Selling information solutions is far more than simply selling raw data. It’s about providing actionable insights and prompts for decision-making, transforming data into a powerful tool that drives business growth.
Survey respondents on average reported that selling data solutions accounted for only 18% of their revenues from data monetization activities, making it the least prevalent of the three approaches in the improve-wrap-sell framework. No doubt, this reflects the complexities of selling information solutions.
Solutions That Offer Data
Example: A retail analytics company provides raw transaction data to retailers. This data includes detailed sales records, customer demographics, and purchasing patterns. Retailers can use this data to understand sales trends, inventory needs, and customer preferences. However, the utility of this raw data depends heavily on the retailer’s ability to analyze and interpret it effectively.
Solutions That Offer Insights
Example: A healthcare analytics firm sells reports that analyze patient data to identify trends in disease outbreaks. These insights help hospitals and clinics prepare for potential increases in patient volume and adjust their resources accordingly. The firm uses data science techniques to process raw data into comprehensible insights that healthcare providers can act upon immediately.
Solutions That Prompt or Trigger Action
Example: An e-commerce platform provides a recommendation engine that analyzes customer behavior and triggers personalized marketing actions. When a customer shows interest in a particular product category, the engine sends personalized emails or app notifications suggesting similar products. This not only enhances customer engagement but also drives sales through timely, relevant suggestions.
Consider a business case where a logistics company leverages its vast amounts of shipping data to create an information solution:
Creating Value
Realizing Value
Data Management (all three)
Collecting, processing, and ensuring the integrity of data across all types of solutions.
Data Platform (all three)
Building and maintaining platforms that support data storage, processing, and delivery.
Data Science (reporting and statistics)
Applying analytical methods to derive insights and trends from raw data.
Customer Understanding (all three)
Deep knowledge of customer needs and pain points to tailor solutions effectively.
Acceptable Data Use (all three)
Ensuring compliance with data privacy laws and ethical guidelines to maintain trust and legality.
Ownership typically lies with a cross-functional team involving:
Chief Data Officer (CDO): Oversees data management and strategy.
Chief Marketing Officer (CMO): Ensures solutions align with market needs and customer insights.
Product Managers: Develop and refine the data products based on customer feedback and technical capabilities.
Who knows the most in your organization about the customers and their struggles?
Identify internal experts who have deep customer insights and can guide product development.
Which of your customers might work with you on this?
Look for early adopters who are willing to provide feedback and collaborate on refining the solution.
What would make your solution unique?
Determine the unique value propositions that differentiate your solution from competitors.
Where are you in building the capabilities needed to create such a data-selling solution?
Assess your current capabilities in data management, platform development, and customer understanding.
How, and whom in your organization, would sell these solutions?
Define the sales strategy and identify the teams responsible for bringing the solution to market.
By addressing these aspects, organizations can effectively develop, market, and sell comprehensive information solutions that provide significant value to their customers.
Book Reference
For a deeper understanding, refer to Data is Everybody’s Business: The Fundamentals of Data Monetization by Barbara Wixom, Cynthia Beath, and Leslie Owens.
