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Leveraging New Data Sources to Improve Retail Pricing

Written By: Gargi Sarma

Introduction:


Technology and data have become more and more important in determining business strategies, causing a seismic shift in the retail sector in recent years. Because their products are perishable, retailers, particularly those in the fresh produce industry, face particular difficulties. To optimize profitability in this industry while guaranteeing customer satisfaction and reducing waste, pricing strategies must be constantly optimized due to their high sensitivity. Retailers now have access to powerful tools that can completely change how they approach pricing and customer engagement thanks to the emergence of a wide range of data sources and cutting-edge technologies.


Retailers often find it difficult to determine the right price for fresh produce. There are many variables that can cause price fluctuations, such as supply and demand, weather, and transportation expenses. Fresh produce also has a short shelf life, so merchants must exercise caution when pricing their goods to avoid losing them to deterioration.


Several new data sources have surfaced in recent years that can assist retailers in raising the price of their fresh produce. Expiration date, motion sensor, time temperature, in-store traffic detection, customer behavior, Universal Product Code (UPC) databases, food hygiene rating, image classification, and demographic shopping purchases are some of the data sources used in these analyses.


Retailers can gain a better understanding of their customers' needs and preferences, the factors influencing the demand for fresh produce, and the expenses related to selling fresh produce by utilizing these new data sources. The prices of fresh produce can then be set with greater accuracy and competitiveness using this information.


Here are some particular instances of how retailers can enhance their fresh produce pricing through the use of new data sources:


Using Data on Expiration Dates to Determine the Best Prices:


Keeping inventory under control to reduce waste and product spoilage is one of the major issues facing the fresh produce industry. Retailers can use expiration date data to implement dynamic pricing strategies that take perishable goods' remaining shelf life into account. Retailers can maximize revenue and minimize waste by adjusting prices in real-time by analyzing this data and encouraging customers to buy products that are getting close to expiration. In order to maintain customer satisfaction and prevent financial losses, retailers can also employ proactive markdown strategies to ensure timely sales of products that are getting close to expiration.


Figure: Examples of Fresh Produce Dashboards (Source: Freshmade, SiFoodSoftware)


For example, Walmart creates "Best If Used By" labels for its fresh produce based on expiration date information. Customers can identify products that are safe to eat and still fresh with the help of these labels. Walmart plans its discounts and promotions using expiration date data as well. For instance, when fresh produce is about to expire, Walmart might give discounts.


Using Time-Temperature Datasets to Improve Pricing and Quality Assurance:


To maintain fresh produce's quality and increase its shelf life, ideal storage conditions must be maintained. Time-temperature datasets can offer insightful information about the storage and transportation conditions of goods. Retailers can ensure transparency and establish customer trust by offering discounts or promotions on products that have undergone suboptimal temperature conditions by incorporating this data into their pricing strategies. Additionally, based on the product's freshness, this data can help retailers adjust prices in a way that keeps quality standards high while still being competitive.


For example, Albertsons Companies tracks the temperature of its fresh produce while it is being transported using time-temperature data. Moreover, Albertsons makes use of this information to spot any possible issues with its storage facilities.


Making Strategic Pricing Decisions by Optimizing In-Store Traffic Detection Data:


Pricing strategies must be customized to maximize sales and improve customer satisfaction, which requires an understanding of customer foot traffic and behavior within retail establishments. Retailers can identify popular product categories and peak shopping hours with the help of in-store traffic detection data, which makes it easier to implement dynamic pricing during periods of high traffic. Retailers can enhance revenue and improve customer engagement by adjusting pricing to stimulate demand and optimize sales through the analysis of customer behavior patterns.


For example, Kroger determines which aisles in its stores are the most popular by using data from motion sensors. After that, Kroger arranges its highest-margin items in these aisles using the information provided. Additionally, Kroger tracks customer traffic patterns throughout the day and week using data from motion sensors. The staffing levels and store hours are optimized with the help of this data.


Customization of Customer Experience via Demographical Purchase Data:


The incorporation of Demographical Shopping Purchases Data allows retailers to develop customized pricing plans that accommodate a wide range of consumer preferences. Retailers may target certain customer segments with product recommendations and pricing promotions by examining demographic data like age, income, and purchasing patterns. This customized approach increases repeat business, builds brand loyalty, and improves customer satisfaction—all of which contribute to higher sales and a competitive advantage in the marketplace.


Figure: Examples of Customer Analytics Dashboards (Source: Fiverr, SlideTeam)


For example, Amazon recommends products to its customers based on information about their behavior. Additionally, Amazon sends its customers personalized emails and promotions using this data.


Using Image Classification to Improve Pricing and Product Placement Strategies:


Retailers no longer have to worry about managing product placement and pricing within their stores thanks to image classification technology. Retailers can optimize product displays to draw customers in and sway their decisions to buy by analyzing photos of products and customer interactions. Retailers can also dynamically modify prices in response to customer engagement with particular products by combining image classification with pricing strategies. This increases sales and facilitates effective inventory turnover.


For example, Kroger tracks customer traffic patterns in its stores using image classification technology. In addition to identifying popular products, Kroger uses this technology to deter theft.


Using Food Hygiene Rating Datasets to Improve Food Safety and Price Transparency:


Upholding food safety regulations is crucial for establishing credibility and trust with consumers in the fresh produce industry. Retailers can openly convey that they comply with strict food safety regulations by incorporating Food Hygiene Rating Datasets into their pricing strategies. Retailers can boost sales and foster brand loyalty by providing customers with confidence through the display of food hygiene ratings alongside product prices.


Furthermore, by guaranteeing that only the best products make it onto the shelves, this integration promotes proactive quality management and improves the overall customer experience.


For example, food businesses can access food hygiene rating datasets published by the Food Standards Agency (FSA) in the United Kingdom. Retailers can utilize this data to find food establishments with excellent hygiene ratings and purchase their fresh produce from them.


Utilizing Universal Product Code (UPC) Databases to Transform Retail Pricing:


With the help of the Universal Product Code (UPC) Database, retailers can simplify pricing and inventory management by accessing a vast amount of product data. Retailers may precisely track product sales, keep an eye on demand patterns, and modify prices in real-time to reflect changes in the market by utilizing UPC data. Retailers can enhance sales and profitability by implementing competitive pricing strategies that align with customer preferences, optimize product assortments, and make well-informed pricing decisions with the help of this data-driven approach.


For example, when customers scan their loyalty cards at the checkout counter, Walgreens uses the UPC database to look up product information. This enables Walgreens to give customers individualized promotions and accurate pricing.


Voice of the customer (VoC) data to increased customer satisfaction, loyalty, and sales:


VoC data can be used by retailers to distinguish between price-sensitive and non-sensitive customers. The development of focused pricing strategies can then be done using this information. For instance, in order to entice price-conscious customers to make a purchase, a retailer might offer discounts or promotions. VoC data can also be used to determine how much consumers are willing to spend on various goods and services. Setting prices that are both profitable and competitive can be done with this information. VoC data can be used to track how valuable customers believe something to be. The retailer can utilize this information to pinpoint areas in which its value proposition needs to be strengthened. For instance, a retailer may discover that consumers do not think a particular good or service is worth the additional money, so they are unwilling to pay a premium for it.


For example, Amazon creates customized pricing recommendations for customers based on VoC data. VoC data is also used by the business to track client satisfaction with pricing. Walmart determines the price elasticity of various products using VoC data. Additionally, the business develops pricing strategies for both its online and offline channels using VoC data. Target analyzes customer perceptions of value using VoC data. Additionally, the business develops pricing strategies that complement its brand positioning using VoC data.


Next-generation Retail Pricing Strategies:


Retailers are constantly being provided with new tools and solutions by the rapidly changing retail technology landscape, which allows them to improve the overall customer experience and rethink their pricing strategies. Incorporating cutting-edge technologies like blockchain, AI, and machine learning can improve the efficacy of data-driven pricing in the fresh produce industry. With the help of AI-powered algorithms, retailers can proactively modify their pricing strategies to maximize sales and profitability by analyzing complex datasets, identifying pricing trends, and predicting customer preferences. Additionally, blockchain technology promotes trust and accountability between retailers and customers by providing increased security and transparency in pricing transactions. Retailers can gain a competitive advantage in the market and position themselves as leaders in the field of data-driven retail pricing by adopting these new technologies.


Examples:


Optimizing Dynamic Pricing for Perishable Goods with FreshMart: FreshMart, a well-known supermarket chain with a focus on fresh produce, used customer behavior analysis and expiration date data to drive its data-driven pricing strategy. FreshMart increased overall sales by 12% in just six months while reducing product waste by 30% through dynamic pricing adjustments based on customer demand patterns and product shelf life. The effective application of data-driven pricing increased customer loyalty and trust while also improving profitability.


GreenGrove - Improving Product Openness and Client Involvement: Premium organic food retailer GreenGrove used Image Classification and Food Hygiene Rating Datasets in their pricing and product display strategies. GreenGrove's customer base was made to feel authentic and trusted by giving them clear information about food safety ratings and displaying product images in an eye-catching manner. This strategy increased customer engagement by 20% and retention by 25%, demonstrating the beneficial effects of data-driven pricing on customer satisfaction and brand loyalty.


Conclusion:


With the ever-changing retail landscape, the conventional approach to retail pricing has been redefined by the integration of various data sources and state-of-the-art technologies. In the fresh produce industry, retailers must leverage data to optimize pricing strategies, reduce product waste, and improve customer engagement. By utilizing Expiration Date Data, Time-Temperature Datasets, Customer Behavior insights, and a host of other data sources, retailers can make well-informed pricing decisions that not only increase sales and profitability but also cultivate enduring customer loyalty.


About RapidPricer


RapidPricer helps automate pricing, promotions, and assortment for retailers. The company has capabilities in retail pricing, artificial intelligence, and deep learning to compute merchandising actions for real-time execution in a retail environment.


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