In their excellent Harvard Business Review article “How Smart, Connected Products are Transforming Competition”, Michael Porter and James Heppelmann analyse the impact of IoT on products and competition. They provide a list of five strategic mistakes to avoid:
- Adding functionality customers don’t want to pay for
- Underestimating security and privacy risks
- Failing to anticipate new competitive threats
- Waiting too long to get started
- Overestimating internal capabilities.
This post concentrates on the one that comes up time after time in my conversations with key IoT people in Finland’s largest industrial companies - adding functionality customers don’t want to pay for.
Challenges related to the IoT are primarily challenges related to customer and business value.
A company we work with recently described their IoT work succinctly: “We are far ahead, but we’re just getting started”. This reflects the typical situation well: technical enablers are in place and technical innovation has taken place, but they’re still taking their first steps on the road to customer and business value.
Systematic value innovation is a cultural thing, driven by corporate structures. Large, established companies are, by design, built for incremental innovation and risk mitigation via planning. Typically, this involves detailed designs of the road ahead based on what has worked before, choosing overarching solutions that match a set of customer problems collected over a long period of time, detailed ROI calculations for future returns and lengthy product development cycles.
The above approach works when a thorough understanding of the problem domain exists, no major disruptive forces are in effect and market is known as well as stable, with competition taking place between known peers operating on a similar frequency.
We don’t yet know whether IoT is the third IT-driven productivity leap, after computers and the internet, as prophesied by the likes of Gartner and GE, or just hype that eventually settles down to become a force for incremental rather than disruptive change. Regardless of one’s stance on IoT, running into a situation where the customer value of your solution is not self-evident is a sign that the old approaches are not really working.
Start with customer value
A lean service creation (LSC) approach turns the process upside down and starts with the value.
- Find a problem worth solving. Fall in love with your customer’s real problem, not your solutions to hypothetical problems.
- Prove solution feasibility. Solve the problem, proving customer value with a small audience. If you aren’t able to capture value with a small group, you won’t be able to do it at all
- Create a plan for proving product-market fit. You’ve solved the problem for a small group of people, but does it scale to be a viable business?
- Prove business viability. Solve the scaled problem, capturing the value with larger audience
- Create a plan for scaling up. Now that you’ve learned what the problem, solution and market truly are, figure out the expected returns.
- Productise and scale. Build new functionality, measure impact and learn from measurements. Iterate. Based on what you learn, objectively pivot or kill functionality not worth having.
How is this any better than the “traditional" approach?
LSC starts with customer value. Proving value is the first gate, so you’ll never run into a situation where you’re missing customer value in an existing solution.
From proving customer value, LSC moves rapidly to proving business value. To get past the second gate, you have to prove that customer value will actually scale into a sensible business.
Typically, new, innovative services are emergent markets for companies developing them. Market-sizing by peering into a crystal ball is highly unreliable. LSC makes no “vanity calculations”, but starts small and scales only when the problem, solution and market are understood and accurate ROI calculations can be carried out.
Last but not least, our fast paced, digitally enhanced reality resembles a complex system more than a complicated but predictable clockwork. It makes more sense to test with smaller steps, measuring the real impact and learning from it, than trying to plan the final outcome of a set of actions that take place over a long period of time.
When results are available quickly, it’s possible to focus on those areas where the measured impact aligns with the expectations.