According to Gartner’s Future of Sales 2025 report, 60% of B2B sales organizations will transition from experience- and intuition-based selling to data-driven selling by 2025.
However, the transition to data-driven decision-making will demand increased expertise in both understanding and applying data. Associate Professor Sanna-Katriina Asikainen, Head of the Department of Marketing at Aalto University, emphasizes that data-driven decisions require a tremendous amount of supporting data. This is why experience- and intuition-based decision-making has remained crucial in many areas, such as emerging industries. Research shows that when an industry becomes more mature, and enough data on it has been accumulated, data-driven decisions become superior to those based on intuition. Meanwhile, the opposite can be true in situations where this level of maturity is still looming on the horizon.
“The value of any given data always depends on the context at hand, as does the selection of the data to be used,” notes Asikainen.
At its best, data allows companies to contrast various areas that would otherwise be difficult to compare. However, successfully using data in decision-making requires critical reflection on the data being used and understanding the context in which the data is to be applied.
The value of any given data always depends on the context at hand, as does the selection of the data to be used."
One common pitfall for companies is using only data available to them at a given time, even if it isn’t applicable to the situation at hand. In addition, the data being used can become biased for a number of reasons. As an example, Asikainen highlights an international company whose sales reward system and bonus-related decisions for the sales teams in two different countries were based on numerical feedback from supervisors instead of actual performance data. Not only was the data biased due to the differences in individual response styles, but also due to cultural reasons, since managers in the U.S. give feedback differently than managers in Japan.
Data – a blessing or a curse?
Using data to support your decision-making, or even as its basis, requires critical thinking, knowing the data’s origin and reliability, understanding its applicability, and various analytical skills.
Data can be a blessing when it allows companies to compare and contrast complex entities when making decisions or when they want to predict the consequences and results of their actions. However, data-driven decision-making can be a curse when it is used without an understanding of the data’s nature and applicability. At worst, data that is unsuitable for the context at hand can steer a company’s decision-making in the completely wrong direction.
Above all, data should be used to support your decision-making and as a tool for comparing various alternatives. The shift towards data-driven decision-making is already underway, and few companies would dare to deny the importance of data in a world where it is easier to collect and utilize than ever before. However, the successful utilization of data requires developing one’s competence and allocating the necessary resources.
Boosting product development and modeling with data
In addition to decision-making, data can be used to identify new markets and demands. By analyzing market sentiments in the form of data, businesses can identify silent signals and demands that are not being met by any existing solutions on the market.
“Humans are not being replaced. We can discover data and the silent signals present in the market and integrate them into our product development, sales, and marketing activities. Data can work alongside humans and allow us to make more informed decisions,” explains Sami Grönstrand, Industry Solution Experience Senior Manager at Dassault Systèmes.
Data can work alongside humans and allow us to make more informed decisions.”
Design Thinking emphasizes rapid development and functionality testing practices early in the product development process. As an example, Grönstrand highlights a data-enabled product development solution used in complex manufacturing processes: the virtual twin. Instead of testing and developing a product with physical prototypes, companies can use virtual twins to model their products and features virtually based on specific data inputs. This allows customers to rapidly test the digital twin, provide feedback on its functionality, and shape future development targets.
Concretizing the value in sales touchpoints
“Customers today like to gather a lot of information before they contact any sales personnel,” Grönstrand notes.
The role of data in sales-related contact points has also become increasingly important. More and more of today’s conscious consumers are likely to analyze and compare products and solutions before initiating their first point of contact with sales.
The data collected by companies can be harnessed and used for their sales. Data can help concretely demonstrate a product’s benefits to customers, such as the savings it will bring or its greater reliability compared to other solutions on the market. In essence, data can be used not only to support decision-making and detect silent signals but also to verify the added value that a product or solution will provide.
Asikainen notes that a key weakness shared by many companies is how they integrate data into the different parts of their business in a comprehensive manner. A company’s various parts, such as its R&D or sales, may already utilize data in their decision-making – however, this utilization can be very isolated in nature, resulting in a non-uniform approach to how the company collects and processes data as a whole.