As organizations mature and become more complex, aversion to risk increases, resulting in a slow decision process. Yet the world around is not standing still, and the speed of change continues to accelerate.
Nimble young businesses, who live by the Lean Startup approach of building, measuring, and learning, move from nothing to a product customers love in what appears, from an established company perspective at least, virtually no time. Startups leave established organizations in the rear-view mirror because they optimize for simplicity and velocity. Startups practice the lean methodology to avoid spending time on things that won’t deliver value. They prevent waste by learning early and quickly where they are wrong.
Mastering simplicity and velocity requires practice. This article will provide a mental model with tools to help organizations of any age and size to focus on what matters and get to the answer faster. It is inspired by sources such as Eric Ries’ book “The Leaders Guide” and Pivotal’s associated workshop organized in 2016. An accompanying podcast episode guides you through the steps in audio format.
As a side note, I am here referring to products, but the same steps can be applied to services, companies, go-to-market strategies, geographical expansions, and much more. This framework can also be used in a group setting, with all the key stakeholders collaborating and engaging actively.
As we consider the strategic clarity around the vision, we naturally form expectations, assumptions, and hypotheses related to the customers, operational execution, or timelines, just to name a few. All these are predictions, and we need to be mindful of them. Too often, we make a “prediction” after the fact. Yet the importance of learning from being wrong is where the actual value of predictions resides. Let’s get started:
List as many relevant assumptions as possible in 3–5 minutes considering your product.
List everything you already believe about your product. List your predictions for the future. List every expected product feature. Who are your key audiences? What is your strategy for growth and success?
Two main categories of assumptions should emerge from a product perspective:
- Technical assumptions are about the design and creation of the product, the features, requirements, customer requests, specifications, usability, reliability, and feasibility. You should also consider how your product delivers value to the customers once they begin using it.
- Commercial assumptions are about what customers or partners want. Consider customers, forecasts, marketing strategies, distribution channels, sales plans, competitors, and partners. What are your growth hypotheses? How will new customers discover your offering? How will early adopters help spread awareness? What are your plans for scaling?
[ 3-minute break starts now for you to put that list of assumptions on paper ]
Congratulations. Now read through the list you just created slowly. Take a moment with each assumption to consider that maybe you’re wrong. When you find that thought which makes you uncomfortable, circle it.
Through this process, you will find that a handful of assumptions are hidden among the well-established facts and straightforward deductions that require courage to state, and you just circled some of them. These are the ones you want to focus on.
The trick is now to take these circled assumptions and rewrite each in the form of a prediction:
If I do X, then Y will happen.
Why does it make you uncomfortable? What would be the impact if you were wrong? How will you prove that these predictions are correct?
The Lean Startup framework is designed around the build, measure, and learn loop, iterating through an idea to become a minimum viable product (MVP) that generates data. You are looking for evidence to guide decision-making as quickly as possible.
The best evidence you can get is by asking customers to exchange something of value. Seeking proof for our predictions helps us understand if we are on the right path or need to change. It also allows you better understand your customers, identifying what is important to them. Therefore, in the context of this framework:
Proof = Measured Customer Action
We need to identify ways to have the customer exchange something of value with you — it can be time, money, reputation, etc. The book “Testing Business Ideas” in the Business Model Canvas collection has plenty of examples on how to gather proof.
Drawing from the key predictions you’ve listed earlier, rephrase these in a format that will allow you to gather proof from the people you hope will find enough value in your product to make some investment in it. This time, use the following format to rewrite your list of predictions:
If I do X, my customer will do Y
By now, your list of customer-centric and provable predictions is taking shape and might even appear a little daunting. Going through each key prediction one by one, focus on the following:
Take one minute to write down all the things that need to be done to prove the prediction
List every step, feature, activity, supply, approval, any items required. How long will it take? How expensive? How many people are required to validate that prediction?
Something tells me that in established organizations, seemingly straightforward predictions will take months and large teams to validate — if you can get approval in the first place!
On each key prediction, the next step is to consider the following:
Think about what you need to learn and consider how you could get a good-enough result in ½ the time, in 2 weeks or even two days.
What is the worst that could happen if you experimented in a crappy way? What might you learn if you did it that way? Remember that the goal is not perfection in answer; it’s to increase the confidence level enough so that you can “un-circle” that assumption from the uncomfortable ones.
The goal of simplifying is to ensure we don’t waste time and skills over-delivering features that are unimportant to the customer and to validate the prediction. That’s why you hear startups focusing on the mythical MVP. Simplifying enables us to invest our skills, values, and intuition into work that truly matters. Be ruthless and think YAGNI: you ain’t gonna need it!
You now have a list of critical predictions that require proof and an idea of how you can validate things in a matter of days. Many methods and tools exist to iterate on the learning. Strategyzer provides valuable tools in their series of books around the Business Model Canvas. One is the combination of a Test Card and a Learning Card.
You describe your prediction on the Test Card before you conduct your experiment. You consider how critical it is, your plan for testing the assumption and how expensive/complex it will be, the metrics you will gather, how much time it will require to collect, and finally the criteria for success (remember the customer-centric way of writing your prediction above).
The Learning Card, to be completed after you conducted your experiment, will include a description of the prediction you looked at, what you observed with a reliability indication, lessons/insights gathered, and next step decisions/action plans.
Remember that lean metrics measure movement towards the goal and need to be actionable, accessible, and auditable. Therefore, the best metrics are understandable and comparable. They are either a rate or ratio; they indicate behavioral change. Ultimately, if a metric doesn’t impact your decision-making, it’s a BAD metric!
Based on validating your predictions through this process, you will be in a much better position to pivot or persevere, delivering products handcrafted with care to delight customers!