BPM Analytics

Dynamic pricing for perishable goods: A data-driven digital transformation approach

In today’s digitally transformed market, businesses face new challenges and opportunities. One significant change affecting the perishable goods market involves pricing strategies, particularly in industries that deal with perishable goods, where time-sensitive sales play a crucial role. Dynamic pricing, driven by data and digital analytics, is emerging as a vital tool for maximising revenue and reducing waste.

The global retail sector is expected to exceed USD 30 trillion in 2024, a substantial rise from 2023 numbers. However, retail margins continue to swing between rather narrow margins and the most critical factor contributing to this is waste, especially waste in perishable goods. The United Nations Environment Programme's (UNEP) Food Waste Index Report 2024 indicates that last year, globally, over a billion tons of food were wasted, and over 12 per cent of this waste was attributed to grocery retailers.

Understanding dynamic pricing

Dynamic pricing is a strategy that involves setting flexible prices for products based on existing market demands, competitor pricing, customer behaviour and other variables. Implementing dynamic pricing is akin to adjusting prices on the fly in response to several factors. The goal is to optimise prices to match the product's perceived value at any given time, ultimately maximising revenue. For perishable goods, since the value diminishes as they near expiration, dynamic pricing allows businesses to adjust prices according to shelf life and fluctuations in demand.

Well-known examples of dynamic pricing can be seen in industries such as airlines, e-commerce and hotels, where price shifts happen based on demand and supply patterns. However, for perishable goods such as fresh food, flowers or pharmaceutical goods, dynamic pricing acquires additional complexity. Unlike non-perishable products, these goods must be sold within a narrow time period, or they lose value entirely, making real-time data and predictive analytics even more crucial.

The role of data in dynamic pricing

The success of dynamic pricing strategies for perishable goods relies on good and reliable data. Data sources may include customer buying habits, seasonal trends, historical sales data, competitor pricing, supply chain information and even weather changes. For instance, the demand for certain food items may spike because of weather changes, cultural and religious events, sports events and various other holidays. Capturing and analysing such data points enables retailers and suppliers to make real-time pricing adjustments to capitalise on these trends.

Implementing machine learning (ML) algorithms to analyse this data allows companies to predict demand patterns and price sensitivity. Advanced analytics can reveal insights such as the likelihood of a product selling within a certain time period, the optimal discount rate to encourage sales, and customer segments most likely to respond to price changes. By leveraging data, businesses can avoid significant losses from unsold products and enhance their pricing strategies with an accuracy that was earlier not easy to attain.

Benefits of dynamic pricing for perishable goods

1. Minimising waste: Traditional fixed or static pricing models often lead to unsold goods, which contribute to high levels of waste. By adopting dynamic pricing, businesses can adjust prices to encourage sales as certain goods approach their expiration dates. For example, a grocery store can discount dairy products as they near the end of their shelf life, and that can boost sales while reducing waste.
2. Optimising revenue: Instead of offering steep discounts in a last-ditch effort to sell old or ageing inventory, dynamic pricing allows companies to adjust prices gradually. Retailers can maximise revenue by lowering prices just enough to stimulate demand, rather than applying huge discounts that ultimately cut into profits.
3. Enhanced customer satisfaction: Customers also benefit from dynamic pricing, as they get to purchase goods at lower prices although the products are close to expiration. This fosters customer loyalty, particularly for budget-conscious shoppers who are drawn to discounted, high-quality products that they can use quickly.
4. Increased efficiency: Through predictive data analytics, companies can streamline their supply chain and inventory management processes. Dynamic pricing based on real-time data helps in better inventory turnover rates, and frees up space and capital for fresh, new high-demand products.
5. Improving competitive positioning: Real-time, data-driven pricing adjustments enable companies to respond quickly to market changes. This flexibility can help businesses maintain a competitive edge, especially in markets where other retailers may be slow to react to rapid changes in demand and supply.

Implementing a data-driven dynamic pricing system

To implement a successful data-driven dynamic pricing system, businesses need to integrate several key components:

1. Data collection and integration: Collecting data from diverse sources, such as POS systems, CRM software and external data like competitor prices, is important. Modern technologies like IoT sensors can also help by tracking the shelf life of goods in real time. Large retailers are known to collect
2. Advanced analytics and machine learning: ML algorithms are critical in analysing vast amounts of data to identify pricing patterns and forecast demand. These algorithms can process and evaluate hundreds of data points in real-time, making accurate pricing recommendations. For instance, an algorithm might identify that certain fruits sell more slowly during colder months, prompting automatic price adjustments based on seasonal availability.
3. Automated pricing tools: Many businesses are now using automated tools that can adjust prices without manual intervention. These tools, informed by data and artificial intelligence (AI), enable retailers to change prices based on real-time insights, minimising the need for constant human oversight and improving efficiency.
4. Customer segmentation and behaviour analysis: Understanding customer preferences and behaviour is vital for success. Dynamic pricing works best when it targets the right customers, especially those more likely to respond to discounted products. Segmenting customers based on purchasing frequency, price sensitivity and product preferences enables businesses to personalise their pricing strategies, creating a more tailored shopping experience. Demographics and geographical location are other factors that can influence customer segmentation.

Overcoming challenges in dynamic pricing for perishables

Dynamic pricing for perishable goods presents some unique challenges.

• Price changes must be made thoughtfully to avoid alienating customers who perceive frequent adjustments as unfair or confusing.
• Transparency is essential; businesses should consider displaying price change information or offering customers an explanation of the factors driving price adjustments.
• Another challenge lies in avoiding excessive discounts. While price reductions encourage sales, they must be balanced to protect profit margins. Overuse of discounts can train customers to wait for price cuts, reducing overall brand value.
• Additionally, managing data privacy is crucial. While data-driven pricing relies on customer information, businesses must comply with regulations like GDPR to protect consumer data.

The future of dynamic pricing in perishable goods

Dynamic pricing in perishable goods is likely to evolve as technology continues to advance. The use of AI, IoT and big data will only improve the process, making pricing systems more efficient and responsive to market changes. With further developments, retailers could even adjust prices at the individual level, offering personalised discounts to loyal customers or customising offers based on specific behaviours.

In conclusion, dynamic pricing for perishable goods is a powerful tool that, when implemented with a data-driven approach, provides retailers with the flexibility to optimise revenue, reduce waste and enhance customer satisfaction. Although there are challenges, technology and data analytics are smoothing the way for innovative solutions that enable smarter, more profitable pricing models in the perishable goods sector. As the digital transformation of retail continues, dynamic pricing will likely become a standard practice, benefiting businesses and consumers alike.

How can Infosys BPM help?

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