Predictive Analytics – How Optimized Pricing increases Revenue and boost Sales

Introduction to Predictive Analytics

Ever wondered how airline prices work, what process is involved before fixing up a price for any product. Well, whatever the product may be, the true fact is that the pricing to sell the product was probably done using some mathematical calculations. Consider, a simple example: Airlines have varied prices doled out to its consumers, sometimes the fare is exorbitant, while sometimes the fare is very less. These types of pricing techniques is called as Price optimization, the very goal behind it is to attract potential consumers based on the volume.

Pricing plays a key role in affecting sales, gross margin and customer experience in any retail organization. A minor change in the price of a product can significantly enhance the demand, increase revenue and importantly provide a much needed boost to the retailer’s public image; however, this effect can also work the other way around.

A best solution to this problem is to optimize price. By optimized pricing, it’s possible to increase the sell-through rate and revenue of the product. Our focus, for the rest of the blog will be to find out how optimal pricing can impact sales, revenue and volume of the product.

Price Optimization – The Problem

To understand the problem of price optimization, let’s consider this scenario: A customer enters a retail shop looking to buy a specific product, the customer finds this product, but, before proceeding to buy, he decides to check the price online, he finds that some other retailer is offering the product at a little lesser price (this may include discounts, and other offers) and hence, he decides to leave. The scenario depicts the loss of a possible sale to a retailer. In order to survive in the market competitively, the retailer should make the product available to the consumer at attractive prices and at the same time keeping the profit margin in check.

So, what is Price Optimization? Price Optimization is a data science technique which uses the power of both Predictive and Prescriptive Analytics, to determine the optimal baseline price for an item within a category.

Having said that, what is Predictive and Prescriptive Analytics; A simple answer for this question is that, predictive analytics answers about the future: “What could happen in the future, if the same trend follows”, whereas, prescriptive analytics, answers about the possible outcomes and solutions to the future trends: “What should we do, if this happens?”.

Why do we need both predictive and prescriptive analytics, well, the ability to predict the future and optimize prices based on the said trends is the key strategy to win the market. Let’s consider that a retailer want’s to offer discount on it’s products, how will it find the optimal price range, through which it can make the products attractive, competitive and at the same time profitable. The question here is to find the optimal discount range for the product, in order to understand this, let’s ask our-self the basic question of why do the retailer’s offer discount on their range of products from time to time; There are many a reason to explain why discount’s are offered, some of them to begin with are;

• The product is slow moving.
• To increase the store foot-fall.
• To boost sales for a short period.
• To boost revenue for a short period.
• To clear out the current inventory and make way for next stock.
• Promotional/Marketing Campaign.

The reasons may be many, but, the end answer is to maximize revenues. Traditionally, all retailers have a legacy method to determine the maximum discount price which can be given for a product. These methods are usually straight forward and are insufficient because it does not take multiple factors into account before arriving at a optimal price range.

Data Loading

Data is collected from various sources and loaded to data warehouse using Pentaho+ Data Integration. The Pentaho+ Data Integration Tool performs the cleansing, transformation, applying rules and stores in data warehouse.

This data is further used for the exploratory data analysis, creating data pipeline and building model.

Price Optimization – The Solution

The initial step to go about this problem is to understand the price elasticity model. In general, the cheaper the price of the product, the more it sells. The same principle may not be applicable for the premium range of products, as in this case, the higher the price, the better the sales. Price Elasticity determines how the pricing impacts the demand of all the products to be sold. Once, the price elasticity is established, then the next step is to find the price level which will maximize the revenue.

Figure – 1, depicts typical price elasticity curve. The curve is plotted with revenue against sales. From the figure, it can be noted that at low prices you tend to saturate the market and the sales does not depend on the price levels and at very high prices, the sale level drops down and you are evidently out of the market. Between the two extremes of sales and revenue, lies the optimal price.

So, how do you determine the price level through which it’s possible to maximize the revenue and at the same time business needs are not affected? Well, by, understanding how the pricing rules works with respect to each businesses and using data science to leverage the optimization techniques, it’s highly possible to support the businesses primary and secondary goals, including higher sales, increased revenue and increased volumes.

Solution: The best way to address the price optimization problem would be an end-to-end predictive model which takes multiple factors into consideration and provides an optimal price range for the product. One needs to understand that each businesses have different needs and consumer demands, a model which works well for a particular business can’t possibly work well for other business. Some of the factor’s which are generally taken into consideration while designing a predictive model are:

• Inventory levels.
• Safety stock.
• Future Consumer Demand.
• Triggered Events and
• Pricing Models

While, these are just a few, there are many more factors which need to be taken into consideration in order to make a model successful.

Conclusion

Price Optimization is more widely used than ever before. With a prefect optimized pricing model, one can;

• Set the right prices for the products to achieve the targeted pricing goal.
• Increase customer foot-falls.
• Increase in sales and revenue.
• Maximum Stock Utilization.

The value of Optimization is that it is using science to compute decisions. We can also design optimization to work in combination with human decision maker by supporting “what if” analysis.