The future of Dynamic Pricing in eCommerce is AI-driven
AI-driven dynamic price optimization is fundamentally altering the way businesses approach pricing.
The Dynamic Pricing model is here to stay
Pricing remains the fastest and most efficient way of driving up margins for businesses. According to pricing expert Rafi Mohammad, “The beauty of focusing on pricing is its immediate effect.” And as one study highlighted, top performers across industries were twice as likely to increase prices dynamically.
Price is what you pay, value is what you get. - Warren Buffet
For businesses, having a sophisticated dynamic pricing strategy in place remains critical. It involves adjusting the cost of products or services with the aim of guaranteeing a purchase in the very first instance by displaying a price that provides the most value to consumers. To quote Alex Shartsis, “dynamic pricing is a problem only AI can solve.
The immediate effect of pricing on margins
Dynamic pricing optimization can improve overall margins, create better value propositions and provide consumers with bespoke pricing experiences. According to McKinsey, leveraging a dynamic pricing model helped their customers increase sales by up to 5%.
How a Dynamic Pricing strategy works
Implementing AI-driven pricing transformations through ML algorithms involves performing granular customer segmentation analytics through the use of cluster analysis tools.
Customer segmentation allows businesses to determine customer persona groups based on common characteristics and target them with precision by analyzing customer behavior and purchasing patterns alongside a host of other variables.
Perform granular customer segmentation and determine customer persona groups
ML pricing algorithms hold clear and distinct advantages for dynamic pricing in retail as they can factor up to sixty variables compared to the three that earlier rule-based algorithms could leverage. Some of the variables influencing pricing decisions are sales and transaction data, product master data, cost data, historical prices, marketing data, competitor data, time-oriented data, and region-specific data.
Modules of Dynamic pricing in retail
Different modules are required to manage complexity while dealing with the various product categories and market responses in retail dynamic pricing.
- Using long-tail module algorithms, dynamic pricing optimization can be made even on newer products with no-historical data.
- A multi-factor elasticity module can calculate the impact of price on demand while factoring in seasonality and cannibalization.
- As customers tend to remember the price of Key-Value Items (KVI’s), KVI modules are designed to manage consumer price perception.
- A competitive response module harnesses granular pricing data from competitors to give a real-time pricing advantage to businesses.
- Time-based pricing modules utilize historical data to perform pricing decisions based on seasonality, peak shopping hours, shipping options, and the expiry date of products.
- Adaptive multi-agent systems from the field of distributed AI utilize reinforcement learning algorithms such as Q-learning to set prices in a competitive marketplace by solving problems using an organizational framework.
E-commerce dynamic pricing
Remaining competitive within eCommerce, where pricing and inventory management are intrinsically bound together, entails leveraging an AI-driven dynamic pricing strategy to the fullest. According to a PwC report, 60% of shoppers base their purchasing decisions based on price.
Predictive analytics to perform price forecasting
With the advances in AI, dynamic price optimization has never proven to be more advantageous to implement. E-commerce enterprises can now tap into real-time data and use predictive analytics to perform price forecasting. Amazon made 2.5 million repricing decisions every single day, which was estimated to have helped Amazon increase profits by 25%.
Roadblocks to implementation
Perception of Unfairness, Price Wars, and Data Sparsity
A dynamic pricing tool, however, has its own downsides. One of the major drawbacks is the perception of personalized pricing as unfair to regular customers, which can come at the cost of brand loyalty. And often, the negative publicity incurred as a result of pricing gone wrong can have a lasting impact on the business’s reputation. The key to tackling this problem is by understanding consumer price sensibility and offering improved discounting to existing customers.
The moment you make a mistake in pricing, you’re eating into your reputation or your profits.- Katherine Paine
Retail dynamic pricing that is based on competitors’ responses without implementing guardrails risks creating a race-to-the-bottom scenario. The most accurate dynamic pricing model should have competitive guardrails built into it to prevent both undervaluing and overvaluing products.
Data sparsity is another factor that affects the algorithm’s ability to perform dynamic pricing optimization effectively. This scenario emerges where too little data is available to train ML models. A clustering algorithm can solve this by associating a new product with existing products to obtain a probable price segment.
Solve data sparsity using clustering algorithms
The feeling of having paid more than what a product or service is worth is a problem something only a nuanced AI can tackle. These nuances are the mainstay of training data that needs to be complete, error-free, and consistent of the kind guaranteed by Taskmonk.
The Taskmonk Advantage
One of the most direct drivers of accuracy in ML models is the data that is used to train them. Algorithms that leverage ML methods such as deep learning or reinforcement learning for more accurate results are only as good as the training data and the human-in-the-loop solutions. The absence of quality training data necessitates cumbersome processes like feature engineering and sparsity reduction to make lower quality data useful.
High-quality labeled data using human-in-the-loop solutions
The need for structured and untainted historical data is a prerequisite for the accuracy of ML models. With its expertise in the eCommerce industry, Taskmonk lets you maintain a competitive edge, allowing you to procure high-quality labeled data to create a dynamic pricing tool for your enterprise. A highly customizable labeler-centric platform like Taskmonk lets you harness the power of consumer data and perform intelligent retail dynamic pricing solutions to meet the challenges of evolving AI.