Sequence: Concierge MVP [LC08]

To develop product craft, we need to know the steps involved and understand how they interact.
Sequence: Concierge MVP [LC08]

In Process vs. Sequence, I argued that our generic sequences — things like the design thinking process, the double diamond, our product operating model, or strategy cycle — don't constructively characterize our work. They're idealized visions, teaching tools, and after-the-fact abstractions that we fit around past experiences.

These processes don't express how any effort actually unfolds. That is characterized by a sequence of discrete and specific steps that weave together and feed into and back from one another, collectively growing and unfolding a concrete instantiation of the team's shared vision.

To build constructive product sensibility, we need to understand how the work looks at this sequence-of-steps level. In that vein, today's update is straightforward: we'll scan a set of steps from a real initiative, and then I suggest a similar exercise for you and your team.

The sequence of steps

We're looking at a concierge (human-effort-driven) MVP that went from an idea to an evolving prototype built on live customer data, ultimately launching into a multimillion-dollar-ARR commercial product line.

The team's PM, Tom Alterman, explains the project in a webinar. Here, our goal is only to look at some of the key steps that express the work sequence of the project. The sequence picks up after the green light to form a small team and attempt to prove we could create a viable solution for construction users' submittal tracking process.

This effort, in a rough semblance of order, contained...

  • Field visits with customers build a first-hand perspective of the problem space
  • Ongoing and active discussion and problem-space sketches
  • Collecting the sales team's experience in the area
  • Reviewing related customer support cases and prior context
  • Visualizing our view of core product scenarios and JTBDs
  • Prioritizing opportunities, risks, and hypotheses for learning
  • Surveying customers for problem sizing and first-draft segmentation
  • Hypothesizing the 'ideal customer profile' and their current behaviors
  • Soliciting stakeholder input on current-state understanding
  • Scoping the initial rails for a learning-initiative-slash-concierge-MVP (just "MVP" after this)
  • Setting up infrastructure and toolset for the MVP
  • Designing the concept or prototype flows for the MVP
  • Conducting pre-flight demand testing for MVP participation
  • Defining legal agreements and data policy for MVP participation
  • Dividing the team's outreach, monitoring, and communication responsibilities
  • Recruiting customers for MVP participation
  • Setting up procedures to receive live customer data updates
  • Building a spreadsheet-driven data model prototype
  • Manually updating live customer data into Google Sheets
  • Iterating on the offering itself and its core value proposition
  • Iterating on the internal view of risks/hypotheses/opportunities
  • Testing different approaches to data visualization
  • Running recurring check-ins with MVP customers
  • Piping Google Sheets data into a live product front-end
  • Iterating through a range of features and user-facing conceptual models
  • Discovering a massive and solvable user problem outside of our initial scope
  • Cadenced communication with internal stakeholders and the org at large
  • Defining a stable value proposition and ICP for a full product offering
  • Scoping technical and design lift for moving off of MVP to beta
  • Defining the minimum scope for beta testing (without the training wheels)
  • Making the internal case for the new commercial product line
  • Transitioning alpha customers out of the MVP

... and ends in this scope as the team starts working with Sales, Support, Marketing, Legal, and Comms to prepare for a commercial launch.

These potential steps are a mix of decision points, convergences, hypothesis-driven action, and recurring or ongoing activities. Most of these steps are not surprising. A few of these steps are purely emergent, convergent, or unexpected.

Every meaningful step changes the work-system state. After every step, some kind of new and different action is possible or necessary. And a majority of them are things we can understand, anticipate, and design our initiative sequence to support.

Building our collective craft

What I'm trying to get at, in this meandering example, is that if we want to do our work well — to build the right learning in the right place, to make the right decision at the right time — we need to work with this kind of sequenced granularity to bring our efforts to life.

We need to know the steps involved and understand how they interact.

We can teach the theory through abstract processes and case studies. But building individual and collective craft-sensibility is an artifact of experience and reflection: doing the work and paying attention is how we begin to see and feel the structures and sequences that create healthy work.

If you want to see these sequences for yourself, take your most recent project and try to sketch out a step list. Discuss and embellish it with your teammates to see just how much was involved. Then look back at your plan and see how much of that work was explicitly anticipated and accounted for.


This is Loops and Cycles, a weekly mailing list exploring the processes that produce good products (and organizations). Share it with your team, send it to a friend, or send me your thoughts and questions.

About the author
Dave Hora

Dave Hora

Research consultant and product strategist. || Understand what is being considered. Focus on user needs. Visualize the challenge at hand.

Dave's Research Co.

shaping useful product lines — consulting, writing, advising

Dave's Research Co.

Great! You’ve successfully signed up.

Welcome back! You've successfully signed in.

You've successfully subscribed to Dave's Research Co..

Success! Check your email for magic link to sign-in.

Success! Your billing info has been updated.

Your billing was not updated.