Data Commercialization Moves to Insights Commercialization
Let’s face it. The data economy is primarily about insights, not data. Which customers are most likely to churn? Which media instruments are best fitted for the task if I want to sell X in channel Y, and what is the optimal marketing spend for each instrument? How can I maximize and optimize the availability and utilization rates of my machines and systems on the shop floor? Decision-makers want answers to these types of concrete business and operational questions. This cannot be achieved by means of good instincts and intuition that used to drive successful business strategies.
Sharing is caring — and good business as well
A growing number of organizations in the private and public sectors recognize the opportunities and business potential that their data provides. The mission is to turn their data into data assets and insights-driven services and commercialize and monetize on them. According to the recent Forrester survey, 1/3 of the firms stated that commercializing data or sharing it for revenue generation purposes with partners or customers is one of the key tasks on their to-do list. Instead of putting their data to work only internally to improve operational efficiency, previously unthought-of external and internal stakeholders could benefit from it as well. Sharing delivers value and enables new sources of business and revenue.
All of this means that it is not enough to think in terms of raw-data-as-a-service and letting partners or customers figure it out what to do with it. Not surprisingly, an increasing number of organizations are creating more advanced and refined data-driven products and services. In the process, they move from traditional discrete manufacturing operations or raw-data-as-a-service business companies to insights services providers that offer more complete decision-making support and action plans.
IDC stated in its recent survey that over 70% of large organizations are already purchasing external data and by 2019 almost all of the surveyed companies are likely to do the same. However, purchasing and capturing all of this data (structured, semi-structured, and raw) does not mean that these organizations have the skills and resources needed to sift through it, make sense of it, and use insights effectively for value- and revenue-generating business decisions and actions — whether it is about targeting new markets, driving operational efficiencies, developing new sources of revenue, or gaining better insights into the customer and prospect base.
Sources of insights
In our customer base, we have come to notice that there are most typically three types of data used to create insights:
- Data for better understanding customer insights and powering a continuous dialog with customers. The retail sector has traditionally been generating detailed data on consumer behavior and purchase history through various PoS, loyalty management, transactional, and customer relationship management systems. Exploiting these data to increase basket or average order value and optimize margins remains a challenge with traditional business intelligence tools and data architectures. As consumers are increasingly empowered by data and pervasive mobility, as well as access to competitive pricing and product information while visiting stores or commerce sites, retailers’ ability to integrate and gain true insights into data from online (clickstreams, social media, etc.), in-store customer experience (PoS, surveys, CRM, etc.), and various market and environmental factors is pivotal. As is the capacity to react in (near) real time.
The application of the insights gained here can be versatile: personalized offers, store design and layout, assortment management and optimization, real-time pricing optimization, and data-driven stock and ordering. Oftentimes, the data acting as the foundation for the insights is syndicated by nature — a situation where third-party data is integrated into the retailer’s or the insights services provider’s data. Many marketing data providers have moved in this inevitable direction of delivering true decision-making support for sales, marketing, and broader business development decisions by implementing the “data refinery” service on top of their raw data sources.
- Usage data which covers the type of user interaction and web data that is captured during the process of usage of a web property or a data asset. Data, insights, and analytics best practices combine to generate actionable insights for specific business use cases. These use cases are specific and concrete and intended to help companies achieve specific business goals, such as driving more traffic from web shops to physical stores or focusing on the right development activities to make the web store convert in line with expected business goals.
Many insights services providers offer a user interface for the business users, as they need a user interface to view the data coming from web and social media analytics systems and other relevant data sources and analytics and derive conclusions and course of actions. It also serves those users who do not have knowledge, skills, or interest in dealing with complex, raw data (95+% of us).
- Data from connected products and “things” provides an opportunity to sell insights based on these smart products and IoT analytics. This is also in line with recent developments where makers of industrial machinery are creating new business models by using IoT links and data to offer their products as a service. For example, shipboard sensors monitor things like generators, engines, air conditioning, various fuel meters, etc. The ability to identify that fuel meter readings are correlated with the amount of power used by refrigerated containers provides lots of useful insights. This data can be used to determine optimum operating parameters by modifying power output from the generators. In one particular case, the use of multivariate predictive maintenance analysis revealed that running more generators at lower power is a more efficient approach than maxing out a few.
This kind of approach also strikes an agreement between a vendor and a customer for a certain kind of outcome rather than a certain kind of functionality. No wonder a number of discrete manufacturing companies are rebuilding their entire software and analytics approach and essentially transforming themselves into insights services providers.
Final thoughts
For many organizations investing in the process of extracting truly actionable insights can be challenging. To do this, organizations need to have the resources, skills (and employer brand to attract these skills), governance, architecture, and other key enablers in place which has turned out to be a very challenging proposition for many of them. This stands as one of the main reasons why insights-as-a-service has turned out to be a viable solution.
In the next blog post, we will explore some of the key data and insights commercialization and monetization models for insights service providers.
