Product management has significantly transformed over the last decade and as we progress through the 2020’s will continue to transform at a rapid pace. The evolution of product management is heavily pegged to changes in customer needs, the way these needs are fulfilled and the underlying technology that enables solutions.
If you look at the product management roles currently being recruited for, they are dominated by demand for skills focused around the customer experience and the development of software vs. purely physical product.
Marc Andreessen saw this coming in his 2011 essay where he famously penned ‘Software is eating the world, in all sectors’. From this, Andreessen asserted that in the future every company will become a software company.
True to Andreessen’s prediction, we’ve witnessed explosive growth in the demand for software accompanied equally by strong growth in internet connected devices.
As a result, product managers now find themselves covering both the physical and digital side of customer experience. Additionally, this marriage has seen a wave of enabling capabilities grow in importance including data and analytics, real-time applications, artificial intelligence and security.
The connection economy now permeates everything, from the moment we wake up, to the moment we go to sleep at night; we are connected by the devices we wear, the devices we use to communicate, through to the hardware that we interact with in our homes. The internet of things (IoT) is truly embedded in our daily lives which places a demand on product managers to be able to navigate a complex, multi-dimensional domain.
So where to start in defining your IoT product strategy?
Fundamentally, the approach should be no different than defining a physical or digital product strategy, i.e. understand the market context, distill out the core customer problem, achieve problem/solution fit, identify options to fulfill the customer ‘job to be done’, define a roadmap to gain traction, achieve product/market fit, then scale.
When it comes to IoT it’s about the ecosystem.
The critical success factor for defining an IoT strategy is to think with an ecosystem driven mindset. Given the complex orchestration of hardware, software, connectivity, security and data, the strategy and resulting action plan needs to factor in multiple layers of interaction.
The illustration below provides a simplified view of the components and layers involved in an IoT solution and the interaction between these layers. As the customer journey scales to more devices, more applications and more points of connectivity, the challenges to deliver a cohesive value proposition grow exponentially.
A simple framework for a complex problem.
Daniel Elizalde, who teaches IoT product strategy at Stanford, has defined a simple decision framework to help steer your journey in developing an IoT product strategy. His framework and accompanying list of questions go a long way to help to make the right decisions for the benefit of your customers and business.
It starts with the user. This is where consideration and analysis of the customer journey plays a critical role in understanding the impact that each touch point plays in providing a great customer experience and value to your business.
Technology is always second to understanding the customer.
The specific technology you might use is not important at this stage, rather, gaining coherent and actionable insight around your customers, their pain points and jobs to be done at this early stage is fundamental. Getting this right unlocks all the corresponding decisions you’ll need to make across data requirements, business value, technology choice, security, fulfilling standards and regulations.
Having confidence that you understand what matters most for your customers and how you best fulfill their needs dramatically reduces the risk of investment across the IoT ecosystem and scaling your product offer.
Start by considering the six key IoT decision areas.
Here are some questions suggested by Daniel Elizalde to consider as part of using the IoT decision framework:
1. User experience:
- Who are your primary and secondary users?
- What are their needs?
- What would make for a great experience at each layer of the stack?
- How do we create a cohesive experience across every touchpoint?
- How should data flow through the stack to fulfil the user’s needs?
- What types of data does your device need to produce?
- How much data should be transmitted to the cloud and how often?
- Do you need to perform analytics at the edge, in the cloud, or both?
- What are your business objectives?
- How will you derive value from the experience (revenue, cost savings, retention, etc)?
- What is the associated business model and which layers of the IoT tech stack will you monetise?
- What are the costs to provide your service at each layer of the stack?
- Will you build, buy or partner at each layer of the stack?
- What technology is required at each layer (eg. sensors, device hardware, software, communication topology, communication protocols)?
- What cloud platform will you use?
- What APIs are required?
- How can each layer of the stack be compromised and how to respond when your devices are hacked?
- How can you reduce the risk of the stack being compromised?
- Who will conduct security testing and at what frequency ongoing?
6. Standards & Regulations
- What standards and regulations will affect your product at each layer of the stack?
- Does your industry have a standard data format or communications protocol that will enable your product to talk to other devices?
- Do your customers require you to meet certain safety or cloud security requirements?
- What laws must your product comply with at each layer?
As previously mentioned, of critical importance in answering the questions above is to take on an ecosystem mindset. In doing so you’ll find defining your IoT product strategy is not a once off effort. Given the knock-on effect of decisions in one area of the ecosystem on others, will require ongoing iteration and review of decisions made in the preceding layers.
To combat this complexity and the myriad of factors involved in IoT solutions, an experimentation based approach will pay dividends. Taking a learning approach will help to de-risk your efforts to scale by identifying early on where your decisions across the IoT stack are working and where they have failed with limited risk and limited investment.