Many executives are seeking a “digital transformation” as a lofty solution.
But transformative change really happens in sprints.
That’s because experimentation is the agile approach to business evolution.
When it comes to business evolution—you don’t know, what you don’t know. If you aren’t learning, your business is not evolving at the pace you need to surpass your competitors.
Let’s start with a story…
In the late 1990s, business leaders were hyped on the possibilities the internet offered. Countless start-ups sprouted up to get an early grasp on web business.
But by 2002, when the dot.com bubble burst, many companies struggled to survive. Companies like Pets.com, WorldCom, and WebVan failed completely, and other organizations experienced declining revenues after a period of optimistic growth.
André Morys, Managing Partner of GO Group Digital and Co-Founder of konversionsKRAFT, lived through this experience. In the first five years, it seemed like he had hit pay dirt with his business. His team was confident about their future direction until 2002 when they struggled with declining revenue of 60% over three months.
But when André reflects back on his company’s history, this struggle provided an unprecedented opportunity to learn about leadership and finance, culture and motivation. The aftermath of the dot.com bubble accelerated his understanding of how to strategically evolve his business for future growth.
There are many parallels in today’s market. 52% of the Fortune 500 since 2000 don’t exist anymore. And business leaders are constantly battling this threat.
Today, many Executive teams are aspiring to the “digital transformation” solution because how can you keep pace with the market, with the rapid technological change?
In this post, you will learn key insights from André Morys, adapted from his presentation, “The Source of Disruption Is in the Mind of the Customer,” at Unbounce’s 2018 CTA conference.
These key insights include:
- Why we should be focusing on velocity of learnings (not tests) by prioritizing impactful experiments
- How to speak the same language as your Executive team by pinpointing their emotions and motivations
- And why a valuable customer experience is at the heart of business evolution.
Scoping out the big picture: The Gartner Hype Cycle
Roy Amara, a researcher, scientist and futurist, claimed that we tend to overestimate the effects of technology in the short term and underestimate the effects in the long term. This phenomenon is called Amara’s Law.
When we think big picture about digital transformation, this forecast is true. We are hyped up on the new tools and technologies when we first adopt them, but once they present challenges, we can become discouraged. Because how can really leverage technology to solve our business problems?
The research firm, Gartner, furthered Amara’s Law by introducing the concept of a hype cycle. When it comes to experimentation, WiderFunnel traces the maturity of organizations through its five different stages, including:
- The Technology Trigger: You are excited at the possibilities of experimentation but business impact is yet to be proven at this initial stage.
- Peak of Inflated Expectations: Early adopters claim success with testing at this stage, but you might be failing to properly leverage experimentation as a strategy.
- The Trough of Disillusionment: Initial hype is tapering off. Internal ennthusiasm fades. You might recognize at this stage that experimentation must provide results to continue the investment.
- The Slope of Enlightenment: Experimentation is starting to show its possibilities as you understand how to better leverage testing to create business impact.
- And the Plateau of Productivity: With consistent bottom-line impacts, you can now start to leverage experimentation as an organizational strategy for business evolution.
Technology can be a solution to business evolution, but leaders need to strategize how to leverage technology to solve real business problems. André articulated that such challenges are actually what is driving your digital transformation.
As you start to scale your experimentation program, these learnings make your team’s workflow more efficient and allow you to zero in on the hypotheses that can make the most impact.
The good news: You can accelerate your learnings for how to evolve your business through experimentation. Even if these seem small wins, compounded over time, you are truly driving your organization’s growth through digital technology. (For example, what is the calculated impact of a reported 2% lift over 50 experiments? It’s not 100%; it’s a compounded 264%!)
Once organizations introduce a defined process and protocol, have systems and procedures in place for prioritizing experiments by impact, they are able to scale their programs for long-term business evolution.
- Scaling experimentation: The 5 key pillars of maturity: On-demand webinar recording with Director of Strategy, Nick So
Addressing your strategic blind spots: The Dunning-Kruger Effect
It is far more common for people to allow ego to stand in the way of learning.
If you are relying on the HiPPO’s strategy for business evolution, how confident are you in their abilities? And do you think they have the competence to judge their limitations?
When André reflects back on his first five years of business before the dot.com bubble burst, he sees how his confidence was an example of the Dunning-Kruger Effect.
The Dunning-Kruger Effect is a cognitive bias where an individual with low ability have mistaken confidence and believe they are more competent than they are.
And people with high competence often view themselves as having lower abilities than in actuality. As André states, “You don’t know, what you don’t know.”
This cognitive bias has been explored in depth by psychologists David Dunning and Justin Kruger. Their research shows that people that suffer from the Dunning-Kruger Effect may resist constructive criticism if it doesn’t align with their own self-perception. They may question the evaluation and even deem the process as flawed.
Kruger and Dunning’s interpretation is that accurately assessing skill level relies on some of the same core abilities as actually performing that skill, so the least competent suffer a double deficit. Not only are they incompetent, but they lack the mental tools to judge their own incompetence.
When it comes to innovation, the Dunning-Kruger Effect creates a blind spot for threats and opportunities that can affect your business success. Instead, a business leader needs to always interrogate their perception of reality to get closer to the truth.
Truth―more precisely, an accurate understanding of reality―is the essential foundation for producing good outcomes.
André now sees how the growth and learning that came out of the dot.com bubble challenged his own self-perception. He began to understand the implications of his business decisions, and he became more in tune with the possibilities of the unknown.
Applying his own professional and personal learnings from this experience to the world of experimentation, André sees a chasm between the manager’s aspirations of digital transformation and the optimizer’s experiments that lead to the desired data-driven decision making.
“The problem is that [the Dunning-Kruger Effect] happens in organizations all the time,” explains André.
“Many optimizers are very skilled in experimentation, they know everything about it: about A/B Testing, confidence levels and statistics, testing tools and psychology. Whereas management has no idea; they don’t get it. They are talking about digital transformation as a big project, while their optimization team is really doing the work that is needed.”
What makes André’s argument that organizations need to focus on learnings so compelling is that a culture of experimentation—testing and learning—can really drive your business evolution and lead to the hockey-stick growth that you need to sustain your market.
But the Executive team and the tactical experts need to get on the same page, especially when it comes to successful business evolution.
As an Optimization Champion, you are the catalyst.
Change-agents build bridges between their peers, empowering them to accept change as it comes. They understand how to build and nurture relationships in order to find common ground with others. They are organized and understand how to speak to c-level executives clearly.
André’s comparison of Optimization Champions and Executive teams within an organization with the Dunning-Kruger effect is insightful.
He argued that Optimization Champions have a high-level of competence, but they don’t understand how they can position their work to gain Executive buy-in because they are too immersed into their specialization.
But he also emphasized that Optimization Champions truly are the catalysts of digital transformation. His advice is simple: Optimization Champions need to speak the same language as the Executive team.
Optimizers should stop talking about uplifts and statistics and A/B tests. They should talk about what A/B testing changes within an organization. They should report business impact, not statistical confidence levels, so they are compatible, so they are speaking at the manager’s level.
Experimentation drives the digital transformation at many successful organizations. Just look at Airbnb, or Uber, or Facebook. These organizations test and learn their way to business evolution.
André points out that experimentation facilitates an organization’s digital transformation, but many managers just don’t know it yet.
Your communication of experimentation’s value needs to be accessible to those who aren’t educated in the technical aspects.
And that’s exactly what André recommends. Understand your experimentation program’s internal stakeholders—your Executive team. Understand their fears and anxieties, their emotions and motivations when communicating your experimentation program’s value.
As a marketer, you are best prepared for this task as you can craft your internal stakeholder’s persona, so you can demonstrate—in their language—the impact of your program on the business.
Build your internal stakeholder persona to get buy-in!
In your experimentation sprints, prioritize business impact over speed.
If you’re not failing, you’re not pushing your limits, and if you’re not pushing your limits, you’re not maximizing your potential.”
In the world of experimentation, there is an emphasis on experimentation velocity. It makes sense: the more tests that you are running, the more insights you can obtain about your business.
But the buzz around velocity has led many leaders to focus on speed, rather than the quality of insights and the business impact of experiments.
And if you aren’t focusing on business impact, you’ll never get on the same page as your Executive team.
If you decide to test many ideas, quickly, you are sacrificing your ability to really validate and leverage an idea. One winning A/B test may mean quick conversion rate lift, but it doesn’t mean you’ve explored the full potential of that idea.
Experimentation sprints are chock full of business insights and impact—exactly what organizations need to continuously evolve their businesses.
That’s why our emphasis as optimizers should be on velocity of learnings, not just experiments.
It’s an agile approach to business evolution. And that’s a sentiment that was echoed by Johnny Russo, in “The 5 Pillars of Digital Transformation Strategy at Mark’s.”
“Because how do we process all this change and learning, without being efficient?” he described.
“Those organizations first to adapt, will be most prepared. And so I think the foundation has to be an agile methodology.”
But optimizers need to ensure they are driving higher impact experiments and deeper learnings by implementing rigorous processes. A prioritization framework ensures you are launching experiments that have the highest potential for results, on the most important areas of your business, with the easiest implementation.
- How are you tracking your learnings from each experiment for future hypothesis development? Evaluate your experiment archive
- How to create the most powerful marketing test hypotheses
- How to be a heavy hitter in enterprise e-commerce CRO: 8 tactics to optimize your optimization program
But to increase experiment velocity, you need a defined optimization process.
According to the “State of Experimentation Maturity 2018” original research report, experiment velocity is a focal point for most organizations.
52% of Small and Medium Enterprises and 64% of Large Enterprises in the survey indicated they plan to increase experiment velocity in the next year.
However, only 24% and 23% (respectively) of these organizations plan to increase budget, which can only add emphasis on workflow efficiency and prioritization so that you are not straining your resources.
“One of the most common roadblocks to increasing velocity is workflow efficiency. Review and document your workflows from ideation to analysis to ensure seamless experiment execution,” explains Natasha Wahid in the research report.
“Another common roadblock is a lack of resources, particularly in Design and Web Development. Ensure you have the right team in place to up your velocity, and plan for possible bottlenecks.”
As André articulated, focusing on business impact instead of speed will ensure you are learning faster than your competition.
That’s because digital transformation is not about implementing technology, it’s about leveraging technology to accelerate your business.
How to use the PIE Prioritization Framework to identify the most impactful experiments.
You can’t test everywhere or everything at once. With limited time and resources and, most importantly, limited traffic to allocate to each test, prioritization is a key part of your experimentation plan.
Prioritizing where you invest energy will give you better returns by emphasizing pages that are more important to your business.
The PIE Framework is made up of the three criteria you should consider to prioritize which pages to test and in which order: Potential, Importance, and Ease.
Potential: How much improvement can be made on this page(s)? You should prioritize your worst performers. This should take into account your web analytics data, customer data, and expert heuristic analysis of user scenarios.
Importance: How valuable is the traffic to this page(s)? Your most important pages are those with the highest volume and the costliest traffic. You may have identified pages that perform terribly, but if they don’t have significant volume of costly traffic, they aren’t experimentation priorities.
Ease: How difficult will it be to implement an experiment on a page or template? The final consideration is the degree of difficulty of actually running a test on this page, which includes technical implementation, and organizational or political barriers.
The less time and resources you need to invest for the same return, the better. This includes both technical and “political” ease. A page that would be technically easy to test on may have many stakeholders or vested interests that can cause barriers (like your homepage, for example).
You can quantify each of your potential opportunities based on these criteria to create your test priority list. We use the PIE Framework in a table to turn all of these data inputs into an objective number ranking.
Learn more about PIE
Seek the “truth” in a delightful customer experience.
Imagine your a taxi service. And you are seeing Uber’s market success as a threat to your livelihood.
What makes them more successful?
Get out the whiteboards and some might write: “We need an app!” or “We need to hire a data scientist!” Because on the superficial level, data and technology seem pivotal to a digital transformation.
But when you look deeper at Uber’s strategy, you will see that they focus on delighting their customers with their experience.
And because of Uber’s success, how people get rides has radically changed.
But Uber is not the only example.
Many businesses have transformed the market through delightful customer experiences.
Amazon makes online ordering a breeze. No more long wait times, André joked that he has to slow down their service so that he will actually be there to accept the order.
And Airbnb—they’ve made vacationing a unique and desirable experience.
André, an expert in emotional marketing and the Limbic Model, emphasized the need to go beyond conversions and focus on a customer experience that delights.
Companies who fail to embrace CX as strategic path to growth won’t just be lagging, they’ll get left behind.
And that’s because you can drive experiments with the most business impact by honing in on your customer experience.
That means, you have to understand your customer—their fears and anxieties, their thoughts and desires—when designing an experience to meet their emotional needs and states.
The most successful organizations have honed in on what makes their experience delightful. In “Moving the needle: Strategic metric setting for your experimentation program,” I talked about creating a True North metric that align to your value proposition, as a way of creating internal focus on your customer experience.
Slack, a team collaboration program focuses on optimizing for teams that send over 2,000 messages.
LinkedIn focuses on quality sign-up, ensuring that new users are actively making connections on the social platform.
And Facebook optimizes for new users who make 10 friends in seven days.
See it’s not tools and technologies that evolve these businesses. It’s how they leverage digital technologies across the organization to solve real business problems.
And when you align your customer experience goals with your experimentation program, you are the competition.
You learn and adapt with each new experiment to what your customer wants and needs from your experience. That’s how you drive business impact. That’s how you get internal buy-in.
But what’s more—that’s how you stay relevant in the market.
And if you are experimenting, keep slogging through the “trough of disillusionment”.
André Morys knows from experience that business that do, can reach enlightenment.
What are your biggest challenges with experimentation that can be attributed to the “trough of disillusionment”? We’d love to hear your comments!
Learn how to scale your experimentation program!
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