Product Strategy: Bridging the Chasm of Frustration06/18
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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:
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:
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: