Build Successful Products with an Impact Mindset
Over the last decade, I have focused on creating and deploying products that create a lasting impact for users. This journey has brought me to the cutting edge of Behavioral Science, studying under the leading minds of the field at the University of Pennsylvania through the forefront of behavioral design, being Microsoft’s first Behavioral Researcher, and more recently, to the startup space creating novel methodologies to measure and maximize feature impact. Through this experience, I formed a product development philosophy called the Impact Mindset. This approach focuses on developing solutions that work for users and drive sustained business value. I am excited to share these insights with a broader audience by launching Desired Outcome Labs!
The need for Evidence-Backed Decision Making
Throughout my career, I have found one recurring mistake that product teams of all sizes and maturity fall victim to: the belief that usage equates to success. When a team uses surface-level metrics such as daily active usage and Net Promoter Score as proxies for fulfilling user needs, they are setting themselves up for long-term failure. While these leading indicators are necessary to ensure that a product grows, they do nothing to share what it does for the end-user. Only through identifying and measuring specific behaviors that a feature changes and how it impacts user outcomes can a company genuinely believe its product will sustain the immediate success shown at launch.
Teams that rely overly on usage metrics will likely fall into a Build Trap, a never-ending pursuit of creating the subsequent customer request, without ever circling back to assess what is working and what should be altered or deprecated. Leadership follows the same pattern of incentivizing output over outcomes, with KPIs such as shipped features. While a highly effective team churning out can be an outstanding place to be, if these features spike usage only to quickly drop when users recognize it does not provide them any value, then this pursuit is destined to lead to a product with high-churn and little opportunity for upswell, a challenging space.
Similarly, Behavioral Science shows us that people do not always know what is best for them and don’t always act according to their long-term desires. The result is the usage of services only to, at some point, discover their discontent when it is not working and decide to pursue an alternative. The term “leaky bucket” is in most product teams' vocabulary. It describes a usage funnel where several customers drop here and there—diagnosing why can be a challenge in itself but is incredibly difficult when the purpose of a feature is not spelled out and there are no metrics to determine if it is fulfilling needs.
In response, applied research teams such as UX Research and Market Insights have responded by doing more studies focused on creating mental models describing user needs. While these are effective to a certain point, product teams with misaligned incentives combined with the lack of a connection to business outcomes yield many of these insights to remain untouched in research readouts. The lack of a cohesive structure and approach to incorporating insights into the product development cycle means that even when the right insights have been created, it doesn’t guarantee that a product team will ever remember to incorporate them into their solution design. In turn, UX research and similar teams have begun to appear as a “nice-to-have” rather than the real cost-savings and value-creating function they can be.
All of this will only be exacerbated by AI. Features will become cheaper to create as AI allows engineers to increase their output rapidly. Yet, this doesn’t mean these new experiences will be more valuable for customers. Products will live up to their full potential through the connection to user needs that these customers seek to fulfill.
Introducing Impact Mindset
Through my experience helping companies of all sizes better understand what they are building for and measuring to ensure that it is working, I have formed a new philosophy of product development, the Impact Mindset. Rooted in evidence-based decision making, this approach focuses on designing and iteration of features to ensure they create their intended impact. While I outlined this in much greater detail in my upcoming book published by New Riders, Bridging Intentions to Impact, I will lay out the core tenets below.
At the center of an Impact Mindset is creating a User Outcome Connection for each core feature within a product. This framework begins with the specific behaviors that a feature is intended to alter through usage. These behaviors are then connected to the user outcomes the customer brings to a platform intending to fulfill. The final step is the connection to business outcomes such that when a feature increases a behavior, the team can be confident it will drive sustained business value.
User Outcome Connection for Business to Consumer (B2C) and Business to Business (B2B)
When teams build out a User Outcome Connection for all of their core features, they add a new layer of definition to find what a feature should be doing for customers. At the scale of a product, this adds new visibility into the inner-workings of what the team expects a user will do and receive from the interaction. It also creates a need to validate each of these connections such that a team can confidently believe that designing a feature to impact specific behavior will, in turn, satisfy outcomes and create a business impact.
The validation process requires the creation of new metrics, which directly address the common pitfalls many teams find themselves in when measuring products, defining success based purely on surface metrics such as usage and satisfaction. Defining specific behaviors and outcomes that a feature is intended to impact creates natural potential to generate new metrics. I outline five levels of metrics that are needed to truly measure whether a feature is successful.
Success Metric Levels: Usage, Usability, Behavioral, User, and Business Outcomes
If the creation of these metrics were easy, everyone would be doing it. Yes, these are not inherently challenging to create. The functional definition of these new metrics is what commonly holds teams back, and thus the potential value for filling out a User Outcome Connecion becomes even more present. With the desired outcomes laid out, its shifts from a lack of knowledge to a technical problem, which many data infrastructure’s are already equipped to handle. Event data from systems ranging from Segment through Shopify can be used for the creation of behavioral outcomes. That same data can commonly be used for outcome metrics especially when combined with attitudinal feedback collected from customers through surveys and interviews.
Constructing the process of validating these user outcome connections, requires a team to adapt, experimentation culture. Many firms espouse this type of thinking, but rarely put together the processes and structures required to fully do it, leading to a wide chasm between the most experimentation heavy cultures, versus those who are just beginning to scratch the surface. As a team begins to shift its collective structure towards that of one which allows for more testing and learning, the final piece of an Impact Mindset becomes essential, the creation of a centralized insights hub.
Most digital product companies have a minimum of user research, market insight, and data science teams. While each of these bring a specialization, under the lens of the Impact Mindset, they are all pursuing the generation of evidence utilizing slightly different methods. Whether structurally a company chooses to colocate all these teams is not the real question, instead, it is the need to centralize all of these teams' evidence into a singular home that can become the basis of product development.
Fully realizing the Impact Mindset means new feature creation beginning with the defining of behaviors, user outcomes, and business impacts based on the insights of previous research. When there are gaps, they are noted for future validation. Solutions are then designed to alter specific behaviors and studies are done to test whether initial attempts are successful. A loop of refinements is closed, ensuring a feature moves closer to the best solution for altering user behaviors.
Launching a feature does not conclude this process, but shifts to a monitoring phase, with a scheduled revisitation of these core success metrics to ensure long-term behavioral change is being accomplished, and that is translating to successful business outcomes. As this process is repeated for multiple features, the learnings are brought back to the centralized insights hub such that future features can be built expediently based on prior learning.
My response is Desired Outcome Labs
Adapting an Impact Mindset may be a novel concept, but all of the component parts are already in the lexicon of most product teams. By starting Desired Outcome Labs, I hope to create scalable services that will empower more teams to follow through with this vision. Whether it be through speaking engagements to share these frameworks and processes at a high-level or through workshops and consulting engagements to be a catalyst for follow-through, I am excited to enter this space to help more teams deliver on their desired impact.
For more information on this and all of my other writing, check out my contents and resources page. To begin a discussion click here to schedule a time for us to chat!
