Understanding the Basics of Price Optimization - Baltimore Post-ExaminerBaltimore Post-Examiner

Understanding the Basics of Price Optimization

The concept of price optimization can be hard to comprehend. There are different terms to understand along with many associated concepts that it can be a bit confusing. So what is price optimization and how is it used?

The Basics of Price Optimization

Price Optimization is the process of determining the best prices for your products based on various factors.  it uses a lot of data and mathematical models to determine the best prices.

At the basic level price optimization helps you determine the impact of prices on demand. It helps find how customers will react to price changes of different products. This involves studying your historical data, collecting data about market demand, competitor strategies, competitors sales, seasonality, and other factors.

There are numerous price optimization software available to help you do this.

Price Optimization and Related Concepts

When talking about Price Optimization you may be confused by the different terms associated with pricing. Are Price Optimization and Dynamic Pricing the same? What about pricing strategies? What factors should be considered for the process of optimization? How does machine learning help in price optimization?

Dynamic Pricing and Price Optimization

Dynamic pricing is the process of continuously adjusting prices in response to competitor moves, customer preferences, market demand etc. Dynamic pricing is a pricing strategy that can be used for price optimization. Price Optimization is a complex and more encompassing process.

Pricing Strategies

Pricing strategies are different pricing models that are used to set prices based on product life cycle, target customer segment etc. Pricing strategies can be used in price optimization. An example of pricing strategy is competitive pricing, where prices are adjusted based on competition. Dynamic pricing is also a pricing strategy.

Data Required for Price Optimization

Based on the complexity of your operation you will need to consider many different data. You will consider all your internal data, also factoring in external data like market demand, competition strategies, and other factors.

Internal data will include your product data, sales, customer data, etc. If you have an online site where customers can leave reviews, then these reviews will also be used in the analysis process for price optimization.

External data may be as varied as market trend, competitor pricing, weather forecasts etc. You can dig deeper and collect customer reviews on review sites, information from social media shares and comments etc. 

Prioritize your data. Are your product sales impacted by season? Is the target customer segment very price sensitive? Do you need to consider competitor pricing more than your historical data? Do you need to focus more on your internal data than external factors?

How do you get access to external data? You can find third party data providers. Look for reputable providers who collect and sell these data legally and provide reliable access to the data

Machine Learning and Price Optimization

Previously, retailers relied on straightforward pricing methods like following the MSRP, or setting Keystone pricing. Later, they started analysing their historical internal data to determine pricing for their products. Now, in this information age with access to so many sources of data,  new technologies are being used in retail. 

Price Optimization software now integrate machine learning. These systems keep learning and adapting with each iteration. That is, they analyse data and provide you pricing suggestions. You implement these suggestions and feed the results to the application. It then analyses the results to determine what has worked and what has failed. Based on this, the next time they provide you better suggestions. So these ML systems keep learning, adapting, and evolving with time.

Benefits of Machine Learning Price Optimization Tools

These tools are trained by providing initial input data, defining goals, analysis of data for reaching those goals, the results of which are the pricing suggestions. The input data includes internal and external data. You also train these systems to identify associations. For instance, you train them to spot identical products and similar products. This can be very helpful. For instance, if you are launching a new product, there will be no historical data for it. However, the ML tool can spot similar products and use that data to set the initial pricing points for this new item, They will  evolve to device better strategies as the historical data accumulates for this product.

There are other side benefits of AI-led Price Optimization. For instance,how does the sale of individual items in closely associated product segments affect each other – like shirts and pants. Can certain products that are related but not so closely associated, like pens and notepads, benefit from being grouped together? 

These and other benefits that result from using ML technology in price optimization can really help you take your retail business to the next level.

Whatever the size of your retail  operation, Price Optimization is an important process that you cannot ignore. It is now easy to implement price optimization solutions even for the small retailer. These options are available as SaaS, in a subscription-based model. Most of these, like Intelligence Node InOptimizer, also use big data to harvest varied sources of information to get better insights and provide better results. 


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