Monetization of Algorithms
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The document discusses algorithms, their properties, use cases, and monetization channels. It defines an algorithm and outlines factors that determine an algorithm's uniqueness and value, such as proprietary data sets. It also covers algorithm types, including those that are intrinsically or extrinsically valuable. The document proposes several monetization models, including direct payment for algorithm results, incorporating algorithms into solutions, and indirect payment based on the value of algorithm-informed decisions. Examples are provided for each channel.
Monetization of Algorithms
- 1. Afik Gal, 2014 The newest version of this document can be found at http://severalthingscometomind.com
- 2. • Algorithm is “a set of rules that precisely defines a sequence of operations.” • The monetized value of an algorithm is dependent on the uniqueness ability to replicate its function but mostly on in its results and the value they generate • Some types of algorithms add value intrinsically to a solutionproduct while some effect the company or the product extrinsically. The monetization models depend on these types and use cases • This presentation will review- algorithm properties, use case and monetization channels
- 3. Quality 1. Lift 2. Specificitysensitivity Uniquenessreplicability 1. Proprietary data sets (e.g. long time, unique population) 2. Advanced research 3. Extensive calibration fitting through Machine Learning 4. Patent protection • “Hard Math” • “Like any other patent” • “Knowledge cannot be patented”
- 4. UsabilityResources • Speed • Storage • CPUPower consumption • Need for connectivity (e.g. with cloud) • Need for customizations needed (e.g. readmission.. tailoring of rules to different populations)
- 5. Ext/Int Type Description and examples I Recipe Rules and logic that were created through research, meta-analyses and etc. Clinical guidelines, regression models I Transform Algorithms that create value through the transformation of data/signals Encryption, Compression, AudioVideo encoding E/I Discover Algorithms that discovergenerate new knowledge that is used as part of the solution Machine language, predictive, data mining, simulation, forecasting E/I Solve Algorithms that solve a complex problem as part of the solution Personalization, recommendation, matching, searching, optimization Extrinsic - Usage of algorithms outside of a product to inform ,rank and influence decision making processes and reduce the risks of new business models (game theory, Real options analysis, Decision Field theory, forecasting, simulation and etc.)
- 6. Channel Description Recipe Transform Discover Solve Direct Payment for the direct results of the algorithm X X** X Part of a solution Payment for the solution which utilizes the algorithm X* X X X Indirect (Extrinsic) Payment based on value of the decisionindirect implications of the results of the algorithms applied X X * Joint creation models such as open sourcing the algorithms can be used to create value without fear of losing potential revenue from the algorithm itself ** Generally requires uploading/giving access to considerable amounts of data and present an opportunity for analytics brokerage
- 7. • “Algorithm as a Service” – Providing an “algorithmic” brain for others to use – Revenue streams: usage, charging for resources consumed, professional services for integrationcustomization – Examples: IBM’s Watson, Wolfram Alpha, Algorithms.io, Lumiata, BigML • Software bundle (tools) – Revenue streams: license, sale, professional services – Examples: SPSS, R, SAS, Knime • Analytics Brokerage
- 8. • The outputs of the algorithm are a core component of a solution and help differentiate its value proposition • The monetized value stems from the value of the solution and is therefore dependent on additional elements to be realized • Examples – Behavior change (WellDoc, Omada, Jiff) – eCommerce (Amazon, StitchFix) – Clinical Decision Support(Zynz, MedCPU) – Jet engines (GE)
- 9. • Useful for use cases of decision support and enabling adoption of new business models (reducing risk) • Monetization is by the value of the advicedecision • Revenue streams: Value of the decisions +/- professional services • Examples: – GE’s Jet engine uptime model – MyBuys advertising model – Netflix’s big data approach to content creation – Starbucks’ branch selection – Algorithms to anticipate and maybe prevent wars – Mental Analytics to improve athletics performance
- 10. Channel Feasibility Example Direct Complex to design as it may require full access to real world results of the buyer andor complex contracting Algorithms for data cleaning charged by the value of the data filtered/found…and not by its volume Part of a solution The solution itself can be a “fee for value” solution Behavior change solution that is reimbursed by its success (e.g. weight loss intervention) Indirect Possible as the monetization model is flexible and usually ad- hoc % of money earnedsaved by a decision