Don’t Leave Change to Chance: Success Depends on Ongoing Learning

Published in

Accelerate Innovation

8 min readAug 7, 2019

Recently, I wrote a post reflecting on the high failure rate of digital transformation projects amongst large enterprises. I paralleled this with the slim odds startups face when initially going to market, leveraging thinking from Bruce Cleveland’s book, Traversing the Traction Gap.

The major takeaway I concluded, was a need for both startups and enterprises to practice ongoing learning and quick adaptation in order to successfully bring new products or services to market.

But implementing ongoing learning is hard — especially in a large organization that has comfortably been operating in optimization mode for years. Shifting to a focus on learning and discovery requires opening oneself up to totally new possibilities. It requires a market-first approach — looking not only to ones ideal or existing customers for inspiration, but also to influencers, competitors, and non-customers who share the space. It isn’t enough to simply add features to existing models; the most successful and scalable startups and transformations bring something completely new to the market.

Breakthrough, real-world solutions create their own market categories — they are revolutionary. But turning a radical idea into a tangible, fully-functional product, and positioning yourself in the market in the right way, at the right time, is a challenge that many (actually most) individuals crumble in the face of. Along the journey company leaders will have to face their assumptions and preconceived notions — they will come up against themselves, required to question the very ideas they founded their solutions upon. Many factors come into play affecting success, including ego. So, let it go.

Focused learning, the ongoing evaluation and re-evaluation of outcomes at a regular cadence, and exercising an established framework for decision making ensure you’re not just gambling on an untested hypothesis. Test it — validation at every stage of growth is the key to company building success. Validate outcomes at a regular cadence.

What does learning look like in the context of company building?

In business, learning is conducted in order to impact outcomes. We learn because we want to influence or change the results we are currently getting. To do this, we must understand how to learn effectively, as be disciplined in our learning process.

There are countless schools of scientific and psychological thought around learning, but nearly all agree that learning is demonstrated by a permanent change in behavior. The permanent part is essential.

For the sake of this article, and to avoid diving into a discussion of Pavlov’s dogs, I’ve summarized the commonly identified modes of human learning and broken them down into three core methods:

1. We learn by doing — through practical experience: We do something, we have positive or negative outcomes, it impacts how we proceed from there. It’s ingrained in most people.

2. We learn by observing others — we mimic what other people do: A big part of learning in our culture, including within companies. We mimic the behaviors of our seniors. The CEO sets the tone for the company.

3. We learn by interacting with others: A different viewpoint or perspective is presented and through the back and forth of conversation our understanding of a topic changes leading to a new perspective.

If you’re pivoting, you’re committing to leaving behind what you used to do and doing something different instead, and you’re bringing your entire team and all of your resources with you. It’s essential you have a learning strategy and a framework for decision making in place.

It is not simply enough to have data or to be collecting huge quantities of data. That data must be framed specifically within the scope of the problem one is trying to solve. The solution design process identifies the data domains that are within the scope of the problem.

It is essential to have direction before diving in.

As Cleveland outlines, having direction, “is a key distinction to avoid lapsing into an ad hoc product process where decisions are made based on an intense review of customer feedback without vision for overall market or the future of the product or its features.”[i]

That said, when enacting the sort of learning that’s required to create a whole new category, it’s still essential to set boundaries. This is especially true for established enterprises who are already established in their existing category. Branching out into new ones requires a highly intentional approach. Setting clear project parameters is what gives these endeavors scope.

Bound, Liberate, Accelerate.

Constraining a problem or question within a specific hypothesis and then conducting experiments bounds the collection and use of data within specific parameters and gives the validation process scope. Once run, you take whatever outcomes you find and you begin the process again, defining a new set of parameters, forming a new hypothesis, and conducting a new experiment.

Just like in the scientific method, experiments almost never prove something to be absolutely true, they’re simply a means of gathering evidence and data, testing, modifying and retesting a hypothesis — the output supports or does not support, it never proves.

When you add constraints, and clearly define the parameters of your research/experiment, you’re able to learn more effectively and in a much more focused way.

You don’t want to pivot aimlessly and endlessly, so clearly defining the scope of each project is essential to generating impactful outcomes. On the micro level, this is done through project parameters, and on the macro level, your company’s offering is bounded by your purpose and the mission, vision, and values you’ve identified — something Tom Mohr, of CEO Quest, writes about in great depth in Scaling the Revenue Engine. It’s a fine line, but there needs to be some structure — if you’re too open to learning and changing, your discovery system becomes unstable and the team will begin to feel like they’re suffering from whiplash.

That’s why discipline is an essential part of this process — both in terms of method and in the rigor with which one actually translates learning into application/action.

The challenge of knowing how to learn effectively, or how to implement an impactful learning cycle, is the reason why so many early stage startups go into accelerators. They have a great idea, but they don’t necessarily have the experience to bring it to fruition, they need advisors and support to teach them how to learn. A great idea doesn’t guarantee success — a lot of great ideas fail because they fail on execution.

Teams are human and without the discipline of a learning cycle, they have the tendency to get sidetracked or go off the track completely and lose direction.

Cadence

Learning cycles are about cadence — we’re coming together as a team and circling back to our hypothesis regularly, evaluating new evidence to determine what we’ve learned. The coming together is essential, whether that’s weekly, monthly or quarterly.

Cleveland touches on this when he writes about how to effectively leverage data for market research:

“You can use the data to fix problems with execution by looking deep into the funnel, but at some point you get diminishing returns. At that key moment, you need to be able to look up, get a view of the horizon, and seek new direction. Once you have identified this new direction, you can dig right back into the data to make the execution possible.

The key, then, to being data-driven is having the flexibility to shift your focus from the forest to the trees and back again as the situation requires.” [ii]

It’s about having a framework for communication that creates alignment. Most of us can be disciplined by ourselves, but to build a company, we need to be disciplined together — in alignment with each other.

The cadence for circling back as a group — the length of a learning cycle — varies depending on size of organization. In a startup, teams are small and the cadence for evaluating progress should be short — weekly, or even more regular. In a large organization, team leads may hold daily or weekly updates, while directors may only come together only quarterly. At each level of hierarchy, the period of metrics, data, and KPIs being analyzed is zoomed out, with trends being evaluated over increasing lengths of time.

Even if you have a great organization where everybody’s really disciplined, unless you’re all coming together at the same time and align ourselves with each other, you’re not going to have the outcomes that you’re looking for.

Alignment and discipline almost operate in a loop, mutually dependent on each other — better discipline fosters better alignment, but having alignment will naturally create better disciplines. This loop operates in conjunction with a decision-making framework — the process used to determine changes to the company’s current course.

To me, that’s what the CEO’s job is, to provide a framework for learning, decision making, and delivering results.

Trials and Errors — The Human Factor and Beyond

When it comes to company building, leaders must take on the perspective of good scientists: open to whatever outcomes an experiment reveals. They must evaluate those outcomes in as unbiased a way as possible, and then go back to their hypothesis, repeatedly tweaking it based on each of the outcomes, at a specific cadence, validating over and over. Learning in company building is never just a once and done.

Often, what keeps people from learning effectively is jumping to conclusions. Skipping steps. It’s something that when I reflect on, I catch myself doing — it’s essential to be aware of this tendency. Leaders become too excited about the outcomes and results they believe to be true and think they can get their faster by skipping parts of the learning process — what’s really true is that doing the steps correctly will get you there fastest. Validating your ideas before trying to execute them will save you time and money in the long run.

The steps we often want to skip, are the learning steps — perhaps because they’re time consuming, perhaps because they seem boring or dry, but for success, it’s essential to not get caught in this trap.

It’s something Bruce Cleveland comes back to time and time again: the failure of both startups and enterprises to collect adequate market research to validate their market category is a major factor in why more than 80% of startups and enterprise digital transformation projects fail. They’re working towards outcomes they’ve arbitrarily or instinctually decided are desired, or have made guesses around based on limited evidence. The reality is, they’ve tried to shortcut the validation of their solution, which is the learning step.

You can’t skip steps — you need to execute steps at speed.

Company and transformation leaders must be open to the outcomes of their research and validation, whatever they may be.

Ignoring learning once you’ve done it, just because it doesn’t fit what you wanted to discover or what you believe, is the surest way to sink a company.

To learn more about Accelerate Innovation, contact Jacob Sandler at jacob@accelerateinnovation.com to set up an appointment.

Notes:

[i] Cleveland, Bruce. Traversing The Traction Gap. Radius Book Group, 2019.

[ii] Ibid

Originally published at https://www.accelerateinnovation.com on August 7, 2020.