Brand Recognition, Customer Loyalty, Expansion Model

1. Brand Recognition Model
The Brand Recognition Model evaluates the awareness levels of a brand across different geographical scopes and its marketing reach. The data comprises:
Awareness Level Local: Simulated using a Beta distribution with parameters (2, 5), this variable represents local brand awareness as a percentage, scaled to a range of 0 to 100. The skewed distribution suggests that while some individuals have high awareness, many have lower levels.
Awareness Level Regional: This variable is generated using a Beta distribution (5, 2), reflecting a higher average awareness level compared to local awareness. It indicates that regional marketing efforts may be more effective.
Awareness Level National: Similar to the previous variables, this is derived from a Beta distribution (3, 7), indicating a tendency towards lower national awareness levels.
Marketing Reach Online: This variable follows a normal distribution with a mean of 5000 and a standard deviation of 1000, representing the online reach of marketing efforts. This reflects typical variations in online audience engagement.
Marketing Reach Offline: Generated from a log-normal distribution (mean=8, std=0.5), this variable captures the offline marketing reach, emphasizing that most values will cluster around the mean with a long tail towards higher values.
Brand Recall: This percentage is drawn from a uniform distribution between 30 and 80, indicating variability in how well customers remember the brand.
The summary statistics for this model provide insights into central tendencies and dispersions for each variable, facilitating an understanding of brand recognition dynamics.
2. Customer Loyalty Model
The Customer Loyalty Model focuses on key metrics that indicate customer retention and satisfaction:
Customer Retention Rate (CRR): This percentage is generated using a Beta distribution (2, 5), reflecting the likelihood of customers continuing their relationship with the brand. The resulting values suggest that while some customers remain loyal, there are challenges in retaining others.
Net Promoter Score (NPS): Simulated using a Poisson distribution with an average score of 7, this variable measures customer willingness to recommend the brand to others. The NPS is crucial for assessing overall customer satisfaction and loyalty.
Customer Lifetime Value (CLV): This metric is derived from a log-normal distribution (mean=9, std=0.3), indicating the potential revenue generated from customers over their lifetime. The skew towards higher values reflects the presence of high-value customers.
Summary statistics for this model will reveal insights into customer loyalty metrics and highlight areas for improvement in retention strategies.
3. Expansion Potential Model
The Expansion Potential Model assesses factors influencing a company's ability to grow and adapt.