How Machine Learning Can help Retailers in Pricing Optimization.
Understanding the Challenges of Pricing and Why Retailers cannot Afford to Ignore Pricing Optimization
Pricing is an important factor and a fundamental problem in retail. It is one of the strategy parameters that can determine the success of a business brand. Hence, retailers pay much focus on pricing strategies and often experiment with new technology, process changes, and methods to control prices while optimizing revenue and profits. The market is volatile these days especially due to COVID-19, whose impact reflects on the retailers too. This situation has encouraged them to rely on other solutions like machine learning for pricing optimization. Machine learning is currently a better alternative as it is based on data that sustains the physical retail outlet and e-commerce sites.
Gone are the days when pricing seemed like a random number pulled from a bingo ball. Pricing is now a definitive element in predicting sales and market success. So, optimizing the pricing to balance value with profit can have a tremendous effect on the success of a business venture, this implies that once the right price is found, everything becomes more manageable, from sales and marketing to growth and profitability. This process involves the study and analysis of various databases. These are customer survey data, demographic and psychographic data, historical sales data, operating costs, inventories, machine learning outputs, subscription lifetime value (if any), and churn data. All these help to understand the market situation, the amount customer is willing to offer, and the profit margin probability. In short, it helps to find a perfect balance of profit, value, and desire. Opting for human-centric decisions can not only make retailers vulnerable to mistakes but are also limited in the scope of factors managers can consider at once.
This is where machine learning can save retailers from the excruciating task of pricing decisions. Machine learning models apply complex algorithms to consider a variety of factors (some of which are mentioned above), parse and process data, and come up with the right prices for thousands of products. These models can detect patterns within the data it is given, which allows it to price items based on factors that the retailer may not have even been aware of. Further, they can predict how customers will react to specific prices and forecast demand for a given product. This helps retailers in planning their pricing strategy as per the requirement, i.e., competitive pricing, dynamic pricing, keystone pricing, and many more. Apart from that, machine learning models can employ linear regression and recurrent neural networks (RNNS) for early anticipation of future trends in market and customer behavior. It can also be used as a web crawler and scrape information on prices of similar products, reviews about said products, and what the competitor price history is.
So it is clear that machine learning is the optimal solution for price optimization. No matter whatever machine learning model is selected for the same, retailers must keep in mind that to learn about buyer personas. Plus, the software should have the option for localization of pricing and competitive monitoring. Plus, it ought to make moving from one-time payments to a subscription business model smooth and straightforward, letting you join the subscription economy without losing customer satisfaction.