Written By: Gargi Sarma
Decisions in the retail industry are frequently made with a certain amount of intuition, a skill for recognizing market trends and customer demands that are derived from experience and gut feeling. While there is no doubting the importance of intuition, the retail pricing environment is changing and necessitates a new outlook—a blending of innovation and tradition. Welcome to the era of retail pricing supported by analytics, where data-driven insights and retail intuition coexist harmoniously and promise a better, more successful future for your company.
Science vs Intuition
The two opposing strategies for determining prices for products are science-based pricing and intuition-based pricing. In order to establish the best price points, science-based pricing carefully examines market data, competitive pricing, previous sales trends, and mathematical models. This strategy places a high value on objectivity and seeks to maximize profitability by coordinating prices with market forces and customer trends.
However, intuition-based pricing is based on the knowledge and expertise of business experts. It entails setting prices based on intuition, specialist expertise, and a thorough comprehension of client preferences. This strategy may be quick and adaptable, but it may not always make the most of data-driven insights, which could result in missed revenue opportunities or price misalignments.
In reality, a blend of the two methods frequently produces the best outcomes. While intuition-based pricing allows for flexibility and adaptation to particular market conditions, science-based pricing offers a strong foundation. Setting prices that are competitive, lucrative, and appealing to their target market can be accomplished by firms by finding the correct balance between these two approaches. In the end, the decision between science and intuition in pricing depends on the particular setting, sector, and objectives of the company.
The Evolution of Retail Analytics
An avalanche of data regarding customer behavior, market dynamics, and product performance has arrived with the advent of the digital age. As a retailer, you are aware that one of the key elements affecting your bottom line is pricing. But with so many variables to take into account, it can be challenging to determine how to set pricing that is both profitable and competitive. Here, analytics can be useful. You may make more strategic selections that will boost sales and profitability by using data to guide your pricing decisions.
Following are some pointers for using analytics to develop a successful retail pricing strategy:
Category management: By finding top-performing categories and items, data analytics assists merchants in optimizing their product assortments. Retailers can decide which categories to extend, decrease, or launch by analyzing sales data, customer preferences, and market trends. Matching the product mix to consumer demand, ensures that sales and profitability will rise.
Promotion effectiveness: Retailers can utilize data analytics to evaluate the effectiveness of promotions and marketing activities. They can assess which tactics work best for increasing sales and luring customers by examining sales data before, during, and after promotions. This enables the optimization of promotional strategies to enhance ROI.
Price optimization: Data analytics is essential for determining the appropriate prices for goods. To choose the best price points, retailers can use historical sales data, rival pricing data, and market demand signals. In order to keep prices competitive and increase profitability, dynamic pricing algorithms can be used to change prices in real-time based on variables including demand, inventory levels, and competitor activity.
Competitor analysis: Data analytics enables merchants to track and assess the actions of their rivals. This entails monitoring competition prices, product selections, and client feedback. Retailers may make educated decisions regarding their own product offerings and price strategies, ensuring they remain competitive, by learning how competitors position themselves in the market.
Figure: Example of Competitive Price Analysis based on Market Share
Customer insights: Data analytics provide insightful information on the preferences and behavior of customers. Retailers can divide their consumer base into segments, examine buying trends, and pinpoint their most valuable clients. By using this data, product recommendations, marketing tactics, and pricing incentives can be tailored to particular customer categories, increasing client retention and loyalty.
Inventory control: For retailers, effective inventory control is essential. Accurate demand forecasting, cost-cutting, and stockout prevention are all made possible by data analytics. Predictive analytics is a tool that retailers can use to streamline their supply chain and make sure the proper products are accessible when they are needed.
Market trend analysis: Retailers may remain ahead of changing market trends by examining external data sources including social media trends, economic indicators, and industry publications. Decisions about product procurement, inventory management, and marketing tactics can be informed by this information.
Examples of how retailers are utilizing analytics to guide their pricing strategies are shown below:
Amazon: Amazon bases the prices of its products on a range of data and analytics. Customer demand is one of the most crucial variables that Amazon takes into account. To forecast the number of units of a product that will be sold at various price points, Amazon uses forecasting models. In order to gauge how sensitive consumer demand is to price fluctuations, Amazon also uses price elasticity.
Walmart: Walmart employs analytics in a variety of methods to determine product prices. Walmart employs analytics, among other things, to monitor and improve its cost structure. With the help of this data, Walmart is able to spot places where expenses can be cut, which it can then pass along to customers in the form of lower prices.
Target: Target uses analytics in a variety of ways to determine product prices. Target employs analytics to, among other things, categorize customers and comprehend their purchasing patterns. Target makes use of this data to determine prices that appeal to certain client groups. Target employs analytics to keep tabs on competitor pricing and make sure its rates are reasonable.
Overall, a pricing plan supported by analytics is a potent instrument that can aid businesses in maximizing their sales and market share. Retailers can consider a number of aspects, such as customer demand, competitive pricing, and cost structure, when setting prices by employing data and analytics. This can assist retailers in making better price selections that produce better outcomes.
For retailers looking to acquire a competitive edge, data analytics is a priceless tool. Retailers may make educated decisions that result in more sales, improved profitability, and a better understanding of their target market by utilizing data-driven insights in areas like competitor analysis, category management, promotions, and pricing. Since it enables companies to adapt and flourish in a market that is changing quickly, it is increasingly becoming a pillar of success in the retail sector.
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.