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2.4.1 Bernoulli Random Variable and its Distribution

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    6.6.2 Santoor's evolution
    
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  Module 07 Strategizing Pricing and Distribution
  - 7.1 Price Mix
    7.1 Price Mix
    
    7.1.1 Framework of Pricing
    
    7.1.2 The Language of Pricing
    
    7.1.3 Price Setting Process
    
    7.1.4 Methods of Pricing
    
    7.1.5 Price Discrimination
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    7.2.2 Distribution Channels
    
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    7.3.1 Types of wholesaling intermediaries
    
    7.3.2 Types of Retailing Intermediaries
    
    7.3.3 Functions of Distribution Channels
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    7.4.1 Channel Length
    
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    7.4.3 Channel Design Process
    
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    7.4.6 Horizontal VS Vertical Channels
    
    7.4.7 Omni-Channel and Multi-Channel Retailing
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    7.5 Retailing
    
    7.5.1 Introduction to Retailing
    
    7.5.2 Roles and Responsibilities in Retailing
    
    7.5.3 Characteristics of Retailing
    
    7.5.4 Retail Formats
  - 7.6 D'Light Case Study
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    7.6.1 Introduction to D'Light
    
    7.6.2 D'Light Analysis
    
    7.6.3 Market Context for D'Light
    
    7.6.4 Distribution Channels for D'Light
    
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  Module 08 Strategizing Promotions
  - 8.6.1 Digital Marketing : An Overview
  - 8.1 Introduction to Promotion
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    8.1.1 Introduction to Promotion
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    8.2 Marketing Communications
    
    8.2.1 Macro Communication
    
    8.2.2 Micro Communication
    
    8.2.3 Tools and Techniques of Effective Communication
    
    8.2.4 Sales Promotions
    
    8.2.5 Developing Effective Ad Campaigns
    
    8.2.6 Ideal Ad Campaigns : Objectives of Ad Communications
    
    8.2.7 Designing Marketing Communications
  - 8.3 Budgeting
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    8.3.1 Budgeting
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    8.4 Personal Selling
    
    8.4.1 Personal Selling
    
    8.4.2 Sales Process in B2B Market
    
    8.4.3 Sales Process in B2C Market
  - 8.5 Sales Force Management
    8.5 Sales Force Management
    
    8.5.1 Designing Effective Sales Force
    
    8.5.2 Managing the Sales Force

2.4.1 Bernoulli Random Variable and its Distribution¶

Definition¶

Bernoulli Random Variable: A discrete random variable that takes only two possible outcomes, typically coded as 1 (success) and 0 (failure).
Probability Mass Function (PMF): f(y)=p if y=1 and f(y)=1−p if y=0, where p is the probability of success and q = 1−p is the probability of failure.

Properties¶

Examples¶

Coin Toss:
Success (Heads) = 1 with probability p
Failure (Tails) = 0 with probability 1-p
Sales Call:
Success (Sale made) = 1 with probability p
Failure (No sale) = 0 with probability 1-p
Customer Survey:
Success (Satisfied) = 1 with probability p
Failure (Not satisfied) = 0 with probability 1-p
Store Demand:
Success (Demand > 10 units) = 1 with probability p
Failure (Demand ≤ 10 units) = 0 with probability 1-p

Application¶

Die Toss:
Define success for getting a number less than or equal to 2 on a fair six-sided die:

Calculation Example¶

For a scenario with a probability of success p=0.2:

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