Dynamic Gradient Scoring Model (DGSM)


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Dynamic Gradient Scoring Model (DGSM)

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Dynamic Gradient Scoring Model (DGSM)

  1. 1. * “Running an Agile Fortune 500 Company” Aditya Yadav, aditya.yadav@gmail.com in.linkedin.com/in/adityayadav76
  2. 2. * A Typical Global Company * Fortune 500/1000 * 200 Divisions * 40 Countries * 25000 Employees *
  3. 3. * @ Acme Inc.
  4. 4. * Original Question “We use the Weighted Scoring Model in all our decision making. Is there anything else we should be looking at?” * The Correct Question - “Short Answer Yes! DGSM. The long answer is we need to understand the mathematical properties of various Scoring Models to understand their limitations and benefits and when to use them and not.” *
  5. 5. * And The Philosophy Behind The Answer
  6. 6. * The process of applying a predictive model to a set of data is * * referred to as scoring the data. Statistics has procedures for building predictive models such as regression, clustering, tree, and neural network models. Or in the simplest case, a scoring model is a formula that assigns points based on known information to predict an unknown future outcome. The most well known example of a scoring model is the “credit score” or “FICO score” used by lenders to predict the probability of a customer defaulting on a loan. A credit score rank orders customers by the probability they will default, with a high score indicating a low probability of default and a low score indicating a high probability of default. This probability helps the lender to determine whether to accept or reject a customer’s application, as well as how to price a loan if granted. *
  7. 7. * Credit scoring models. There are different classes of credit scoring models. Generic models (such as the “FICO score” and other scores provided by the credit bureaus) make use of data reported by lenders to the credit bureaus. Based on a customer’s credit history, a score is calculated to predict the likelihood of a customer defaulting on a new account. Custom models typically make use of both credit bureau data and other application data (such as income, time at residence, etc.). Custom models are developed by a lender based on the performance of accounts in its own portfolio. * Behavior scoring models use credit and account performance data to determine whether to increase credit lines, re-price accounts, etc. * Collections scoring models utilize credit and account performance data to determine collections strategies. For example, how often to call a delinquent account and whether to sell an account or outsource to a collection agency. * Revenue scoring models are used to predict how long a customer will stay on the books, the amount of fee revenue an account will generate, etc. Scoring models are also used in other industries and for other functions, such as insurance underwriting and marketing. *
  8. 8. * A Weighted Scoring Model includes a list of relevant criteria the decision maker wishes to take into account when evaluating choices from the candidate menu. * Choices are then rated by decision makers on each criterion, typically on a numerical scale with anchor phrases. * Finally, multiplying these scores by weightings and adding them up across all criteria will produce a score that represents the merit of the choice. * Higher scores designate choices of higher merit. The Weighted Scoring Model can be designed specifically for any given selection situation. *
  9. 9. Weighted Scoring Model Features Experience Price Total Score Choice 1 Weight Score (0-10) 5 9 8 4 6 8 7.35294118 Self Explanatory!!! * Choice 2 Weight Score (0-10) Features 5 7 Experienc e 8 9 Price 4 4 Total Score 7.235294118
  10. 10. Static Gradient Scoring Model Choice 1 Choice 2 Weight Score (0-10) Weight Score (0-10) Features 1 9 Features 1 7 Experienc Experience 0.9 6 e 0.9 9 Price 0.81 8 Price 0.81 4 Total Score 7.70479705 Total Score 6.767527675 * Gradient alpha = 0.9 * Criteria listed in order of decreasing priority from top to bottom, and the gradient weightages are applied *
  11. 11. Dynamic Gradient Scoring Model Choice 1 Choice 2 Weight Score (0-10) Weight Score (0-10) Features 1 9 Features 0.9 7 Experienc Experience 0.81 6 e 1 9 Price 0.9 8 Price 0.81 4 Total Score 7.77121771 Total Score 6.841328413 * First scores are listed down for each Choice * Gradient weightages are applied for each choice, in the decreasing order of the scores for each of the choices. * The order of weightages is not fixed. It depends on the scores of each of the choices. *
  12. 12. * Reverse Ranking – Dynamic Gradient Scoring Model
  13. 13. Reverse Rank - Dynamic Gradient Scoring Model Choice 1 Choice 2 Weight Rank Weight Score (0-10) Features 1 2 Features 0.9 1 Experience 0.81 1 Experience 1 2 Price 0.9 2 Price 0.81 1 Total Score 1.70110701 Total Score 1.36900369 * We normally rank things 1,2,3. Reverse Ranking would mean the best * * * is 3, second best 2,1 etc. i.e. in the reverse order Humans are very poor in accurately measuring something but great at relatively ranking one or more choices, per attribute one at a time RR-DGSM is hence more consistent, if all the choices are available upfront. DGSM is great for a more continual evaluation as more and more choices become available, but with some effort RR-DGSM can also be applied continously *
  14. 14. * The perspective in Weighted Scoring Model was the Evaluators Perspective (hence his choice of weights and his limited choice of criteria) * * * * The perspective in the Dynamic Gradient Scoring Model is the perspective of the Value of the choice itself. * * * Virtually independent of the selected set of criteria’s if the DGSM criteria set is huge e.g. 100 etc. * When evaluating the Value of a Choice you should probably limit # of criteria to be used by min & max. There is a penalty in the algorithm for poor scoring additional criteria's you add for a choice. * * Allows comparison of hugely differing choices also “Apple and Oranges Comparison” The dynamic gradient scoring model has the highest chance of Assessing Value!!! Captured in the Value Net. And it’s the simplest way to make multi-dimensional choices/decisions You can probably have 100’s of criteria's and use the scores of what Quality’s are available for each of the choices and arrive at a Probable Value!!! In the Value Net for each of the choices. Mostly independent of subjective weight choices, i.e. uses fixed gradient weights The choice of alpha/gradient and #significant-digits impacts the number of selection criteria that are effectively used out of the entire set e.g. of 100 criteria's You have to select * * * * * Alpha e.g. = 0.9 ( < 1) signifying gradient Criteria List (Evolving list) normally 10X of #criteria’s/choice #Significant Digits for total score Min & Max # of criteria’s used per choice The RR-DGSM is the more natural and consistent than DGSM because humans are poor at accurately estimating a value and great at comparative ranking between 2 or more choices. *
  15. 15. *
  16. 16. * <Hypothetical> * John wants to buy a shirt he creates his criteria (i) 36-38 inches size * * * * * * * * (ii) colorful. Does a WSM analysis between PE and LP And he finalizes on PE and sticks to that brand for 4 years His wife Jane notes the Top Qualities of PE and LP as [PE = #Stores Worldwide (6/10), Colors 8/10], and [LP = Brand Image (9/10), Formal Wear Range(8.5/10), Quality (8.8/10) ] She does a DGSM Analysis and concludes there is more Value in LP compared to PE. Both are in the budget of her husband, which is not an issue Earlier WSM analysis 4 years ago was myopic in its outlook Her husband is better off capturing the Most Value LP offers DGSM has a higher chance of capturing Value. *
  17. 17. * * * * * * * * An Internet Giant has a 10 point criteria to hire engineers John appears for interviews and scores 6.7 on a WSM of 10 Engineer Hiring Criteria points. He is rejected. Jane from the People Analytics Department of the company collects 10 criteria’s each for hiring Engineers, Product Managers, Cultural Fit, Top Executive’s, Customer Facing Roles, Business Saavy, Radical Outlook/Innovation etc. She rescores John on the total list using DGSM picking 2 criteria from Engineer Hiring list, 1 from PM, 1 from Top Executive, 1 from Innovation (capability) She finds out that John infact has a score of 8.4/10 and brings tremendous Value to the company. She cancels the previous decision and makes an offer to John to join the company Again DGSM captures The Value John Brings and not a fitment to a niche criteria like WSM *
  18. 18. * Similar to earlier * Jane qualifies each vendor using some very basic Entry Gate criteria * She understands that a vendor relationship is a strategic relationship and not a one off tactical one. She decides Not to use a WSM analysis. * Then she does a DGSM analysis of every vendor using a large 200 criteria list of Vendor Quality’s * She makes a decision to award the project on Total Value offered by the Vendors * The vendor ends up being a strategic partner to the company delivering immense value to its business goals *
  19. 19. * * * * Jane is the Director of Strategy at ACME co. * She realizes that for her organization to succeed they will need to evaluate cross cutting projects that deliver maximum value * She also realizes that historically the projects of maximum value have always been shot down by one or the other function for failing to meet their criteria * * * * * * * * She also realizes that most projects cannot be compared to each other very easily She finds that Finance has a 10 point criteria list for projects. Engineering has a different 8 point list for projects HR would have a different 5 point criteria…. And so on with every function like Marketing, Sales, Operations… She creates a big list of all criteria’s used in her company And does a DGSM Scoring of all proposed projects across the company She does a rough costing for all projects She cuts off the sorted list based on the budget limitations She ends up with a list of projects that deliver the most value to the organization She goes ahead and authorizes them The company makes a tremendous leap in innovation, quality, profitability, and growth. *
  20. 20. Aditya!!! *