Create a Dynamic Pricing Strategy using Machine Learning
Supply and demand are something many companies must consider. A dynamic pricing strategy can help solve fluctuating supply and demand problems by changing the price of an item or service to meet consumer demand.
Leveraging pricing power is a great way to achieve business goals. And did you know that machine learning is the best way to leverage pricing power? Read on to find out more!
Let’s use the ever-popular Amazon.com as an example of fluctuating prices. Amazon.com typically updates most of their product prices daily, as they would not want to update too often (such as multiple times a day) or too slowly (such as once a week).
Especially during the holiday season (Black Friday, Cyber Monday, etc.), Amazon may adjust their price more quickly (typically lower the price to increase sales).
E-commerce companies are adapting to a dynamic pricing strategy to remain competitive with a growing number of online stores. Brick-and-mortar stores, however, are also using dynamic pricing to adjust to supply and demand situations.
There are a few key goals for a great dynamic pricing strategy.
One is to try to maximize your revenue. Another is to maximize your profit.
Revenue and profit, however, are not always aligned. Sometimes you can maximize your revenue but with that you might also increase your costs.
For example, to increase your revenue you could decide to increase your inventory and maintain a high price. But you may not be able to sell all your inventory in time and you then liquidate some of the inventory at the end to make room for new products. In those cases, you may maximize your revenue but not necessarily maximize your profit.
Another goal is to maximize your profit margin.
This is tied to inventory management. If you want to really maximize your profit margin, you aggressively try to manage your inventory. You don’t want to procure too much. You want to have a high confidence that you can sell everything in time.
The last goal is to maximize your market share.
When you try to maximize your market share, typically there is a profit constraint. And sometimes you say, okay, I can make zero profit on this product but I will try to sell as much as possible.
Sometimes you can even lose money. This is very often the case for Amazon.com as, every week, they have top 10 loss leaders. They intentionally lose money on those products to try to maximize their market share.
Those are the four business objectives to a great business strategy. To achieve this strategy, companies need to identify and classify their product. Products that have been on the market for a while have stabilized and are a cash cow. The market share is stable and already dominant in the market.
You may already have 80% of the market share, so to grow to 90% may not be necessary. But for new products, especially the ones that try to disrupt the market, you should try to maximize your market share. Then you set a lower goal of the profit or revenue.
There are also three implementation objectives to consider. Demand forecasting, dynamic pricing, and inventory optimization. These three are very closely related.
Dynamic pricing depends on demand for the product or service you provide. You have the pricing that can be tuned for your business and you have the inventory. The inventory can be changed, but not always. In those cases, you can adjust your supply and demand relationship to try striking a better balance point to achieve your goal.
If we look at dynamic pricing, the classic approach is: identify what is the key category—what product generates the most revenue and how it relates to your business KPI (Key Performance Indicator).
Whether the KPI is revenue-based, profit-based, or combined, you identify which group of items should be used. Once you identify each key category, you can create a more advanced strategy.
Take stock trading pair pricing as an example. You have two products, A and B, that you want to sell. To sell more of A, you raise the price for B. People look at A and say it provides more value. This makes the decision to buy easier.
Of course, you need to do all this within compliance. You cannot just randomly raise the price for your products. There is a limitation to how much you can raise the price.
There are some special cases. For example, what we just mentioned is if you have pricing power. That means you can control your price and adjust it as frequently as daily.
But in a lot of cases, a company does not have pricing power. Such as when wholesale companies sell through a channel. Those companies cannot frequently adjust the channel’s price.
We have a client (ABC) that signed a deal with a major channel company (XYZ) for a one-year contract to sell a product. And during that contract period, ABC could not change the price. Even after the contract expired and they renewed, they could not change the price. XYZ company required ABC to prove the price needed to increase to cover manufacturing costs.
In this case, ABC did not have pricing power. XYZ controlled the price, but they did have to abide by the MAP (Minimum Advertised Price). This means that XYZ could never advertise below the agreed-upon pricing.
Another factor to consider is to decide how much product is sold into each channel. You can sell to multiple channels and, depending on your agreement, you can change the product price accordingly.
A lot of companies are pricing their products based on human intuition. But to be effective at dynamic pricing, you need to appropriately forecast supply and demand. And attempting to forecast on your own, knowing how and when to tune the price, is very difficult to do on your own.
This is where machine learning can help.
How can machine learning help create a dynamic pricing strategy? Well, humans are not as proficient as computers at quantifying things.
For example, rebates are a powerful promotion tool. Even when using channel sales, you can run a rebate.
The question is, what is the best rebate to give the customer and still make a profit? $5? $10? $100? Humans have a difficult time trying to find the optimal price point. Machines learning, however, can easily find the right price if appropriately used.
The more data you have, the better. Companies with many customers have a lot of data points that can be fed to the machine learning model and generate an output of the best dynamic pricing to use.
ElectrifAi machine learning models have proven to be very capable at producing reliable results. From dynamic pricing to price optimization, we have the domain expertise to help companies reduce costs, increase revenue, and decrease risk.
Contact ElectrifAi today to find out how machine learning can help you create a dynamic pricing strategy to grow your business profit margins and increase customer satisfaction.
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