Continuous Foresight: Visualize Strategy By Testing For Customer Change

By Andre Marquis and Diana Jovin

In prior posts, we’ve talked about why large companies need innovation at scale.  In contrast to innovation efforts that incubate a small number of ideas, innovation programs that test dozens to hundreds of business model innovations in a systematic way can deliver significant benefits to large companies. This large-scale testing gives valuable data about which new business model opportunities customers are ready to embrace and, just as importantly, which ones they are not prepared for today.  With the data obtained from this type of continuous, systematic testing, large companies can make strategy a data-driven discipline and align their innovation investments in a capital-efficient way that generates measurable results quickly.

Why Scaled Innovation Testing Is Essential

Most of the companies we work with have historically taken an organization-centric approach to innovation. They describe their strategy from the company’s perspective – as sustaining or disruptive, design-centered, open or ambidextrous.  The challenge with taking an organization-centric approach is that it tends to focus on current capabilities instead of figuring out when important customers are willing to change. Bill Gross of Idealabs analyzed his investments in this TED Talk and showed that timing was the most important factor in startup success, not the idea, team, business model, or investment amount. We can reinforce this observation across the thousands of innovation teams we work with.

A common example we see today – is it essential to invest in blockchain, data platforms, or AI business models? Those are testable strategic hypotheses, yet almost all the innovation programs we see do a very poor job systematically segmenting customers and then systematically testing across business model options to generate strategic insights. At best, a few innovation teams will do some “lean startup” work to try a couple of options.

Getting customer segmentation and timing right is ever more critical because there is increasingly less and less friction in two customer adoption dimension:

  • when customers will change their behavior
  • how much change they are willing to embrace to adopt new business models

Why Now? Technology Permanently Has Increased The Speed Of Customer Change

The key contributor to this lower friction behind customer change is technology. Successive waves of technology innovation over the last several decades have dramatically accelerated how we communicate in commerce and socially:

Technology WaveMarket Impact
80sSemiconductor/PCComputing productivity to processes formerly managed by phone and by paper
90sInternetDisintermediation and self-service to markets that had been serviced by distribution channels
00sSmartphone/app economyScale and continuous presence to community and communication
10sRealtime economyRealtime data in business operations and apps, with operational flexibility – the ability to scale resources up and down through cloud-native architectures.
Waves of Technology and Customer Change

Each of these waves of technology has enabled markets to change more rapidly, at a greater scale, and with a greater variety of business models. The former mainstay method of relying on one’s distribution channels and salesforce for market feedback and market share is long gone.

Consider, for example, a physical product such as fiberglass insulation. It is essential. It keeps us warm and cool in our homes daily, but we give it little thought.  In the early ‘90s, we interviewed a number of dealers around the country who carried this product for the construction market.  They stocked physical products, typically allocating warehouse space to three suppliers – one which gave them the best service and price for volume, a second which ensured availability, and third for niche features or significantly lower pricing.  This approach to decision-making was repeated in dealers around the country.  Fast forward to today – and manufacturers can market to consumers and builders directly.  Shipping options have expanded – including drop-shipping where the product is never on a shelf at all.  Many more people than the dealer can influence reviews and opinions.  And there are now prefab homes (e.g. Tiny Homes and instant ADUs) that are also being marketed directly to consumers.  The various ways to reach the end goal – of being the selected product inside a new structure – have increased. And, the channels for contributing and soliciting market feedback have increased.  The insulation dealer is no longer the primary influencer in a purchase decision or in the customer-manufacturer feedback cycle.

In almost every industry, the opportunities for business model innovation have increased. Technology has allowed the elements of customer value to be pulled apart and reassembled in new and interesting ways, and then delivered at scale.  Thales S. Teixeira and Greg Piechota’s recent book, “Unlocking the Customer Value Chain: How Decoupling Drives Consumer Disruption” has many good examples of how industries have quickly shifted based on value chain reconfiguration. Even with an agile process, delivering new features in existing products through existing value chains is no longer enough.

Continuous Customer-Driven Testing Delivers Continuous Foresight

Companies face the challenge of figuring out what strategies to pursue without good feedback mechanisms. The traditional product development process “feature innovation” race locks companies into their existing marketing and sales feedback channels – a classic innovators dilemma. Innovation incubators don’t test enough opportunities to inform the larger company’s strategy. Idea competitions don’t actually test the ideas generated on customers. Creating “innovation outposts” is still seen as a valid operational model. When we see companies discuss innovation strategy at Board meetings, it is about individual startup teams’ fate instead of reviewing the data from systematically testing a portfolio of options.

Rapid, scaled business model innovation is a different approach. Rather than considering whether customers need an incremental product feature or employee-generated ideas, companies use their innovation process to generate ideas across a spread of new product and business model opportunities structured way that spans potential future strategy options. Each new business model tested can now consider: 

  • Does a new business model affect existing customers?
  • Does it “resegment” existing customers into adopters and laggards based on their desire to adopt a new offering immediately and what are those segment boundaries?
  • Does it capture completely new customers?
  • Does it require a customer acquisition and value delivery pipeline that is different from existing lines of business?
  • Does it require Open Innovation – collaboration with partners and startups – deliver?
  • Is there evidence that massive customer is likely to happen relatively soon, that is, there are the makings of a unicorn?
  • What span of business model innovations is important to consider to detect potential unicorns and big wins?

Early Customer Traction With New Business Models Becomes The Key Indicator 

By conducting continuous large-scale experimentation, companies can gain a view of how a particular business model innovations affect customer segments across a market.  For example, are no customers interested based doing customer interviews, are some willing to try minimum viable products but will not commit to buying, or is there growing segment-by-segment adoption? Companies can then visualize:

  • How the composition of customer segments in their market is changing
  • How understanding new customer segment boundaries and adoption patterns affect their current customer mix and revenue patterns
  • What upcoming product and new business model portfolio changes are needed to contribute to customer mix and retention goals?
  • Whether strategy needs to direct new business model testing at retaining customers, growing revenue from customers, or creating new customers

Understanding how markets are changing and how that intersects with innovation investment can help companies be proactive about leading market change rather than be reactive to trends and trying to catch up to startups that already have traction.

Driving Strategy with Continuous Foresight: The Value Of Innovation Programs 

Broad-scale business model innovation testing also helps lay the foundation for an agile, continuously adaptive strategy.  This should be a conscious goal of innovation programs – contributing to strategy and forward-looking investments based on real customer traction data.

Two key elements to driving an agile, adaptive strategy are continuous testing and broad testing:

  • Continuous testing is necessary to understand the timing and consequences of market change. For example, if you think back to the iPhone introduction, it was initially perceived as a niche product, focused on early adopters who appreciated the design and were willing to pay for it.  Nokia still dominated the market with its broad portfolio of flip-phone designs, and consumers expected keypads on their phones. One could think about the iPhone as “not my market,” or “not likely to be the mass part of my market.” The introduction of the app store in 2008 was the key driver of the mass adoption of the iPhone. It then became a vehicle for an application universe that customers wanted. As it turns out, the phone itself was only a small part of the customer value. Continuous testing helps companies stay on top of assumptions about market dynamics and market direction.
  • Breadth of testing across types of business model innovations and customer segments is also essential. By considering many business model innovation dimensions – customer, product, channel, delivery, finance, etc. – companies can obtain an unparalleled view of predictive markers about where a market is headed.  This, in turn, drives focus on the strategic themes that are getting customer traction.

Remember, that timing – having the right offering at the right time for the right customer – is the key to innovation success and avoiding disruption. The bigger idea is that larger companies can be well-positioned to out-compete startups by using their innovation programs as a sensing network for strategies. Visualizing business model tests’ success across customer segments and strategic options is a way to generate continuous foresight.  

We see lots of companies investing in startup scouting and corporate venture capital, which is another way of trying to generate strategic foresight. Unfortunately, that foresight often happens too late – after startups are already successfully disrupting the market and taking customers. We also worry that outsourcing to startups is simply giving up on creating the capacity to continuously innovate inside a company. That strategy ensures disruption!

Finally, we also see companies using trend research, scenario planning, technology mapping, and the like as their primary way of setting strategy. Those all assume we can predict the future. Unfortunately, when it comes to innovation, No One Can Pick Winners.

This is why our work has been so focused not only on running business model innovation as a scalable business process at the team level and putting each team’s validation results in the context of strategic hypotheses. It allows companies to visualize where early customer traction is occurring relative to strategic options. So foresight is based on real data from real customers in real-time. How do you visualize your innovation portfolio?

Ask Us!

If you want to learn more about our system for launching new businesses while generating continuous foresight, contact us here. Now is an exciting time in business model innovation!

Sustaining Versus Disruptive Innovation: Does it Matter?

by Andre Marquis and Diana Jovin

Since Clayton Christensen’s groundbreaking book The Innovator’s Dilemma came out in 1997, much discussion about innovation has centered on the differences between sustaining and disruptive innovation. Christensen noted that large organizations were good at sustaining innovation, that is, the types of innovation that move product features and value forward to existing core customers and markets. He further observed that large organizations were less successful at disruptive innovation – the type of innovation that creates or redefines markets. He attributed this to organizational dynamics that keep business resources allocated towards existing markets and business models. Disruptive innovation seemed to be the domain of startups and the lack of existing infrastructure or customers that influence corporate behavior explained why startups could leapfrog incumbents and thrive with substantially less access to capital and customers.

This book sparked two decades of excellent discussions about whether large companies can pursue disruptive innovation or whether it is necessarily the domain of startups. McKinsey proposed a time horizon model: the more disruptive an innovation, the further out on the time horizon it would be for returns. They described this as a Horizon 1, 2, 3 model, and noted that companies should maintain a mix of innovation investment types to meet short-term and long-term horizon benefits. Eric Schmidt of Google took this one step further, describing the appropriate allocation of resources amongst the three time horizons as (70/20/10) follows, with the expected return from each horizon reversed (10/20/70). 

Another innovation concept is the idea of “ambidextrous” organizations, that is organizations that adopt a dual-mode structure in which one set of organization, processes, and norms are adopted to serve core markets, and another are adopted to “free” business units to pursue disruptive innovation. The objective of this dual-mode structure is to allow companies to remain good at steady improvements in an existing business and get better at breakthrough innovation through the creation of separate divisions that can operate with different underlying norms, processes, and regulations more favorable to fast-moving, disruptive ideas. The notions of ambidexterity and Lean Startup posit that different organizational cultures based on the level of disruption is a key ingredient to successful innovation outcomes.

But, do these distinctions really matter and are they based on the right set of assumptions? Some ideas to consider:

Deciding whether an innovation is sustaining or disruptive is no longer useful.

  • Technology has now advanced to a point where new innovations can disrupt entire markets in a very short time period. In 2013, unicorn startups (greater than $1B) were developed in only four ecosystems. Now, they are being produced in more than 80 worldwide. Whether an idea is developed by a startup or a large organization, it can reach scale very quickly. The “when” assumptions behind time horizons are not very useful.
  • An idea can seem disruptive at the start and as it is tested on customers, evolve to be more incremental, or vice versa. If customers are willing to change quickly, an incremental idea can suddenly need to become a disruptive business model change to be successful. We see companies miss these opportunities all the time.
  • An understanding of whether an idea is sustaining or disruptive is really an after-the-fact assessment. What is more important for driving the strategic success of your business is driving your culture (incentives and decision-making) using forward-looking indicators – how and where is change occurring and how should you be responding to adapt?

We see a lot of companies pursue what they see as a multi-horizon strategy, with agile product development focused on the core business (Horizon 1), a small Lean Startup accelerator or two to explore Horizon 2 opportunities, and idea competitions and incubators for Horizon 3 projects. The outcome of this is that the Horizon 2 and 3 projects have no opportunity to change the direction of the Horizon 1 business. A new way to think about this is not about horizons, but how to create a feedback system to visualize where and how customers are willing to change. Understanding and valuing early customer change indicators not only leads to new customers and new business models, but also an understanding of how customer change is disrupting your core business and if you need to react right now (Horizon 1) or can wait (Horizon 3).

Creating innovation-customer change feedback cycles can be the key to getting out of the Horizon and “what does ambidexterity mean to us?” trap.

  • Run innovation at scale. Testing and experimenting with dozens to hundreds of ideas, rather than a few, gives you a landscape of real-time data on how customer segments are actually shifting and how quickly.
  • Stop projects that aren’t driving customers to change their behavior early in your product development process. Often you can do that with customer interviews alone.
  • Insight derived from innovation at scale and early customer traction needs to feed back directly into business units rather than occur in isolated outposts or incubators. That lets you adjust your strategy continuously at both the innovation team level (Should we keep investing in this team’s specific business model?) and at the Board level (Which strategies are actually getting customer adoption? Should we be adjusting our core business strategy and operations to take advantage of this newfound willingness to change?).

We most often see companies trying to pick Idea Competition winners based on no actual customer data on the one hand and discussing what the success or failure of a single innovation team means to their strategy without rigorously testing a range of opportunities on customers on the other. This is trying to fly while almost completely blind.

Here are some questions to ask about your innovation efforts and whether they could drive your current and future strategies. Do you have an innovation process that lets you:

  • See where disruption is occurring in existing, adjacent, and new markets?
  • Drive a consistent, measurable, improvable innovation process?
  • Create closed-loop feedback with your existing sales organization?
  • Involve employees from all parts of your organization so you can take advantage of all of their knowledge and touchpoints across different customer types and geographies?
  • Drive Open Innovation partnerships with large companies and startups in a way that you can rapidly stop bad projects while getting strategic insights on what partnerships are actually driving early customer traction?

A recent McKinsey study noted that only 6% of those surveyed were satisfied with their innovation efforts. That makes sense to us as so much innovation happens in outposts or idea competitions that have no opportunity to drive strategic insights while actually launching new businesses. We see lots of programs run on ideation platforms or using PDF printouts of canvases paired with PowerPoint presentations. Would you run finance, product development, or sales using those simple kinds of tools at your company?

Steve Blank’s The Four Steps to the Epiphany was published 15 years ago and was a significant step in understanding how to operationalize customer-driven business model validation.

We are at the beginning of a new era that builds on his validation ideas, but with a much bigger vision – a real-time innovation process run on a digital platform can drive strategy across multiple horizons and create an adaptive, optimizing innovation culture. Is your organization getting ready for the digital transformation of innovation?

We are happy to share more of what we’ve learned about building efficient digital innovation ecosystems that continuously generate strategic insights. We call that capability Innovation Performance Management. You can contact us at Hypershift Systems.

Don’t Innovate Like Steve Jobs

When it comes to creating the next new, innovative business, no one can pick winners – not even the geniuses of Silicon Valley. The evidence for this is overwhelming. At any one time, there are more than 20,000 funded startups here in the San Francisco Bay Area. The big successes we get are from lots and lots of smart people trying small improvements until one of them works. Venture funds are organized around the expectation that the majority of their portfolio firms will fail.

When I’m giving a talk and point out that no one can pick winners, inevitably a hand goes up in the audience and someone asks a version of, “Well, what about Steve Jobs? He was always right.” Sigh.

Let’s look more closely at the track record of this gifted genius and his amazing collaborators who have had such a big impact on how billions of people live their lives every day.

What I learned from my research is that even for a genius, innovation:

Continue reading “Don’t Innovate Like Steve Jobs”

The Agile Business Model Innovation Manifesto

Creating new business models is hard. Creating an organization that is continuously creating new business models is extremely hard. The closest I’ve seen is organizations running an Agile Product Development (APD) process. APD focuses on creating a culture and processes that support small, semi-autonomous, high performance teams.

APD has done a lot to radically accelerate product delivery, better value employees, prioritize decision making and show how organizations can have speed and stability. With the DevOps revolution evolving into the GitOps revolution, the increase in speed between Agile organizations and organizations that use waterfall product development models can easily top an order of magnitude.

Unfortunately, APD isn’t the answer when it comes to business model innovation. The brightest minds in Agile lay this out well even if they don’t say the words “business model innovation” when they talk about where APD has challenges:

Continue reading “The Agile Business Model Innovation Manifesto”

Agile Business Model Innovation: Because No One Can Pick Winners

We’ve coached over a thousand startups and large company teams working to create new products based on innovative business models. The number one mistake they making is simply recognizing that when it comes to innovation, no one can pick winners. The data is overwhelming:

  • Only 12% of venture capital funds outperform the market (1)
  • Just 7% of angel investments account for 75% of the returns (2)
  • YCombinator, perhaps the top Silicon Valley accelerator, made 940 investments to get 8 $1b valuation startups – a 0.009 “unicorn” yield (3)

Innovation efforts don’t fail because of stupidity. They fail because of math. Even Steve Jobs couldn’t pick winners. After the successful Apple II, he sold only 50,000 Apple IIIs and roughly 100,000 Lisa’s. That’s at a time when over 1.3 million PCs were being sold per year. The original Macintosh sold 320,000 units in 1984 against 2 million PCs and Mac sales promptly went down to 200,000 units in 1985. NeXT sold about 50,000 computers in a market where 80 million PCs were being sold each year. Pixar was an unsuccessful hardware company, then a somewhat successful software company, and finally, a very successful movie company. Even for a genius, innovation is hard. (The One Device has a more nuanced view of Job’s prodigious talents while making us remember the iPod Hi-Fi, ROKR phone, and long, twisty road to the iPhone.)

Continue reading “Agile Business Model Innovation: Because No One Can Pick Winners”

Blowing up the Business Plan at UC Berkeley

A post I wrote with Steve Blank for his blog:

When I came to U.C. Berkeley in 2010 to run the Lester Center for Entrepreneurship in the Haas School of Business we were teaching entrepreneurship the same way as when I was a student back in 1995. Our core MBA class used the seminal textbook New Venture Creation by Jeffrey Timmons of Babson College that was first published in 1977. The final deliverable for that class was a 30-page business plan. We had multiple business plan competitions. As I looked around at other schools, I saw pretty much the same landscape – business plan classes, business plan competitions and loosely coupled accelerators that focused primarily on mentoring.

Over my career as a serial entrepreneur I observed that since the late 1990s, no early-stage Silicon Valley investor had used business plans to screen investments. Even those who asked for them never read them. Traction and evidence from customers were what investors were looking for – even in “slow” sectors like healthcare and energy. There had been tectonic shifts in the startup world, but our business school curriculum had barely moved.

Continue reading “Blowing up the Business Plan at UC Berkeley”

About Andre Marquis

Andre Marquis is the CEO of Hypershift Systems and Founder of the Innovation Acceleration Group at the University of California Berkeley. Mr. Marquis has a long record of successful startups with a particular focus on winning in markets where scalability is critical. Two companies Mr. Marquis helped start became publicly traded and a third was acquired by Amazon for over $190 million. The Chorus Group at Eli Lilly, which he co-founded, is a significant corporate lean business model innovation that sped up and lowered the cost of drug development by almost an order of magnitude. His most recent startup, Amplyx Pharmaceuticals, has raised almost $200m.

Andre was the Executive Director of the Lester Center for Entrepreneurship where and his team built a global-scale business model validation process that delivers funded startups and breakthrough product and service innovations for corporations. His Hypershift methodology has scaled to support rigorously testing hundreds of business model innovations per year and delivered breakthrough results to >1,200 teams. Real innovation has a high failure rate and requires testing lots of opportunities (scale) and testing them rigorously (repeatable process). Hypershift delivers all four: acceleration+education+scale+repeatable process and is delivering a consistent pipeline of $100m revenue potential businesses.

Examples include the BOSCH global business model innovation accelerator (>120 teams/year), reality TV show America’s Greatest Makers, Bay Area NSF Innovation Corps, Innovate for Digital India, Berkeley LAUNCH Startup Accelerator (teams raise over $10m yearly) and programs delivered in dozens of countries for governments, universities and companies including Intel, HP, BOSCH, and more. The Hypershift process drives rapid digital transformation and enduring culture change.

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