Jan 20 2022

The Price Is Right: How Retail Price Optimization Can Maximize Sales

Competitive product pricing is critical for sustainable sales. Here’s how technology can help retailers identify optimal prices.

The price of retail goods continues to rise. According to data from the Federal Reserve Bank of St. Louis, the Consumer Price Index hit a new high point, at 280.192, in December 2021. According to the U.S. Census Bureau, meanwhile, overall retail sales volumes continue to rise.

While both numbers seem like good news for retailers, the reality is more complicated. Continued supply chain delays, increased shipping costs and staffing challenges make finding the right price point a balancing act — too low, and businesses lose potential revenue; too high, and they risk losing customers.

Retail price optimization can identify ideal price points and deliver sustainable sales. Here’s how.

What Is Retail Price Optimization?

According to Kevin Yarnell, Americas Retail Lead for Cisco, “Retail price optimization is the use of mathematical analysis by a company to determine how customers will respond to different prices for its products and services, through different channels.”

In practice, this combines historical and current pricing and consumer data to model potential outcomes if prices are changed. The more accurate the model and its conclusions, the better-equipped retailers are to determine optimal price points.

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What Challenges Exist in Price Optimization?

As noted by Yarnell, three core challenges exist in price optimization:

  • Data collection: Given the sheer amount of pricing and consumer data now available, retailers often struggle to find and collect reliable and relevant data points. To address this challenge, Yarnell suggests the use of analytic technologies, Internet of Things sensors and machine learning tools capable of automating the collection process to provide accurate data.
  • Analysis paralysis: With data in hand, retailers need to make sure it gets to the right people. “Analysis paralysis is basically wasted time, when the correct business units don’t have the necessary information they need to make changes,” says Yarnell. “This lack of movement can allow competition to take away market share due to slow reaction times.”
  • Product formatting: Yarnell also notes that retailers must understand how customers consume products — and what format they prefer — in order to optimize price. For example, e-commerce pricing may differ significantly from the curbside pickup for the same product, depending on both consumer preference and overall demand.

What Kind of Technologies Can Help Optimize Pricing?

To optimize pricing, retailers need technologies that can analyze buyer behaviors, understand customer journeys and connect with consumers to create brand loyalty.

“By using video analytics tools such as Cisco Meraki or DNA Spaces, retailers can better understand dwell times within stores and offer promotional messaging via dynamic signage that is customized to specific demographics,” says Yarnell.

These tools also provide the actionable data retailers need to create in-store merchandising plans that optimize sales volume based on both price and location.

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Yarnell also points to the use of natural language processing tools that make it possible to identify friction points in the purchasing process that may impact consumer conversion. Eliminating these points wherever possible can help retailers maximize product price adjustments.

In addition, companies can benefit from multichannel communication strategies that let them customize consumer offers. “These offers create a sense of brand loyalty,” says Yarnell, “and make the buying decision easier for the consumer.”

Robust brand loyalty also makes it possible to optimize prices with less pushback from customers.

How Do Models Help Price Optimization?

Price optimization models combine current and past purchasing data to help predict future outcomes.

Automation is essential to both the speed and accuracy of these models. “By eliminating manual processes, information gathered provides a more accurate picture to help pinpoint necessary change,” Yarnell explains.

That makes sense: Given the sheer amount of pricing and consumer data now generated by retailers every day, attempting to manually identify and sort key sources is an exercise in frustration. By the time enough data is gathered to make pricing predictions, market trends will have already moved on.

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How Is Machine Learning Used?

Machine learning offers a way to streamline the collection and analysis of retail pricing data at scale.

“Machine learning algorithms allow the data to be gathered in much larger quantities and aligned more closely to the business objectives of the retailer,” says Yarnell, “thus allowing more variables to be input than traditional pricing models. Factors such as weather, historical data, marketing campaigns and seasonal inventories can all be factored into the algorithm as the data is collected and compared, and this output provides a clearer picture to take action for each business unit.”

He notes that price optimization tools equipped with machine learning also make it possible for retailers to keep pace with rapidly changing consumer expectations and supply chain trends. “The tools allow the ability to ‘learn’ and get more precise by constantly evaluating the data to find the optimal price point for retailers.”

Price optimization leverages instinct and information in equal parts. Retailers need to trust their experience when it comes to finding optimal price ranges, but can streamline the speed and accuracy of the process by implementing agile analytics tools bolstered by machine learning algorithms.

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