Rubric

Scoring Engine

Identify High Value Emerging Brands Early in theier business Lifecycle.

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Rubric

Consumer Appeal

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Organic Propensity

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Intervention Value Add

Consumer Appeal

Consumer Appeal

+

Organic Propensity

+

Intervention Value Add

Consumer Appeal

Understanding the impact of a set of inputs (e.g. Branding, Product, Merchandizing, website Quality and other INPUTS) on online purchase decisions.

How we Do It …..

Psychological mechanisms that can be invoked by Startups to mitigate problems of information asymmetry via signaling to bolster consumers’ purchase intention under the influence of trust.

 

The Outputs = A scoring Mechanism to Create forward lookin mettrics to identify High Potential Brands

Consumer Appeal – Understanding the impact of a set of inputs (e.g. Branding, Product, Merchandizing, website Quality and other INPUTS) on online purchase decisions.

How we Do It …..

Psychological mechanisms that can be invoked by Startups to mitigate problems of information asymmetry via signaling to bolster consumers’ purchase intention under the influence of trust.

 

GROWTH PROPENCITY – Forward Looking Scoring Framework that looks at TODAYS Market Conditions, Consumer Behavior and Demand curves to create Forward looking metrics. We then Score Consumer Brands against theses forward looking metrics to identity Brands that a positioned convert these opportunities. 

Think Right Place Right Time. We have build a massive data-lake and using Machine Learning to create a Opportunity Horizon for 

For a Brand to accelerate its Grwth curve, they opprunity need to be there and then they much position thier o

 

 

How we Do It …..

Psychological mechanisms that can be invoked by Startups to mitigate problems of information asymmetry via signaling to bolster consumers’ purchase intention under the influence of trust.

 

Forward Looking Growth – Ignore historical data. Look for a

 

How We do it….

Leverage Machine Learning to create a nural map between data-sets from multiple sources and create a Deep Learning Model that looks for Causation.

Baseline Drift Score – 

Propencity Socre  

Look at Causation not Corellation

Collect and connect Data from Scources.

Bring it together and benchmarking 

The 3 Pillars of The Rubric

Causation Not Correlation

Dont Look at historical data and find corellation. What has happende in the past will not repeat itself.

We look for Input that resulted in the desred output.

Continuous Learning

Dont Look at historical data and find corellation. What has happende in the past will not repeat itself.

We look for Input that resulted in the desred output.

Continuous Learning

Dont Look at historical data and find corellation. What has happende in the past will not repeat itself.

We look for Input that resulted in the desred output.

How we do it

C

HData – How we collect and process data