Product Strategy: Bridging the Chasm of Frustration - Hypergiant

Product Strategy: Bridging the Chasm of Frustration

06/18

TOPICS

#ARTIFICIAL INTELLIGENCE #DIVERSIFIED INVESTMENTS #PORTFOLIO MANAGEMENT #VENTURE CAPITAL #INDUSTRY





As clients move away from engagements that are strictly strategic, limited to user experience and visual design, or focused solely on digital development, there is an increased need for studios and agencies to provide services that holistically engage strategy, design, and AI development players throughout each project’s lifecycle. Moreover, when the goal is creating a digital product, experience, or service, projects’ strategic aspects must be reimagined.



Enter product strategy, an arena for building multifaceted approaches based on several key principles:

  • A focus on innovation over optimization and a belief that “parity is for chumps”;


  • A willingness to be provocative with a purpose and a penchant for speaking the truth;


  • Providing a bridge between business strategy and the product roadmap;


  • Rapid ideation, creation, and articulation at a high degree of fidelity;


  • An awareness of the various machine intelligence (MI) applications and techniques available.




  • The Art of Making Progress

    Initially, attempts to bridge “Design Thinking” and (for lack of a better term) “Design Doing” left a gap between the traditional application of service-design-led strategy to business transformation and the somewhat-more-tactical methodologies underlining digital product creation. For clients, this marked a clear chasm of frustration — a yawning divide between the opportunities unearthed in a strategic or discovery phase (or service design blueprint) and the conviction required to craft a well-defined, service-driven product. Indeed, while colossal maps are brilliant for exposing key user journeys — thereby linking customer lifecycles with a panoply of touch points and familiarizing clients with multiple, differentiated possibilities — they are not always accompanied by direction and prioritization (i.e., “Now, you should do exactly this”).



    Product strategists, on the other hand, burgeon decisiveness via an interplay between design, business, and technology. Their ultimate goal is to craft remarkable products that users love by determining what should be created and why, what well-articulated business problem it solves, and how to best innovate with technology. Traditionally, this involves the following actions and capabilities:

  • Identifying the most important questions that a company’s products and services should address;


  • Translating insights into actionable solutions that can be executed and delivered;


  • Connecting design efforts to an organization’s business strategy;


  • Using technology to transform business needs into improved operations or new offerings;


  • Integrating design as a fundamental aspect of building meaningful products;


  • Prioritizing how products and services are launched;


  • Helping their clients manage change, so that new solutions stick;


  • Providing clients with an understanding of how MI applies to their business needs.


  • By combining the above, product strategists ensure that (at a minimum) the product is purposeful, responsive, intuitive, contextual, and engaging — all while remaining compliant with more specific quality measurements, as well. They carefully weigh the needs of the business, user, and data (especially critical when delivering MI- or AI-powered products) to define the strategy and foster direction. And it is only by synthesizing all three of the aforementioned aspects and rationalizing them against one another that a suitable product is created.



    Taking solely the user’s position when crafting an experience (aka, a user-centric approach), for example, will lead to a product that does not satisfy the needs of the business that is, most likely, footing the bill. Conversely, being responsible with business needs alone is what plagues so many minimum viable products (MVPs) in the larger marketplace. A simple tally of business goals rarely addresses users’ core emotional or logistical needs, and often results in products (or services) that lack the necessary degree of subtlety or nuance.



    The final consideration — that of the data — hinges upon future, data mining expectations. What does the client want to see supported by analytics and what is the roadmap for implementing machine learning (or some other aspect of MI) to deliver on those expectations? How can explicitly-provided data return value to that same user by providing him/her with an elevated vantage point from which to effectively see the interrelationships between data? From a data science perspective, is the data hygienic, properly formatted, or granular enough without creating unnecessary entropy? Many companies gather data, but few companies gather it intelligently.



    Adapting for the Fourth Industrial Revolution

    There are considerable elements related to product strategy that are massively affected by MI and the so-called Fourth Industrial Revolution. Some of the new requirements are as follows:

  • Automate: consider this as one of the most basic forms of MI, and possible in most experiences;


  • Elevate: use MI to provide clients with a 10,000-foot view of their own data and identify relationships that are difficult to see “in the weeds”;


  • Eliminate: apply Progressive Disclosure to ensure that users are not exposed to superfluous information inapplicable to them (properly applied MI offers significant personalization improvements);


  • Modularize: architect products in ways that facilitate MI technology integration in future states;


  • Centralize: encourage clients to create a canonical source of truth when data is spread across multiple, disparate systems (thereby promoting a revisiting of data structure and hygiene, as well);


  • Collect: gracefully introduce explicit data collection opportunities to accompany implicit collection (users’ demographic and psychographic details have tremendous value and considerable risk, as seen with Cambridge Analytica).