Market risk discounting

The less understood late increase in market risk, and what drives it

“To wish was to hope, and to hope was to expect”
― Jane Austen, Sense and Sensibility

Startups typically take one or more of 3 kinds of risk — Market risk, Execution risk and Technology risk. The ones taking Market risk tend to be the ones that are the most exciting. They bet on driving a significant change in customer behavior, examples being Uber believing that people will hail cabs using their phones or Facebook believing that people will share their feelings publicly online with a large group of friends. The promise of such startups is that if they are successfully able to change the customer behavior, they will be best positioned to ride the wave to prominence as that behavior becomes mainstream.

As with any other form of risk, Market risk is not static. It obviously is very high when a startup is just starting out because there is almost zero evidence that the behavior change it intends to drive would ever manifest in reality. As the startup gets more signals around the behavior change being real, Market risk goes down. However, is it possible for the Market risk for the startup to increase again? Yes, but this possibility of a late increase in Market risk is what is not commonly understood. This assumption that Market risk once down would never go back up is what I am calling “Market risk discounting”.

Fig: Market risk discounting — the gap between expectation and reality

Market risk discounting is mostly visible in the hype around startups that DON’T have true network effects. The market crowns them as winners even though the road ahead is very long. However, the ones that do have true network effects are able to avoid this late uptick in Market risk. I understand that Network effects is an overused term; the most clear articulation I have found till date is from Bill Gurley — “To know if you have network effects, plot value to the consumer on Y-axis and penetration of supply on the X-axis, and see if the graph goes up into the right”. The graph for most startups that have limited network effects goes to the right only till a certain point and then plateaus. The late uptick in Market risk in startups without true network effects is because of a few under-appreciated reasons.

📈 Evolving customer expectations

When startups are driving a new customer behavior, they can afford to have a product that is “good enough” on the new customer behavior scale because customers don’t know what else to expect. As the behavior becomes more established, customer expectations increase but the product offering of the startup doesn’t always evolve in accordance. This disconnect is mostly a result of a deeper business model issue, and when this happens, the Market risk for the startup increases again.

Couple of examples of this are eBay and Instacart. eBay started off as AuctionWeb, a way for people to buy and sell online. It was a new concept and auction seemed to be a good mechanism for price discovery. It was intended to be asset-light model (no ownership of inventory and no warehouses) C2C marketplace. That was eBay’s DNA. For a time, it worked great, and eBay led the e-commerce pack.

Over time, as more and more goods started to be sold online, including very standardized goods on Amazon, eBay discovered that auction wasn’t always the best way to shop online for many goods, especially new goods. So it introduced “Buy Now”, which gave a fillip to new goods on eBay. However, eBay’s hesitation to make big capital investments in logistics continued to hold it back. Meanwhile, thanks to Amazon, customer expectations had shot way past. So, though eBay had much more breadth than Amazon, which was just a books retailer at one point, it eventually lost out. Once seemingly at the forefront of the new customer behavior of online shopping, it failed to keep pace with the evolving customer behavior.

I think the same might happen with Instacart. Online grocery shopping is a relatively new concept, and Instacart as emerged as the default choice, especially during Covid-19. While that’s great for Instacart at the moment, if you had a choice of online delivery from other providers, would you still go with Instacart? Instacart is a delivery layer on top of the existing grocery value chain, which by design makes it a luxury. It adds costs to the system without removing any costs.

An ideal online grocery shopping service would be competitive on price, would offer fast delivery, and would have a very low item replacement rate. And how would you shop on this ideal online grocery service? You would definitely not be asked to first select a store! How can you select a store if you don’t know how price competitive that store is for the items you want and whether or not the store is likely to have those items? With Instacart, the customer is playing the same guessing game that they would have to play in the offline world, driving from one store to the next. They are now just playing it online, which is better, but is it really the experience they desire? Sadly, Instacart can’t really fix this problem. Opening warehouses or sourcing directly from distributors would make it competitive with stores, its partners, thereby, risking its business model.

🚫 Neutralized distribution advantage

The biggest reason for startups such as Instacart to go asset-light, and sometimes compromise on the product experience, is to be able to scale quickly to get very large distribution. On the other hand, the reason established companies don’t typically push the envelope on new customer behavior is because they have lost part of the innovation muscle and they don’t have the risk appetite for taking big Market risks. They would rather have a startup create a new customer behavior, and then jump into the action once the behavior is becoming mainstream and the Market risk is lower.

What makes this dynamic interesting is that many times these established companies have some adjacent or direct distribution advantage that they can leverage to easily make up for the time when they were sitting ducks. This is can be seen in what Reliance Jio is doing with JioMart and also what Microsoft Teams is trying to pull off against Slack. BigBasket, Grofers, Amazon, Flipkart, etc. spent years building the online shopping habit in India, with the first two specifically focused on building the online grocery shopping habit. Now once this habit has been well established, Reliance Jio has come with all guns blazing, leveraging the ubiquity of its Jio phone network to launch JioMart.

Similarly, Slack spent years shaping workplace communication to be chat-centric and Microsoft Teams is copying it (with some of its own spin) and leveraging it’s enterprise relationships, and bundling it with it’s other offerings. Once such onslaught of established companies begins, startup’s Market risk increases, and the startup has to have a cost advantage and a vastly superior product to keep up its growth curve.

🤑 Bad money’s bad incentives

Clay Christensen asserted that good money comes from funding sources that are impatient for profit, and patient for growth, while bad money comes from sources that are impatient for growth, yet patient for profit.

Price is one of the most important aspects in customers selecting a particular product. Ideally, price is derived based on customer’s willingness to pay, which should be much higher than the cost to serve if the business is to be sustainable. The challenge with many startups is that VC funding allows them to offer prices that their business models wouldn’t be able to support otherwise. While they know that, they live in the hope (supported by some data crunching) that, at scale, the business model will make those prices possible.

This hope becomes the rationalization for startups to continue to invest into inefficient business models, potentially leaving them unable to serve a large part of the market in the longer term. There is too much money and organization momentum tied to the existing business model. Growth targets are steep and doing a pivot takes courage. What if the pivot doesn’t go well and the startup runs out of cash? This is the typical Innovator’s dilemma that the startup now faces.

💡 Innovation outside Silicon Valley

One of assumptions around Uber’s valuation was that it can win in multiple international markets. However, by the time it got around to really focusing on those markets, it had local competitors (example Ola in India and Grab in Indonesia) offering a similar product. This resulted in Uber opening up multiple battlefronts for itself at the same time, bleeding it out. The Market risk was not around creating the behavior of hailing a cab using one’s phone but around the expectation of that being cheaper than getting a taxi from the street corner.

Though I don’t know what the thinking of the investors at the time was, I would have to imagine that they either thought that Uber had a model that could scale very fast across geographies or they discounted what the entrepreneurs in international markets (India, China and SE Asia) were capable of. The beauty of the internet is that if offers infinite scale but remember that it does that not only for startups in Silicon Valley, but everywhere.

Changing customer behavior is a massive undertaking and it’s a journey. Though the startups that drive this change are winners in their own right, the question is whether they will bear the fruit that’s commensurate with their labor. Understanding Market risk discounting and its drivers can be a helpful tool.

P.S. Opinions in this article are my own and don’t reflect the opinions of any of the companies mentioned

🐦 Twitter @setparth