What Product Managers Can Learn From Portfolio Theory

Risks, Returns, and Product Management  

One of the best things about being a product manager is that the profession requires you to learn about a lot of different disciplines such as software engineering, statistics, marketing, design, and accounting. I’m going to add another to the list – portfolio theory.

Portfolio theory is a framework for establishing a portfolio of financial assets with a balance between risk and return. Thinking about the familiar case of stocks, you can generally expect the stock of an established company with a stable revenue, low debt ratio, and lots of assets (say, Apple) to have less variability in price increase and decrease than a publicly traded biotech firm with no product yet in the market but the potential to dominate that market if they are successful. That biotech firm may create a breakthrough and be worth many multiples of its current value, but it also may end up worthless. Portfolio theory let’s you combine assets across this risk spectrum and maximize your return for your level of acceptable risk.

The portfolio theory framework is also useful for product managers planning feature development and how they approach their market. Just as an investment manager must balance a finite amount of capital to invest in assets lying across a spectrum of risks and returns, a product manager must allocate a finite amount of development time to features which have different levels of risk.

Most generally risk is defined as the probability of a loss multiplied by the magnitude of that loss. In the product manager’s world, ‘loss’ (and ‘gain’) is a bit tougher to define feature by feature, but for our purposes here think of it as your product’s measure of market success through a key performance indicator (KPI) – total revenue, customer acquisition rate, retention rate, etc. The magnitude of that loss (or gain) is the size of that features effect on the KPI.

Scenario: Roadmap Planning

Imagine you are product manager for an email service. You’re planning the product roadmap and have a suite of proposed user interface features that are common among analogous products. Your engineers have a clear understanding of each feature’s scope and the implementing technology. These are low risk features – there’s little chance of your team being unable to implement them. However, since these are market standard features, there’s also little upside – you don’t expect their development to greatly affect a KPI like new user acquisition.

You’ve also come up with another idea – an “automatic email response generator” that uses deep learning and natural language processing to analyzes previously sent emails to propose a response, which the user then modifies and approves. This artificial intelligence augmented response feature would be the first of its kind. You imagine being interviewed by Wired and can see the new user acquisition chart growing exponentially with your market-dominating idea. Most importantly, user research indicates that your target market is clamoring for features that let them spend less time composing emails.

However, your engineering team provides a reality check – the feature will take an immense amount of time and they can’t even determine if it’s viable until they’ve spent months working. It is obvious the user interface and AI-augmented response feature lie at opposite ends of the risk-return spectrum. This scenario really gets to the heart of product management and innovation – is your strategy to pursue the incremental changes and be happy with incremental results, or try upend your target market and reap the larger reward, knowing the probability of failure is high?

Portfolio theory indicates a potential third option – balancing a reduced scope innovative feature with a subset of the incremental user interface features. Perhaps by implementing a deep learning technique that merely searches for the user’s past responses to similar emails, you can lower technology risk and the amount of resources needed to bring the feature to market. If this feature fails, at least you haven’t bet the house on the outcome.

Where the Portfolio Analogy Breaks Down

Portfolio theory allows a product manager a useful framework for thinking through how to deploy finite resources to advance their product. However, it is truly a rough framework since it breaks down in one important point – financial instruments usually allow assessment of years of statistical data indicating the risk and return of your investment options, whereas with a product roadmap you are relying on inference from market data, engineering guidelines, and your own judgement. 

The framework presented here is really just a broad outline focused on risk management – portfolio theory offers more lessons for product managers. The key takeaway is that any organization managing a product needs to understand their tolerance for risk and realize that features have unique risk-return profiles. Risky innovation can potentially be balanced with incremental product improvements and ultimately these tradeoffs lie at the heart of product strategy. A product manager has many responsibilities and risk management is among the most important.