Six Tips for More Accurate Cost Prediction Models
Do you use your data science skills to develop cost prediction models? If so, then clients want to know your results are reliable for accurate expectations. The more reliable the results, the more leaders will factor them into their decisions.
Discover six tips below to improve your accuracy in cost prediction.
1. Consider Dynamic Pricing
Dynamic pricing sets rates based on a customer’s perceived willingness to pay the demanded amount. One example of this concept is Uber’s surge pricing during peak demand. If a person is willing to pay more, they don’t wait as long for a lift.
Cost predictions vary based on the number of people who respond favorably to an offer. Consider an airline that wants to launch a substantially more expensive new route. However, it offers amenities not provided by competitors. Airlines often use dynamic pricing to vary fare prices.
Machine learning can determine a person’s likelihood to purchase something. Optimization algorithms then calculate which factors make them most likely to pay.
Machine learning in cost prediction could determine if a company’s new offering will pay off. It also shows which features are most demanded by future customers. As a result, businesses don’t spend money on things customers don’t want, saving money.
2. Understand Seasonal Fluctuations
Most cost prediction models have demand-based elements. Demand varies based on seasonal factors. Statistics indicate that 45% of wholesalers find it challenging to manage seasonal items, partly because of pricing models that rely on continual demand. The very nature of seasonality is not constant.
Machine learning can predict how weather patterns, holidays and other behaviors impact whether people buy. If companies have too much inventory on hand, they face a cost problem. They may have to significantly reduce prices or consider the cost a loss. Calculating seasonality into cost prediction models is smart, particularly if increased demand during drives supply costs.
For certain products, you may need to create several cost models to reflect seasonal demand. For example, Christmas decorations will see a spike in demand during the winter months. You might also see a small surge in July for businesses with holiday-themed campaigns.
This approach promotes stability when your target audience’s appetite changes. You can maintain consumer expectations without losing money or getting stuck with excess product.
3. Identify Expense Drivers
Achieving accuracy in cost prediction models means understanding which factors increase expenses. Determine the variability in those factors and how they could make models inaccurate if not managed.
In one case, The Metropolitan Sewer District of Greater Cincinnati felt continually challenged by a federal mandate requiring them to keep raw sewage, mixed with storm water, out of waterways during rain. Once the organization started using data analytics software to make decisions, they were able to manage flow levels and control the valves that direct the movement of liquid.
After implementing the new system, the organization reduced sewer overflows by more than 400 million gallons per year. Additionally, it anticipates saving millions in capital investments to control overflow problems. The software reduced costs from $0.23 to $0.01 per gallon of overflow.
You can adopt this approach by figuring out which costs are not well-controlled. Then, determine if or how data analysis could bring expenditures down. If it’s not possible to cut costs dramatically, acknowledge that a cost prediction model must account for unstable expenses that cause inaccuracies.
4. Choose the Right Model
Several cost prediction models exist. Knowing which one to use in a given situation will increase overall accuracy. Should cost and landed cost are two of the most common. Should cost shows how much you should be paying for something based on numerous factors, such as manufacturing, supply and labor costs, as well as profit margins.
The should cost model is particularly helpful when you’re negotiating with the costs of indirect supplies, those needed for ongoing operations. The landed cost model, on the other hand, calculates the fees of getting goods in the door. For example, tariffs, shipping costs and shipment container rentals may affect the conclusions.
The landed cost model could help you determine whether it’s possible to cut costs regarding the transport of supplies. If it’s not, you can consider adjusting the consumer price to make products profitable despite associated expenses to your company.
Understanding when to use each cost prediction model — and what they can tell you — could affect overall accuracy. If you pick the wrong model, you might find unexpected results. However, you shouldn’t look too hard to reach conclusions that aren’t there.
5. Implement Target Shuffling
How can you determine whether there’s a cause-and-effect relationship between variables? As the number of variables goes up, so does the possibility that you’ll identify false patterns. When that happens, your cost prediction models become less reliable. A process called target shuffling can determine the likelihood that something occurred by chance.
In one case study, John Elder, the first person associated with the practice, used target shuffling to convince a reluctant client to invest more money into a hedge fund. Although the hedge fund had a great first year, the client was concerned that the success was a fluke.
First, Elder randomly shuffled the output — also called the target variable. In this case, it was the “buy” or “hold” signal for a given day. This step ends the relationship between the output and the input variables.
Elder then performed 1,000 simulations with the randomized target variable. The goal was to compare how closely the shuffled data mirrored the actual performance statistics. When Elder found the random distribution returned better results in 15 of the simulations, he told his client there was only a 1.5% likelihood the fund’s success was due to luck.
Target shuffling works well if you’re using machine learning. It’s also ideal when the leaders at your business don’t feel confident about indicators of success. Be sure to look at available historical data when making the models. If people assert trends may not hold for the upcoming year, target shuffling could help calm their nerves.
6. Frequently Compare Outcomes
Some companies make the mistake of creating a cost prediction model and leaving it as is for the next year. However, prediction models should not be static. Expecting yours to be could make it inaccurate.
One best practice is to check how close your operating expenses match the prediction models throughout the year. If you notice discrepancies, adjust the model to be accurate for current times. Try to develop trends over time — years, if possible. For example, do you consistently miss the mark in one category?
Although this tip may expose weak points in your methods, try not to feel discouraged. As you periodically create cost prediction models and keep them up-to-date, each will become more accurate. Not only will you access more information, but you’ll gain a wealth of experience. In the end, the company will rely on you for your accurate information.
Accuracy Increases Confidence in Cost Prediction
If a firm’s leaders discover your cost prediction models to be highly accurate, they’ll trust the data — and you. They’ll depend on your predictions when it comes to future decision-making, making you an asset to the company. By following the six tips above, you can improve your accuracy and excel at work.
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Tags: operational performance, Predictive Analytics
