How companies monetize data
Data Monetization is a generally a poorly understood strategy at the corporate level. Here's a brief overview of how businesses should be thinking about creating new revenue streams by leveraging their first-party data (the data that your company creates) better:
Selling data to third parties
One of the most common ways to monetize data is by selling it to other businesses or organizations that can use it for research, marketing, or other purposes. For example, companies like Nielsen and comScore offer data on consumer behavior to businesses looking to improve their marketing strategies.
Here are some things to consider:
Legal and regulatory compliance:?Selling data (anonamized or personalized) requires that all necessary consents and disclosures from customers have been agreed to, typically stipulated in privacy and data policies.
Data quality:?The data being sold needs to be clearly defined using a data definition document that identifies the data and file format, cadence (how often it's updated), cleaned for errors and normalized to an agreed format.
Intellectual property rights:?It's important to ensure that the data does not infringe on any intellectual property rights, including copyright, trademark, and patent rights.
Data security:?How the data is secured, protected and accessed is important given it's important to all stakeholders (business, customers, data consumers). Having a strong strategy for data security including working with penetration and vulnerability testing firms to verify and advise on best-practices is highly encouraged.
Reputation and brand image:?The sale of data can potentially damage a company's reputation and brand image, particularly if customers feel that their privacy has been violated or their data has been misused. A careful legal and marketing approach with appropriate transparency (especially with Customers) about the sale of data is paramount.
Financial considerations:?Monetizing data should including consideration for pricing, contracting and payment terms like any other product or service. It's also important to consider the potential impact on revenue if customers feel that their data is being sold without their consent.
A user experience that can conveys intelligence and superiority backed by rich and reliable data will always resonate better.
Offer data-driven products or services
Companies can also monetize their data by using it to develop new products or services. Consumer companies like Garmin, Apple and Fitbit uses data from their fitness trackers to offer personalized coaching and training programs to its users.
Additional things to consider:
Data quality:?Unreliable data is the death nail for any data-driven product or service. Inaccuracies in the format, quality or cadence of data will clearly lead to a degradation of performance and impact demand for the product.
User experience:?A user experience that can conveys intelligence and superiority backed by rich and reliable data will always resonate better.
Business model:?It's important to consider pricing, revenue models, and other factors when considering the role that data plays in the financial viability of the product or service.
the ability to allocate the right resources at the right time can literally pay for itself.
Use data to optimize operations
Not a product or service but worth re-affirming that internally optimized operations will lead to reduced overheads, lead-times and greater efficiency, ultimately leading to increased profits. A quick example is where business with core logistics and delivery operations like UPS, Uber or products like Google Maps use traffic, driver behaviour, weather data to optimize delivery routes that reduce fuel consumption and delivery lead times.
Some other important considerations:
Business objectives:?Identifying the operations that need to be optimized and the key performance indicators (KPIs) used to measure success is a good place to start.
Data analytics tools and techniques:?There are a wide variety of data analytics tools and techniques available for optimizing operations. It's important to select an approach that best suits your needs:
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Resourcing:?Building a capable data team is still an after-thought for most companies but when it comes to process optimisation, the ability to allocate the right resources at the right time can literally pay for itself.
Change management:?Optimizing operations using data may require changes to business processes, procedures, and workflows. Having an open-minded approach is easier said that done, starting with a smaller business vertical and using that as a launch point helps prove/dis-prove a new approach.
not incorporating buyer behaviour patterns and intelligence back into a product is as good as operating in the dark.
Use data to identify new revenue streams
Data can also be used to identify new revenue streams or business opportunities. Many companies new follow the approach originally outlined by Pinterest, Netflix and Amazon to build recommendation direvtly into their core service offering.
If fact, not incorporating buyer behaviour feedback and intelligence into a product is generally considered as good as operating in the dark.
Many of the same considerations already listed also apply here including data quality, privacy and governance. It's also worth affirming the following:
Great products:?have data capture and insights built in fromt he start
Requirements gathering:?New Product or Service requirements should also define the data analytics objectives
Hire carefully:?Monetization, Product Managers and service managers should be very comfortable with how data science and analytics can add value over time.
License data to partners
Companies monetize their data by licensing it to partners who can use it for their own purposes. For example, car manufacturers work directly with insurance companies who then offer personalized policies to their customers
Things to consider:
Scope of License:?The scope of the license should be clearly defined within a license agreement that stipulates the purpose for which the data can be used, over a specific time period (hopefully not indefinitely) as well as any restrictions.
Data Governance and Security:?Contractually, Data governance and security will be essential to being able to license data to other parties. Having that clearly identified and with assurances in place that also consider the question of accuracy is key.
Liability:?The license agreement should specify the liability of each party in case of data breaches or other data-related issues.
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All comments and feedback welcome - Thanks for reading. Cameron
Cameron runs FScale, a data, cloud and AI advisory company. Reach out to [email protected] for more.