# The New Frontier of Price Optimization

# Optimization and Scaling

Tullika Tiwary

What would be the price of our product or service? This question has bothered businesses forever. Several pricing models have been spawned as a result. But the concept of price optimization is a fairly new one. At least to businesses that are not in the hospitality or airline sector.

Price optimization hasn’t been popular as it requires vast amounts of data on the internal processes, customers, and competitor’s behaviors. And it also takes a lot of time and effort to compute this data to create an optimum pricing model. All this is changing now, thanks to the role of AI and machine learning in the price optimization process.

For example, three researchers at MIT have developed a way for optimizing the prices of hundreds of products in an inventory on a non-stop basis. Data is available at the businesses’ fingertips, and there is an increase in computing speeds too.

While conducting trials with three online retailers, the researchers succeeded in increasing the retailer's profit, revenue, and market share by more than 10 percent. Although the trial was conducted with online retailers, the price optimization solution is also viable for brick and mortar businesses.

Machine learning solutions help in price optimization in three steps:

**Forecasting**

Since some of the products in the retailer's inventory have never been tried for price optimization, it becomes necessary to rely on historical records and sales patterns of similar products. This is exactly what the MIT algorithm does through a machine learning technique called the regression tree.

It analyzes clusters of products similar in sales characteristics to the one being optimized. Then the regression tree, which generates a set of 'what-if' questions, analyzes the price-demand dynamic. Using this data, a new optimized price is determined.

**Learning Curve**

The next step in the price optimization process is the algorithm comparing the price determined above to the real-time sales. The pricing curve is redrawn to match actual results with the predictions. At the end of this step, the algorithm knows how successful the price was in increasing sales. The price-demand curve is then redrawn.

**Optimization and Scaling**

After several rounds of learning, the optimum price of the products is determined. The curve is applied through hundreds of items at different periods.

There are several price optimization tools in the market like this one by Intelligence node that help retailers maximize sales and profits. It helps companies stay updated on competitors' price strategies and deals.

There are several advantages to using machine learning for price optimization, including:

- Machine learning software takes into account a large number of products and analyzes the most minute criteria. There are several parameters, channels, and sources that the algorithm studies. Such fine detailing is impossible through manual means.
- It’s well known that changing the price of a product can affect the sales of other products. Such levels of predictions are difficult for a human to make, but not for machine learning solutions.
- Companies can also change the different KPIs and see how the different pricing models turn out.
- The large amount of data analyzed helps the algorithm predict pricing trends. This helps companies make relevant pricing decisions.
- Machine learning solutions can gather significant information about competitors pricing through websites and social media. They also help collect data about the discounts, deals, customer response to prices, and other key factors.

A study by Bain shows that the top-performing retailers in the market are those that have a dynamic pricing strategy. So, understanding the reaction customers will have to your pricing models is essential. This is only possible through adapting machine learning solutions for price optimization.

They use the major key pricing variables like inventory, season, competitors pricing, etc. to create effective pricing models.

Originally published here.

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Tullika Tiwary

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