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Adding supplemental revenue streams through analytics
As a leader and manager of a data driven organization you may be able to leverage analytics to generate additional revenue streams. If you…
As a leader and manager of a data driven organization you may be able to leverage analytics to generate additional revenue streams. If you are not in the business of selling data (e.g. Jawbone) but are a data driven company (e.g. LinkedIn, Chase, Target), this is your future. So far we have seen consumable analytics come from firms that have a core business of providing insights. Prime examples are companies in the wearable apps and web/business analytics space.
Let us put aside the technical hurdles of setting up analytics products in this post and focus on what kind of customer, when to monetize and what analytics to provide.
Analytics for Businesses
In a recent interview, a CEO of a leading LED manufacturer said “I need analytics on where the fish are swimming right now.” He wanted to know as soon a property is leased or sold so that he could pursue it as a sales lead. This data can be curated by combining data from Zillow, Craigslist and other open data sources.
Companies in a two-sided marketplace business model are in the best situation to provide analytics to both consumers and sellers. Consider a real-estate listings company that can provide realtors with analytics on how their listings are performing and how they are doing compared to other realtors in the area. For listings, providing Google Analytics like information would help sellers understands potential buyers better.
Analytics products that provide interactive automated reports for such businesses can very likely be monetized through a subscription model. Independent and small businesses are continually looking to find prospective clients and to understand the competitive forces in their marketplace. Large businesses may also find performance reports valuable and it may help them compare their services with their competitors.
Analytics for business customers can include
Market Assessment: Analytics around competitors selling similar products along with assessment of demand for those products. eBay, LinkedIn and Craigslist are good examples of two-sided marketplaces which can provide information to sellers about the competitive environment, and demand for items listed. For example, LinkedIn could share information about how many potential candidates meet criteria for job requirements.
Client/Service Performance: Insights about seller’s performance and transactions. A real-estate service could track realtor’s performance (sales) over time and provide analytics about how her listings perform.
Client’s Consumer Behavior: Aggregated demographics data about end-consumers that shed light into buying behavior. Companies like Amazon or eBay can share insights about buyers for particular merchants. This could be data about consumer acquisition funnel or change in Average Order Value (AOV).
Lead Qualification: In cases where consumers are willing to share their data, your service may be able provide sellers with leads and propensity to buy. A good example is recruiters and candidates for job listings on LinkedIn. LinkedIn can share profiles of candidates for job listing that best fit the job description. Similarly, real-estate listing services like Trulia can provide realtors with screens of property buyers who may be interested in appropriate listings.
Insights for Consumers
Below is a snapshot of what LinkedIn will offer if you go to apply for a job using their website. Signing up for a premium account, they’ll give you detailed insights about how many people applied for the role and what LinkedIn estimates your chances are based on your profile.
Consumers only tend to pay for analytics when they have a very specific need for which an investment in analytics results in a financial return. For example someone actively looking for a job or a house may invest in a subscription service that provides them with a competitive advantage over other candidates or properties. In most other cases, an analytics product can be an added feature to your main product.
Types of consumer facing analytics may include
Customized Analysis: Customized recommendations for consumers that provides self-reported data (LinkedIn recommended jobs, Amazon recommended purchases, and Netflix recommended movies). A potential service would be a medical website/app that takes details about your symptoms, x-ray reports, bloodwork, etc. and tells you exactly what type of medical condition you have, and what local doctors specialize in it. Another potential service would provide customized travel itineraries for vacations based on your travel preferences.
Combining Several Data Sources: Consumers provide credentials to their data sources (financial accounts, retirement funds, social networks) and a service combines those sources of data to provide insights. Combining data from all your financial institutions, Mint.com does an excellent job of providing insight about a consumer’s expenditure and investment behavior.
Social Analytics: If your company has millions of consumers and you can aggregate data, you can provide analytics around how a consumer compares to the rest of the population. Spotify in theory could provide analytics around what kind of music your friends like and how your musical tastes differ.
Issues Relating to Privacy
One of the main concerns relating to monetizing data is the questions of privacy and data ownership. If you are looking to monetize data this has be a primary issue that should be addressed upfront. Consumers and businesses want to first make sure that any data that’s either shared with them or collected about them is never sold or publicized to a third party. Any data that can somehow be attributed to a particular user or business is referred to as PII (Personally Identifiable Information). Anyone looking to monetize data must think through how to communicate their commitment to protecting PII data to end customers.
When providing analytics that gives a market or competitive perspective you must aggregate data in a way that no single customer can be identified using that data. Aggregating data to form collective inference enables analysis that can potentially be shared with customers or third parties. Some of the criteria that may be used to ensure that aggregate data is not compromising any customer’s privacy are
Using an adequate sample size for segments and attributes so that a single customer cannot skew analytics
Not splitting data or data segments in a way that a single customer could be identified
Not showing medians, minima and maxima when possible
For companies that collect a lot of data about consumer behavior there are several opportunities to monetize new revenue streams. This can be done by providing analytics to consumers and businesses. While two-sided market places are poised to benefit the most, many businesses can provide valuable actionable insights about service and product usage. When sharing analytics about market assessment and competitive insights privacy issues must be addressed upfront.
Originally published at ehasan.com.