Monday, June 17, 2019

Understanding the innovation options in your business

I'm writing a series of blog posts to document some of the things I've learned in innovation along the way, over 15 years of leading corporate innovation work.  So far I've written several blogs, the first about the importance of defining a why and a how for innovation, the second about the importance of an idea sponsor.

Today I'm going to focus on the key drivers for innovation and explain what drives innovation in many organizations.  And in doing so I'm going to revert to a bit of probability and statistics, using a normal distribution curve.

Which firms innovate and why

Before we go deeply into the analysis, let me first say that most firms innovate, occasionally and sporadically, so to be fair, innovation is often happening in many organizations.  That's important, but what's more important is the nature and type of innovation.  For the vast majority of companies, the vast majority of innovation work is incremental at best, adding a new feature or capability to an existing product.

Now, incremental innovation isn't necessarily easy and it is important.  Incremental innovation is what ensures you have something new and attractive to provide to customers three to six months from now.  It's a new flavoring in an existing soft drink or a new feature for an electronics device.  Incremental innovation is relatively safe, and usually returns a small value for the investment, but rarely fails to flop.  That's why incremental innovation is so valued, and also why it is so dangerous.

Interesting and disruptive innovation

More valuable and far more risky are different types of innovation, those that achieve true breakthroughs or disrupt existing markets or industries, or create something really new and different.  The investments are often higher, and the risks of failure are often far higher with this type of innovation.  This could include innovating to offer a physical product as a service, or introducing an entirely new relationship or business model (ala Airbnb).  Disruptive innovation often originates from outside the standard players in an industry, because most of the existing players have too much to lose to make a significant transformation in their existing industry.

This leads back to the normal distribution curve

All of this leads back to the normal distribution curve.  If we assume that in every industry, companies are distributed across the risk and success curve in a normal (bell shaped) distribution, then the companies in the middle (moderately successful) are likely to conduct incremental innovation.  It's the firms two or three standard distributions away from the mean (industry leaders and industry laggards) which are most likely to conduct interesting, disruptive innovation, for two different rationales:

  • Industry leaders can afford to make big bets and investments, to try to keep a significant differentiation between themselves and the second tier competitors.  The leaders will double down, introducing new capabilities and features at an increasing rate, and will expand the definition of innovation beyond purely product features and shift into channels, service and experience innovation.
  • Industry laggards, falling behind the leaders and the second tier competitors, have no choice but to swing for the fences.  They will conduct disruptive innovation activities because they need to become relevant again to customers and to do that they must change the nature of competition.  These companies are more likely to conduct disruptive innovation that changes offerings and especially business models.
Which company are you?

From this simple analysis we can see there are three categories of companies within an industry (note I am not considering new entrants because they typically try to disrupt the industry or offer a significantly new business model, channel or experience).  Leaders will attempt to reshape the industry with new products, new services and new experiences to lock in their advantage.  Second tier competitors will fiddle around the edges and respond to innovations that the leaders introduce, but most of the second tier competitors will conduct incremental innovation.  Industry laggards, more desperate for attention and needing to change the nature of competition are more willing to take risks and conduct disruptive innovation.

Knowing which kind of company you are in, and the stomach for risk and change in your company and in your industry, will tell you a lot about the potential for the different forms and types of innovation.  Doing incremental innovation in a laggard is whistling past the graveyard.  Most second tier companies prefer to wait to see what the leaders do and then follow those actions, rather than define a new course for the industry.  Leaders lead, until like Apple they no longer innovate and simply rest on their laurels and watch their share get taken away.

What kind of innovation you want to do, and perhaps can do, is to some degree dictated by what kind of firm you work for, its position in the competitive pecking order and the amount of risk it is willing to undertake.
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posted by Jeffrey Phillips at 10:20 AM 0 comments

Wednesday, June 12, 2019

Every idea needs a sponsor

As I noted yesterday, I'm writing a series of posts about what I've learned over the last 15 years of corporate innovation work.  Yesterday I wrote about needing a "why and a how" rather than a "what" for innovation.  Today, I'd like to write about sponsorship, and why it is critical to have engaged sponsors for ideas and for innovation.

This idea is important enough that I took the extra 30 seconds and looked up the definition of sponsor.  The definition that I felt was appropriate for this discourse was "one who assumes responsibility for some other person or thing".  I think this is as close as we can get to what an innovation or idea sponsor is.  Now we just need to understand how this fits into innovation, and why I think it is so important.

Free radicals

Most ideas are free radicals - that is, they don't necessarily "belong" to anyone, and if they are interesting or have real potential impact they are probably a bit dangerous or radical.  Since it is easy to generate lots of ideas, every business has plenty of incremental and disruptive ideas floating around.  It's not hard to generate ideas, obviously, but much more difficult to decide which ideas to test and validate, and even more difficult to convert an idea into a new product or service.  To test and convert ideas successfully and consistently, you'll need to match an idea to a sponsor.  Otherwise, ideas will remain free and interesting, but they won't be implemented.

I've used the term "free radicals" intentionally.  Ideas themselves are necessarily dangerous the way free radicals in our bodies are dangerous, yet ideas left unaccounted for tend to hang around and become the subject of discussion and debate, leading to skepticism and cynicism about innovation.  So over time ideas without sponsors can become like a free radical in the human body, doing unintended damage.


An idea sponsor is simply an individual with enough seniority to ensure that an idea receives the attention, funding and resources it needs to be carefully considered and promoted to a stage where it has a chance to enter new product or service development.  Idea sponsors typically don't sponsor ideas out of the goodness of their hearts - this isn't a charitable exercise - but for purposes of advancing a new opportunity that has value to them or to their business.

This means that ideas need to be matched with people who have enough power or clout to move them ahead, and also matched with people who have an opportunity or need that the idea addresses.  No matter how good an idea is, if there isn't a person who has the need or understands the opportunity and is willing to invest in the idea and push it forward, it will remain a free radical.

There's a potential downside to sponsorship, which we must also consider.  Sometimes "pet" ideas or concepts that aren't really interesting or innovative have sponsors.  Projects or ideas that could not receive investment or funding under other mechanisms will be relabeled as "innovative" and pushed through an innovation process.  That's why every good innovation process has a gating function or steering committee to determine if the idea has real value and impact, as well as having a sponsor.

What do sponsors do for ideas?

Ideas may be free, but managing, evaluating, testing and validating ideas isn't free.  These activities require investment of time and money, and that's where sponsors come in handy.  Given that many organizations have lots of ideas and few resources, resource allocation and prioritization is in order.  For ideas to succeed - moving from the "front end" to product or service development and on to launch, a lot of investment, focus and pressure must be applied.  That only happens when someone takes an interest in an idea and helps focus attention and resources on the idea.

Sponsors help build a business case for the idea, provide resources to shape and vet the idea, encourage the innovation teams when obstacles occur and make the case for the idea to bridge the gap from a concept in the front end to a defined project in product or service development processes.  Sponsors keep attention on an idea, ensure funding flows to critical ideas even when other projects are getting sidelined or killed.

What happens without sponsorship?

When we make a case for a specific course of action - such as assigning sponsorship to ideas - we ought to demonstrate that the course of action has merit over a 'do nothing' or status quo activity.  So let's imagine what happens to ideas in a front end funnel without any sponsorship.

Teams or individuals will generate ideas, and will seek funds to test and validate ideas.  They will have to ascertain what ideas are most valuable and important based on corporate or business unit strategy, since no one will forcefully argue for an idea.  They will have to prioritize a handful of ideas - because they can't work on more than a few at a time - based on their own judgement, since no one is placing emphasis on a specific idea.  The team will have to negotiate for funds to prototype, test or license ideas or conduct research from a funding source who isn't necessarily keen on any specific idea or course of action.  All this time ideas are free radicals, not aligned to any product group or business function, and therefore not receiving any input or shaping by a potential adoptee.

Any idea that must go through a front end process, that must bridge the gap from the front end to product or service development, that must go through a marketing and launch cycle and have an impact on its market needs a sponsor, ideally the same one throughout.  We can debate whether or not small, incremental ideas need sponsorship, but anything reasonably new of different simply cannot run this gauntlet without the careful attention of someone with enough need, vision and clout to make it happen.

How does your organization decide who sponsors ideas?  How do you avoid the pet idea practice?
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posted by Jeffrey Phillips at 7:14 AM 0 comments

Tuesday, June 11, 2019

You need a why and a how, not a what, for innovation

I'm going to start a series of blog posts about what 15 years of innovation consulting has taught me.  I'm happy to say I've learned a lot about innovation in corporations, and also somewhat excited to say I'm still learning, because the pace and nature of innovation is changing so rapidly.  However, there are still so many basic, fundamental things that companies either overlook or fail to realize that going back to the basics is important.

Over the next few blogs posts I'll focus on one aspect of innovation that I believe is overlooked, ignored or simply given short shrift.  I'll explain why I think the innovation method, tool, approach or philosophy is important and why it doesn't receive focus.  Finally, I'll address some thoughts on how to fix it. 

Whether this is your first time innovating, or you consider yourself a grand master of innovation, I hope you'll weigh in.  I've long believed, and along with several others - including Paul Hobcraft - have often suggested, that we need more clarity and transparency around innovation, to remove the mystique and to simplify the work, so we can have more, and better, innovations.

Today, I'm going to focus on what I think is an important fallacy:  We need a what, not a way.

We need a "what"

So often I hear from companies that they need a new something.  It can be a new product, a new process, a new way of working or thinking.  The most common reason they need this new thing - and need it now - is because some other firm has something and they need to respond.  They need a new innovation and they need it now.  To some extent, what the new thing is, and does, isn't as important as its novelty.  I call this fallacy - we need a what.  But what many companies really need is not a "what" - things, products, services are easy to create.  What they really need is a why and a how.

Most often the reason they need a "what" is because the "why" is lacking.  By why I mean a good strategy that defines where they want to compete, what they hope to win.  Too many firms are too focused on the short term, locking in and protecting the market share and product portfolios that they have.  When another firm creates a compelling new product, the company feels it must respond.  So a team is spun up and given an ultimatum - create a new thing that competes with our competitor's new thing.  Very little thought is given to why this should be done or the outcomes or benefits.

You need a clear "why"
So teams race around trying to create a new thing - without the benefit of strategy (the "why") and typically without benefit of the "how" - methods, processes and tools that support innovation.  Creating a new and compelling product or service WITH the how and why in place is challenging.  Doing so without a definitive why and lacking the how is almost impossible.  This is one reason so many innovation projects "fail" - they were rarely defined with success in mind.

Supplemented with a definitive "how"
Nietzsche is quoted as saying "if you have the why for your life you can endure almost any how".  For innovation to thrive, having the "why" - a clear strategy that indicates what innovation should do, how it impacts the business, a way of thinking about the future competitive conditions and putting in place the factors to succeed - is vital.  Lacking that, all innovation success is luck.  Beyond the why is the how.  Nothing in business is left to chance except innovation.  As the demand for innovation increases it is interesting to see so many companies focus so much on day to day operations and leave so little time, energy, experience and enthusiasm for innovation.  These firms lack the "how" and leave much of innovation to chance.

When you have a why (strategy) and a how (process) then innovation opportunities will regularly present themselves.  You won't need suggestion boxes or crowdsourcing to find good ideas - your executives and teams will suggest good ideas because they understand the strategy and know how to create and implement good ideas.

But we have a strategy

Many reading this will argue that they have a strategy, but I'll suggest what they have are at best goals.  Strategies indicate not only direction and destination but also indicate how to get there and most importantly what to ignore or stop doing.  Even in instances where clear strategy exists, it is rarely communicated or well understood below the C-level officers of an organization, and the pressures felt by the mid-management to operate efficiently and with least risk and variance are what win out.  Is your strategy definitive and clear?  Do your people understand the strategy and the implications to their investments and decision making? 

Many innovation teams are left with an unclear goal, asked to respond to a competitor's product or service, unsure how what they create will fit into the strategic direction of the business, and asked to do this work without clear tools, roles or processes. 

In the end, it's rarely one new killer product or service that wins, because competitors and copycats will often quickly match an interesting product or service.  It's the ability to communicate what your company wants to do, and how it should execute on what it wants to do, that drives innovation success.  If you have a good corporate, business unit or product strategy, ensure that innovation supports it.  Also, ensure you communicate it effectively and people understand what the strategy means.  Second, define some innovation methods, processes and tools.  Even a simple "how" helps accelerate innovation work.
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posted by Jeffrey Phillips at 8:26 AM 0 comments

Friday, May 31, 2019

Digital Transformation, Data and Innovation

In my last post I tried to illustrate the importance (and the challenges) of data to digital transformation.  This is often a complex and difficult idea for people to understand - why is "data" so hard?  Why can't computer systems work more effectively?  For example, my father called me over the weekend to ask why his doctors can't get his electronic medical records correct.  Trying to explain all of the databases, the types of data, the different sources of the data and the work required to normalize that data is difficult, even to a guy whose job was in computer systems.

Now, if it is difficult to simply compile and use data (like in an electronic medical record) imagine how much more difficult it is to compile data and use it to make decisions.  The data has value but often has subtle context associated with it that we humans understand, but machines may not, because they haven't been taught the explicit and implicit meanings of data that accompanies the data (what many people call "meta-data").  For example, a sensor or computer may use computer vision to "see" a school bus, and based on its size and shape may recognize that the vehicle is a school bus.

But the computer may lack the intelligence to also understand the shape and size of the children in the bus (small kids - heading to kindergarten, large kids - heading to high school) or the context (empty bus leaving school, full bus arriving at school).  We humans have this insight and understand the less overt concepts around meta-data because we have learned experience.  We know that a school bus has a relatively predictable job - taking kids to and from school - and that at different times and different locations the bus is doing a specific job.

What gets even more complicated is when a predictable activity, like a bus trip, takes place outside of normal hours or normal routes.  When a school bus is used for a field trip or for a sports event, the bus is outside of its normal routes, operating outside of its normal times.  Even we humans may look at a school bus on the highway late in the evening and wonder what they are doing.  Machines can only parse through rules or assumptions when data is out of regular bounds.

Data and digital transformation

There is no digital transformation without data.  It's as simple as that.  Digital transformation is either involved in creating more data (using sensors and IoT devices to gain more data), managing and understanding the data (Big Data, predictive analytics) or using data to make decisions or take actions (autonomous vehicles, robots, AI and machine learning).  None of these things can be accomplished without data.  Once you realize how important and how valuable data is to digital transformation you'll think again about where and how digital transformation can be successful.

I spoke recently with a software partner to ask them how to help clients prioritize what projects they should start for digital transformation.  There were several activities that if they could be automated and digitized would have high return, but obtaining and using the data was proving difficult.  His response was to find the problems or challenges where obtaining and using the data was the easiest, not necessarily where there was the biggest return, because access to good, useful data is that important.

Digital Transformation and Innovation

So, what does this tell us about the impending merger of innovation and digital transformation?  Again, data can be an input to innovation - helping drive the design of new products or identify market needs and gaps, or it can be the result of innovation, creating new products that generate useful data that can be gathered, analyzed and perhaps monetized.

In both activities - digital transformation and innovation - people, consultants, thought leaders - will try to convince you that the digital tools (AI, machine learning, robotics) or key innovation methods or activities are what matter.  Please ignore these arguments.  Where innovation and digital transformation are concerned, what matters is the data.  Data coming into the activity or data resulting from the activity, and the value you can create from the data heading in either direction.
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posted by Jeffrey Phillips at 6:38 AM 0 comments

Tuesday, May 28, 2019

Data, Data everywhere

I'm using a reference to the Rime of the Ancient Mariner for the title of today's post, because there's rarely been a more interesting dichotomy facing most management teams.  As a colleague of mine is fond of pointing out, data is the new oil.  For those old enough to remember the Beverly Hillbillies, Jed struck oil and became rich, and moved to the big city.  In many ways digital transformation and the value of the data it creates will enable many new digital hillbillies to strike it rich, striking gushers of data.

At some level the mere availability of data is valuable.  When no or very little data is available, any data is valuable and precious.  When someone strikes a gusher of data (Facebook, Google, etc) then that data and the access to that data can become profitable.  But what happens in large organizations when data becomes ubiquitous?  What happens when we have thousands of sensors and IoT devices submitting data, along with consumer data and reviews, and social media feeds?  What happens when there's data everywhere, of every type, velocity, validity and all the other V words that experts use (variability, veracity and so on).  Increasingly we aren't sitting on gushers of data, we are swimming in rivers and lakes of data, soon to be oceans of data.  And here's where we circle back to the Ancient Mariner.  We may be afloat in a sea of data, but an awful lot of it isn't valuable or useful, because we can't be quite certain which is recent, valid and most importantly, normalized.

ETL - Phone home
If you had real time access to all the data in the world, and it was all verifiable, accurate and without inherent bias, you'd still face an enormous challenge.  Gaining insight from mechanisms like machine learning and AI will only happen when the machines can read and make sense of the data.  Right now we are generating gushers of sentiment data, quantitative and qualitative data and other kinds of data, that aren't normalized and require some human intervention.  In fact most honest brokers who are dealing in machine learning and AI will tell you that the "long pole" in gaining value from AI and ML is in another acronym: ETL - Extract, Transform and Load.  There are a couple of important activities in that acronym.

Extract - find the data and get it out of the originating system.  This could be data from sensors or devices on other devices like your iPhone or Alexa.  Where the data is generated is often very different from where it is stored, thanks to the cloud.  We've got to find that data and consolidate everything we know about a certain individual or segment or product, and get all of that data into one place.

Transform - Your data can take hundreds of forms - binary, quantitative, analog, digital, hexdecimal, images, voice, text and so on.  Machines can be taught to read and recognize any type of data but they can't easily determine the validity and value of different types of data.  Thus, we must normalize the data to some degree - help machines understand why a picture is worth a thousand words.  And it's this work that will be the biggest barrier to full adoption and use of machine learning and artificial intelligence. In fact we may need machine learning and artificial intelligence just to find, clean, evaluate and normalize all the data we are generating.

Thus, as strategists and innovators we are left in an interesting predicament - the more data we have, the more potential value we have, and the more the problem of actually finding and using the data that matters increases.  This is in fact an exponential problem, because the data is increasing at a rate faster than we can determine how to make sense of it.  At some point only machines will be able to interpret and understand all of the data we generate, so it behooves us to begin to either standardize the data formats, sources or streams (which isn't viable due to competitive differentiation) or to improve the ability to find, clean, standardize, rank and normalize the data we have.  Otherwise we'll sit on oceans of potentially viable data unable to extract the value, as new oceans of data are created.

Having more data than a competitor doesn't convey an advantage unless you can make sense of the data and use it more effectively.  In fact in many cases having more data may make it more difficult to make good decisions and as the volume of data accelerates and the range of data types increases, it will become every more difficult to simply keep pace with the data.  Like the Ancient Mariner you'll be awash in data, floating in data but without an insight to drive your business.

Who is responsible for managing this data?

Here's another interesting challenge - thinking about who is responsible for managing this data.  The traditional IT team has been overwhelmed with simply keeping the operational systems running.  Your email, core systems, financials and other operational systems require constant attention, and constantly upgrading to the latest releases and protecting the data from hackers is a constant struggle.  Does your IT team have the bandwidth and skills to capture, manage and make sense of the data?  Should a data scientist report to your Chief Information Officer?  If not, then where should people who are good at managing data and making sense of the data reside?  What should they do?  Who directs their work?  I'm not sure there's a good answer in many companies to this important question.

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posted by Jeffrey Phillips at 11:54 AM 0 comments

Wednesday, May 15, 2019

Understanding future conditions is vital to create innovative solutions

I'm just wrapping up another project that's focused on trend spotting and scenario planning, working to help a client company understand the emerging competitive conditions their business will face in the next 5-7 years.

While foresighting, trend spotting and scenario planning are exceptionally valuable, many innovation projects simply ignore the benefits they can receive from doing this work.  Instead innovators plunge in to create the products and services they believe customers need.  This approach is why so many innovation projects fail to deliver value - no matter how compelling the innovative product you create is, if the circumstances or environmental conditions change, consumers and their needs will change as well.  To create a really compelling product or service, you need to be able to understand or predict the future conditions and tailor your innovation to those conditions.

Why understanding the future is so important

Many potential customers tell me they'd like to understand the future - in fact they'd pay good money for a forecast that is highly probable.  However they don't believe they are good at predicting what might happen and therefore don't spend time trying, or worse they simply assume the future will be an awful lot similar to current conditions.  They are often shocked when we demonstrate how rapidly customers, technologies, needs and conditions are changing.

Of course it's possible to create a compelling product or service and have it well-received in the market without doing foresighting or scenario planning, it's just much more likely that you'll miss evolving needs or opportunities and the product you create will fall flat.  If we accept that people acquire goods and services to fulfill "jobs to be done" or to satisfy needs, then we must also accept that jobs and needs are fungible and change over time, and that new entrants and new substitutes arise all the time.  Ignoring future conditions that will shape needs, wants and especially ability to pay is a recipe for disaster.

Insightful yet inexpensive

Further, foresighting and trend spotting when done correctly creates an opportunity to gather insights on what might happen and how the company should be prepared to act.  Foresighting is valuable to understand emerging needs, but also useful to understand emerging threats and opportunities.  Doing this work is relatively simple with good participation and facilitation, and creates insights that shape your innovation activities.  It is powerful and insightful, while also being a relatively inexpensive investment.

Its importance and value are increasing

Foresighting and trend spotting are becoming ever more important, as the nature of change is changing and the rate of change is increasing.  Digital transformation will create disruptive change in many companies and will create new types of demand.  New generations of consumers are emerging with different concepts about acquisition and ownership.  Understanding the evolving future and identifying emerging needs before or as they happen is more important than getting a new feature on an existing product which may be obsolete or unnecessary by the time you get the new feature installed.

Practicing the Future

Instead of wandering blindly into the emerging future, you should be practicing it regularly.  Conducting trend spotting and scenario planning activities won't guarantee a perfect understanding of the future, but can give you good insights into emerging market conditions and the potential for new segments, new customers and importantly new threats or competitors.  Having seen how things may unfold will prepare you to put new capabilities in place and to anticipate when the tipping point arrives.

If you want to understand how to do this work well, or need help doing a foresighting or scenario planning activity, contact us.  We have tremendous experience doing this work and identifying the emerging opportunities that will shape future innovation.
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posted by Jeffrey Phillips at 10:08 AM 0 comments

Thursday, May 09, 2019

Digital Innovation meets As a service

For a while now I've been considering the impact of all the emerging digital transformation tools on innovation.  Artificial intelligence, machine learning, IoT, blockchain and a host of other technologies will have a bit impact on how corporations conduct work and create new insights and new products and services.  However, I'm increasingly of the opinion that we are guilty of focusing on the technology and ignoring the real benefit of these technologies and other IT-influenced changes, and what customers really want.  As these technologies are implemented, the real benefit will be the data they generate and companies will have to confront the question - how do we gather, use and most importantly, monetize all of the data?  And this, I think, is where the real impact on innovation will be felt.

Two converging themes

There are two really interesting and potentially impactful converging themes in innovation, both of them led ultimately by the increasing power of information technology and ubiquitous connectivity.  The first is digital transformation - the ability to both generate vast amounts of data from sensors and IoT devices, as well as to manage the data and make sense of it using other technologies like Artificial Intelligence, Machine learning or predictive analytics.  The second is the increasing demand for solutions, not products.  I think increasingly people will want to acquire solutions "as a service" or will be happy to share data about device usage in order to receive a less expensive product. 

Companies can provide products "as a service" by changing value propositions and business models, and can further lower the cost of the service by collecting and monetizing the data generated by usage or by pushing other offers to the user of the device.  An entire generation is entering the workforce that grew up with Facebook and Google, so these data exchange models are well understood and already accepted.  The big challenge is for "as a service" solutions to move from the purely digital world (search, social networks) to physical products.  In some senses the shift has occurred for larger capital goods like aircraft engines.  GE doesn't sell the engines, they sell flight hours of operation.  Michelin has a "tires as a service" offering, which is where this really becomes interesting, because tires are a consumable commodity.  If we reach the point where consumables can be offered "as a service" then almost any physical product can be offered as a service, which will have to be supported by new business models.  Further, many of the "as a service" models will be funded to some extent by data, either harvested from the device or information pushed to the device.

Why a new "whole solution" emerges

If these ideas above are true, they have significant impact on innovation and how it is conducted today.  In the past, Geoffrey Moore created the idea of the "whole product" to cross the adoption chasm.  If the arguments above are true, we need to consider a new "whole solution" model to compete in the digital innovation economy, where the value proposition of the physical product shrinks but is augmented by value from the data that surrounds it, the customer experience that empowers it, new business models that sustain it and ecosystem partners who fulfill the promise.

Yes, companies that make physical products will continue to make physical products, but increasingly they'll find that customers expect a more holistic "whole solution" which will incorporate data (from the digital transformation application).  That data may originate from sensors on the product, from a bluetooth connection between the product and the smart phone or device the owner possesses, or eventually from ubiquitous 5G, which just creates a virtual network between any internet enabled device anywhere.

Customer experience
Beyond the physical product and the data that enables, surrounds or funds it, the customer experience will need to change.  In the Michelin example, tires as a service indicates that my experience expectations are actually higher than if I manage the tires myself, because my expectation is that Michelin has experts on staff ready to identify any issue, and who can guarantee that I get the most value and longest life from my tires.  In this example my expected experience is that I do nothing and simultaneously get more, which means my experience expectations are vastly increased.

Business Models
Finally, those products, data and experiences will come wrapped in a different business model - or, more likely two or three different business models.  Again, Michelin is a great example.  Michelin still sells tires to consumers without any support or "as a service" offering, as well as providing an "as a service" offer.  The revenue models, service models, pricing, support and warranty options for these two delivery models are significantly different, and any company that embarks on an "as a service" offer will encounter those who prefer to acquire and own a device or product, and those who are happy to use it as a service, so multiple business models will be required.

The challenge for innovators

I'll submit that the converging factors - increased data generation and management, and increasing expectations of products as service - are here and will continue to converge.  This means that innovators must decide how and when to include data as a component of the offering, and how to shape and ensure customer experience and be prepared to offer multiple, concurrent business models based on the same product.  In other words, are innovators ready to vastly accelerate innovation thinking and options, and work well beyond the innovation requirements of the physical product to include data, customer experience, business models and other factors?

If not, what will it take to develop the innovation teams and skills in order to compete in this market?  The impact to innovation is real and cannot be denied.  Can you and your team rethink and revise how you innovate?  If you need help, we can help think through not only the core product, but how and where data is important, the customer experiences expected by consumers and the requisite business models, as well as critical ecosystem partners.

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posted by Jeffrey Phillips at 6:39 AM 0 comments