6 min readHow IoT data can reduce new product development costs & create new revenue [...]
We are in an innovation economy. That means that significant R&D and new product development (NPD) expenditure is key for technology-reliant companies to keep pace with the competition. Across all industries, companies spend an average of 10% of their overall revenue on R&D, with businesses in healthcare, tech and telecommunications spending as much as 13% to 16%.
Obviously, gathering information on customer usage and satisfaction accounts for a large portion of these costs. Manufacturers have traditionally relied on tools like focus groups, surveys, online reviews, customer service calls and product returns for insight.
These methods, however, are largely reactive in nature, and tend to be rather costly to implement at scale. They also fail to provide a detailed understanding of actual product usage. Indeed, it can be hard to isolate specific technical or design issues that are impacting the customer experience.
Usage data from IoT-connected products changes this. These real-time metrics allow product managers to develop a picture of how a product functions after it leaves the factory floor. The major advantage? It is free of the distortions of memory and bias.
Data from connected products can reveal a lot about how they are performing and being used. The “lifecycle” score you can derive from IoT analytics can help you answer questions like:
Product performance metrics can be linked to the timing of customer claims, issues and returns. Those help to isolate where specific problems are rising. Likewise, comparing usage behaviour to customer turnover can reveal the precise moment (and in what precise manner) a product’s performance has degraded to the point when it is no longer meeting a customer’s needs.
Deeper insight into usage also allows for more dynamic sales and marketing techniques. With a more granular understanding of actual usage conditions, appliance companies can introduce usage-based pricing, leasing and warranty plans. Meanwhile, others are identifying new opportunities to upsell service and support options throughout the product’s life-cycle.
As we’ve noted before, finding the right analytics tool for IoT can have a decisive effect on ROI. IoT products produce a vast amount of data, but it’s only useful if you’re able to parse relevant from irrelevant information.
Using either a purpose-built IoT analytics platform or one extensively customized for the purpose will ensure you’re asking and getting answers to key business questions.
A recent discussion paper on AI applications for business from McKinsey includes a revealing case study on how new technologies are impacting manufacturers. Insights derived from IoT data can create efficiencies by reducing waste during the design process.
McKinsey refers to this as the “design for manufacturability” ethos, where machine learning integrates production and client feedback in real-time in order to continuously refine the product’s design.
In a sense, there is no “final” product, as iterative modifications and improvements can be made throughout its life-cycle.
Every customer becomes a source for ongoing bug testing—just as an automotive company will crash test a car thousands of times to account for variations, data from IoT refrigeration units will eventually reveal what parts are most likely to fail over time, and which highly-touted features see little real use.
This effectively allows manufacturers to transition from a repair-and-replace maintenance model to a predict-and-prevent approach.
This provides a clearer rationale for making and enforcing value decisions (ex. safety versus cost), and managing relationships with suppliers. Being able to identify the exact pain points within a supply chain of component manufacturers (i.e. which parts are causing the performance to degrade) means it’s a much simpler matter to manage accountability.
IoT provides manufacturers with data to improve R&D and design processes. Based on the actual usage data of existing products, manufacturers can determine what functions they should add, what parts they should improve, and what features they should remove.
They can also use the usage data to help product managers quickly assess whether a prototype would be likely to succeed or fail in the market—and why.
This is a clear example of where IoT analytics present a significant improvement over traditional qualitative assessments of customer desires. It’s difficult for even a particularly savvy focus group tester to put into words what issues they’re having using a product, or how it might be improved.
Consider the following case study of a robot vacuum cleaner. The product was innovative but had numerous pain points, such as:
The solution is in the data, but not all platforms are capable of deriving actionable insights from it. In this case, the manufacturer used Mnubo’s SmartObjects platform to ingest and analyze the data, leveraging two specific dashboards:
The insights the manufacturer received from the IoT analytics platform allowed it to derive real business value by:
You can summarize the answer to the question “How can I reduce R&D costs?” like this: