Exploring dynamic pricing using DecisionRules

Why dynamic pricing?

As with many things today, the idea of dynamic pricing came along with the evolution of the internet. At first, the static pricing, the strategy very much present in brick & mortar stores, was sufficient, but as more and more people became accustomed to shopping online the demands on price change velocity rose sharply mainly due to the following two reasons:

Availability of comparison

It became very easy for a regular customer to compare prices of a particular product in many different stores with minimal effort. Now we even have a website specifically designed for this purpose, therefore a majority of customers will have done at least a nominal amount of comparisons pricing.

Frequent market fluctuations

As the availability of comparisons emerged, more frequent price fluctuations across the market have emerged. Many companies are now watching market prices, demand, stock, and other factors and setting their prices based on them daily. This now makes keeping competitive prices even more difficult than before, doing so manually might now be impossible.

Implementing dynamic pricing in DecisionRules

We want to show you how you might be able to tackle the problem of dynamic pricing using DecisionRules to

• Create and manage different pricing strategies
• Leverage the information you have about the pricing of your competition
• Use data gathered using data scraping services
• Adjust your pricing strategy based on any variables as you see fit

For this purpose, we have created a dynamic pricing model to showcase one of the possible solutions.

Specific Pricing strategies

Dynamic pricing might be considered a blanket term for many particular pricing strategies that share some similarities. As there are an infinite number of possible variables based on which we might create our prices, there are also infinite possible pricing strategies to choose from. For your model, we will choose a subset of the most common strategies that are implemented and do not require terabytes of data and an analyst for each product's performance. The model is designed in a way in which creating a new strategy is as simple as creating a simple table, therefore should you want to experiment with your own more complex rules, the model will allow you to do so easily.

The implemented strategies are:

Cost-Plus

This is one of the simplest strategies, which tries to price items as low as possible, while still retaining a preset margin. It takes into account your total cost and to create a final price it adds your predefined margin, in order to create the lowest feasible price

Competition Matching

A big draw to dynamic pricing is the possibility of keeping your prices competitive. This strategy allows you to match the current prices of your selected competitors; you no longer have to manually reset prices whenever a competitor's product goes on sale

Relative Positioning

Similarly to Competition Matching, this strategy allows you to follow the prices of your competitors but remainlower or higher by a set percentage. This is a strategy for products for which you do not want to follow competitor’s prices exactly because you either want to be cheaper or your product has other advantages, that allow you to maintain a higher price

Absolute Positioning

Similar to Relative Positioning, but the price difference is a preset value, not a percentage

Goal oriented

If you want to sell a certain amount of products over a period of time, this will be your strategy of choice. The strategy is designed to look at your goal sales for the product and lower the price periodically, should it be selling too slowly to reach your goal

Change oriented

This is a strategy that compares the sales of a product over the previous period and the current one. It tries to increase the sales or maintain the current level of sales by adjusting the price according to the development of the current sales curve.

Stock oriented

A strategy that takes into account how many units of an item are in stock. A typical use for it might be to lower the price of an item that you have overstocked or increase the price of an item that is close to being sold out.

For any of your products, you can choose a fitting strategy that would align with your goals.

Smart Rounding

An undisputed part of modern pricing is the rounding (up ro down) of prices. Surely you have heard of how an item that is priced at $19.99 feels much more like a $10 item to a customer than a $20 one, regardless of being just one cent shy of it. In our model, we offer you an option to use several rounding features

• Setting prices to the nearest .99
• Setting up the number of significant digits - 19999.99 does not look as appealing, 19900 might be a better option for you
• Setting up which number the prices can end in - After calculation, a $3.13 price of either 3.15 or 3.19 might look better
• Choosing whether to allow lowering the calculated price to reach a “better looking” price

Conclusion

This article serves as an introduction to the concept of dynamic pricing as such, and an overview of several basic strategies contained within it. In the following article, we will describe how this pricing model can be implemented in DecisionRules, how to use it for your own applications, and which alterations you might want to make to the model to help it fit your needs.