The last 200 years of economic growth have led to societies prevailing over the scarcity of goods. After the industrial revolution, we have based the economy on manufacturers capitalizing on our desire to buy new things and replace our old ones.
Industrial companies can produce massive amounts of goods cheaply and efficiently, while their marketing and sales departments are left to figure out how to push these products to unsuspecting price-sensitive customers. Businesses compete on price while ignoring the reasons the customers are buying the goods in the first place – their real needs.
The information gap between the customer and the manufacturing process makes one feel like society has lost its common sense. Buying a blender that breaks down in a year and repeating that for ten years is amazingly more expensive than getting one that makes smoothies for a decade, even if you switch the blades a couple of times during its lifetime. The throwaway products are not only a bad business model – a reasonable customer will change brands right away – it is also remarkably harmful to the environment.
Luckily, digitalized manufacturing and customer data analytics are creating new means to battle this throwaway consumerism. Now, as much as 90% of goods are sold to replace previous ones. The simple truth is that you wouldn’t constantly need to buy new things if the ones you got were serving their real purpose and came with good services, maintenance, and updates.
This would not only be good for the sustainability of the planet but it would allow businesses to create entirely new service business models connected to the goods they sell.
What if the aforementioned blender had a sensor which could signal to the manufacturer how their product is being used and if a component was breaking down? The data could be transmitted directly to the manufacturer, who could proactively take care of the blender before it breaks and before the user is long gone as a customer. Any object can be turned into a data stream and thus a service item with the internet of things.
There is a clear trend among market leaders to decouple their revenue from manufacturing volume by replacing goods with services. Concentrating on services built around goods increases customer loyalty, reduces marketing costs, and also makes the company a better long-term investment compared to manufacturers that mainly focus on their production processes. Just look at their stock prices.
For example, the globally successful Finnish elevator company KONE offers sophisticated product maintenance and urban planning services. They aim to make their products last as long as possible and try to understand the needs arising from the customer’s environment. Service-focused thinking is also what their brand is about. KONE is not a product-focused elevator company, but, as they put it, their job is to provide a service: maintain smooth People Flow.
Another huge industrial giant, GE, has gone through a century of technological disruption. A company which started out as a light bulb manufacturer makes most of its revenue today from services. Even after selling off most of its financial services, GE is still heavily invested in producing services around energy, aviation, and medical technology to name a few of its business areas. GE has embraced a data-driven equipment service approach called asset performance management. This means utilizing the industrial internet of things to provide comprehensive digital maintenance and optimization services for the machinery the company produces.
In addition to offering services along with goods, the future of manufacturing will be transformed by the rapid explosion of customer data. Ecommerce solutions, search engines, and marketing automation all take place online and record customer behavior. The right analysis of this data can take manufacturing to a totally new starting point, where customers’ needs are understood from the very beginning.
Revenue science, meaning the data science behind new revenue streams, is the combination of customer data analytics together with marketing and sales automation. It allows the manufacturers and service providers to know what the customer really wants and needs. A business can then adjust its production process and operations to fit this demand. Customers already assume that their changing personal demands should be met instantly, and they are voting with their wallets.
Personalized goods and services are the keys to customer loyalty and revenue growth. Unfortunately, that is not how most factories are set up in our current system of high volumes and mass distribution.
While revenue science is the answer to the unpredictable needs of individual customers, the industrial answer is to modify processes and resources to provide mass personalization. Technological development has reached a level where manufacturing and distribution can be set up to create cost-efficient personalized products and services with the help of digital commerce and content. For example, smaller inventories, the internet of things, and flexible manufacturing have brought on a revolution in supply chains. With customer data and digital user interfaces for customizing products, companies can offer a completely new and more competitive value proposition.
Even though the technology to serve customer needs better exists, this transformation necessitates a change in strategy and operations. However, the very first step needed is organizational change: fusing industrial operations with revenue-generating functions, namely sales and marketing. Often, these tend to operate in separate silos, identifiable right down to the way their employees talk and dress.
To reach new business models, core processes like production, supply chains, marketing, and customer service will have to be restructured to be driven by customer data. In human resources, emphasizing skills like design and co-creation will promote customer-oriented thinking. Likewise, in data management, data protection processes need to be in place to put customers in charge of their own data. Overall, organizational processes that support the holistic flow of data from customers to the production line can shift a company from a mere manufacturer to a sophisticated, predictive service provider.
Mass personalization improves manufacturing companies’ growth while clearing the path for a more sustainable consumer society. No one wants products that they have to replace all too soon. We want products that are customized to our needs based on our user data – and supported by services that keep the products evergreen for years to come.