Leverage Hypothesis-Based Investment Decisions To Accelerate Products To Market

[Fred Champlain]

Being a product manager isn’t easy.  Product managers are frequently required to make nonstop high-stakes decisions, often armed with ambiguous information and under severe time constraints driven by accelerating market cycle times.

Better Decisions Faster

Fueled by disruptions in technology, the competitive landscape is constantly being redefined with new threats surfacing continuously, resulting in increasing marketplace volatility.  GenAI is the latest disruption that, in a short eighteen months, has already reshaped markets with much more to come, with an exponential impact significantly greater than prior disruptions from mobile computing and cloud technologies.

Technology acceleration is driving customers to expect the continuous evolution of the products they use to constantly deliver new functionality and features that provide additional value.  Likewise, enterprises expect their products to overperform to provide ever-greater market share, revenue, and profitability. And competitors expect to erode these factors in their rivals in an ongoing effort to disrupt, displace competitors, and dominate the market - resulting in high pressure on product managers to make better decisions faster.

Data-Driven Decisions

How can a product manager give themselves an advantage?  Increasingly it’s through agile product management and the idea of hypothesis-based decision making that lies at the core of customer-centric product design.

What is hypothesis-based decision-making?  It is the use of one or more experiments conducted by releasing a relatively small increment of new product functionality or a design change, to prove (or disprove) a theory of whether the impact of the change meets or exceeds a predetermined expected target, such as whether changing the UI of a mobile app results in a 2% increase in the average revenue for an e-commerce app.

If an outcome fails to meet the expectation, the hypothesis is proven false.  Otherwise, the hypothesis is proven true. Either way - by using a hypothesis-based approach, for example with A/B testing - we obtain value telemetry from our customers that rapidly determines our hypothesis outcome and informs our next product investment decision.

Agile Product Management

This hypothesis mechanism is at the core of the Lean Startup method that is central to agile product management: building products incrementally, guided by a series of small bets that quickly inform decisions that are based on customer feedback using a Build, Measure, Learn cycle. Lean portfolio management gains customer-centricity, and investments become aligned to support continuous adjustment to product fit to adapt to changing customer needs.

Regardless of how much primary and secondary research has been done, or how many design thinking workshops have been conducted - directly learning from customers provides as optimal of a path as possible through the cone of uncertainty.  Data-driven decisions lower risk and increase the likelihood of successful outcomes over gut-based decisions, accelerating the time to market of innovation investments resulting in improved product agility.

Managing Investment Risk

Hypothesis-based investment decisions are key to avoiding the risk of investing too much, too early in products when we might not yet understand our competitive market fit, or from investing in products that are currently sufficiently meeting customer expectations.

When a hypothesis is disproven, the option to pivot exploration using a new hypothesis trims risk exposure by avoiding going down an unattractive path.  At any point a decision to stop investing can be made – to either outright abandon a feature or product if the market is no longer aligned with our objectives, or potentially pause investment until our OKRs for the product trend to underperforming, reducing the risk of misaligned and unproductive investments.

As a result of using a structured experimental design approach to decisions, a learning loop is created that shapes the refinement of our value propositions using a sense and respond adaptive investment approach that leverages direct customer evidence to minimize investment risks.

ServiceNow SPM

Developing and testing hypotheses provides a data-driven foundation for product evolution, and it improves alignment between work and value delivery.  But it only works if an organization has the ability to carry out testing of hypotheses in an effective and efficient manner.  That’s where ServiceNow comes in. With ServiceNow SPM (Strategic Portfolio Management), product manager requests for piloting or prototyping in support of hypotheses are submitted to a central portfolio demand funnel across the value streams of an enterprise, resulting in allocation of funding and resources to development value stream portfolios where product capabilities are created and maintained.  With SPM, requests are aligned with the overall strategic roadmap and organizational priorities for funding and capacity allocations, while providing visibility to all stakeholders.

Using SPM product managers can track the flow of work across the NOW platform regardless of how it is being delivered, including release management and product operations, all in a secure and seamless environment. Continuously tracking operational metrics and value telemetry informs hypotheses in real-time, providing organizations with a complete end-to-end transparent ecosystem for data-driven product management, enabling product managers to focus on what they do best: delivering the right value to customers at the right time.