Restaurants, deliveries and data

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.

Two way due diligence

In a cash-and-stock or all-stock acquisition, does due diligence take place one way or both ways?

This is a relevant question because not only are shareholders of the acquiring company acquiring shares of the target company, but shareholders of the target company are also acquiring shares of the acquiring company.

Take, for example, Foodpanda’s acquisition of Tastykhana in 2014. The source of this snippet, of course, is the brilliant Mint story about Foodpanda earlier this week.

Shachin Bharadwaj, founder of TastyKhana, a Pune-based start-up that Foodpanda acquired in November 2014. After spending two months inside the company, Bharadwaj was disturbed about the lack of processes and had uncovered several discrepancies—fake orders, fake restaurants, no automation, overdependence on open Excel sheets, which were prone to manipulation, and suspicion over contracts awarded to vendors.

“I know I am making allegations,” he told the people in the room. “All I am asking is that we do an independent audit.”

The others were not interested.

“The past is the past,” said Malhotra. “Let’s just resolve the differences and find a way forward for you and Rohit to work together.”

So Foodpanda acquired Tastykhana, and the Tastykhana founder (who became a Foodpanda employee) later found out that Foodpanda wasn’t the company he had assumed it was, and now owning shares of a company he had overestimated, he rightly felt shafted. It’s unlikely that due diligence happened “the other way” in this acquisition.

I had written on LinkedIn a while back about how employees accepting stock in a company that is hiring them are implicitly investing financially in the company, and that they need to be able to do due diligence before they make such an investment. Acquisition works in a similar way.

So I’m repeating myself yet again in this blog post, but is “reverse due diligence” (acquiree checking acquirer’s books) a standard practice in the M&A industry? Does this work differently in big company markets and in startups? Do acquirers get pissed off when acquirees want to do due diligence before getting acquired (when being paid in stock)?

Note that this doesn’t apply to all-cash deals.

Hyperlocal and inventory intelligence

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.

Chew on it!

Market depth, pricing and subsidies

A few days back I had written about how startups should determine how much to subsidise their customers during the growth phase – subsidise to the extent of the long-term price. If you subsidise too much initially, elasticity might hit you when you eventually have to raise prices, and that can set you back.

The problem is in determining what this long-term sustainable price will be. In “one-sided markets” where the company manufactures or assembles stuff and sells it on, it is relatively easy, since the costs are well known. The problem lies in two-sided markets, where the long-term sustainable price is a function of the long-term sustainable volume.

A “bug” of any market is transaction costs, and this is especially the case in a two-sided market. If you are a taxi driver on Ola or Uber platform, the time you need to wait for the next ride or distance you travel to pick up your next customer are transaction costs. And the more “liquid” the market (more customers and more drivers), the lesser these transaction costs, and the more the money you make.

In other words, the denser a market, the lower the price required to match demand and supply, with the savings coming out of savings in transaction costs.

So if you are a two-sided market, the long-term sustainable price on your platform is a function of how big your market will be, and so in order to determine how much to subsidise (which is a function of long-term sustainable price), you need to be able to forecast how big the market will be. And subsidise accordingly.

It is well possible that overly optimistic founders might be too bullish about the eventual size of their platform, and this can lead to subsidising to an extent greater than the extent dictated by the long term market size. And some data points from the Indian “marketplace industry” show that this has possibly happened in India.

Having remained credit card only for a long time now, Uber has started accepting cash payments – in order to attract customers who are not comfortable transacting money online. This belated opening shows that Uber perhaps didn’t hit the numbers they had hoped to, using their traditional credit card / wallet model.

Uber has problems on the driver side, too, with an increasing number of its drivers turning out to be rather rude (this is anecdata from several sources, I must confess), refusing rides, fighting with passengers, etc. Competitor Ola has started buying cars and loaning them to drivers, perhaps indicating that the driver side of the market hasn’t grown to their expectations. They are all indicative of overestimation of market size, and an attempt to somehow hit that size rather than operating at the lower equilibrium.

So an additional risk in running marketplaces is that if you overestimate market size, you might end up overdoing the subsidies that you provide to build up the market. And at some point in time you have to roll back those subsidies, which might lead to shrinkage of the market and a possible death spiral.

Now apply this model to your favourite marketplace, and tell me what you think of them.

The service marketplace paradox

This came out of a conversation a few weeks back, and resurfaced in a conversation yesterday. There is a fundamental paradox in service marketplaces – the more useless the general quality of service is, the more useful the marketplace. Let me explain.

Let us take the market for plumbers, for example. There are several hyperlocal service marketplaces in India (like HouseJoy or LocalOye) which supply plumbers on demand. Their biggest challenge is offline transactions (as this article about US-based HomeJoy describes) – once two sides of the market are introduced to each other, they take further transactions online.

Thus, there is a huge amount of activity and value taken offline once the introduction has been made, as the long tail of the client/pro relationship takes hold. Clients are perpetually motivated to move their pro relationships off platform, because it’s one less intermediary to go through to directly access the pros they love.

In other words, once I’ve discovered a plumber through HouseJoy (for example), and find his work to be good, the next time I need a plumber I’ll simply call him rather than call HouseJoy (cutting out the middleman). If the plumber is reliable and produces reasonable service, HouseJoy has practically lost me as a customer for plumbing services for a long time.

On the other hand, if the plumber I used the first time is good but not reliable (doesn’t arrive on time the next time I call him), I’m likely to use HouseJoy (or a competitor)  the next time round. In other words, the worse the service providers are (in terms of reliability, not quality of work), the greater the likelihood of the platform getting business!

This is the fundamental paradox of service marketplaces. When services are reliable, you don’t need a marketplace. So if you need a marketplace only if services are unreliable, the server side of the marketplace is full of unreliable people. The hope, and the value that the marketplace adds, is that by aggregating a bunch of unreliable people, some level of reliability is guaranteed. The question is how sustainable this is.

Think of this another way – the level of reliability offered by a marketplace can be described as the sum of reliability of service providers and reliability of the marketplace itself. So for a given level of overall reliability, the marketplace adds more value if individual service providers are less reliable!

Extending this model to other marketplaces and services is left as an exercise to the reader. Feel free to use the comments section to write your analysis.


On multitasking, queues and call centres

Queues and call centres, with linear processing, are inefficient as they result in low utilisation. 

I recently read this excellent article by Tim Harford about multitasking.  In this, he talks about research which says that multitasking makes you ineffective because of high cost of context switching, something that I’ve come to learn over the last three years. He also has this nice piece on ADHD here:

“You’re letting more information into your cognitive workspace, and that information can be consciously or unconsciously combined,” says Carson. Two other psychologists, Holly White and Priti Shah, found a similar pattern for people suffering from attention deficit hyperactivity disorder (ADHD).

It would be wrong to romanticise potentially disabling conditions such as ADHD. All these studies were conducted on university students, people who had already demonstrated an ability to function well. But their conditions weren’t necessarily trivial — to participate in the White/Shah experiment, students had to have a clinical diagnosis of ADHD, meaning that their condition was troubling enough to prompt them to seek professional help.

This piece, however, is not about multitasking at the personal level. It is not about ADHD, either. It is about Adigas, and Citibank, and call centres.

As I had mentioned in a blog post yesterday, I visited Vasudev Adigas in Jayanagar 8th block on Sunday, after a really long gap. They have completely revamped and redesigned the restaurant, changing the place of the cash counter, food counters, kitchens and what not. Most of the design is good, and speeds up processes. Except for the cash counter.

A “feature” of cash counters at South Indian fast food restaurants is that there is no queueing. Counters are placed in a way that people can crowd around it from all directions. While this leads to some confusion and encourages bad behaviour, it also ensures that the person at the cash counter is always productive. If one customer is dillydallying about her order, the cashier can simply process another order before the first customer has made up her mind.

The new cash counter here, however, have very restricted access which makes it hard for the guy at the counter to multitask. As a consequence, his utilisation is low (customer take time to make up their minds), and the average wait is longer. And there is no queueing either, so there is no reduction in bad behaviour also.

For a similar reason, call centres are ineffective – they result in low utilisation on the part of both the customer and the call centre “executive”. It is unlikely that you spend all your time talking, and there is significant amount of time wasted in being put on hold or listening to boilerplate messages.

For example, I need to talk to Citibank because they’ve issued me a new debit card but haven’t sent me a PIN. When I call them, I’ll have to enter my account number multiple times, waste time listening to their options read out in a linear fashion and simply wait listening to random music when they inevitably put me on hold. On the “executive”‘s side, there will be time taken to verify stuff – when they are stuck with me while they might be serving another customer instead.

Call centres are a vestige of the 1990s (or earlier outside India), when phones were plentiful and internet not so. A significantly superior mechanism is to replace the call centre with chat – either through a web interface or through an app. Chat allows both the customer and the executive to multitask, and not waste time in meaningless tasks. Authentication can be superior to that on the phone, no time is lost navigating (since nothing needs to be read out linearly), and utilisation of both the customer and the executive is really high.

Yet, Citibank doesn’t offer this option. Neither do a lot of other supposedly progressive organisations. And that is disrespect to the time of both their customers and their “executives”. Hopefully, they’ll offer a chat option soon.

As for Adigas, the redesign has been after their new PE money came in. I’m less bullish about their changing their billing counter design.

Characterising network effects

Met a bunch of people for drinks this evening. Most of the conversation was just okay. But there was this little bit about network effects. Where I figured out how to calculate whether network effects are present in an industry. It all came out of Kingsley claiming that the age of network effects is over, and there are no more network effects left.

The discussion presently moved to how you discover whether there exist network effects in different industries. Does the fact that  Amazon’s marketshare is nowhere close to that of a monopoly mean that there are no network effects in e-commerce marketplaces? Doesn’t Google have network effects in that given the larger number of people searching on the platform, there are more clicks and more opportunity for learning (for Google) and hence better results?

At a point of time in the conversation, I made the statement that Google (in particular and search engines in general) has “partial network effects”, in that more users means more learning and hence more results. And that for this reason Bing or any other competitor can’t match up.

So how can we characterise whether an industry has network effects, and if so, to what degree? Thinking about it, it’s rather simple. In a “normal” non-networked industry, the value of the user base is directly proportional to the number of users. Going by Metcalfe’s Law, in a fully networked industry, the value of the user base is directly proportional to the square of the number of users. An industry with “partial network effects” should surely have its value a power between 1 and 2 of the number of users?

Here’s how we figure out how networked an industry is. Take all the players in the industry and tabulate the size of the user base and the value of each of the players (excluding very small players). Plot them on a log-log plot, and measure the slope. If the slope of this log-log plot is close to 1, it means that the industry is not networked at all. If the slope is close to 2, it means it has “full network effects”. And the numbers in between represent the spectrum of possible values.

Rather simple, isn’t it? This is why I love drinking sessions, for they allow you to unleash such thoughts. Oh, and I “recorded” this thought by sending a WhatsApp voice message with the gist of the above content to Hariba. He replied with “keep them coming” or some such thing, but this was all it was for this evening.