Retail Price Optimization and How Retailers Can Maximize Sales

The price of retail goods continues to rise. According to data from the Federal Reserve Bank of St. Louis, the consumer price index reached a new high, at 280.192, in December 2021. According to the US Census Bureau, at the same time, the overall volume of sales retail continues to increase.

While both of these numbers sound like good news for retailers, the reality is more complicated. Ongoing supply chain delays, rising shipping costs, and staffing issues make finding the right price 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 drive sustainable sales. Here’s how.

What is Retail Price Optimization?

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

In practice, this combines historical and current prices 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 levels.

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What challenges exist in price optimization?

As Yarnell noted, three main challenges exist in price optimization:

  • Data gathering: With the vast amount of price 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 analytical technologies, Internet of Things sensors, and machine learning tools that can automate the collection process to deliver accurate data.
  • Paralysis of analysis: With the data in hand, retailers need to make sure it gets to the right people. “Analysis paralysis is essentially a waste of time, when the right business units don’t have the information they need to make changes,” Yarnell says. “This lack of movement may allow competition to take market share due to slow reaction times.”
  • Product formatting: Yarnell also notes that retailers need to understand how customers are consuming products — and what format they prefer — in order to optimize price. For example, e-commerce prices can differ significantly from curbside pickup for the same product, depending on both consumer preferences and overall demand.

What kind of technologies can help optimize prices?

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

“Using video analytics tools like Cisco Meraki or DNA Spaces, retailers can better understand in-store wait times and deliver promotional messaging through dynamic signage tailored to specific demographics,” Yarnell says.

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

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

Additionally, businesses can benefit from multi-channel communication strategies that allow them to personalize consumer offerings. “These offers create a sense of brand loyalty,” Yarnell says, “and make the buying decision easier for the consumer.”

Strong brand loyalty also helps optimize pricing with less customer rejection.

How do models help optimize pricing?

Price optimization models combine current and past purchase data to help predict future results.

Automation is key to both the speed and accuracy of these models. “By eliminating manual processes, the information gathered provides a more accurate picture to help identify needed changes,” says Yarnell.

It makes sense: given the massive amount of price and consumer data that retailers generate every day, trying to manually identify and sort through key sources is a frustrating exercise. By the time enough data is gathered to make price predictions, market trends will have already moved.

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How is machine learning used?

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

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

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

Pricing optimization relies on equal parts instinct and information. Retailers should trust their experience when it comes to finding optimal price ranges, but they can streamline the speed and accuracy of the process by implementing agile analytics tools bolstered by learning algorithms Automatique.