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.

1. Afik Gal, 2014
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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