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Question 18.1
Describe analytics models and data that could be used to make good recommendations to the power company.
Here are some questions to consider:
The bottom-line question is which shutoffs should be done each month, given the capacity constraints. One consideration is that some of the capacity – the workers’ time – is taken up by travel, so maybe the shutoffs can be scheduled in a way that increases the number of them that can be done.
Not every shutoff is equal. Some shutoffs shouldn’t be done at all, because if the power is left on, those people are likely to pay the bill eventually. How can you identify which shutoffs should or shouldn’t be done? And among the ones to shut off, how should they be prioritized?
Think about the problem and your approach. Then talk about it with other learners, and share and combine your ideas. And then, put your approaches up on the discussion forum, and give feedback and suggestions to each other.
You can use the {given, use, to} format to guide the discussions: Given {data}, use {model} to {result}.
Have fun! Taking a real problem, and thinking through the modeling and data process to build a good solution framework, is my favorite part of analytics.
Ans:
Problem Statement: How can a utility company identify the most effective and advantageous approach to disconnecting power for customers who possess the ability to pay but express no intention of settling their bills?
Inquiries:
1.
Which power disconnections should be performed on a monthly basis?
2.
How can you recognize customers who do not plan to settle their payments?
3.
What are the expenses associated with power disconnections compared to maintaining uninterrupted service?
4.
What is the optimal scheduling strategy for power shutoffs, considering service and travel time?
5.
What criteria should be used to prioritize power disconnections?
6.
Is it advisable for the power company to increase its service personnel to facilitate more efficient shutoff procedures?
Approach 1: Logistic Regression and Optimization
We address and resolve this issue through two distinct approaches. In Approach 1, we aim to detect customers lacking the intention to settle their power bills. This involves employing logistic regression, a soft classifier. Our goal is to minimize false negatives, where we identify a customer as unwilling to pay, considering other potential factors contributing to non-payment. Consequently, we
solely consider classifier values falling within a 99.5 percent confidence interval. Subsequently, we assess the cost associated with powering down compared to maintaining power. If the cost of
shutting down power is lower than keeping it on, we compute the optimal schedule for shutoffs. Otherwise, we resort to Approach 2, detailed below.
1.
Step 1: Identifying Customers with No Intention of Paying Their Power Bill
Given: Customer data (history, residence, income, credit score).
Use: Logistic regression model to predict non-payment likelihood.
To: Set a threshold (>0.50) to avoid false negatives and focus on high-confidence predictions.
To address this issue, we would construct a logistic regression model aimed at predicting the probability that a customer will not fulfill their payment obligation.
Essential predictors may encompass the duration of the customer's association with the service provider, consecutive months of non-payment, type of residence (apartment or home), lease term duration, annual income, rent/mortgage amount, and credit score.
The underlying concept of this model is to spotlight users who, despite having sufficient income, exhibit a pattern of unreliable payments or do not intend to pay as they are in short-term living arrangements.
The threshold for deciding whether a customer's power should be disconnected should be set at >0.50.
Given the substantial customer base of a large power company, it is reasonable to assume that only a small percentage are not meeting their financial obligations.
Additional costs associated with cutting off a customer's power, such as potential loss of future business, damage to the company's social standing, logistical expenses, etc., need to be considered.
Consequently, it is prudent to assume that the cost savings from disconnecting a customer's power are relatively modest compared to the company's overall revenue. Therefore, power disconnection should be implemented only in cases where there is a high likelihood that the customer has no intention of settling their bill.
2.
Step 2:
Assessing Model Quality
The outcomes derived from the logistic regression model will be utilized for a comprehensive cost analysis, gauging the overall effectiveness of the predictions.
The power company could supply historical data regarding average power consumption per month. Alternatively, a forecasting model incorporating seasonality could predict a specific customer's power usage, although this would require sufficient usage history, which might not always be available.
The average or predicted usage for each customer would be applied in conjunction with the provided table to compute the total cost associated with the model.
Should the calculated cost exceed the anticipated or acceptable threshold, a reevaluation of the logistic regression approach might be warranted.
Customer will Pay
Customer will not Pay
Customer will Pay
Zero cost
Cost attributed to "expense associated with exceeding service demands when activating/deactivating power"
Customer will not Pay
Cost attributed to "Revenue depletion resulting from ongoing missed payments"
Cost savings achieved by "disconnecting a customer's power"
Prediction
Actuals
3.
Step 3: There are 3 options for step 3:
Option 1: Customer Clusterization for Power Shutoff:
In this phase, the objective is to categorize customers whose power should be shut off according to the model's prediction into clusters based on their geographical locations.
o
I recommend employing a k-means clustering model and determining the optimal number of clusters to enable a single employee to systematically turn off every customer's power in a specific cluster during a shift.
o
Denser areas would necessitate a greater number of clusters, while less dense areas would require fewer clusters.
o
Subsequently, a power company employee could choose a cluster to disconnect power, prioritizing those clusters that offer the highest value to the company.
A challenge with this method lies in its inclination to prioritize power disconnection based on location rather than potentially focusing on customers from whom the company gains the most value by shutting off power.
Option 2: Optimization Model for Power Shutoff Decision:
Utilizing an optimization model could effectively prioritize the disconnection of customers' power, focusing on achieving the greatest cost savings for the power company.
o
Each customer would be associated with variables such as their predicted/average power usage, a binary variable indicating whether the power should be shut off, and a variable representing the required service time.
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o
The optimization's objective would be to maximize the potential cost savings from shutting off customers' power.
o
Constraints for this model might involve specifying a maximum number of allowed shut-offs, establishing a minimum savings threshold for a shut-off to occur, determining a maximum total time required for service, and other relevant factors.
A challenge with this method is its omission of the physical location of customers when determining which shut-offs should be executed, thereby presenting significant logistical challenges in implementation.
Option 3:
Integration of Approaches:
The primary challenge in this stage is determining how to balance the logistical feasibility of disconnecting customers' power (considering service times, drive times,
etc.) while simultaneously prioritizing shut-offs that offer the most value to the power company.
A potential resolution would involve initially employing the optimization model to identify customers where the most value would be gained by shutting off their power. Subsequently, a clustering model could be applied to select groups of these customers where power can be logistically shut off by a single employee.
o
These clusters could also guide decisions on whether the power company should hire additional employees to disconnect power—evaluating, for instance,
if the value gained by shutting off power at additional clusters exceeds the cost of employing additional personnel.
Approach 2: Soft Warning and Punitive Messages
The effectiveness of analytical models hinges on the quality of the data used for their training. External factors and noise can also impact the accuracy of these models in real-world scenarios. Given the high cost associated with false misclassification in this context, we implement a strategy involving soft warning and punitive messages to alert customers about potential consequences if they fail to settle their bills. In this approach, the logistic classification model is applied, and customers with confidence levels between 95 percent and 99.5 percent are identified as those less likely to pay their bills. Subsequently, we employ two sequential steps based on the outcome at each stage.
I.
Warning Message: Your electricity consumption this month has exceeded 150% of your average consumption. Kindly use the provided link to settle your electricity bill in installments.
II.
Punitive Message:
Non-payment of your power bill can adversely impact your credit history and may result in a power shutoff. Promptly settle your power bills to avoid any potential consequences.
Transition Plan:
The implementation of the analytical model in a production or real-life environment necessitates thorough testing and analysis of results obtained through simulation. To facilitate a seamless transition to the recommended models outlined above, we propose the use of simulation and A/B testing.
Simulation Model:
This involves creating an emulation of our production environment, where real-time data is collected. The classification and optimization models discussed earlier are employed to make predictions, and the predicted outcomes are then compared with the actual results. This step is crucial to ensure the comprehensiveness, accuracy, and error-free nature of our models. Additionally, we recommend this step for potential model enhancements based on the collected data.
A/B Testing:
Once we have confidence in our final model, A/B testing is employed to estimate the cost savings for the company. Cost savings are determined by the difference between the implementation cost of the analytics model and the savings generated from the predictive model
.