What could be similar between two industries as different as Product Management and Trading? The title looks like a misnomer and probably even an oxymoron. At one end, we have product management, where many different design, engineering and marketing practices 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
Regardless of their specialisation, every trader, including capital markets like stocks and funds or exotic instruments such as sports betting, poker etc. makes a successful trade when he takes bets with a positive payoff and capped downside risk. If we make more than it costs us, the bet will be a positive payoff, 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, many product managers in the Indian Product space have started to think in probabilistic 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. Read about Tez/GooglePay in detail here.
The other recent but quite interesting set of products design relying on probability, betting and auction are the ones from CRED.
CRED had this interesting public spending game where if you finish among the top 50 or 25 leaderboards in terms of spend within a window. You get to win certain prices, such as Air Pods. From what I understand, they have since launched 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 is the auction house. This is an English auction where the highest bids win, 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 leaderboard a day before the contest ended. Interestingly, you could still win air pod pros at the 50th position with spending 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.
With just a minimal marketing spend of ~20K*50 which translates to Rs 10L, CRED can drive incremental spending from many users. This might be a cheaper way to drive engagement and spending among existing users than other marketing means, such as ads, discounts etc.
It’s difficult to know if the experiment succeeded and by what margin. We cannot draw any judgments without data, how many people spend money during this window. If I’m spending a buck to earn ten and my margins are bigger than 10%, I should run these as frequently as possible without dragging the overall consumer experience.
The participants in the auction, aka the users, spend money to win the air pods and are incentivised to continue spending to ensure their spot on the leaderboard. If they lose out just by a small margin, their spending would be useless, and the payoff would be negative. This sunk cost fallacy would drive lots of spending by users.
The game design then had a manual update where the leaderboard was updated every 24 hrs. With a more frequent leaderboard updater, they could have driven a faster feedback loop and more competitive spending.
Another interesting concept is to bring liquidity into the system by running an exchange between buyers and sellers. A stock exchange is a place to match buyers and sellers with an orderbook consisting of bids and asks.
A trade is complete when an order matches. A seller sells at the bid price, or the buyer buys at the asking 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 cost? How can we manage these two priorities in terms of price and speed? The exchange has these two different types of orders to manage: 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 can get the quantity we need to buy or sell.
- Limit Order: Here, price is the key. Use this order when you don’t want to optimise for speed but for price. The order might never get fulfilled 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 higher margins 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 is even listed. This is the time to start working on the optimization aspect of the system.
At the current stage, the priorities likely were market share and growing the size of the marketplaces. This meant all decision making was made purely from a marketing point of view. This also meant funding and carrying out trades or the order full-fills that 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 offering 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 decision-making, I’m willing to pay a ridiculous delivery fee and buy things at any price when partying. For these items, your order is in bulk, but you want a fast order delivery. Price is mostly not a consideration because the quantity is large, and I want to chill at the party rather than try to optimise the discount.
Swiggy and Zomato have a prioritization algorithm that probably tries to gauge order priority and executes accordingly. 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. It will be interesting to know whether they explicitly mark them as those or if a hidden tag is already being used to prioritise orders accordingly.
There are many more interesting concepts to be picked up from, but that’s for discussion in some other post!