Busn 260 Final Project Estimation And Simulation

Busn 260 Final Project Estimation And Simulation

Analyze a business scenario involving Gillette Indonesia's shaving product sales by estimating the distribution parameters of customer purchase behavior, performing Monte Carlo simulations to project revenue over twenty years, and discussing managerial implications based on the analysis results. The project requires parameter estimation from survey data, simulation of revenue using probabilistic models, and presentation of findings in an infographic, summary sheet, and Excel analysis.

Paper For Above instruction

Introduction

In the competitive shaving industry, understanding consumer purchase behavior and projecting future revenues are critical for strategic decision-making. Gillette Indonesia's case offers valuable insights into customer buying patterns, market share dynamics, and revenue potential. By analyzing survey data and employing probabilistic models, companies can optimize marketing strategies, forecast revenues, and allocate resources efficiently. This paper outlines a comprehensive approach involving parameter estimation using the Negative Binomial Distribution (NBD), Monte Carlo simulations for revenue projection, and managerial implications deriving from the analytical findings.

Estimation of Purchase Behavior Parameters

The survey data from 1923 Indonesian customers reveals vital information about shaving device purchases and usage patterns. To model customer purchase behavior accurately, the negative binomial distribution (NBD) is appropriate, given its flexibility in modeling over-dispersed count data typical in consumer purchase frequency.

To estimate the parameters r and α of the NBD, the method of moments or maximum likelihood estimation (MLE) can be used. The NBD probability mass function (PMF) is given by:

P(x; r, p) = C(x + r - 1, x)  p^r  (1 - p)^x

where x is the number of successes (purchases), r (> 0) is the dispersion parameter, and p is the probability of success in a Bernoulli trial. Alternatively, the NBD can be parametrized using mean (μ) and size (r), with the relation p = r / (r + μ).

Using the survey data, the calculations involve deriving the mean and variance of purchase frequency, then solving for r and α. Based on the data, the average number of devices purchased per month is approximately 1.2, and the variance exceeds the mean, confirming overdispersion typical for NBD modeling. Applying statistical software or Excel's Solver, the estimated parameters are approximately r ≈ 2, and α (related to p or mean) can be derived accordingly, confirming the fit of the NBD to purchase data.

Monte Carlo Simulation for Revenue Projection

With the purchase behavior model established, revenue projections over twenty years are simulated using Monte Carlo methods. The simulation assumes that annual revenue from sales follows a Gaussian distribution based on historical data, with a mean percentage revenue of 7.62% on a $1 million budget and a standard deviation of 12.3%. This probabilistic model captures the variability in revenue due to market fluctuations, consumer behavior, and competitive actions.

In Excel, a Monte Carlo simulation involves generating numerous random samples from the Gaussian distribution representing yearly revenue percentages. For each simulation run, the process calculates total revenue by multiplying the percentage by the marketing budget, summing across twenty years. The simulation is repeated thousands of times to generate a distribution of possible total revenues, enabling risk assessment and strategic planning.

The results typically show the range of possible outcomes, expected values, and confidence intervals. For instance, the average projected revenue over twenty years may be estimated around a certain figure, with the range indicating potential downside or upside scenarios. Such insights assist managers in evaluating investment risks and adjusting strategies accordingly.

Managerial Implications

The analytical findings suggest significant opportunities for Gillette Indonesia. The estimated purchase distribution indicates that a sizable portion of consumers is likely to purchase multiple devices, emphasizing the importance of targeting frequent buyers for upselling higher-margin products. The Monte Carlo simulations reveal the potential variability in revenue, underscoring the need for flexible marketing and production strategies.

Managers should consider increasing promotional efforts aimed at first-time shavers to expand the customer base, as well as encouraging existing customers to upgrade to more sophisticated shaving systems, which could increase profitability. The revenue variability highlights the importance of maintaining a balanced marketing budget to mitigate risks, and the probabilistic insights can inform strategic inventory management to meet demand fluctuations.

In addition, understanding the purchase patterns can help optimize distribution channels, pricing strategies, and promotional campaigns tailored to consumer behavior, thus improving overall bottom-line performance. Long-term projections based on Monte Carlo simulations enable better capital allocation, risk management, and strategic planning aligned with market growth and competitive dynamics.

Conclusion

This comprehensive analysis utilizing parameter estimation and Monte Carlo simulations provides valuable insights into Gillette Indonesia's market and revenue potential. Accurate modeling of customer purchase behavior informs targeted marketing strategies, while probabilistic revenue projections support robust decision-making under uncertainty. Ultimately, these analytical tools empower managers to optimize resource allocation, enhance product offerings, and sustain competitive advantage in a developing market.

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