Incremental innovation: the need for data insights
Today’s economy is in a state of constant flux, with new, dramatic disruptors appearing all the time. Despite how sudden these changes may seem, the vast majority are the result of incremental innovation.
Today’s business landscape is characterized by two principle actors: technology and the consumer. Although technological innovation has never been so crucial to business success, the same innovations are heightening customer expectations. Subsequently, brands are shifting towards a more customer-centric approach. As consumers become increasingly savvy, competition on the market intensifies, driving the pace of innovation. Therefore, there is an enormous amount of focus on disruptive innovation and the creation of new markets for technologies, products, and services. A classic example is Netflix, whose digital streaming services forced DVD rental companies into bankruptcy.
These breakthroughs have transformed the way that consumers conduct their day-to-day lives. However, these dramatic shifts often began with a simple idea that was enhanced by incremental innovation. By gradually improving a simple proposition, businesses could effectively monetize their ideas and encourage mass adoption. One of the most successful examples of this approach is the iPhone, where incremental innovation led to market dominance. However, tech is not the only sector where this strategy presents significant opportunity. Now, all industries should seek to exploit strategies focused on incremental innovation. In this article, we discuss the advantages and challenges of this approach to business development.
Incremental innovation and risk
Whilst disruptive innovation is certainly astonishingly profitable, the importance of incremental innovation cannot be overlooked. Incremental enhancement provide a route towards sustainable improvements to products, services, functions, and systems. Often, necessity is what drives gradual improvement, where small inconveniences drive the impulse toward innovation. However, innovation invariably comes with some risk; for example, the demand for goods or services is likely to be uncertain. Equally, consumers may lack understanding or the investment itself may present a significant risk. Therefore, many companies enter into phases of innovation with a certain degree of trepidation.
Mitigating risk through intelligence
However, according to a study conducted by researcher Altin Kadareja, these risks can be mitigated if businesses leverage customer insights. Furthermore, in an article published by management consultants McKinsey & Company, the firm stated that “understanding and acting on the probable contours of change requires reflection and a deep knowledge of customer behavior, industry dynamics, and feedback loops. These insights can help players reshape their business models to exploit structural changes and cushion potential shocks.”
As such, it is essential that companies adopt a two-pronged approach to innovation. Not only should they know what consumers want and how their product or service meets these demands, but they also need to understand what factors may trigger resistance to innovation amongst potential customers. Although the business cannot influence customers’ decisions directly, they can develop a strategy for managing resistant behavior. With this plan, they can seek to overcome sales objections.
The importance of granular customer insights
Research shows that businesses that leverage customer insights outperform competitors by 25% in gross margin. However, many businesses approach data looking for generalizations or pervasive trends. Although this intelligence is useful, more often than not, a granular approach to data generates the most meaningful insights. Therefore, businesses should harness big data to extract this intelligence. By segmenting customers according to data that go beyond the usual information (demographics, spending) towards more detailed insights (unstructured text data, mouse strokes, usage patterns) businesses can gain a deeper understanding of consumer preferences.
According to McKinsey, the ultimate goal of this approach is to “extract value in a rapidly changing space, companies must not only focus on the fraction of users who drive the economics but also simultaneously build a diversity of business models to address the broader audience.”
By listening more carefully to consumers, businesses can apply these insights to their incremental innovation strategy. Thus, brands can concurrently meet customer needs and manage resistance to innovation. This, in turn, leads to great customer loyalty and reduced churn.
Applying big data to product development
Big data is a critical enabler of innovation and product improvement. Every design process has to have the customer at the center; from here, companies can identify the best way to meet customer needs and capitalize on them. In addition, big data facilitates enhanced quality control and streamlined production line processes. Through leveraging data, companies can ensure every decision is evidence-based, actionable, and quantifiable.
Moreover, the success of a product hinges on robust market analysis. The incorporation of granular customer insights in the early stages of product development is undoubtedly a critical condition for success. This is because this drives the product development team to meet consumer preferences and nurture a relationship between product and consumer. After all, developing a product without the consumer at the center is likely to lead to costly adjustments later down the line.
Nonetheless, many companies are only using a fraction of the data available to them. Enormous legacy systems, siloed data, and time-consuming manual processes are frequent obstacles. As a result, organizations attempt to leverage insights from incomplete data sets, which impacts the accuracy or decision-making processes. Therefore, it is essential that before embarking on a big data project, companies have a robust data hygiene strategy in place. As such, the data governance process is an important stage of incremental innovation, as these small cultural and processual changes facilitate disruptive innovation on a sustainable basis.
Why data maturity is essential to incremental innovation
In the customer-centric era, it is critical that businesses continue to innovate. Brands need to constantly add value to the customer journey, driving better service and economic growth.
However, as is proven, innovation is not necessarily an expedited process; now, innovation is a process of continuous improvement. These reinventions and adjustments respond directly to consumer preferences and habits, nurturing receptive attitudes and smooth transitions to mass adoption.
In order to achieve this, companies should look to big data to guide their strategic decisions. Many firms are at very different stages in their data maturity and developing a big data plan is an important route to the competitive advantage. Moreover, a robust understanding of customer preferences is essential to the development of an innovative new product.
With detailed insights into customer needs and wants, companies can create products that meet these requirements. After all, the numbers show the success of data-driven strategy; companies that leverage analytics are 5–6 times more profitable than their competitors. Equally, companies that implement big data as part of their marketing strategy improve ROI by 15–20%.