P14 Model Rantai Markov

Summary

This video script discusses the application of the Markov Chain model to predict changes in market share over time. The model is demonstrated through an example of four products (A, B, C, D), showing how customer movements between products are tracked using transition probabilities. It explains the process of constructing a transition matrix, calculating retention rates, and using matrix multiplication to predict market share in future periods. The script provides a step-by-step guide on how to model customer shifts and calculate market share for subsequent periods, emphasizing the importance of accurate transition data for reliable predictions.

Takeaways

• 😀 The Markov Chain model helps companies predict changes in market share over time by analyzing customer transitions between competing brands.
• 😀 Transition matrices are essential in determining the probability of customers switching between brands, which influences market share predictions.
• 😀 Retention refers to customers who stay with the same brand, and it is represented as the diagonal elements in the transition matrix.
• 😀 Customer movements between brands are tracked by calculating how many customers each brand gains and loses to others.
• 😀 The model calculates transition probabilities by dividing the number of customers gained from a brand by the total customers of the gaining brand.
• 😀 The sum of transition probabilities in each row of the matrix must always equal 1, ensuring that no customers are 'lost'.
• 😀 Market share for each period is predicted by multiplying the transition matrix with the market share of the previous period.
• 😀 The process can be repeated to predict market shares for future periods by using the market share of the most recent period and the same transition probabilities.
• 😀 In the example, the market share for the second period is predicted to be 0.225 for A, 0.29 for B, 0.23 for C, and 0.255 for D.
• 😀 The model allows businesses to simulate future changes in market share, helping them adjust their strategies for customer retention and acquisition.

Q & A

What is the main purpose of using a Markov Chain model in the context of market share prediction?

The main purpose is to predict the changes in market share for different products over time, based on transition probabilities of customers moving between products.

How is the initial customer data used in the Markov Chain model?

The initial customer data represents the market share for each product at the start of the period, which is then used to calculate transition probabilities and forecast future market shares.

What does the transition matrix represent in the Markov Chain model?

The transition matrix represents the probabilities of customers switching from one product to another, either by gaining or losing customers between products during a given period.

How are retention rates calculated in this model?

Retention rates are calculated by measuring the number of customers who remain with a product over a period, i.e., the customers who do not switch to other products.

How is the transition probability calculated between two products?

The transition probability is calculated by dividing the number of customers who moved from one product to another by the total number of customers of the source product.

What happens when the transition probabilities are summed up across all products?

The sum of the transition probabilities should equal 1, ensuring that all customer transitions between products are accounted for correctly, without any missing or over-counting.

What is the purpose of multiplying the transition matrix with the market share vector?

Multiplying the transition matrix with the market share vector calculates the predicted market share for the next period, based on the current market distribution and the transition probabilities.

How is market share calculated for each product in the initial period?

Market share for each product is calculated by dividing the number of customers for each product by the total number of customers across all products, providing a proportion of total market share.

How is the accuracy of the transition probabilities verified?

The accuracy is verified by checking if the sum of the transition probabilities for each product equals 1, indicating correct probability distribution and no errors in the calculations.

Can the Markov Chain model be used to predict market share for multiple future periods?

Yes, by iterating the process and using the market share from each period as the input for the next, the model can forecast market share for multiple future periods.