The second most common reason why VCs pass on an investment is some version of “it’s not big enough.” For a VC to generate a great fund-level return, they typically need to invest in at least one company that has billions of dollars of enterprise value. To do that, most VCs decide that each one of their investments needs to have the potential to exit at or above that amount, even if it’s very unlikely to be the reality for every single investment.
The problem is, most really exciting companies seem “not big enough” to a lot of investors, especially really early on. These startups are often going after markets that don’t currently exist or seem like a niche opportunity (but in reality, are much bigger).
So if you’re a founder choosing to take the VC path, how can you counter investors’ objections about market size?
Below are some different approaches. Keep in mind that some of these are left-brain sort of approaches and others are more right-brain. Both are important and could be effective for different sorts of investors (and different sorts of founders). And if you gravitate towards one, keep in mind that investors that make team decisions will come at this question from multiple angles.
Most market sizes are top-down. “The market for marketing software is $XB dollars so it’s big enough to support some really big companies.” It’s the simplest way to think about market size, so most investors will gravitate that way, especially if you are building a company that is going after an EXISTING market.
One way to augment this is to essentially take the same approach but show brick-by-brick how your market opportunity may be bigger than it seems. This means showing:
You will still need to be going after a pretty large core business for this to resonate in any way. But doing a build up like this can be effective when a prospective investor does believe that the market is somewhat big but would love to see more upside to get fully comfortable.
The previous approach completely fails when you’re talking about markets that don’t quite exist yet or when an investor is not at least on the fence about market size.
Another approach is to do a bottoms-up analysis to demonstrate the scale of market demand for a service like yours. Start with the total number of potential end-users, and use reasonable estimates around customer demand, pricing, market share, etc. The key things that you’ll be pushed on with this sort of an analysis is a) how you are defining the reasonable scope and segmentation of the potential customers, b) how realistic your market share assumptions are, and c) the fact that this is really all conjecture.
One way to address c) is to include solid data points that lend credibility to your assumptions (like a reasonable estimate of how much customers already spend to solve a similar problem or some ROI analysis on your product/service that can be used to estimate reasonable pricing and the “no-brainer-ness” of what you are proposing). Also keep in mind the “vitamin vs. pain-killer” analogy. Bottoms-up approaches tend to work better for “pain-killers” than “vitamins,” even if the ROI of the vitamin seems to hold together.
Being in lock-step with a broad mega-trend is another way that investors get over a seemingly small market. This means that the investor (consciously or not) believes that the mega-trend will either a) drive massive market growth or b) drive the new company to have unusually high market share.
A simple example of this was the shift of enterprise software to the cloud. Once investors believed this was happening, it became more reasonable to think that a new software product in a specific vertical might enjoy extremely rapid adoption and enough market share to build to $100M+ in revenue and $1B+ in enterprise value reasonably quickly. Without this mega-trend, it’s harder to believe this because the pace of adoption may be too slow and it would be too difficult to dislodge existing players with a similar approach without being 10X better, faster, or cheaper.
Another example is IoT. Historically, investors have hated the idea of investing in consumer electronic products. Any early investor or operator at Ring will tell you that early on, almost nobody believed that a smart doorbell company could be “big enough.” But as a mega-trend emerged in this category, we saw more suspension of disbelief in this area for a period of time, for better or worse.
Using analogies can be tricky because they may not land. But if they do, I find that a lot of investors often get fixated on an analogy and that can sufficiently build conviction. When doing this though, it’s important to not just list out similar companies or big exits in the space, but internalize what those analogies communicate.
For example, if there have been some large exits in a seemingly small market, this can be a blessing or a curse. Yes, those analogies exist, but how well do investors know the comp that you are citing? Was it actually a really teeny business bought for pure strategic reasons? Are there actually only one or two buyers who would pay that kind of premium? How many investors would take that bet?
Productivity software is in this category. One could point to companies like Evernote, Sunrise, Acompli, etc. as examples of companies with really nice exits or private market valuations. But looking at this another way, one could say, “Wow, outside of Microsoft, who will pay a premium? The best companies only exited for at most a couple hundred million? Wow, doesn’t Evernote show that it’s really tough to be a truly venture-scale, independent company in this category?”
I find that the best analogies are ones that tend to connect to one of two things. Either, it ties to a mega-trend. For example, “Just like the shift to the cloud allowed for the rise of great companies in different categories, the shift to mobile computing in the enterprise will do the same. So this application that does X is the beginning of a mobile-first HR product that will be like Workday but for mobile.”
The second analogy is to connect yourself to a company with a similar ethos or founders with the same super-power. This is a lot harder to do, and probably happens by inception more than through direct argument. You would probably not say “We are just like the Airbnb founders, so you should believe we can make this work.” But I have heard investors who have gotten to know founders over some time say something like “Wow, these founders are unbelievably obsessed with design and user experience in a way I haven’t seen since (person X). Maybe they really can pull it off!”
This is some version of “Today we are doing X, but that just puts us in a great position to do Y, which is obviously huge.” There are a couple flavors of this.
The first is the bank-shot. This is where X is actually not the foundation of a great sustainable business but could be a gateway to more. A lot of VCs have a hard time with bank-shots, unless you are already demonstrating some really remarkable traction. Usually, the right approach here is to focus on growth and scale as quickly and efficiently as possible when accomplishing X, and make most of your money doing Y down the road when you have a network effect, customer lock-in, or can provide a valuable service that no one else could provide without your scale.
The second version of this is when X is actually pretty decent. Maybe it won’t be “the next Facebook,” but it could certainly get you to a pretty attractive place. Usually, this works well when the underlying business could be profitable and decently large without being too capital-intensive, which gives you more freedom to pursue the bigger opportunity as a next step. This allows an investor to say to themselves, “I could reasonably get a 5–10X on the core business, and there is some small probability that this could actually be a 20X or more.” Usually, this means that the company is in a market that has decent prospects for future funding or M&A, such that if the business hits a double but not a home run, it still could be a good outcome.
One additional approach that I’ve seen founders use quite successfully is what I’ll call “the future bet.” The approach here is to deflect discussions about current market size and focus the discussion on a single, simple bet about the future.
For example, this can be used in almost any rental or sharing economy company (clothing, transportation, equipment, etc). Even though most rental markets aren’t very large, the bet goes something like “do you really believe that people are going to continue spending thousands of money on products that not utilized 90+% of the time? Our bet is that consumers are rapidly moving away from ownership towards sharing and renting, and those multiple billions of dollars are going to shift towards the companies that get this right.
First, don’t forget about what margins mean for market potential. High-margin businesses like software or marketplaces (when revenue is correctly accounted for) can support 10X+ revenue multiples. So the bar for a large scale opportunity is the potential to generate hundreds of millions of dollars in revenue to be worth billions of dollars down the road. For low-margin businesses, the revenue bar for a larger scale opportunity is higher. So when you are talking about how your business can build using a bottoms-up analysis or comparatives, make sure you keep this in mind.
Second, the landscape of potential acquirers plays into this discussion as well. Generally, I don’t recommend founders spend too much time talking about buyers and M&A opportunities, and we don’t obsess over it much here at NextView. But when you are a company that may very well find that the market opportunity is not as big as one thought or hoped, it’s comforting to be in a category with a strong set of folks who would buy you for a reasonable amount. Most investors don’t really focus on downside protection. But psychologically, this could make a difference when one is on the fence because of market size or the risk associated with a bank shot / scope-expansion strategy.
Here at Bowery Capital, we continue to outline where CxOs are in their current enterprise technology upgrade cycle, a trend that lies at the core of our investment thesis. As a result of this shift, we expect “next-generation” technology spend to hit $468B over the next ten years as legacy technologies are swapped out for new. In addition, we see some of this $468B coming from net new areas of spend where humans are displaced by new process, new system, or new automation software. Taking a look at exit data since our last update on this, we continue to believe that we’re early in this cycle. Cumulative revenues of exited next-generation companies is roughly $60-70B of replacement which in our models represent only about 15-20% of the estimated market opportunity. Over the last few years, a number of key themes have emerged corroborating our view that most of the enterprise opportunity lies ahead of us and that enterprise technology spend shifts are upcoming.
1. The Displacement Of On-Premise Solutions Is Early. There’s a cloud option out there for almost every IT workload, but a survey from the Uptime Institute indicates that about two-thirds of enterprise computing is still done in company-owned data centers. While projections vary and you are still seeing product end markets in SaaS infrastructure and SaaS applications grow by 40%+ y/y we are still moving in the right direction with a lot of opportunity. The story is not fully written and enterprise technology spend shifts will continue to occur here.
2. Traditional Vendors Are Increasingly Investing In Next-Gen Offerings. Startups aren’t the only ones taking advantage of this massive shift in spending. The “tech titans” are fully offering as-a-service versions of their own products. Microsoft, Oracle, SAP, and other company’s cloud revenue continues to grow at a huge clip. IDC has predicted correctly every year for the past three years that about 20%+ of all new business software purchases will be SaaS, benefitting long-standing tech leaders and startups alike.
3. Next-Gen Solutions Are Growing The Overall Market. By replacing spend previously allocated to services or personnel, next-gen solutions are bringing new dollars into the tech market. Yesterday’s personnel or consulting expenditures become tomorrow’s SaaS or IoT revenues. This is the core message behind Marc Andreessen’s now-famous “Software Is Eating The World” piece. The key takeaway here is that even Fortune 500 companies are starting to understand that every non-core function has the potential to be replaced by newer, easier-to-adopt alternatives; and their spending behavior is beginning to reflect that mindset as companies work with increasingly young and innovative vendors.
4. Greenfield Opportunities In Vertical Markets Remain Untapped. Several cloud software companies have already made waves serving vertical markets: Veeva Systems, Fleetmatics, Guidewire, and MindBodyare just a few public-company examples. Per Bain research, however, fewer then 15% of companies in transportation, energy, manufacturing, and several other sectors view themselves as being active cloud software adopters. As we detail in another recent report of ours (“Opportunities In Vertical Software”), the Bowery team expects many more vertical SaaS success stories as the specialization of enterprise tech continues and we will continue to make investments here beyond our portfolio beyond companies like Transfix (trucking), CredSimple (healthcare), and Fero Labs (manufacturing).
5. As Consumers Migrate To Mobile, Companies Need Next-Gen Tools To Follow. It’s already well known that consumers are rapidly adopting mobile to manage nearly every aspect of their lives, including how they buy products and services. Certain categories of commerce moved online seemingly overnight (e.g. flowers, office supplies), for example, and the same is happening in mobile. In order to market to, track, engage with and support customers on new platforms like mobile, enterprises must employ next-gen solutions that can transact in new forms of data. Connected industrial sensors, in-store beacons, mobile marketing attribution and mobile CDN are just a few examples.
The enterprise spend shift to cloud software is underway, and by our measure, we’re still early in this cycle. Over the next few years, growth in enterprise tech upgrades will likely out-shadow anything we’ve seen to date. And that means unprecedented opportunity for smart investors, corporate innovators, and startup founders alike that want to get in on these enterprise technology spend shifts.
Consumer companies are the ones that drive the headlines, that generate the most clicks on Techcrunch, and are top of mind for many in the tech industry. So I’d like to celebrate this brief point in time where the enterprise strikes back. While one of the darlings of the last 10 years, Facebook, is getting pummeled, the enterprise market is back in the spotlight.
Look at the Dropbox IPO which priced above its initial value and came out white hot at the end of one of the worst weeks in stock market performance. Couple that with Mulesoft being bought for 21x TTM revenue (see Tomasz Tunguz analysis) at $6.5 billion and Pivotal’s recent S-1 filing and you can see why the enterprise market has everyone’s attention again. However, I’ve been around the markets long enough to know that this too shall pass.
The real story in my mind is about what’s next. It’s true that Salesforce and Workday have created some of the biggest returns in recent enterprise memory. And with that, VC money poured into every category imaginable as every VC and entrepreneur scrambled to create a new system of record…until there were no more new systems of record to be created. My view is that we will see many more of these application layer companies go public in the next couple of years and that will be awesome for sure. There will also still be some amazing companies that raise their Series C, D and beyond funding rounds with scaling metrics. There will also be the few new SaaS app founders who have incredible domain expertise reinventing pieces of the old guard public SaaS companies.
However as a first check investor in enterprise startups, the companies that truly get my attention are more of the infrastructure layer companies like Mulesoft and Pivotal. We are at the beginning stages of one of the biggest IT shifts in history as legacy workloads in the enterprise continue to move to a cloud-native architecture. Being in NYC working with many of the 52 Fortune 500 companies who are undergoing their own migrations and challenges makes us even more excited about what’s ahead. The problem is that as an investor in infrastructure, it’s quite scary to enter a world where AWS commoditizes every bit of infrastructure and elephants like Microsoft and Google are not far behind. Despite that, it’s also hard to ignore the following facts:
and many more threads which can create new billion dollar outcomes. Key here is tying this all to a business problem to solve and not just having infrastructure for infrastructure’s sake.
Salesforce clearly sees the future and it’s in moving a layer deeper into the infrastructure stack, and combining the world of application with back-end and cloud with on-prem. The irony is that the company that led the “no software” movement is the one that bought Mulesoft, a company where 1/2 of its revenue is from software installed on-premise. What Salesforce clearly understands is that in the world of enterprise, integration becomes king as organizations constantly look to get disparate applications, databases and other systems to talk to each other.
“Every digital transformation starts and ends with the customer,” Salesforce CEO Marc Benioff said in a statement. “Together, Salesforce and MuleSoft will enable customers to connect all of the information throughout their enterprise across all public and private clouds and data sources — radically enhancing innovation.”
It’s a digital transformation journey, one that every Fortune 1000 is undergoing. In a world where Gartner predicts that 75% of new applications supporting digital businesses will be built not bought by 2020″, you can see why Mulesoft’s integration platform helps Salesforce future proof itself and embed itself in a future where developers rule.
If you are looking for a story about how large enterprises digitally transform themselves into agile software organizations (to the extent they can), then I suggest reading Pivotal’s recently filed S-1 on Friday. Their ascent over the last 5 years mirrors many of the trends we are hearing about on a daily basis; cloud in all forms — public, private, hybrid, and multi; agility; rise of developers; monolithic apps to microservices, containers, continuous integration/deployment, abstraction of ops and infrastructure, and every Fortune 500 is a software company in disguise. Their growth to over $509mm of revenue from $281mm 2 years ago is a case in point. What Pivotal understood early is that there is no digital transformation and agile application development without infrastructure spend. Benioff clearly understands this which is why he paid such a high multiple for Mulesoft.
For those that don’t know what Pivotal does, here is what they do in a nutshell:
PCF accelerates and streamlines software development by reducing the complexity of building, deploying and operating modern applications. PCF integrates an expansive set of critical, modern software technologies to provide a turnkey cloud-native platform. PCF combines leading open-source software with our robust proprietary software to meet the exacting enterprise-grade requirements of large organizations, including the ability to operate and manage software across private and public cloud environments, such as Amazon Web Services, Microsoft Azure, Google Cloud Platform, VMware vSphere and OpenStack. PCF is sold on a subscription basis.
I’ve been fortunate to have a chance to watch closely through my first check into Greenplum many moons ago which ultimately sold to EMC and spun back out as Pivotal (along with some VMWare assets). I also remember the journey the founders were taking on when they decided to sell into P&L units at Fortune 500s charged with making a more agile company. Instead of selling infrastructure to IT, they were able to sell a vision of how P&L units could deliver on their goals faster. Difficult in the beginning, but proved out over time. These P&L units were the one’s charged with creating the bank of the future, the hotel of the future, the insurance company of the future, all centered around a better customer experience driven off of one platform that allowed developers to be more productive and delivered on any cloud.
My only fear about all of this enterprise infrastructure excitement is that like the SaaS markets of yesteryear, this attention will attract way too much venture capital, driving up prices, and reducing opportunities to create meaningful exits. It’s great that enterprise infrastructure is top of mind, but part of me prefers for it to stay in the background, stealthily delivering amazing results.
Is your experimentation program experiencing push-back from other departments? Marketers and designers who own the brand? Developers with myriad other priorities? Product owners who’ve spent months developing a new feature?
The reality is that experimentation programs often lose steam because they are operating within a silo.
Problems arise when people outside of the optimization team don’t understand the why behind experimentation. When test goals aren’t aligned with other teams’ KPIs. When experimentation successes aren’t celebrated within the organization-at-large.
Optimization champions can struggle to scale their experimentation programs from department initiatives to an organizational strategy. Because to scale, you need buy-in.
Most companies have a few people who are optimizers by nature, interest, or experience. Some may even have a “growth team.” But what really moves the dial is when everyone in the company is on board and thinks this way reflexively, with full support from C-level leaders.– Krista Seiden, Global Analytics Education Lead, Google
But getting buy-in for any initiative – especially one that challenges norms, like experimentation – is no easy task. Particularly if your organization suffers from silo mentality.
In this post, we outline a 5-step process for blasting through the silo-mentality blocks in your organization to create a culture of experimentation.
Our 5-step process for destroying silos so you can scale your experimentation program:
At WiderFunnel, we often hear questions like: How can I get other people on board with our experimentation program? How can I create an organizational culture of experimentation, when our team seems to be working in a bubble?
When a company operates in silos, people have fewer opportunities to understand the priorities of other departments. Teams can become more insular. They may place greater emphasis on their own KPIs, rather than working with the team-at-large towards the organization’s business goals.
But it’s not silos that are necessarily the problem, it’s silo mentality.
And when an experimentation mindset is only adopted by the optimization team, silo mentality can be a major barrier to scaling your program to create a culture of experimentation.
Silo mentality causes people to withhold information, ignore external priorities, and delay processes where other teams are involved. All in an effort to ensure their team’s success over another’s.
Within a silo, members can suffer from groupthink, emphasizing conformity over confrontation and allowing weak ideas or processes to go unchallenged. They rely on intuition to guide their practices, and resist change because it’s new, uncomfortable, and different.
At its worst, silo mentality can point to adversarial dynamics between teams and their leads. It points to internal conflict, either between management as they fight over limited resources or compete to rise to the upper echelons of your organization.
Silo mentality often comes down to leaders, who are creating the goals and priorities for their teams. If team leads experience conflict, this us-against-them mentality can trickle down to their reports.
Managers, particularly under stress, may feel threatened by another manager’s initiatives. This is because silos often form in organizations where leaders are competing. Or, they appear in organizations where there is a clear divide between upper management and employees.
Unfortunately, silo mentality is a pain point for many optimization champions. But every department is a stakeholder in your organization’s growth. And to enable a strong organizational culture of experimentation, every department needs to understand the value of testing—the why.
So, let’s dive in and explore our 5-step process for breaking down silo mentality. At the heart of this process is creating an understanding of what experimentation can do for the individual, the department, and the organization-at-large.
You may be thinking: What does a “culture of experimentation” even look like?
That’s a great question.
A culture of experimentation is about humility and fearlessness. It means that your organization will use testing as a way to let your customers guide your decision making.
Ask yourself these questions to create a vision for your experimentation program:
In traditional business settings, leadership often takes a top-down approach to communication. But experimentation flips this dynamic on its head. Instead of the HiPPO (highest paid person’s opinion) calling all the shots, all ideas must be tested before initiatives can be implemented.
To me, a culture of experimentation is simply measured by the number of people in an organization who are able to admit, ‘I don’t know the answer to that question, but I know how to get it’.
If people within your organization are telling you ‘This is what our customers want’ (without the data to back it up) then you have a problem. Organizations that excel at experimenting aren’t better at knowing what customers want, they are just better at knowing how to find out.–Mike St, Laurent, WiderFunnel Senior Optimization Strategist
The most effective way to persuade others to adopt an experimentation mindset is to subscribe to your vision. You need to demonstrate the test-and-learn values of an Optimization Champion. Values like:
We listen to our gut, then test what it says.
We gather market research, then test it.
We create best practices, then test them.
We listen to our opinions, then test them.
We hear the advice of others, then test it.
We hear the advice of experts, then test it.
We believe in art and science, creativity and discipline, intuition and evidence, and continuous improvement.
We aim for marketing insights.
We aim to improve business results.
We test because it works.
Scientific testing is our crucible for decision-making.
– Chris Goward in “The Optimization Champion’s Handbook”
Once you have clarified your vision, write it down in a declarative statement. And be ready to communicate your vision over. And over. And over.
You can’t achieve a culture of experimentation by yourself. You need testing allies.
Other department leads can help create momentum, acting as internal influencers, inspiring others to adopt an experimentation mindset in their workflow. They can help spread the gospel of a test-and-learn culture.
When executives embrace failure as an inherent and integral part of the learning process, there is a trickle-down effect on the entire enterprise. Increasingly, more employees from more departments are eager to learn about the customer experience and contribute their ideas. With more individuals invested and involved, it’s easier for a company to gain a deeper understanding of its customer base and make informed decisions that drive business value.– Optimizely’s “Creating a Culture of Experimentation”
To do this, you need to understand what will motivate stakeholders to fully adopt an experimentation mindset; how to incentivize them to champion the cause of experimentation. And of course, not everyone will prescribe to your vision.
At least not right away. It may take some finesse. In her Growth & Conversion Virtual Summit presentation, Gretchen Gary, Product Manager at HP, outlined three different types of stakeholders that may have difficulty engaging in a testing culture.
The underlying emotions for all three types of stakeholders are the same:
Your job is to inspire them to overcome these emotions. You need to communicate the possibilities of experimentation to each department in a way that makes sense for them – particularly in terms of their own performance.
What’s in it for your stakeholders?
You, the Optimization Champion, will need to mitigate different perspectives, opinions, and knowledge levels of testing. You’ll want to:
The best thing you can do is try to familiarize yourself with [other team’s] KPIs so you can speak their language and know what might drive them to be more involved with your program.– Gretchen Gary
Support your vision of experimentation by building a business case. Leverage existing case studies to demonstrate how similar organizations have achieved growth. And show, through real-world examples, how different internal teams — from product development to marketing, from branding to IT — have incorporated experimentation into their workflows.
It’s important to create an experimentation protocol so that people across your organization understand how and when they can contribute to the experimentation program.
Remove bottlenecks and unify siloed and understaffed teams by formalizing an optimization methodology, empowering individuals and groups to take ownership to execute it.– Hudson Arnold, Senior Strategy Consultant at Optimizely
A standard process enables any employee to know when they can take ownership over a test and when they’ll need to collaborate with other stakeholders.
Building a test protocol is essential. If I’ve learned anything over the last six years, it is that you really have to have formal test protocol so everyone is aware of how the testing tool works, how a test is operated and performed, and then how you’re reading out your results. You will always get questions about the credibility of the result, so the more education you can do there, the better.– Gretchen Gary
First, evaluate how your experimentation program is currently structurally organized. And think about the ideal structure for your organization and business needs.
Experimentation programs often fall into one of the following organizational structures:
Regardless of how you structure your program, education is a major part of ensuring success when experimentation is a company-wide initiative. Anyone involved in testing should understand the ultimate goals, the experimentation methodology, and how to properly design tests to reveal growth and insights.
When clarifying your organization’s experimentation methodology, you should:
“Every department should have complete access to and be encouraged to submit ideas for experimentation. But this should only be done when the company is also confident it can complete the feedback loop and provide explanation as to the acceptance or rejection of every single idea,” Mike St. Laurent explains.
“An incomplete feedback loop – where people’s ideas get lost in a black hole – is one of the most detrimental things that can happen to the testing culture. Until a feedback loop can be established, it is better for a more isolated testing team to prove the value of the program, without the stressors caused by other parts of the organization getting involved.”
Different departments in your organization offer unique insight, experience, and expertise that can lead to experiment hypotheses. Experimentation protocol should communicate why your organization is testing, and how and when people can contribute.
If silo mentality is limiting your experimentation program, cross-functional teams may be an ideal solution. On cross-functional teams, each member has a different area of expertise and can bring a unique perspective to testing.
Eliminate the territoriality of small teams,” advises Deborah Wahl, CMO of Cadillac and former CMO of McDonald’s. “[Leverage] small, cross-functional teams rather than teams at-large and really get people committed to working towards the same goal.
When you form cross-functional teams, everyone benefits by gaining a deeper understanding of what drives other teams, what KPIs measure their success, and how experimentation can help everyone solve real business problems. They can also generate a wealth of experiment ideas.
Hypothesis volume is (after all) one of the biggest roadblocks that organizations experience in their optimization programs.
Cross-functional teams can channel the conflict associated with silo mentality toward innovative solutions since they help to break down the silo characteristic of groupthink.
How to move from groupthink to think tank
Bruce Tuckman’s theory of group development provides a unique lens for the problem of collaboration within teams. He breaks down the four stages of group development:
In the first stage, forming, a team comes together to learn about the goals of other team members and they become acquainted with the goals of the group. In this case, the goal is growth through experimentation.
Everyone is more polite in this stage, but they are primarily still oriented toward their own desires for an outcome. They are invested in their own KPIs, rather than aligning on a common goal. And that’s fine, because they’re just getting to know each other.
In the second stage, storming, the group learns to trust each other. And conflict starts to rear its head in group discussions, either when members offer different perspectives or when different members make power plays based on title, role, or status within the organization.
But for the team to work, people need to work outside the norms of hierarchy and seniority in favor of collaboration.
In this stage, members feel the excitement of pursuing the goals of the team, but they also may feel suspicion or anxiety over other member’s contributions. You want to make sure this stage happens so that people feel comfortable raising unconventional or even controversial perspectives.
In the context of experimentation, one person’s opinion won’t win out over another person’s opinion. Rather both opinions can be put to the test.
“I find [experimentation] has been a great way to settle disputes over experience and priorities. Basically you just need to find out what people want to know, and offer answers via testing. And that in itself is gaining trust through collaboration. And to do so you need to deliver value to all KPIs, not just the KPIs that your program will be measured on. Aligning on common goals for design, support, operations, and others will really help to drive relevancy of your program,” explains Gretchen Gary.
It’s important to enable the right kind of conflict—the kind that can propel your experimentation program toward new ideas and solutions.
The third stage, norming, is when members of the group start to accept the challenge of meeting their shared goal. They understand the perspectives of others and become tolerant of different working or communication styles. They start to find momentum in the ideation process, and start working out solutions to the problems that arise.
And the last stage, performing, is when the team becomes self-sufficient. They are now committed to the goal and competent at decision-making. And conflict, when it arises, is effectively channeled through to workable solutions.
Teams may go through these stages again and again. And it’s necessary that they do so.
Because you want weak ideas to be challenged. And you want innovative ideas to be applied in your experimentation program.
Free-flowing internal communication is essential in maintaining and scaling experimentation at your organization.
You should be spreading experiment research, results, and learnings across departments. Those learnings can inform other team’s initiatives, and plant the seed for further marketing hypotheses.
Information has a shelf-life in this era of rapid change. So, the more fluid your internal communication, the more central and accessible your data, the more likely it will be put to use.
How are customer learnings and insights shared at your organization?
One method for sharing information is to create an “intelligence report.”
An intelligence report combines data generated from your organization and data derived from external sources. Paired with stories and insights about experimentation, an intelligence report can be a helpful tool for inciting creativity and generating experimentation ideas.
Another method is to provide regular company-wide results presentations. This creates an opportunity for team members and leaders to hear recent results and customer insights, and be inspired to adopt the test-and-learn mindset. It also provides a space for individuals to express their objections, which is essential in breaking down the silo mindset.
But sharing insights can be also be more informal.
WiderFunnel Strategist Dennis Pavlina shares how one of his clients posts recent test results in the bathroom stalls of their office building to encourage engagement.
A new idea doesn’t get anywhere unless someone champions it, but it’s championship without ownership. Keep it fun and find a way to celebrate the failures. Every failure has a great nugget in it, so how do you pull those out and show people what they gain from it, because that’s what makes the next phase successful.– Deborah Wahl
Whatever tactic you find most effective for your organization, information dissemination is key. As is giving credit for experiment wins! At WiderFunnel, we credit every single contributor – ideator, strategist, designer, developer, project manager, and more – when we share our experiment results. Because it takes a team to make truly drive growth with testing.
A lot of what we talked about in this post is about building trust.
People need to trust systems, procedures and methodologies for them to work. And every initiative in breaking down silos should be geared towards earning that trust.
Because trust is buy-in. It’s a commitment to the process.
Creating and maintaining a culture of experimentation doesn’t happen in a straightforward, sequential manner. It’s an iterative process. For example, you’ll want to:
Because a culture of experimentation is about continuous exploration and validation. And it’s about testing and optimizing what you’ve learned as an organization. Which means you’ll need to apply these concepts over and over.
Make the terms a part of your vocabulary. Make the steps a part of your routine. Day in and day out.