Does Using Data Analytics Improve Price Points in Retail?
August 8, 2023
How can your business stay competitive in retail? Pricing analytics is a valuable tool to help optimize pricing strategies to maximize revenue and profits. By using advanced analytics to inform pricing decisions, retailers can make more informed, data-driven decisions based on customer behavior and market conditions.
Data, including internal data such as customer behavior and sales data, and external data such as market and competitor data drive pricing analytics. There are many techniques in the pricing analytics arsenal that retailers can use to analyze this data to gain insights into how customers will respond to different prices. Here’s a summary of some of the top methods.
Price optimization analyzes customer and market data to find the ideal price point for a product or service. This data analytics method is based on price sensitivity, how changes in the price of products/services affect customer demand, and how much profit the retailer can earn from selling the product or service at a certain price.
Price sensitivity differs greatly across different consumers. For this reason, it’s useful to use segmentation to divide a market into distinct groups of consumers with different price sensitivities. Businesses can charge more and increase profits for some segments, and for others, businesses can offer discounts and price promotions to increase conversion.
Dynamic pricing automatically adjusts the price of a product or service in real-time based on customer demand, competition and other factors. By using real-time analytics to set prices that reflect current conditions, retailers can better optimize their revenue or provide than with traditional pricing strategies that set prices based on fixed costs and profit margins.
Dynamic pricing is especially valuable in dynamic markets where factors such as supply and demand, seasonal patterns and competitive pricing change quickly. This method is useful across any selling channel, but real-time analytics can be essential for competing in e-commerce. For example, Amazon dominates the e-commerce market with its ability to rapidly and frequently change prices to undercut competitors.
Price Gap Analysis
Price gap analysis compares the price of a company’s product or service to those of its competitors. Using price gap analysis shows if a product/service is priced above the market, below the market or on par with the market.
“Minding the gap” depends on the company’s price strategy for the product/service. For example, a company usually prices national brands a certain percentage higher than private labels or generics. Price gaps may vary at different retailers and a retailer may want to adjust price since some brands prefer a more consistent price because most consumers shop at multiple retailers.
Bundle Price Analysis
Bundle price analytics involves setting the optimal discounted price for a bundle of multiple products or services, taking into account the price of individual prices in the bundle, production costs, desired profit margin and expected customer demand. The goal of bundle price analytics is to encourage customers to purchase more items with a cost-effective way to buy the products together. This type of data analytics helps retailers increase their revenue and improve customer loyalty and retention.
Businesses need to make informed decisions about pricing to be successful in retail using data analytics. Pricing analytics can help transform pricing through better data science if its built on the right platform where data can be collected, cleansed, managed, and analyzed in a centralized location. See how the Actian Data Platform makes price analytics easy.
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