The cost of retail goods keeps going up. As per data from the Federal Reserve Bank of St. Louis, the Consumer Price Index reached a new high of 280.192 in December 2021. Concurrently, the U.S. Census Bureau reports that overall retail sales continue to rise.
While these numbers may seem promising for retailers, the reality is more complex. Persistent supply chain delays, increased shipping expenses, and staffing issues turn setting the right price into a delicate balancing act. If the price is set too low, businesses miss out on potential revenue; if it’s too high, they risk losing customers.
Retail price optimization can pinpoint ideal price points, leading to sustainable sales growth.
Here’s a closer look at how it works.
What Is Retail Price Optimization?
Kevin Yarnell, Americas Retail Lead for Cisco, defines retail price optimization as “the use of mathematical analysis by a company to determine how customers will react to different prices for its products and services, across various channels.”
In practice, this involves analyzing historical and current pricing data along with consumer information to model potential outcomes when prices are adjusted. The more accurate the model and its conclusions, the better retailers can identify the optimal price points.
What Challenges Exist in Price Optimization?
Three primary challenges exist in price optimization:
- 1. Data Collection: Due to the vast amount of pricing and consumer data now available, retailers often struggle to gather reliable and relevant data. To address this, Yarnell recommends using analytic technologies, Internet of Things sensors, and machine learning tools to automate data collection for accuracy.
- 2. Analysis Paralysis: Once data is collected, retailers must ensure it reaches the right people. Yarnell warns against “analysis paralysis,” where necessary information doesn’t reach the correct business units in a timely manner. This delay can allow competitors to gain market share due to slow response times.
- 3. Product Formatting: Retailers need to understand how customers consume products and their format preferences to optimize pricing. For instance, e-commerce prices may differ significantly from curbside pickup prices for the same product, depending on consumer preferences and overall demand.
What Technologies Can Aid in Pricing Optimization?
Retailers require technologies capable of analyzing consumer behaviors, understanding customer journeys, and fostering brand loyalty to optimize pricing. These tools provide actionable data for creating in-store merchandising plans that maximize sales based on both price and location.
Additionally, natural language processing tools help identify friction points in the purchasing process that may affect consumer conversion. Eliminating these points whenever possible can assist retailers in maximizing product price adjustments.
Multichannel communication strategies can also be beneficial, allowing retailers to personalize consumer offers. These offers cultivate brand loyalty and simplify the purchasing decision for consumers, ultimately making it easier to optimize prices with less resistance from customers.
How Do Price Optimization Solutions Assist in Price Optimization?
Retail price optimization models utilize current and past purchasing data to predict future outcomes. Automation plays a crucial role in the speed and accuracy of these models. By eliminating manual processes, the gathered information offers a more precise picture to pinpoint necessary changes.
This approach makes sense considering the sheer volume of pricing and consumer data generated by retailers daily. Attempting to manually identify and organize key sources can be frustrating, and by the time enough data is gathered to make pricing predictions, market trends may have already shifted.
How Is Machine Learning Employed?
Machine learning simplifies the collection and analysis of retail pricing data on a large scale. Machine learning algorithms enable the collection of data in larger quantities and align it more closely with the retailer’s business objectives. This allows for more variables to be considered than traditional pricing models. Factors such as weather, historical data, marketing campaigns, and seasonal inventories can all be integrated into the algorithm as data is collected and compared. This output provides a clearer picture for each business unit to take action.
Price optimization tools equipped with machine learning also help retailers stay in step with rapidly changing consumer expectations and supply chain trends. These tools continuously evaluate data to determine the optimal price point for retailers.
Price optimization relies on both instinct and information. Retailers should trust their experience in finding the optimal price range but can enhance the process’s speed and accuracy by implementing agile analytics tools bolstered by machine learning algorithms.
