Consider a home appliance manufacturer whose annual customer satisfaction survey consistently ranks ‘energy savings’ as the top-valued feature. Based on this finding, the product team channels significant investment into the next model’s energy management system. Then usage sensors embedded in the product tell a different story: most customers never activate the energy-saving mode, while the quick-cycle option is used an average of four times per week. The survey did not give a wrong answer to the right question; it gave a right answer to the wrong question. This is precisely the gap that the Internet of Things — IoT — is entering the business agenda to close.
IoT refers to an architecture in which sensors, connectivity modules, and data transmission infrastructure embedded in physical products record how those products are actually used in the real world. In Turkey, the technology is currently being piloted in select sectors — home appliances, industrial equipment, and automotive components. The underlying logic, however, is straightforward: reverse the information asymmetry between manufacturer and customer. Customers cannot always accurately report how they use a product; sometimes they simply do not remember, and sometimes they are not even aware of their own patterns. Sensor data closes that gap directly.
The most significant contribution of usage data is removing assumption from the product development cycle. Traditional processes rely on focus groups, surveys, and field observations — all of which are both costly and susceptible to selection bias. Sensor data, by contrast, shows which features are used and how often, at which step users abandon a process, and under what operating conditions specific failure modes are triggered. An industrial compressor manufacturer reviewing this data might discover that warranty claims cluster within a specific pressure range — the very range the user manual defines as ‘normal operating conditions.’ That is a finding no survey or focus group can produce.
From a segmentation standpoint, usage data goes well beyond demographic and transactional records. When a SME segments its customers by purchase frequency or order volume, it gets a serviceable picture. When it segments by product usage intensity and pattern, a far more actionable distinction emerges: a customer who buys infrequently but uses the product intensively has entirely different support, training, and product development needs than a customer who buys often but operates the product well below its standard capabilities. Marketing investment made without seeing this distinction delivers the right message to the wrong segment.
The integration of usage data with ERP and enterprise software ecosystems is an inseparable part of this discussion. Raw sensor data carries no value on its own; it becomes meaningful only when combined with sales, service, inventory, and product lifecycle data. A service department that knows how many operating hours a specific component typically runs before failure can shift from a reactive service model to a proactive maintenance model. That shift improves customer satisfaction and reduces service costs — a directly visible improvement in the TCO calculation. In Turkey, realizing this kind of integration requires that both the ERP infrastructure and the data collection layer be designed together from the outset.
That said, the model faces serious practical obstacles. The first is data volume and quality: sensors generate continuous streams of data, but cleaning, classifying, and converting that data into meaningful reports requires substantial technical capacity. The second is customer privacy and data ownership — particularly in B2C products, the questions of what data is collected, where it is stored, and how customers are informed demand both an ethical and a legal framework. The third is organizational readiness: the analytical capability needed to interpret usage data and bridge the gap between product and marketing teams simply does not exist in most SMEs yet. Building the technology is far easier than operating it.
For managers, the core question is this: before committing to an IoT investment, have you defined which business question you are trying to answer? The cost of collecting usage data is not limited to hardware and connectivity infrastructure; it encompasses data management, analytical capacity, and process change costs as well. If the business question — which product feature to eliminate, which service model to redesign, which customer segment to treat differently — is not defined upfront, the collected data sits in storage as an expensive liability rather than an asset. Companies that approach IoT not as a technology project but as a deliberate effort to rebuild customer understanding are the ones generating real returns on this investment.
This article was originally written in Turkish by Gökhan MERCANOĞLU on April 21, 2014 and has been automatically translated into English and other languages using machine translation.