It’s relatively common for IoT initiatives to begin somewhere in the realm of product management. Often, they find inception with a product-minded executive attempting some form of world domination or at least market differentiation by launching a new product, improving an existing offering, or enhancing a method of production that leads to lower costs, higher quality, or greater output. This is not an accident but rather a direct result of what product management truly is — a nuanced practice of making informed, strategic bets in the market. IoT initiatives need tireless champions who are devout students of their customers and well-equipped at building and aligning internal and external coalitions for strategic investment. Product managers, because of the nature of their discipline, tend to be relatively well equipped for such a challenge.
However, even if you’re starting out equipped and seasoned, the journey can be quite long and arduous. Gathering customer or operator feedback, developing data-backed business cases, garnering investment, and building cross-organization coalitions to support your initiatives are all individually complex endeavors. It usually helps to begin with a proven design or discovery process or methodologies like Zero Waste Engineering™ to provide you with a more systematic approach to breaking down each of these challenges as you test your value-based hypotheses and refine your business case for investment.
As a continuation of our recent series about common outcomes and tools desired by key stakeholders of an IoT system or initiative, this post briefly surveys a few common areas and ways product managers view and evaluate data from their IoT system to identify opportunities (or connect dots) to ultimately inform and refine future roadmap decisions.
Product Usage Data
Product usage data is all about tracking and understanding which features your customers are using most frequently and how. This begins with a solid understanding of desired outcomes in regards to how various users interface with devices and components in the system. Tooling abounds in this space and companies will often arrive at using some combination of off the shelf software along with proprietary or custom tools to track and analyze how your product is being used. Various degrees of machine learning and data science methodologies might also be applied given the toolset chosen. The IoT system architecture, fiscal, and timeline constraints will ultimately influence the level of tooling employed in a given initiative. More specifically, the ecosystem itself will greatly influence this, and whether you’ve already invested Azure IoT Hub or AWS IoT Core. If you are interested in learning more about applying different data science tools, you may find this recent post interesting.
With the right toolset, a product manager would be keen to evaluate product usage data to gain a better understanding of:
- Which features are most frequently used?
- Which features are infrequently or never used?
- How long does it take a user to perform primary, secondary, and tertiary tasks?
- Which roles use the product or portions of the product most often
- Are diagnostic and system event notifications being properly acknowledged in a timely manner?
- Are system events or diagnostics subsequently followed by the expected and correct set of remediation tasks?
- Are users executing workflows as designed or departing from intended next steps?
- Does the product usage data follow any interesting patterns (daily, weekly, seasonal)?
- What time(s) of day is usage highest?
Analyzing product usage data to gain insights against the above questions can help inform feature enhancements and modifications to drive better system outcomes and more effective user experiences. When looking at product usage data it’s wise to also ask how does this data align with my real-world understanding of the user, their environment, and their active intent.
Additionally, coupling usage data analysis with live observational studies and interviews can be a truly powerful combination in turning initial insights into future features and product enhancements on the IoT product roadmap.
Service Usage and Performance Data
In addition to evaluating product usage data at a user and feature level, IoT product managers will also evaluate how the system itself and underlying services are performing by investigating the following:
- Configurations — How are devices and services typically configured across deployments? Are there consistent patterns or anomalies in service and environment settings, devices or asset configurations?
- Performance — How are services in the system performing when it comes to tracking device data and metrics? Are response times sufficient for various API calls and will the system scale as more devices and sites come online? Can you sustainably support the level of IOPS experienced in the system? What performance limitations exist in the architecture?
- When it comes to gaining insights in relation to these questions it is helpful to frequently engage in proactive performance testing and monitoring so that you are continuously and iteratively finding ways to improve the IoT ecosystem you’ve established. Occasionally, performance findings and metrics will also corroborate product usage data findings.
Customer Request and Support Tickets
Finally, triaging, analyzing, and grouping support tickets and customer requests is a great low barrier and highly valuable way to further refine your IoT product roadmap. You will usually get a better sense of urgency across enhancements and features which is a good sanity check for how you have your workstreams scheduled and aligned. It’s also critical to perform root cause analysis of issues so that the true, complete problem is defined and subsequently addressed by new proposed solutions in the roadmap. This also implies the need for a connected data pipeline to perform the analysis and ensure you can actually arrive at a root cause in the first place.
Ultimately, there is no silver bullet to building out your product roadmap and designing or refining the IoT system that supports it. It’s a complex, multifaceted challenge that requires synthesis across various dimensions. We’ve mentioned 3 key sources of input for your roadmap in this post but you may also find primary market and industry research as additional critical influences. Given the complexity, it’s important to work in a regular, iterative rhythm of inspection and adaptation as you evolve your product.
To learn more about how we have approached product strategy or roadmap challenges, contact us today and we’ll be happy to dive deeper into best practices and provide an initial evaluation for how we can help you achieve your goals.