Data monetization opportunities are determined by defining a problem to be solved in terms of focus and state. If the focus of the problem to be solved is external and the state is new, then the defined monetization opportunity is in creating new business models using data. Businesses that recognize they can create new products and services that reach existing and/or new customers based on data they own or acquire quickly realize that the information business requires a separate and distinct business model. The environment for information businesses to succeed must be agile and able to adapt quickly as change requirements emerge.
The difference between using data to increase market share and data monetization that requires new business models is the shift in focus of the problem. A new business model is required because most established business processes in traditional businesses answer the needs of the business, not of the customer. When the focus of the problem is on the customer’s needs and the business works to use data to solve new customer challenges (sometimes before the customers are even aware of the problem) there is a fundamental paradigm shift that the traditional business model cannot address. In some organizations this approach to data monetization requires the creation of a new business unit and in other cases spin-offs are created to avoid any long-standing political obstacles.
Innovation is the key to collecting data to solve new customer challenges. Behavior data and customer preference data are both valuable to informing businesses what is important to their consumers. The typical approach is to use data to expand customer profiles with identification capabilities as granular as “segments of one.” However, emerging research suggests individual actions may not be the greatest predictors of consumer action and digitized community characteristics and behaviors may be greater predictors. The challenge is that consumers come and go through digital communities frequently. Understanding individual behaviors and preferences relative to these communities require innovative, real-time methods of data collection.
Once collected, the data packaging requires a team of specialists as reporting and analytics are not just technical or mathematical processes. Yes, it is a science, but not just for a single data scientist. Packaging data in these agile business models requires a data science team composed of technical engineers, data gurus and business professionals with a deep understanding of the customers’ needs. The key factor to remember with packaging information for this monetization approach is the information or derived insights must be valuable to the customer within the scope of their specific need, pain or problem.
The delivery methods of data and insights to customers for these monetization opportunities will vary greatly and will depend upon the specific context of customers’ intended use. Remember, because the state of the customer problem being solved in these opportunities is new, not all customers will be prepared to act. The delivery of these products and services should be so compelling that the customer should immediately understand the value and take action without needing additional justification or consideration.
New business models are built to manage the fundamental shift in focus from focusing on business needs to focusing on answering the needs and challenges of customers. Rather than asking “How can we use data to grow revenues?” the question becomes “How can we use our data to make generate products and services that solve our customer’s challenges and make their lives easier?” This requires innovative data collection techniques and an extremely knowledgeable, cross-functional data science team to deliver data and insights that are valuable within the scope of the customer’s needs. The delivery of these monetized products and services must be power-packed with not only the solution to the problem but also the compelling justification to take action immediately.
Anne Buff is a thought leader on SAS's best practices team.
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