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.”
*
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.
*
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.
* 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.
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.
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.
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.
*
Reverse Ranking – Dynamic Gradient
Scoring Model
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.
*
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.
*
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.
*
*
*
*
*
*
*
*
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.
* 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.
*
*
*
*
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.
*
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