Lessons for Product Managers from Trading — Startup Analytics

Abhinav Unnam
6 min readOct 15, 2021

The title looks like a misnomer and probably even an oxymoron. What could be similar between two industries as different as these? At one end we have product management where, a multitude of different practices from design, engineering and marketing come together.

While on the other hand, trading is a purely intellectual or alpha driven industry. Find your edge and manage your risk, you should be able to trade your way to profits! What lessons can product managers learn from trading?

“Amateurs think about how much money they can make. Professionals think about how much money they could lose.”

Jack Schwager

Think In Terms of Probability

Every single trader whether that’s capital markets aka stocks, funds etc or exotic instruments such as sports betting, poker etc. A successful trader takes bets with the positive payoff and capped downside risk.

A bet has a positive payoff if we will make more than what it’s gonna cost us, adjusted probability wise. This is where LTV and CAC come into play. We should only onboard the market segment where our LTV > CAC (Positive Pay off).

Recently a lot of products in the Indian Product space have also started to think on those terms. The first notable one was Google Pay’s scratch cards which were based on probabilistic payout instead of fixed cashback. This allowed them to keep the audience engaged and interested with smaller overall spending on scratch cards. This is discussed in detail over here.

The other recent but quite interesting set of products design relying on probability, betting and auction are the ones from CRED.

Auction Design

CRED had this interesting public spending game where if you finish among the top 50 or 25 leaderboards in terms of spend within a time window. You get to win certain prices such as Air Pods. From what I understand, they have since managed to launch multiple such games with prizes for maximum spending.

This specific use case of product comes from understanding how auctions work and how an auction house can design incentives to jack up bids. In this case, CRED being the auction house.

This is an English auction, where the highest bids wins and bids are public. The other types of auction designs include closed envelope bids used for tendering. Wholesale retail uses the concept of dutch auction where the bids decrease as we go and the size of bids might not be the same.

This was an instance of the leader board a day before the contest ended. The interesting thing was you could still win air pod pros at the 50th position with spends of only 5K. But the situation could change just a few hours before the deadline when more people might rush with sudden spending to sneak out a win.

Specific Details

With just minimal marketing spend of ~20K*50 which translates to Rs 10L, if CRED can drive incremental spending across a good chunk of users. This might be a cheaper way to drive engagement and spending among existing users compared to other means of marketing such as ads, discounts etc.

It’s difficult to know how much of a success, the experiment was. Without data on how many people spend money during this window, it’s kinda difficult to make judgments. If I’m spending a buck to earn ten and my margins are bigger than 10%. I should be running these as frequently as I can without dragging the overall consumer experience.

The ones participating in the auction aka the users who are spending money to win the air pods will be incentivised to keep spending to ensure they are consistently on the leaderboard winning the stage. If they lose out just by a small margin, their entire spending would be useless and the payoff would be negative. This sunk cost fallacy would drive lots of spending by users.

The game design at that point in time had a manual update where the leaderboard was getting updated every 24 hrs. With a more frequent leader board updater, they could have driven a faster feedback loop and more competitive spending.

Order Management

Another very interesting concept is to bring liquidity and manage priorities when running an exchange to match buyers and sellers. A stock exchange is a place to match buyers and sellers with an orderbook consisting of bids and asks.

A trade happens when an order gets matched. Either a seller sells at the bid price or the buyer buys at the ask price. Besides the prices, we also have the quantity specific to each bid/ask. The quantity does not need to match completely, we can even have partial order fills.

What if I wanna purchase a specific quantity of stock but only a specific price? On the other hand, what if, someone wants to sell at any price? How do we manage these two priorities in terms of price and urgency? The exchange thereby has these two different types of orders to manage the two: limit and market orders.

  • Market Order: This allows you to sell at any price. The key is the speed of execution. It can be buying or selling. We might not get the price, but we will be able to get the quantity we need to buy or sell.
  • Limit Order: Here, price is the key. Use this order, when you don’t wanna optimise for speed but for price. The order might never even get full filled if the price is too far off from the existing ones.

So, why is this esoteric concept useful for product managers? Both Swiggy and Zomato are now large scale food delivery or rather food order management systems. Their job is not just to get more orders to people but even drive the overall market liquidity and thereby higher net profits for themselves.

Without a conscious effort to bring in these two concepts in some form, it will be difficult for them to squeeze the juice out of the systems. Given that both products have outcompeted all competition and one of them even listed. This is the time to start working on the optimization aspect of the system.

Specific Cases

At the current stage, the priorities likely were market share and growing the size of the market places. This meant all decision making was made purely from a marketing point of view. This also meant funding and carrying out trades or order full fills which were negative pay off for the companies. This is well understood across the firms and the consumers as well.

Zomato briefly did have the concept of an additional delivery fee for faster delivery. For a token amount of Rs 10, they could guarantee order delivery within 30 minutes. This was indeed a step in the right direction. This concept mimics the market order. It has since been withdrawn from the app.

Swiggy has started giving out 1–1 offers on selected restaurants and their specific items. I believe this is a way for restaurants to get rid of excess inventory on specific food items which could have gone to waste but at the same time, allows Swiggy to increase their order volume and grow them.

Based on their series of product decisions, it’s clear that the Indian audience does not want to pay for urgent orders. But from my own personal decision making, I’m willing to pay a ridiculous delivery fee and buy things at any price when partying.

At these items, your order is in bulk but you want a fast order delivery. Price is mostly not a consideration because quantity is large and I want to chill at the party rather than try to optimise the discount.

Swiggy and Zomato, definitely have a prioritization algorithm that probably tries to gauge order priority and accordingly executes. If there is a way to take explicit user input and jump the queue or something, more liquidity can be generated.

They need to start thinking in terms of market and limit orders. Whether they explicitly mark as those or there is a hidden tag already being used to accordingly prioritise orders will be interesting to know.

There are a lot more interesting concepts to be picked up from but that’s for discussion in some other post!

Originally published at https://startupanalytics.in.