Allocation 3M combines three mathematical statistical models to help you analyze resource allocation issues in life, such as time allocation, manpower allocation, commodity allocation, etc. These models provide three allocation strategies based on the past data of allocation targets: high expected growth rate, low volatility and volatility parity. When you are facing resource allocation decisions, these strategies provide you with reference information on the data side.
Allocation 3M contains four tools:
Add Data:
Add the allocation target data you want to calculate.
For example, you can allocate your resources to VIP customers based on their monthly purchases. Resources can be service hours, membership gifts or membership points.
For example, you can allocate your study time based on the weekly test scores of online courses.
Mean Variance Model:
The mean variance model calculates the expected growth rate and volatility based on the past data of the allocation target. Use Monte Carlo method to find the allocation ratio with the highest expected growth rate under a given volatility or the allocation ratio with the lowest volatility under a given expected growth rate.
For example, you plan to meet with 10 VIP customers next week for product sales, and you want to allocate resources based on the past purchase records of these 10 customers. You can use this model to calculate the expected purchase growth rate, volatility, and resource allocation ratio for these 10 customers this month. You need to put in a lot of effort to successfully sell. The model is purely based on data, and it reminds you to allocate more resources to customers with the highest expected purchase growth rate under a given volatility.
Black–Litterman Model:
The Black-Litterman model combines the mean variance model, Bayesian estimation method, and users’ views on the expected growth rate to calculate the resource allocation ratio. If you have your own views on the future expected growth rate, you can use the Black-Litterman model to calculate the allocation ratio with the highest expected growth rate under a given volatility or the allocation ratio with the lowest volatility under a given expected growth rate.
For example, you plan to meet with 10 VIP customers next week for product sales, and you want to allocate resources based on the past purchase records of these 10 customers. This model calculates the expected growth rate of each customer based on the customer’s past purchase records. This expected growth rate is one of the factors for calculating the allocation ratio. If you have your own views on the expected growth rate of customers in the future, you can adjust the expected growth rate of the model to calculate the expected purchase growth rate, volatility and resource allocation ratio of these 10 customers this month.
Risk Parity Model:
Unlike the mean variance model and the Black-Litterman model, which are designed to optimize expected growth rates, the risk parity model is designed to optimize volatility. The risk parity model uses Newton’s method to calculate an approximate resource allocation ratio so that the volatility contribution of each data to the data combination is consistent.
For example, you plan to meet with 10 VIP customers next week for product sales, and you want to allocate resources based on the past purchase records of these 10 customers. This model calculates the purchase volatility of each customer based on the customer’s past purchase records, and calculates the expected purchase growth rate, volatility and resource allocation ratio of these 10 customers this month under the premise that each customer has the same volatility contribution . You can use this model to avoid allocating most of the resources to a few customers, but to allocate resources to more customers to improve sales stability.
