse – there is no real reasons sometimes to provide same day delivery. If you offer next day delivery then you know tomorrow delivery volumes beforehand, and it will be easy for you to stock up. These guys had this process, it seems, where you have to order for the same day and if the thing runs out you don’t get it at all.
In any case, three days after my half-fulfilled order had been delivered I got a mail that refund had been initiated for the items I had ordered but hadn’t arrived.
It was like writing a cheque. Cheques are inefficient because between the time it is written and encashed, neither the giver nor the receiver has access to the funds (online transfer such as IMPS, on the other hand, ensures that the money is in either the giver or receiver’s account at all points in time).
So my order which had been partially fulfilled was in a similar trishanku state – I didn’t know if it would arrive or if I should order the same items from elsewhere. In case I waited I would have the risk of getting the stuff even later (since I’d delay order from elsewhere).
It was only after it failed to arrive on Wednesday (and I got the mail) that I was able to place an order from elsewhere. Hopefully this one won’t get into trishanku state as well.
I must warn that this is a super long post. Also I wonder if I should put this on medium in order to get more footage.
Most models of disease spread use what is known as a “SIR” framework. This Numberphile video gives a good primer into this framework.
The problem with the framework is that it’s too simplistic. It depends primarily on one parameter “R0”, which is the average number of people that each infected patient infects. When R0 is high, each patient infects a number of other people, and the disease spreads fast. With a low R0, the disease spreads slow. It was the SIR model that was used to produce all those “flatten the curve” pictures that we were bombarded with a week or two back.
There is a second parameter as well – the recovery or removal rate. Some diseases are so lethal that they have a high removal rate (eg. Ebola), and this puts a natural limit on how much the disease can spread, since infected people die before they can infect too many people.
In any case, such modelling is great for academic studies, and post-facto analyses where R0 can be estimated. As we are currently in the middle of an epidemic, this kind of simplistic modelling can’t take us far. Nobody has a clue yet on what the R0 for covid-19 is. Nobody knows what proportion of total cases are asymptomatic. Nobody knows the mortality rate.
And things are changing well-at-a-faster-rate. Governments are imposing distancing of various forms. First offices were shut down. Then shops were shut down. Now everything is shut down, and many of us have been asked to step out “only to get necessities”. And in such dynamic and fast-changing environments, a simplistic model such as the SIR can only take us so far, and uncertainty in estimating R0 means it can be pretty much useless as well.
In this context, I thought I’ll simulate a few real-life situations, and try to model the spread of the disease in these situations. This can give us an insight into what kind of services are more dangerous than others, and how we could potentially “get back to life” after going through an initial period of lockdown.
The basic assumption I’ve made is that the longer you spend with an infected person, the greater the chance of getting infected yourself. This is not an unreasonable assumption because the spread happens through activities such as sneezing, touching, inadvertently dropping droplets of your saliva on to the other person, and so on, each of which is more likely the longer the time you spend with someone.
Some basic modelling revealed that this can be modelled as a sort of negative exponential curve that looks like this.
T is the number of hours you spend with the other person. is a parameter of transmission – the higher it is, the more likely the disease with transmit (holding the amount of time spent together constant).
The function looks like this:
We have no clue what is, but I’ll make an educated guess based on some limited data I’ve seen. I’ll take a conservative estimate and say that if an uninfected person spends 24 hours with an infected person, the former has a 50% chance of getting the disease from the latter.
This gives the value of to be 0.02888 per hour. We will now use this to model various scenarios.
Delivery
This is the simplest model I built. There is one shop, and N customers. Customers come one at a time and spend a fixed amount of time (1 or 2 or 5 minutes) at the shop, which has one shopkeeper. Initially, a proportion of the population is infected, and we assume that the shopkeeper is uninfected.
And then we model the transmission – based on our , for a two minute interaction, the probability of transmission is %.
In hindsight, I realised that this kind of a set up better describes “delivery” than a shop. With a 0.1% probability the delivery person gets infected from an infected customer during a delivery. With the same probability an infected delivery person infects a customer. The only way the disease can spread through this “shop” is for the shopkeeper / delivery person to be uninfected.
How does it play out? I simulated 10000 paths where one guy delivers to 1000 homes (maybe over the course of a week? that doesn’t matter as long as the overall infected rate in the population otherwise is constant), and spends exactly two minutes at each delivery, which is made to a single person. Let’s take a few cases, with different base cases of incidence of the disease – 0.1%, 0.2%, 0.5%, 1%, 2%, 5%, 10%, 20% and 50%.
The number of NEW people infected in each case is graphed here (we don’t care how many got the disease otherwise. We’re modelling how many got it from our “shop”). The right side graph excludes the case of zero new infections, just to show you the scale of the problem.
Notice this – even when 50% of the population is infected, as long as the shopkeeper or delivery person is not initially infected, the chances of additional infections through 2-minute delivery are MINUSCULE. A strong case for policy-makers to enable delivery of all kinds, essential or inessential.
2. SHOP
Now, let’s complicate matters a little bit. Instead of a delivery person going to each home, let’s assume a shop. Multiple people can be in the shop at the same time, and there can be more than one shopkeeper.
Let’s use the assumptions of standard queueing theory, and assume that the inter-arrival time for customers is guided by an Exponential distribution, and the time they spend in the shop is also guided by an Exponential distribution.
At the time when customers are in the shop, any infected customer (or shopkeeper) inside can infect any other customer or shopkeeper. So if you spend 2 minutes in a shop where there is 1 infected person, our calculation above tells us that you have a 0.1% chance of being infected yourself. If there are 10 infected people in the shop and you spend 2 minutes there, this is akin to spending 20 minutes with one infected person, and you have a 1% chance of getting infected.
Let’s consider two or three scenarios here. First is the “normal” case where one customer arrives every 5 minutes, and each customer spends 10 minutes in the shop (note that the shop can “serve” multiple customers simultaneously, so the queue doesn’t blow up here). Again let’s take a total of 1000 customers (assume a 24/7 open shop), and one shopkeeper.
Notice that there is significant transmission of infection here, even though we started with 5% of the population being infected. On average, another 3% of the population gets infected! Open supermarkets with usual crowd can result in significant transmission.
Does keeping the shop open with some sort of social distancing (let’s see only one-fourth as many people arrive) work? So people arrive with an average gap of 20 minutes, and still spend 10 minutes in the shop. There are still 10 shopkeepers. What does it look like when we start with 5% of the people being infected?
The graph is pretty much identical so I’m not bothering to put that here!
3. Office
This scenario simulates for N people who are working together for a certain number of hours. We assume that exactly one person is infected at the beginning of the meeting. We also assume that once a person is infected, she can start infecting others in the very next minute (with our transmission probability).
How does the infection grow in this case? This is an easier simulation than the earlier one so we can run 10000 Monte Carlo paths. Let’s say we have a “meeting” with 40 people (could just be 40 people working in a small room) which lasts 4 hours. If we start with one infected person, this is how the number of infected grows over the 4 hours.
The spread is massive! When you have a large bunch of people in a small closed space over a significant period of time, the infection spreads rapidly among them. Even if you take a 10 person meeting over an hour, one infected person at the start can result in an average of 0.3 other people being infected by the end of the meeting.
10 persons meeting over 8 hours (a small office) with one initially infected means 3.5 others (on average) being infected by the end of the day.
Offices are dangerous places for the infection to spread. Even after the lockdown is lifted, some sort of work from home regulations need to be in place until the infection has been fully brought under control.
4. Conferences
This is another form of “meeting”, except that at each point in time, people don’t engage with the whole room, but only a handful of others. These groups form at random, changing every minute, and infection can spread only within a particular group.
Let’s take a 100 person conference with 1 initially infected person. Let’s assume it lasts 8 hours. Depending upon how many people come together at a time, the spread of the infection rapidly changes, as can be seen in the graph below.
If people talk two at a time, there’s a 63% probability that the infection doesn’t spread at all. If they talk 5 at a time, this probability is cut by half. And if people congregate 10 at a time, there’s only a 11% chance that by the end of the day the infection HASN’T propagated!
One takeaway from this is that even once offices start functioning, they need to impose social distancing measures (until the virus has been completely wiped out). All large-ish meetings by video conference. A certain proportion of workers working from home by rotation.
Last night we decided we wanted pizza from dominos for dinner. Having been used to Swiggy, I instinctively googled for dominos and tried to place the order online.
There is one major fuckup with the dominos website – it asks you to pick the retail outlet closest to you, rather than taking your location and picking it yourself. And so it happened that we picked an outlet not closest to us.
I quickly got a call from the guy at the outlet where my order had gone, expressing his inability to deliver it, and saying he’ll cancel my order. I gave him a mouthful – it’s 2016, and why couldn’t he have simply transferred the order to the outlet that is supposed to service me?
I was considering cancelling the order and not ordering again (a self-injurious move, since we wanted Dominos pizza, not just pizza), when the guy from the outlet in whose coverage area I fell called. He explained the situation once again, saying my original order was to be cancelled, and he would have to take a new order.
Again – it wasn’t just a fuckup in the payment in the Dominos system, in which case they could’ve simply transferred my order to this new guy. So I had to repeat my entire order once again to this guy (not so much of a problem since I was only getting one pizza) and my address as well (it’s a long address which I prefer filling online).
Then there was the small matter of payment – one reason I’d ordered online was that I could pay electronically (I used PayTM). When I asked him if I could pay online for the new order he said I had to repeat the entire process of online ordering – there was no order ID against which I could simply logon and pay.
I played my trump card at this time – asked him to make sure the delivery guy had change for Rs. 2000 (I’d lined up at a bank 2 weeks back and withdrawn a month’s worth of cash, only that it was all in Rs. 2000 notes). He instantly agreed. Half an hour later, the pizza, along with change for Rs. 2000 was at my door.
The good thing about the experience was that the delivery process was smooth, and more importantly, the outlet where my order reached had taken initiative in communicating it to the outlet under whose coverage my house fell – the salespersons weren’t willing to take a chance to miss a sale that had fallen at their door.
The bad thing is that Jubilant Foodworks’ technology sucks, big time. Thanks to the heavily funded and highly unprofitable startups we usually order from, we’re used to a high level of technology from the food delivery kind of businesses. Given that Jubilant is a highly profitable company it shouldn’t be too hard for them to license the software of one of these new so-called “foodtech” companies to further enhance the experience.
No clue why they haven’t done it yet!
PS: I realise I’ve written this blogpost in the style I used to write in over a decade ago. Some habits die hard.
As a rule, Indian fathers are not terribly close to their kids (my father was a major exception to this rule), and I lay the blame on a “traditional practice” in Indian families.
This is the concept of “baaNantana” (don’t know words in other Indian languages) where the woman goes to her parents’ house for childbirth, and stays there till the child is a few months old, before returning to her own house. And this contributes to several reasons which contribute to distance between fathers and children.
For starters, the woman’s house and her parents’ house may not be in the same city or region, putting a physical distance between the father and the baby. Thus, for the first few months of the baby, there is little contact between them, and when the baby finally goes to live with its father, he is already a distant figure. And unless the father makes special efforts to bond with his child, this distance is only bound to grow.
Secondly, in India, childbirth and associated activities are generally seen as a primarily female pursuit. It is the mother’s parents (primarily mother’s mother) who accompany her to the hospital, and be there with her until childbirth. The father generally only makes a guest appearance where he appears, carries the baby for a bit, hands it back and disappears.
And then every subsequent activity of the mother is directed by her own female relatives, and the father has little to do in the process. Even if he is physically proximate to the baby (by virtue of living not too far from his in-laws), the “culture” of baby-related activities being female pursuits means that he is not a primary actor any more, and he generally prefers to hand over the baby to a “female elder” when it cries, rather than to learn to pacify it himself.
Given this background, I’m really impressed with the efforts of CloudNine, the hospital where my daughter was born, in involving the father in the delivery process and beyond. For starters, the hospital insists that the father be present at the time of delivery, and cut the baby’s cord. While this was always known, what I was pleasantly surprised was the process afterwards.
A couple of hours after my wife and daughter came to their room, a nurse materialised, offering to teach her how to breastfeed. I readied myself to be sent out for the process, but there was no such attempt. In fact, the nurse seemed encouraging of me watching on – the hospital has perhaps realised (maybe belatedly in the Indian context) that the wife’s boobs are unlikely to be a novelty to a man, and so there is absolutely no reason to send him out!
On the other hand, the joy in watching your child feed directly from your wife is totally unmitigated!
Then later in the evening on Thursday, another nurse materialised, to take my wife for bath. That time, both my motherinlaw and I were there in the room with her. The nurse presently put my motherinlaw in charge of looking after the baby, and asked me to accompany her to help give my wife a bath. When my motherinlaw gestured that she could help out with the bath, the nurse firmly said that she wants me to come.
Apart from the hospital’s efforts I’ve been doing my own efforts to make sure I bond with the baby. Rather than sending off my wife to her parents’ place for baaNantana, I’ve instead convinced them to come live with us for a month, to help us deal with the new baby. I’ve learnt to carry the baby in different ways and change diapers, and I’m trying to learn to calm the baby when she cries (lack of boobs is a big impediment in this process).
And I’ve found that the more involved I am with the baby, the more responsible I feel in taking care of her and looking after her. The more I’m sent to “do my thing” while others take care of the baby, the more I feel like handing her off to someone else when she cries, rather than pacifying her myself!
Thinking back, perhaps one reason my father was able to bond with me was that he lived fairly close to my maternal grandfather’s place when I was born, and even though my mother was away on “baaNantana”, he made sure to come see us for a few hours every day, and carry me. Hopefully I can propagate this process with my daughter!
Readers of this blog might be aware that I’m not a big fan of hyperlocal grocery delivery firm Grofers’s business model. The problem is that there are no costs saved to make Grofers its margin – apart from the retail inventory expense incurred at the retailer (from whom Grofers procures), there is also the last mile delivery expense that is incurred which doesn’t leave much profits.
The reason for Grofers scaling back from nine cities in India, however, is not related to this. It is more to do with market size and scale.
Given the uncertainties in terms of demand and service times, a business such as Grofers makes sense only when there is a minimum critical mass in terms of demand. Serving a locality with only one delivery person doesn’t make sense, for example, since uncertainty in demand will mean that either that delivery person is underworked or service levels cannot be guaranteed.
If the average demand in an area can support more delivery persons, though, this can smoothen out the uncertainty (that aggregation smoothens uncertainty is one of the fundamental principles of operations) and higher service levels can be guaranteed without building in too much slack.
While the cities that Grofers has pulled back from are not small (Mysore/Vizag/Coimbatore etc) it is unlikely that any of them would have had the size and density of demand in order to support a scale of operations which would make sense for Grofers. There are several reasons for this.
Firstly, Grofers only captures the incremental demand for grocery delivery, and with most small retailers already offering grocery delivery, the value Grofers adds is to deliver from large retailers. While I don’t have data to support this, my hypothesis is that large retailers have a smaller share in small cities thus cutting Grofers’s natural market.
Next, the transaction cost of travelling to the store is lesser in smaller cities, given shorter travel times (on account of both size and traffic), further cutting demand for on-demand delivery. Thirdly, while smartphones are widespread across the country, my hypothesis (again don’t have data to support this) is that usage is lower in smaller cities (compared to larger cities). Fourthly, smaller cities are likely to be less dense than larger cities (data on this should be available but NED to compile it now) meaning delivery personnel have to cover larger areas.
Some thinking can lead to more such reasons, but the basic point is that not only are these cities small, but demand for on-demand hyperlocal grocery delivery is also much lower (on a per capita basis) than in larger cities for several reasons.
These two factors have together meant that the scale (and density) of demand that is necessary for Grofers to be viable as a business was simply not there in these cities. So it’s a logical move for them to pull out.
This doesn’t answer, however, the question of why Grofers entered these cities in the first place, since the above factors should’ve been apparent before the entry. My hypothesis here is that some fast-growing startups measure their growth in terms of the number of cities they’re in. I’ll elaborate on that on another day.
Delivery aggregators are moving customer data away from the retailer, who now has less knowledge about his customer.
Ever since data collection and analysis became cheap (with cloud-based on-demand web servers and MapReduce), there have been attempts to collect as much data as possible and use it to do better business. I must admit to being part of this racket, too, as I try to convince potential clients to hire me so that I can tell them what to do with their data and how.
And one of the more popular areas where people have been trying to use data is in getting to “know their customer”. This is not a particularly new exercise – supermarkets, for example, have been offering loyalty cards so that they can correlate purchases across visits and get to know you better (as part of a consulting assignment, I once sat with my clients looking at a few supermarket bills. It was incredible how much we humans could infer about the customers by looking at those bills).
The recent tradition (after it has become possible to analyse large amounts of data) is to capture “loyalties” across several stores or brands, so that affinities can be tracked across them and customer can be understood better. Given data privacy issues, this has typically been done by third party agents, who then sell back the insights to the companies whose data they collect. An early example of this is Payback, which links activities on your ICICI Bank account with other products (telecom providers, retailers, etc.) to gain superior insights on what you are like.
Nowadays, with cookie farming on the web, this is more common, and you have sites that track your web cookies to figure out correlations between your activities, and thus infer your lifestyle, so that better advertisements can be targeted at you.
In the last two or three years, significant investments have been made by restaurants and retailers to install devices to get to know their customers better. Traditional retailers are being fitted with point-of-sale devices (provision of these devices is a highly fragmented market). Restaurants are trying to introduce loyalty schemes (again a highly fragmented market). This is all an attempt to better get to know the customer. Except that middlemen are ruining it.
I’ve written a fair bit on middleman apps such as Grofers or Swiggy. They are basically delivery apps, which pick up goods for you from a store and deliver it to your place. A useful service, though as I suggest in my posts linked above, probably overvalued. As the share of a restaurant or store’s business goes to such intermediaries, though, there is another threat to the restaurant – lack of customer data.
When Grofers buys my groceries from my nearby store, it is unlikely to tell the store who it is buying for. Similarly when Swiggy buys my food from a restaurant. This means loyalty schemes of these sellers will go for a toss. Of course not offering the same loyalty program to delivery companies is a no-brainer. But what the sellers are also missing out on is the customer data that they would have otherwise captured (had they sold directly to the customer).
A good thing about Grofers or Swiggy is that they’ve hit the market at a time when sellers are yet to fully realise the benefits of capturing customer data, so they may be able to capture such data for cheap, and maybe sell it back to their seller clients. Yet, if you are a retailer who is selling to such aggregators and you value your customer data, make sure you get your pound of flesh from these guys.
The number of potential learnings from today’s story in Mint (disclosure: I write regularly for that paper) on Foodpanda are immense. I’ll focus on only one of them in this blog post. This is a quote from the beginning of the piece:
But just as he placed the order, one of the men realized the restaurant had shut down sometime back. In fact, he knew for sure that it had wound up. Then, how come it was still live on Foodpanda? The order had gone through. Foodpanda had accepted it. He wondered and waited.
After about 10 minutes, he received a call. From the Foodpanda call centre. The guy at the other end was apologetic:
“I am sorry, sir, but your order cannot be processed because of a technical issue.”
“What do you mean technical issue?” the man said. “Let me tell you something, the restaurant has shut down. Okay.”
I had a similar issue three Sundays back with Swiggy, which is a competitor of Foodpanda. Relatives had come home and we decided to order in. Someone was craving Bisibelebath, and I logged on to Swiggy. Sure enough, the nearby Vasudev Adigas was listed, it said they had Bisibelebath. And so I ordered.
Only to get a call from my “concierge” ten minutes later saying he was at the restaurant and they hadn’t made Bisibelebath that day. I ended up cancelling the order (to their credit, Swiggy refunded my money the same day), and we had to make do with pulao from a nearby restaurant, and some disappointment on having not got the Bisibelebath.
The cancelled order not only caused inconvenience to us, but also to Swiggy because they had needlessly sent a concierge to deliver an impossible order. All because they didn’t have intelligence on the inventory situation.
All this buildup is to make a simple point – that inventory intelligence is important for on-demand hyperlocal startups. Inventory intelligence is a core feature of startups such as Uber or Ola, where availability of nearby cabs is communicated before a booking is accepted. It is the key feature for something like AirBnb, too.
If you don’t know whether what you promise can be delivered or not, you are not only spending for a futile delivery, but also losing the customer’s trust, and this can mean lost future sales.
Keeping track of inventory is not an easy business. It is one thing for an Uber or AirBnB where each service provider has only one product which is mostly sold through you. It is the reason why someone like Practo is selling appointment booking systems to software – it also helps them keep track of appointment inventory, and raise barriers to entry for someone else who wants the same doctor’s inventory.
The challenge is for companies such as Grofers or Swiggy, where each of their sellers have several products. Currently it appears that they are proceeding with “shallow integration”, where they simply have a partnership, but don’t keep track of inventory – and it leads to fiascos like mentioned above.
This is one reason so many people are trying to build billing systems for traditional retailers – currently most of them do their books manually and without technology. While it might still be okay for their business to continue doing that (considering they’ve operated that way for a while now), it makes it impossible for them to share information on inventory. I’m told there is intense competition in this sector, and my money is on a third-party provider of infrastructure who might expose the inventory API to Grofers, PepperTap and any other competitor – for it simply makes no sense for a retailer to get locked in to one delivery company’s infrastructure.
Yet, the problem is easier for the grocery store than it is for the restaurant. For the grocery store, incoming inventory is not hard to track. For a restaurant, it is a problem. Most traditional restaurants are not used to keeping precise track of food that they prepare, and the portion sizes also have some variation in them. And while this might seem like a small problem, the difference between one plate of kesari bhath and zero plates of kesari bhaths is real.
So I recently told someone, “I don’t like your business idea. It’s too brick and mortar for me”. By publicising that I said this, I’m probably ruling myself out of a large number of possible job openings, if I want to get interested in those things. For the buzzwords nowadays in the Indian startup world are implementation, delivery, execution and getting one’s hands dirty. By professing a dislike for “brick and mortar”, I’m basically declaring myself to be a sort of a misfit for the Indian startup world.
Traditionally, things like what I’ve mentioned above – implementation, execution, delivery, etc. have never been sexy. They’ve basically been the necessary work that has had to be done to get full mileage out of one’s sexy work. The sexy work has traditionally been getting ideas, solving problems, negotiating, cutting deals and all such. And in the traditional model the unsexy work has gotten outsourced to the underlings and the less capable and to “Bangalore”.
But then this model wasn’t very sustainable. A bank I used to work for insisted that quants code their own trading algorithms, arguing that the transaction cost of explaining the algorithm to a specialist coder was significantly higher than the cost of coding it themselves. Recently, an interview with Jay Parikh of Facebook revealed that they’ve stopped bifurcating employees as those that do “day to day work” and those that work on “breakthrough ideas”.
Basically, companies started figuring out that the necessary but unsexy work was actually much more critical than they had imagined, but it was hard to motivate people to do a good job of them. So the next natural step was to play up the roles that had traditionally been unsexy. So execution became part of the mantra. Corporate leaders and gurus would talk about how they were successful due to an extreme focus on “rolling up their sleeves and getting their hands dirty”. And it seems to have worked.
Rather, I think it has worked too well. Implementation and execution has been played up so much that nobody can talk much about the kind of work that used to be sexy. So people don’t talk about ideas any more – the consensus seems to be that ideas are cheap and anyone can generate them, and what matters is only execution. Venture capitalists talk about execution, too, and of investing in companies based on the execution capabilities of the founders. And having invested, they drive their investees to simply “execute away”, and get things done.
I don’t have too many closely observed data points to corroborate this, but my reading of the Indian startup scene is that it is full of headless chicken. The focus on execution is so extreme, and the push from founders and venture capitalists in that direction so strong, that it appears that people have stopped thinking any more. And (again, this might appear speculative, and it is, for I don’t have much data to back this up) it appears that such sectors are headed for a kind of equilibrium where extreme execution is the norm, and people who like to deliberate and think before acting are getting weeded out.
I’m not saying that we should not execute, or give execution its due. All I’m saying is that we’ve gone too far in that direction, to a state where thinking might actually be penalised. And it is this bit that needs to be kinda “rolled back”. But then who will execute this roll-back?
I’ve never got a call from a postman asking for the route to my home. I’m assuming none of you have, either. In fact, it is extremely unlikely that anyone even writes the recipient’s phone numbers on any letter or package that is being sent by ordinary post. It is assumed that the address uniquely identifies your house and the postman knows how to get there.
However, every time I get a courier or any other package delivered I’m faced with a constant barrage of calls from the delivery person. And this after living in Jayanagar, which apart from a few dead ends and diagonal roads is like Manhattan in that mains and crosses can be easily used to identify approximate locations of addresses beyond which door numbers uniquely identify houses. The problem is that most delivery persons of “private” courier companies have as their domains areas much larger than Jayanagar, because of which they have little domain knowledge of Jayanagar.
The reason why delivery persons of “private” courier companies have large domain areas is the number of packages that these companies deliver – the market is generally quite fragmented and so the number of packages that a single company has to deliver in a particular area is low, because of which the area assigned to each delivery person is large, because of which the delivery person is unable to “figure out” his complete area, which makes the entire delivery process inefficient.
Package delivery can hence be considered to be a “natural monopoly“, in that it is more efficient for one provider to deliver packages in a particular area than for several providers to deliver in the same area. A single provider delivering packages in an area can have delivery persons who are knowledgeable about the area and can hence deliver with low transaction cost.
Hence there is scope for setting up a company that specialises in last mile delivery of packages, with delivery persons with intimate knowledge of small areas delivering packages in that area. This company can then take over the responsibility for delivery in that area from a large number of courier companies, e-commece companies which have their own logistics, etc. But then that will completely defeat the purpose of a “courier service”!
If only India Post increases reliability to a level where e-commerce players start using it rather than their own delivery services.
(Companies such as Uber, which sends you a different cab each time you call for one and thus has no way to exploit this “natural monopoly”, solve the problem by providing their drivers with GPS and turn by turn navigation. Perhaps courier companies can learn from this?)
The trigger for this post was this Amazon delivery person who kept calling me every two minutes asking me to provide him directions to my house. As if I don’t have any better job. I told him that figuring out addresses is a part of his job and he can’t outsource it to me. I’m not at home so I don’t know if he’s even delivered the package.
If you were to do a ranking of Masale Dose in different restaurants in Bangalore, I would say that the clear winner would be the one served at The Restaurant Formerly Known as Central Tiffin Room (TRFKACTR, now known as Shree Sagar). Soaked in ghee (melted butter), extremely crisp on the outside and soft on the inside, and served with two awesome chutneys, it is an experience every visitor to Bangalore must experience (Warning: Not good for your lipid profile, though). Except if you go on a Sunday morning.
The first time I visited TRFKACTR was on a Sunday morning in early 2010. While I was quite impressed by the product itself, I wasn’t so impressed by the ambiance and the operations. It was a Sunday morning and the restaurant was crowded. People were waiting around all over the place waiting to get a seat. Waiters would do nothing to assist you to get a table. And once seated, service was inefficient and slow – the waiters didn’t show any urgency given the size of the crowd at hand. It would remain my last visit to TRFKACTR in close to two years.
And then I shifted my residence, and moved to a house within two kilometres of TRFKACTR . I’ve since visited the restaurant several times (I’ve lost count), and have come away impressed each time. On none of my subsequent visits have I had any complaints about the service and operations, either. I’ve got a table immediately (though usually shared with strangers, as is the practice in such restaurants), been relieved that the waiters are actually not in a hurry and leisurely enjoyed my Butter Masale Dose without being bothered by crowds waiting to grab my seat. In the process I’ve also understood why the waiters didn’t show any urgency on that crowded Sunday morning when I first visited.
I had breakfast at TRFKACTR this morning, and the restaurant looked like this:
While this is an extreme case – I went early on a drizzly morning, and the restaurant had just opened – the thing with TRFKACTR is that most of the time it runs at or just below capacity. On any given day, as long as it is not a Sunday morning, you can expect to find a seat as soon as you visit the restaurant. You get served at a leisurely pace (though not too leisurely – this restaurant relies on high table turnover), and can eat in peace.
We need to recognize that “business as usual” in TRFKACTR involves the restaurant running at or close to capacity, and the operations at the restaurant have been optimized for this. That operations are stretched on a Sunday morning is not bad planning by the restaurant – it is a conscious decision by the restaurant that the crowds are a once-in-a-week occurrence and they will not optimize for that. While it might make sense to learn and plan for a different set of procedures on Sunday morning, we need to keep in mind that kitchen and table capacity are limited (slow service at the table on my first visit was perhaps due to a constraint on kitchen capacity) and differential pricing for Sundays is unlikely to go down well with customers.
Instead, what has happened is that customers (the regulars, at least) have learnt that the restaurant is really crowded on Sunday mornings and have shifted their gratification via Butter Masale Dose to other days. It is very likely that a majority the crowd that still comes to the restaurant on a Sunday morning consists of “tourists” – non-regulars who want to see what the restaurant is like.
PS: I’ve visited the restaurant once again on a Sunday morning after that initial visit. I had gone alone, but found a seat immediately. It is a possibility that my perception that the restaurant is really crowded on all Sunday mornings suffers from small sample bias.
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