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Solving the Innovation Information Problem to Enable Long-Term New Business Success

Posted by Chris Hartshorn on May 7, 2015 11:28:59 AM

We’re bombarded these days with the potential of data, from understanding and segmenting populations to optimizing manufacturing operations. However, for companies targeting new business in B2B domains, the quality and breadth of data can be limited. While they typically know their existing customers and markets, they struggle in identifying, segmenting, and attacking genuinely new opportunities. Corporate functions such as R&D, venturing, and innovation need to lead the charge into new business areas but often focus on areas that lack a critical mass of quality information around successful and unsuccessful initiatives. As a result, metrics can’t be effectively set and measured, prescriptive action and predictable results are impractical, and proving the value of these innovation-enabling functions to the skeptical CEO is impossible.

What’s the outcome when the relevant corporate functions lack data to successfully drive new business? When the CEO comes under fire – whether from activist investors or antsy boards of directors – the functions and initiatives that can’t show their path to providing value come under threat. Small armies from McKinsey, Bain, BCG, and their ilk come in, every function is told to align better with business units, Horizon 1.5 becomes the new Horizon 3, and the resumes of the innovation leaders and team start being circulated. To avoid this fate, innovation functions have got to get better, understand how to get better, and get better at showing they are better. This is an information problem.

Does any data exist? Sure. One example is an oft-cited McKinsey study into the probability of success for new chemical products, segmented by technology familiarity and market familiarity (see Figure 1). However, rather than helping companies get better at developing new business, this chart has been repeatedly used to reinforce the pull toward incremental, lower-risk, and quicker-to-market but ultimately undifferentiated, business-cannibalizing ideas.

Figure 1: Summary Graphic from “Chemical innovation: An investment for the ages”

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Lux Research has also looked at historical success rates and timelines to success for a number of technology-enabled new business initiatives. However, while the McKinsey study captured dozens of examples from large companies and used dozens of interviews to extract data and draw conclusions, we have analyzed thousands of start-ups and captured their underlying traits with higher granularity. Since our research focuses on stand-alone entities, we have clean dynamics around technology and market familiarity (as it’s all “new-new” to start-ups), and we have clean financial metrics around the outcomes (as they are not muddied by existing products and incremental improvements).

Given that successful innovation efforts have to show financial return, we looked at timelines to profitability from the founding date of these discrete entities and analyzed what factors accelerate this timeline. This metric gives innovation functions guidance for how to unlock the entrepreneurial behaviors that drive progress, deliver growth sooner, and maintain CEO buy-in.

Of the 3,600 companies analyzed, 17% were profitable within five years of the founding date of the venture. The obvious assumption is that these quick-to-profit start-up companies delivered a solution that either caused disruption to incumbents or uncovered a burning unmet need. Indeed, a start-up with strong technology value is 8% more likely to be making money in the first five years. However, a stronger factor for speed to profitability is the quality of partnerships, where companies with strong partnerships were 10% more likely to reach profitability in their first half-decade. More importantly, the better the partnerships, the less the speed-to-profit depends on technology value (see Figure 2) – spending more time talking to the value chain being targeted pays off.

Figure 2: Partnership Quality is Strongest Driver of Profitability at Five Years of Age

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These same patterns carry forward over time. For the first 15 years after a start-up is founded, technology value has relatively little impact on speed to profitability, but a focus on partnership development increases the odds of profitability by up to 20% in some timeframes. It is only beyond the 15 year timeframe that technology quality becomes a strong indicator, with a 20% difference in probability of being profitable between strong and poor technology. By this point, partnerships are the rule, not the exception, so technology becomes a clearly differentiator.

Figure 3: Technology Value is a Critical Factor for Profitability Only in the Long Run

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To validate the predictive value of this analysis, we looked at a set of companies in the water industry, analyzed in 2009, not profitable at that time, with strong technology, and an overall "Positive" Lux Take. The 22 entities matching this description had an average age of 6.5 years since founding in 2009. In the following five years, only one went out of business, two were acquired, and a remarkable eight became profitable. The eleven that remained unprofitable, but still operating, saw average revenue growth of over 300%. A broader study of 256 such ventures across multiple sectors has further reinforced these findings, where the Lux Take, a focus on partnerships, business model, and the management team setting the overarching strategy for the venture, can stack the deck in favor of success.

How should corporate innovation functions – whether it be R&D, venturing, or new business development – use this information? There are now metrics that they can use to examine the traits of every new business development initiative, whether internal or external, and prescribe actions that can deliver success across every timeframe. External venturing efforts use data-driven methods to project profitability and choose the right time to enter. Innovation functions can set expectations for the CEO on timeframes to success and deliver higher probabilities of realizing that success.

The periodic slash and burn campaigns on longer-term innovation portfolios will no longer be needed in organizations that perform and predict that performance. We’ll look at McKinsey’s numbers on the probability of success and timelines thereof as being data inadequacies of a bygone era where our understanding was poor and our actions followed in kind. Corporations can again become innovation engines as opposed to puppets of myopic cost-cutting measures.


For more information on innovation and strategic technologies contact Chris at