Horizontal Segmentation and MindGenomics Veljko Milutinovic, Jakob Salom, Zoran Markovic, Zoran Ognjanovic, With Jasna Matic and Howard Moskowitz, Steve.

Horizontal Segmentation and MindGenomics Veljko Milutinovic, Jakob Salom, Zoran Markovic, Zoran Ognjanovic, With Jasna Matic and Howard Moskowitz, Steve Onufry 1/65

MARKETING: BROADBAND vs TARGETED

Modern Individualized Marketing We don’t like to be tracked!We don’t like to fill forms!

Individualized Marketing with MindGenomics We love value exchange! We love sophisticated math!

Essence HorizontalSegmentation = A method to increase MarketEffectiveness, etc. MindGenomics = A method to implement HorizontalSegmentation Applications = Marketing+Education+Science, etc. Unique for MindGenomics, MG = A 2-stage approach synergizing general and individual 5/65

Phases of Modern Marketing Phase#1: Bringing the customers Phase#2: Maximizing the revenues from customers New trends: Individualized marketing (drawbacks): Individualized marketing, based on demog/behav No MathScience@Ph#1, no CustomerTyping@Ph#2 6/65

MindGenomics in a NutShell Compile-time effort: e.g., Bringing people to a restaurant 7/65

MindGenomics in a NutShell 4. Sophisticated Selection of Marketing Slogans 3. Analysis 2. Market Polling 1. Building the MicroScience Compile-time effort: e.g., Bringing people to a restaurant 4/65

MindGenomics in a NutShell 4. Generation of a Marketing Slogan 3. Analysis 2. Market Polling 1. Building the MicroScience Compile-time effort: e.g., Bringing people to a restaurant 4/65

MindGenomics in a NutShell 4. Generation of a Marketing Slogan 3. Analysis 2. Market Polling 1. Building the MicroScience Run-time effort: e.g., Treating people at arrival to the restaurant Compile-time effort: e.g., Bringing people to a restaurant 4/65

MindGenomics in a NutShell 8. Building the Experience Base 7. Profit Analysis 6. Targeted Action 5. Customer Typing (at reservation time) 4. Generation of a Marketing Slogan 3. Analysis 2. Market Polling 1. Building the MicroScience Run-time effort: e.g., Treating people at arrival to the restaurant Compile-time effort: e.g., Bringing people to a restaurant 4/65

MindGenomics in a NutShell 8. Building the Experience Base 7. Profit Analysis 6. Targeted Action 5. Customer Typing (at reservation time) 4. Generation of a Marketing Slogan 3. Analysis 2. Market Polling 1. Building the MicroScience Run-time effort: e.g., Treating people at arrival to the restaurant Compile-time effort: e.g., e.g., Bringing people to a restaurant 4/65

1. Building the MicroScience Jazz Rock Rap Funk Country Metal Music Veggie Turkey Chicken Seafood Eggs Fruits Food White Red Rosé Sparkling Dessert Fortified Wines Sweet I am in Love Most Beautiful Happy Thoughtful I know It All Smiles Silos (6) Slogans (6x3) 5/65 Combining one Silo and two Elements: 216. Optimal: 40 vignettes to answer! Max48 The MoskowitzJacobs software creates the process: One vignette = 4 elements! Elements developed using psychodynamic principles: 5KEYS (Cognition, Emotion, Behavior, Sensates, Personality)

Example of a Vignette in a Set of 40 (48) Vignettes : Q#1 6/65

Example of a Vignette in a Set of 48 Vignettes: Q#2=? 7/65 How much are you willing to pay for this?

2. Market Polling a. Internet: In USA about $5 (typ = 300) b. NeuroEconomy: Possible Future (MIT) c. Text/Media-Mining: Future Research (ACM iASC-2013) d. Computer/Network-Acceleration: Future Research (ACM ISCA-2013) 8/65

High-Tech Polling 9/65

The Brain Marked with Economically Relevant Areas 10/65

Advances in Media Mining, IEEE/ACM, 2013 Aleksandar Mihajlovic, Vladisav Jelisavcic, Zoran Ognjanovic, Veljko Milutinovic, Zoran Markovic, and Hermann Maurer 11/65

Maxeler Mining from Social Networks Kartelj 201212/65

3. Analysis: Linear Regression 13/65 9 9 0

3. Analysis: Linear Regression 13/65

4. Slogans Slogan #1 Slogan #2 Slogan #3 14/65

4. Slogans Slogan #1 Blackened Louisiana Shrimps: Patriotic Slogan #2 Special: Hypochondriac Slogan #3 Special: PennyWise 14/65

4. Slogans The slogans will bring people to the restaurant! 14/65 Slogan #1 Blackened Louisiana Shrimps: Patriotic Slogan #2 Special: Hypochondriac Slogan #3 Special: PennyWise

5. Customer Typing Classifying each reservation-making or just-arrived customer to a cluster! ? The Major Tradeoff: Likelihood Maximization versus Cluster Count 15/65

5. Customer Typing Classifying each reservation-making or just-arrived customer to a cluster! 42% 20% 38% 15/65 The Major Tradeoff: Likelihood Maximization versus Cluster Count

5. Customer Typing Classifying each reservation-making or just-arrived customer to a cluster! 42% 24% 20% 12% 38% 64% 15/65 The Major Tradeoff: Likelihood Maximization versus Cluster Count

5. Customer Typing Classifying each reservation-making or just-arrived customer to a cluster! 42% 24% 6% 20% 12% 6% 38% 64% 88% 15/65 The Major Tradeoff: Likelihood Maximization versus Cluster Count

6. Targeted Actions What is your music wish? 88% 16/65

7. Profit Analysis 1. 2. e.g., LittleBay@London 3. 4. 17/65

8. Machine Learning Finding missing puzzle elements: For better future estimate 18/65

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN PHASE#1[MG]

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 1 PHASE#1[MG]

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 PHASE#1[MG]

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 PHASE#1[MG]

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 PHASE#1[MG] 4(40VIGNETTES:MARKOV)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 5(PAY) PHASE#1[MG] 4(40VIGNETTES:MARKOV)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 5(PAY) 6(PAY.WEST) PHASE#1[MG] 4(40VIGNETTES:MARKOV)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 5(PAY) 6(PAY.WEST) PHASE#1[MG] 7(PAID) 4(40VIGNETTES:MARKOV)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 5(PAY) 6(PAY.WEST) 8(300) PHASE#1[MG] 7(PAID) 4(40VIGNETTES:MARKOV)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 5(PAY) 6(PAY.WEST) 8(300) 9(300) PHASE#1[MG] 7(PAID) 4(40VIGNETTES:MARKOV)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 5(PAY) 6(PAY.WEST) 8(300) 9(300) 10(TOP 3+3) LIN.REG PHASE#1[MG] 7(PAID) 4(40VIGNETTES:MARKOV)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 5(PAY) 6(PAY.WEST) 8(300) 9(300) 10(TOP 3+3) LIN.REG 11(TOP 3+3) PHASE#1[MG] 7(PAID) 4(40VIGNETTES:MARKOV)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/SPSYHO/AN 2(6+36+300) 1 3 4(40VIGNETTES:MARKOV) 5(PAY) 6(PAY.WEST) 7(PAID) 8(300) 9(300) 10(TOP 3+3) LIN.REG 11(TOP 3+3) 12(TOP 3) PHASE#1[MG]

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG]

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V) 5(BAYES.THEORY)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V) 5(BAYES.THEORY) 6(3)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V) 5(BAYES.THEORY) 6(3) 7(3clicks)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V) 5(BAYES.THEORY) 6(3) 7(3clicks) 8(3clicks)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V) 5(BAYES.THEORY) 6(3) 7(3clicks) 8(3clicks) 9(PLS, ANALYSE)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V) 5(BAYES.THEORY) 6(3) 7(3clicks) 8(3clicks) 9(PLS, ANALYSE) 10(HERE YOU ARE, ON-LINE OR OFF-LINE)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V) 5(BAYES.THEORY) 6(3) 11(OFFER) 7(3clicks) 8(3clicks) 9(PLS, ANALYSE) 10(HERE YOU ARE, ON-LINE OR OFF-LINE)

POLLERS 300 WEST [STANFORD] EAST [HARVARD] MEDIA 30 CUSTOMERS REST MGC/S PSYHO/AN PHASE#2[MG] 1(web) 2(3=?) 3(ok) 4(G+V) 5(BAYES.THEORY) 6(3) 11(OFFER) 7(3clicks) 8(3clicks) 9(PLS, ANALYSE) 10(HERE YOU ARE, ON-LINE OR OFF-LINE) 12(MONTHLY PAYMENT)

DIFF(PHASE#1) NO: DEMOGRAPHY

DIFF(PHASE#1) NO: DEMOGRAPHY YES: TASTE

DIFF(PHASE#1) NO: DEMOGRAPHY YES: TASTE NO: INTUITION

DIFF(PHASE#1) NO: DEMOGRAPHY YES: TASTE NO: INTUITION YES: MATH

DIFF(PHASE#2) NO: FORMS

DIFF(PHASE#2) NO: FORMS YES: VALUE EXCHANGE

DIFF(PHASE#2) NO: FORMS YES: VALUE EXCHANGE NO: TRACKING

DIFF(PHASE#2) NO: FORMS YES: VALUE EXCHANGE NO: TRACKING YES: CUSTOMER SATISFACTION

Horizontal Segmentation So far: N S = 1 How about: N S > 1 One cluster  One Segment! 19/65

Horizontal Segmentation: Example Before: Yogurt - 1000pcs@$1 20/65

Horizontal Segmentation: Example Before: Yogurt - 1000pcs@$1 After: C herry - 1000pcs@$2 Bicycle - 1000pcs@$2 AFLA - 1000pcs@$2 ? - 1000pcs@$2 Typing? 20/65

Horizontal Segmentation: Example Before: Yogurt - 1000pcs@$1 After: C herry - 1000pcs@$2 Bicycle - 1000pcs@$2 AFLA - 1000pcs@$2 ? - 1000pcs@$2 Typing? Self-typing while the customer is at shelves! 20/65

Horizontal Segmentation: Example Before: Yogurt - 1000pcs@$1 After: C herry - 1000pcs@$2 Bicycle - 1000pcs@$2 AFLA - 1000pcs@$2 Yni - 1000pcs@$2 Typing? MG-typing while the customer is at entry! 20/65

MindGenomics: Revisited 21/65

The Ice Point of View: What is MindGenomics? (1) Why? A method based on industrial psychology, mathematics, and DataMining, which can be used to promote products, services, ideas, opinions... What is the essence? If one wants to sell, one has to listen to the needs of the prospect! 22/65

What is MindGenomics? (2) Company assessment! 1. A company with potentials is approached, and their capabilities are estimated, using methods from company auditing and financial engineering. 2. A product is selected with potentials for horizontal segmentation; the question is, how to figure out what the market needs! 23/65

What is MindGenomics? (3) Data gathering! 3. Based on the principles of industrial psychology, a set of N questions is generated, which penetrate deep into the needs of the target group of individuals. 4. A polling partner is engaged to approach the target audience, and to bring back the form filled in, which assumes the existing of a small motivation item. 24/65

78/65 25/65

What is MindGenomics? (4) Data analysis! 5. The results of the poll are analyzed, using a proprietary datamining software, based on sophisticated math, and recommendations are made (for the essence and the form) 6. Crucial Step: Analysis! 26/65

80/65 27/65

An Introduction to Linear Regression Given a data set of n statistical units, a linear regression model assumes that the relationship between the dependent variable y i and the p-vector of regressors x i is linear. This relationship is modelled through a disturbance term or error variable ε i — an unobserved random variable that adds noise to the linear relationship between the dependent variable and regressors. Thus the model takes the form:datastatistical unitslinearrandom variable where T denotes the transpose, so that x i T β is the inner product between vectors x i and β. Often these n equations are stacked together and written in vector form:transposeinner productvectors where: 28/65

Example Figure 3: Example of simple linear regression, which has one independent variable!simple linear regression 29/65

What is MindGenomics? (5) Process analysis! 7. The life time monitoring of the process is absolutely mandatory. 8. Applications span from science and engineering to humanities and arts! What are the alternatives to polling? 9. SocialNetworksDataMining 10. NeuroEconomyMindMining 30/65

Definitions of Basic Terms IdeaMap MindGenomics AddressableMinds RDE 31/65

IdeaMap = A Program Function: Running a study Input: – Demographic (facts for the polling agency) – Silos (concepts of interest for marketing) – Elements (concept related marketing slogans) 32/65

Intermediate Products 1. VIGNETTES 2. POLLING AGENCY INTERFACE 33/65 After the vignettes are created, a human brain action might be needed before the poling starts!.

1. VIGNETTES Vignettes (screens with a scenario and questions): 40 or 48 Scenario (one silo and 3 to 4 elements from other silos) Questions (2 or 3): Think? Like? HowMuch? Scale: Each question on the scale ZERO to 9, or 1 to 9. Important: Questions are NEVER demographic! Questions are: PSYCHOGRAPHIC (What do you like in this 50Y old picture?) BEHAVIOURISIC (Do you like the meeting to be casual?) MERCANTILISTIC (How much are you willing to pay or donate?) Patent: If 2 questions, then: B+M (more common) Patent: Combinations form consistency sets catch liars (acceptable justification for polling re-requests) Patent: Others who praised/liked/bought the same book as you, also purchased... (inserted into IdeaMap) Patent: Number of vignettes is study dependent (inserted into IdeaMap) 34/65

2. POLLING AGENCY INTERFACE Several million people on file: Field House and Amazon Turk Each person willing to read and click 96 times (typical 2 questions, in each one of 48 vignettes) Requests and re-requests (demographic data must be precisely defined in re-requests) 35/65

Output Elements ranked by utility values (each element gets a utility value assigned) Human brain has to be activated at this point! Clusters of mind sets (to select three clusters manually) 36/65

MidGenomics = A Process The scientific process of discovery: a. How people think b. How people feel c. What are they ready to pay d. [and in future, how they change] For the following: a. Product b. Service c. Idea d. [HR] Typing is a part of MG! 37/65

AddressableMinds = A Set of People Set of people who belong to one of the categories discovered by MG! Only such people can be typed! 38/65

RDE = Rules Developed Experimentation Alias: Development of MicroScience Goal: Finding minds of given characteristics Example: Peter Coleman (conflict resolution in Israel) Scenaria: 1. $ to ALL 2. Finding what they like, and make it! 3. The best: Finding mindsets who like to cooperate online, and offering them a business opportunity :) 39/65

A RESTAURANT EXAMPLE Step#1: Talk and Google, to understand the essence! Step#2: Make 6 Silos (e.g., music, food, wines, ambience,...) Step#3: Make 6 elements per Silo (slogans) This results in 36 marketing slogans, some good some bad: IdeaMap will rank them and will give each one a weight Those above a threshold: Good Those below the threshold: Bad Examples: a. Food, wine, and opera - the best choice for the first date b. Select tunes to fit your wine bouquet c.... These should be generated by a PsychoAnalyst! Long story! 40/65

MidGenomics = A Process Step#4: Submit all 36 to IdeaMap, after the MJ is ready for you. This means: An account was formed to which you log in. Upon creation of the account: Type 36 elements and press SUBMIT Step#5: Get 48 vignettes, each one 2 questions; each question 1 to 9. Step#6: Find the polling agency and give them demographic requests for 300 Step#7: Polling in which the polling agency (PA) does not see vignettes! PA sends only the URL to polling individuals (PI) PI = pollees or pollers PIs log in remotely and click 96 times. 41/65

AvgUKFrGerSeg1Seg2 Base Size28773126884231 Constant2183216123 A7 Cereal bar with dried fruits and honey, no sugar added8789-426 A8 Soft and crispy cereal bar, dried fruits, seeds and nuts119139422 A1Crispy cereal bar555614 A9 Premium bar, soft and crunchy with a lot of fruit and chocolate chunks162314131913 B3Cereals from organic production3317-212 A6 Cereal bar with a soft fruit-yogurt filling37-713-312 A3 Crispy cereal bar with milk-chocolate coating161814192111 A4 Chocolate bar with a filling of full grain biscuits and raisins795978 A2Crispy cereal bar with dried fruits17-21-37 Raw Data to Be Analyzed (e.g., Food Industry) 45/65

Mind Genomics Revealed Three Mind Sets (e.g., Segments for Teens Facing Hospital) Seg1Seg2Seg3 Base Size435559 Constant425671 Seg 1 Medical staff always have a smile on their face18-8-3 Medical staff constantly checks up on teenage patients and insure their comfort188-4 Medical staff genuinely tries to help patients which makes teenage patients feel special16-7 Seg2 Medical staff always speaks the truth to the teenage patient no matter how traumatizing as it will help in the long run515-5 Wittiness helps medical staff seem more human215-4 Medical staff communicates and gives advice to teenage patients for their present and future lives315-4 Seg 3 Medical staff develop a teacher-student bond and help teenage patients who want to be medical staff themselves3-825 Medical staff develops bond with teenage patient to make it easy for them to vent7014 Medical staff develop a permanent friendship with teenage patient that carries on even after their release-3-714 46/65

MidGenomics = A Process Step#8: Consistency check by IdeaMap If an inconsistency is caught, MJ re-requests N repeats, and specifies their demographics. If the PA assumes abuse (getting more than 300 w/o pay), MJ shows the inconsistencies! Step#9: Getting reports, manual clustering, and selecting top 3 slogans for each cluster. Step#10: Shipping top 3 slogans for each cluster to media! Billboards, TV, Radio, Web,... Maximal number of slogans for a campaign: 5 47/65

TYPING Axiom#1: The compile time axiom! Type them before they get to the restaurant, e.g., when making the reservation, via web (interactive login) or phone (direct call or call center). Axiom#2: After they come, they do not like to "push buttons", so if you have a "fresh-from-the-oven" offer, or the guest just dropped-in un-announced, the value exchange must be considerable (TABLET with 3 questions to select a free appetizer). Final GOAL: Increased the spending and many happy returns An example: Pfizer Animal Health Care :) 48/65

H2020: ICEs of Europe MindMining = MediaMining + AlgorithmicPlethora 49/65 MoskowitzJacobs@HCNY

The ICE of USA Formed around NYU: Queens College Goals: Teaching + Consulting + Research Teaching: Army of promoters + Educating future experts Consulting: e.g., Pfizer animal health care Private prenatal hospital rooms Student mind sets elaborated (self-esteem, control, concentration) 50/65

Vision for Europe: National ICE Growing MultiNational The MindGenomics Process: Step#1: Getting incorporated Step#2: The 50/65 memorandum/contract with the ICE of USA Step#3: The B2C infrastructure (.ppt explanation + customer contract) The pitch of the explanation: a. If MG is used as a one-step process: Better 5% to 25% or more b. If MG is used as a two-step process: UNIQUE c. Axioms of MG: Think? Like? How much ready to pay/donate? 51/65

Step#4: The First Project From min 1Y to max 3Y Price Structure = Expences + Fee(ICE) Expenses: Polling + Travel (if any) + Specifics (if any) Fee(ICE): MicroScience (one time) + IndividualTyping (one month) = $2K IndividualTyping (monthly) = $200 Sefte = 50% off 52/65

The MindGenomics Process Step#5: LESSONS LEARNED ANALYZED! Step#6: Careful selection of the next 9 projects! 53/65

The MindGenomics Process Step#7: Another 9 projects @ 50% discount in cooperation with ICE(USA) Step#8: Lecturing in front of few big prospects. Definition of a Big Prospect: Chain of Identical Units or Single Multi Variety Production Examples: Dealerships (e.g., VW) or Monopolists (e.g., DELTA) Step#9: Automating the process: Setting the production rules Setting the quality control rules Teaching the HRs Setting the quality control of quality controllers 54/65

Step#10: Lecturing in front of many small firms! Local USA Chamber of Commerce Local National Chamber of Commerce Local Commercial Chambers of Managers Pitch: This is a game of large numbers! Worst seller with MG vs Best seller w/o MG = 23% better The gain is statistical (invisible becomes visible) 55/65

The MindGenomics Process Step#11: Learning How to Close the Sale in Big Noise! Suggestions: Not to hire a marketing agency a. One of co-owners (for big prospects) b. N assistants under one of co-owners (for many small) Step#12: Classification of Master Targets Master targets are classified as: a. Those having a problem e.g., NY Times selling subscription to college students b. Those anticipating the problem e.g., Politicians before entering a campaign 56/65

The MindGenomics Process Step#13: Education at a vector of universities! a. Course structure (e.g., Freshmen's MATH 110 @ NYU): 1/3 Statistics (including linear regression) 1/3 The MG process 1/3 An MG class project b. Invited lectures (e.g., Master and PhD Students) Step#14: All child diseases cured! 57/65

Step#15: H2020 Proposal Preparation Essence: Introduction to a vector of National ICE companies Mechanism: 40 different partners from 40 different EU countries Entrance Tickets: a. Have an educational course (on-site or via-skype) b. Have a paper published at a peer-review conference: Proof of understanding and building credentials c. Have a research idea compatible with the call d. Have a national ICE formed, as an affiliate of ICE.SRB: NGBT (no give before take) 50% stays locally 50% goes to ICE.SRB (for using the brand and technology) Half goes to ICE.USA Half stays at ICE.SRB Half stays with the majority owner Half goes to share-holders Axiomatic issue: Unlimited IntraBorder Growth 58/65

Step#16: Proposal Building on the top of Public Domain Improvements in 8 domain avenues! Step#17: Patenting Dr. Moskowitz holds (alone) 4 major MG patents Step#18: Conferencing e.g., VIPSI Brainstorming: What next? Step#19: Journalization Step#20: Exchange or JPMorgan 59/65 The MindGenomics Process

ESSENCE Experimental design: Sets the vignettes Linear regression: Deconstructs combinations, and detects lies as a byproduct Clustering and discriminate function analysis: Create Qs for Typing Empathy mining: Media based preprocessor - ongoing research! 60/65

OurPapers! Milutinovic, V., A Comparison of Suboptimal Detection Algorithms Applied to the Additive Mix of Orthogonal Sinusoidal Signals, IEEE Transactions on Communicatiions, Vol. COM-36, No. 5, May 1988, pp. 538-543. Milutinovic, V., Knezevic, P., Radunovic, B., Casselman, S., Schewel, J., Obelix Searches Internet Using Customer Data, IEEE COMPUTER, July 2000 (impact factor 2.205/2010). Milutinovic, V., Cvetkovic, D., Mirkovic, J., Genetic Search Based on Multiple Mutation Approaches, IEEE COMPUTER, November 2000 (impact factor 2.205/2010). Milutinovic, V., Ngom, A., Stojmenovic, I., STRIP --- A Strip Based Neural Network Growth Algorithm for Learning Multiple-Valued Functions, IEEE TRANSACTIONS ON NEURAL NETWORKS, March 2001, Vol.12, No.2, pp. 212-227 (impact factor 2.889/2010). 61/65

OurFP7s BalCon ProSense ArtTreat HiPeac 62/65

NB: 1. Using induction (datamining) create rules 2. Using deduction (from global to specific) create questions from which one can deduce 3. Example: Cooperation over children! ENABLERS: 1. Technology enables each individual to be addresses in a different way 2. Condition: Modern behavior 3. Example: Drop in who does not push buttons ;) STRATEGIC LAYERS: 1. SRB -> Europe 2. JPM == World 3. MAT == Universe 63/65

Raw Materials – Silos & Elements Target Customers - Impact on Customer Experience INTERNET IdeaMap™ SURVEY Identify topic area ANALYZED SURVEY RESULTS → Mind Genomics MARKET SEGMENTATION TYPING ENGINE MARKETING PHRASES © 2011 iNovum LLC Process: Develop the Microscience … 64/65

65/65 The best ideas are coming from nature!

Horizontal Segmentation and MindGenomics Veljko Milutinovic, Jakob Salom, Zoran Markovic, Zoran Ognjanovic, With Jasna Matic and Howard Moskowitz, Steve.

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Horizontal Segmentation and MindGenomics Veljko Milutinovic, Jakob Salom, Zoran Markovic, Zoran Ognjanovic, With Jasna Matic and Howard Moskowitz, Steve.

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