Evaluating Startup Investment Potential with Data science
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The document discusses venture capital investing and the importance of data science in evaluating start-up potential, emphasizing that competent management is critical for success. It highlights significant factors influencing start-up success and failures, such as creativity, innovation, and emotional stability, while also proposing a predictive model to assess start-up viability. The findings suggest correlations rather than deterministic outcomes, urging investors to recognize their strengths and mitigate risks in evaluations.
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Evaluating Startup Investment Potential with Data science
- 1. Venture Capital Investing Evaluating Start-up Potential with Data Science
- 2. Working towards a Masters in Psychology as I have more no. of mentors in Psychology than Technology in my startup and is loaded with homework backlogs! N.Sankar TEXTIENT Analytics @_textient About Me
- 3. VCs are constantly in search of Innovative, Disruptive and Competent companies to Invest Start-up Evaluation cannot be a guess work It is difficult to evaluate new start-up applications, its team or a new management without any prior track record. Research by Entrepreneur Weekly, Small Business Development Centre, Bradley University and University of Tennessee found that the #1 reason why start-ups fail is incompetence followed by Lack of Managerial Experience. - The smartest people with innovative ideas and solutions need competence that is required to run a business
- 4. Crucial aspects in Venture Capital Investment Gladstone, Handbook of Private Equity: • VCs first look for what is special / unique about the product. • VCs look at the management team. “They place more emphasis on this. http://bit.ly/18Q8aUy
- 5. Entrepreneur(ial) Characteristics can Shed light on the Potential & Risks Startup Potential
- 6. • Data Science driven Behavioral predictions can significantly help improve Evaluating start-ups, Decision making and Impact Investment outcomes Behavioral Economic Cues to the Rescue
- 7. Cues to the Potential.. Creative outlook Innovativeness Success orientation Mental Balance Cultural outlook Brand/ Market aspects. InnovationTeam Execution Culture Organisation
- 8. Cues to the Risks.. Significant play of Affect, Psychology & Behavioural elements Incompetency The entrepreneur falls in love with the product/business Lack of a clear focus Pride Lack of financial responsibility and awareness Family pressure on time and money commitments Pessimistic Entrepreneur gets worn-out .. Emotional Turbulence (Worry, Anxiety, Despair, Depression etc.) Many believe they can predict the future, rather than try to create a future with their customers. Sources : Entrepreneur Weekly, Small Business Development Center, Bradley Univ, University of Tennessee Research as cited in http://bit.ly/1fQLwNH , The Psychological Price of Entrepreneurship , Inc.com http://bit.ly/1cWZTtC and HBR http://hbs.me/1opOBYr
- 9. Predicted Pattern of a High Growth Tech Startup Sample Profile #1 A Start-up with Multi-million dollars funding from 10+ VCs in Multiple rounds
- 10. Predicted Pattern Psychological Orientation 0% 20% 40% 60% 80% 100% Propensity to Succeed Creativity & Innovativeness Creative Pursuit level Cooperative Sociable Mentally Resilient Demonstrative Care for Stakeholders Market Differentiating Market Nurturing Highly Competent Sample profile #1 : Funded, High growth tech. start-up
- 11. Predicted Pattern of a Failed Startup Sample Profile #2 Start-up had Multi-million dollar funding from a Visionary VC...Shutdown in 1+ years
- 12. Predicted pattern Psychological Orientation 0% 20% 40% 60% 80% 100% Emotional Orientation Cognitive Orientation Propensity to Succeed Creativity & Innovativeness Empathy/Co-operation Sociable & Outgoing Mentally balanced/Resilient Perfection Aims Unity/Fellowship Depersonalization : Possible Incompetency Sample profile #2 : Funded tech start-up , shutdown in 1+ years
- 13. Predicted pattern Low *Survivability Signals 0% 20% 40% 60% 80% 100% Cognitive Orientation Propensity to Succeed Active Pursuit of Novelty,Broad interests Sociable & Outgoing • #2 V.High Novelty • #2+3 Suggests Inward focus, lack of engagement in the tasks at hand Sample profile #2 : Funded tech start-up , shutdown in 1+ years
- 14. Predicted Pattern of a Successfully funded Mobile startup with traction Sample Profile #3 Million dollars, Many investors in a span of ~ 2 years
- 15. Predicted Pattern Psychological Orientation 0% 20% 40% 60% 80% 100% Founder Co-founder *Propensity to Succeed Creativity & Innovativeness *Creative Pursuit level *Managerial /Team-oriented *Sociable & Outgoing Mentally Resilient Involvement * Perhaps call it as Paul Graham’s Start-up team qualities ?! http://bit.ly/1zDvzTa Sample profile #3 : Funded tech start-up Promising with Traction
- 16. Prediction Motivational bases of Attitude & Behavior 0% 20% 40% 60% 80% 100% Founder Co-Founder Power & Esteem Independent Action,Creation & Novelty Group Devotion In-Group Emphais & Social relations Sample profile #3 : Funded tech start-up Promising with Traction
- 17. Prediction Social Values Orientation 0% 20% 40% 60% 80% 100% Founder Co-founder Transaction Oriented Receptive/Caring for others Building Brand Involvement Empowering Partners/Customers Sample profile #3 : Funded tech start-up promising with Traction
- 18. • High Creativity ,Innovation and Propensity to Succeed :Reflected through an Innovative Mobile App, Market launched with Traction • Propensity to Succeed : At Healthy levels across the founding team • Mentally Resilient : Lower pre-disposition to worry, Ability to face pressure . Healthy across • Socialable & Outgoing : CEO complements CTO! Must have for the CEO to take the company forward with all stakeholders • Low Managerial, Co-operation (CTO): Risky for a team oriented play, growth. (This firm has very few in tech team including the CTO,So Perceived risks may be lower..) Prediction Correlation Image Courtesy FlickR: https://www.flickr.com/photos/nasacommons/4940991596/ Sample profile #3 : Funded tech start-up Promising with Traction
- 19. Assessment Creative, Innovative, Relentless Execution, Success driven High Market ‘Take off’ capabilities Focus similarity, Complementing dimensions Low ‘Failure Hazard’ bias (Short to mid term) Penchant for Differentiation, Nurturing Low Potential on Democratization & Growth as a true Organization – Good for Low headcount Orgs, High Risk if one founding member leaves Low Potential to Empower employees, customers and partners Sample profile #3 : Funded tech start-up Promising with Traction
- 20. Predicted Pattern of a startup in Adolescent stage Sample Profile #4 Startup with a 4 years traction
- 21. Predicted Pattern Sampled Motivational base Highly Sincere Low Fun Highly Progressive Low/Medium – Org. Fundamentals Unpredictable/Imitating/Obsessionwithmodernity High AffectionLow Org. Expression Weaker Image • Assessment • A Weaker Image • Weak demonstrative aspects, Dampening environment • Unpredictability in terms of Goals Vs Fundamentals to meet the Goals or aspirations * Sample #4 Startup with a Four year traction Unbalance in Goals
- 22. How is it Predicted ? Startup Potential
- 23. Data in Public domain Your Private Data Start-up Persona predictive model * Affect, Cognitive, Behavioural TEXTIENT Platform TEXTIENT Framework Data Science Cloud computing Analytics /Insights Start-up Potential Modeling & Analytics * Predictive models are based on proven research .
- 24. Potential Application Areas • Angel Investors / Large VCs • Screening Aid, Gold Miner, Risk tool • Crowdfunding – Investment Aid • Investment Marketplaces - Potential Index !
- 25. Summary Data Science & Behavioral Economics models provides a significant opportunity to correlate and assess a Start-up potential for Investors especially in areas where Investors have to do a guess work or they don’t have time or avenues to do even that! Please remember that the findings are not deterministic. It is not true that every startup's characteristics will be the same if they have a high or low potential. These are simply correlations. The proper reading of these findings is to factor in how to identify and build upon Investor's own strengths while creating mechanisms to insulate against acknowledged deficiencies and risks. While I believe the correlations are statistically significant, Weaknesses in the models may include the self- reporting nature and veracity of the data, Major Life events based changes and the possibility of multi- faceted and intentional behavioural aspects in humans.
- 26. If you are Evaluating Start-ups, Reach out to us if we can help you unlock the Unknowns! sankar[at]textient.com
- 27. Behavioral economics helps study the effects of psychological, social, cognitive, and emotional factors on the economic decisions of individuals and institutions and the consequences for market prices, returns, and the resource allocation. Source – Wikipedia http://bit.ly/1gnKPqr Behavioral Economics 101
- 28. The Next revolution will be Psychological, NOT Technological ! @rorysutherland YouTube link http://bit.ly/15T8TCa Inspiration
- 29. THANK YOU!
- 30. The views mentioned in this presentation my own. Data if used, in the document have been sourced from available information in the public domain and has not been authenticated by any statutory authority. Although every reasonable effort is made to present current or appropriate information, there are no guarantees of any kind. Data accuracy cannot be guaranteed. All analysis included herein are based on data from public sources, but no representation or warranty, expressed or implied, is made as to their accuracy, completeness, timeliness, or correctness. I am not liable for any errors or inaccuracies, regardless of cause to you (readers, users). Disclaimer