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Retail Pricing: Using Data to Increase Revenue

In today's hypercompetitive retail landscape, price optimization gives companies an invaluable edge. Data analytics is used to optimize retail pricing by providing insights into consumer desires and behavior that can increase sales and drive customer loyalty. Although the use of big data in price optimization isn't new, recent advances in machine learning and other technologies have enabled retailers to analyze vast amounts of data, often in real time, making it possible to dynamically adjust prices in response to market trends, competitor pricing actions, and evolving demand. 

How Retail Pricing Data Creates Opportunities in Retail 

Because consumer preferences are quick to change and the retail landscape is continually shifting, a data-driven retail pricing strategy is essential. Using data, retailers can achieve a pricing strategy to meet their specific goals, including maximizing per-unit profitability, growing market share, and moving into new markets. Here are five examples of how retail pricing data helps retailers get ahead.

Granular product pricing

Optimizing pricing for individual products can be a complex and time-consuming process. But modern technologies now make it possible to implement data-driven retail pricing strategies beyond the category level. Using machine learning to mine a diversity of data, such as competitor pricing, real time sales data, current inventory levels, purchase history, and product movement, retailers can achieve the optimal price for individual products. 

Pricing by customer segment

Retail pricing tools can be used to gauge the elasticity of demand by customer segment, making it possible to price individual products at levels most likely to appeal to each segment. This highly defined targeting allows retailers to break away from a monolithic approach to product pricing.

Dynamic pricing adjustments

Adjusting prices in real time can help retailers maximize the profitability of each unit sold. Analytics tools identify optimal pricing based on numerous factors, including inventory levels, competitors’ pricing, and current trends in consumer demand. Real-time data is gathered through the continuous monitoring of product pricing fluctuations on a variety of ecommerce platforms, the product manufacturer’s website, third-party marketplaces, and other retail websites. Pricing can then be automatically adjusted based on this information.

Price testing for new products

Determining the best price for new products can be challenging since there is no historical information for the product. Analytics tools can help retailers find the ideal initial price for a new product offering by analyzing relevant factors, including product category, brand, and pricing for similar items already established in the marketplace.

Tailored promotions based on inventory levels

With real-time visibility into inventory levels, retailers can quickly create promotions based on product availability. By analyzing historical data and current data sources, such as weather, social trends, holiday schedules, and levels of current and projected future demand, retailers can balance their inventory levels through the strategic use of price adjustments and promotional discounts. 

In this way, retail analytics insights streamline inventory management efforts so businesses avoid both stockouts and overstock. With real-time tracking, business leaders can see which products are selling quickly, which are slow movers, and which are piling up as dead stock. With this information, teams can make better decisions on when overstocked items should be discounted and how quickly inventory for popular products should be replenished.

Additional Ways Retail Analytics Boosts Growth

Price optimization isn’t the only way retail analytics can benefit today’s companies. Here are four additional ways that retail analytics can boost growth.

Improve the customer experience

Customer data can be analyzed to create incredibly detailed customer profiles. Often referred to as Customer 360, these enriched consumer profiles include demographic and psychographic data compiled from purchase history, customer service interactions, social platforms, and more. With a deeper understanding of customers, retailers can create a highly customized experience that increases loyalty. 

Optimize micro-segmentation

Micro-segmentation moves beyond the traditionally defined market segments, dividing customers into increasingly smaller groups of individuals who share common characteristics. Micro-segmentation may be conducted using a variety of differentiators including demographics, interests, and needs. This allows retailers the ability to optimize product pricing for very specific customer segments and create highly targeted promotions that are more likely to resonate and result in a purchase. 

Reach customers at all relevant touchpoints

Today, retail transactions occur across multiple channels, including online and offline channels, such as social media platforms, ecommerce sites, mobile apps, and brick-and-mortar stores. Retail analytics tools provide insights that retailers can use to more effectively reach customers at every touchpoint for targeted marketing content.

Reduce retail-related expenditures

Retail analytics can help companies reduce business expenses, including warehousing, transportation, and back office costs. Less overstock requires less warehouse space to store the extra inventory. Avoiding frequent stockouts saves the expense of paying for expedited delivery. And automated product pricing frees team members from the manual tasks associated with shifting pricing. 

Improve Retail Pricing Analytics with Snowflake

Retailers with a strong data foundation are well positioned for growth, while those stuck with legacy infrastructures and data silos will struggle to keep pace. Optimize your marketing technology stack with the Snowflake Retail Data Cloud. By harnessing the full power of your retail pricing data, you can better understand the needs of your customers, increase relevance, and deliver seamless experiences. The Data Cloud enables retailers and manufacturers to access, govern, and share data seamlessly to deliver more personalized customer experiences, optimize supply chains, and make data-driven merchandising decisions.