Test and learn approach while launching dynamic pricing

Test and learn approach while launching dynamic pricing
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Dynamic pricing, also known as surge pricing or demand pricing, is an approach where the price of a product or service fluctuates based on factors such as demand, supply, market conditions, or customer behavior. It aims to optimize pricing strategies in real-time to maximize revenue or achieve specific business goals. Implementing dynamic pricing can be challenging, so  T&L framework can be handy to ease the process.

History of the concept

The concept of dynamic pricing can be traced back to the airline industry in the 1980s. Airlines started implementing yield management systems to adjust ticket prices based on factors like seat availability, time until departure, and customer demand. This approach allowed airlines to optimize their revenue and maximize seat occupancy.

eCommerce and Online Retail bring new life to the concept - retailers began experimenting with dynamic pricing. Online marketplaces like Amazon used algorithms to adjust prices based on factors like competitor prices, customer browsing behavior, and demand patterns. This approach enabled retailers to remain competitive in a fast-changing online environment. Emergence of ride-sharing platforms like Uber and Lyft continue to utilize dynamic pricing and elevate it on the next level. These companies introduced surge pricing, where prices increase during periods of high demand or limited supply. This model allowed them to balance supply and demand while incentivizing drivers to meet increased customer needs. Hospitality and travel services are quickly embraced this tech as well.

How current tech state affect dynamic pricing?

The approach to dynamic pricing underwent a significant transformation with the advent of Machine Learning (ML) and Artificial Intelligence (AI) technologies. These advancements revolutionized the way businesses implement and optimize dynamic pricing strategies and open the gate for really big data based models.

ML and AI technologies have transformed dynamic pricing from a rule-based approach to a more sophisticated and adaptive process. They help to process and interpret diverse data sources such as customer behavior, market trends, competitor pricing, and external factors. Access to such data allowing businesses to make predictions about future demand and price elasticity, adjust behaviour accordingly and respond via changing market conditions in real-time.

Launching ML pricing – Test and Learn approach

Implementation and usage of ML pricing models in already existing businesses can be challenging, so we in Rexalto recommend considering Test & learn approach. T&L will help to systematize overall process, set up goals, metrics and set up fixed points to keep all the process according to estimated time and resources.

A few quick tips for easy T&L:

· Always keep in mind your business goals and metrics, handling to much data can be challenging sometimes, but we all here for revenue and superior customer experience.

· Gather relevant historical data on pricing, sales, customer behavior, market trends, and competitor pricing beforehand. This data will serve as the foundation for training and testing the dynamic pricing AI model to better suit your business goals.

· Run test on select markets or product line before cascading it on the whole business. Start with a manageable scope to assess the impact and effectiveness of the AI-driven pricing strategy.

· Establish a baseline by running the existing pricing strategy to serve as a point of comparison to measure the performance of the dynamic pricing AI.

· Monitor and analyze the performance of the AI-driven pricing strategy, comparing it with the baseline results. Collect feedback from sales teams, customers, and other stakeholders to identify any issues or opportunities for improvement.

· Iterate and Refine based on the insights gained from the test phase. Adjust pricing algorithms, input variables, or other parameters to optimize results. Continuously monitor the impact on key metrics and make data-driven adjustments as necessary.

· Scale once the dynamic pricing AI model has demonstrated positive results in the test markets or product lines, gradually expand its implementation across additional markets or product lines. Monitor performance closely during the scaling phase to ensure consistent and favorable outcomes.

Looks simple enough 😂 So AMPE is here to help.

AMPE, as an AI-powered dynamic pricing solution, can greatly enhance the test and learn process:

· leverages machine learning and AI technologies to analyze vast amounts of historical data. It can uncover patterns, trends, and correlations that may be difficult for a human analyst to identify.

· generate optimal pricing recommendations in real-time based on dynamic factors of your choosing. This allows product analysts to quickly test and assess different pricing strategies without the need for manual calculations or guesswork.

· learns and adapts to changing market dynamics and customer preferences. It can adjust pricing algorithms and strategies based on real-time data, enabling product analysts to optimize their test and learn process on the fly and stay responsive to market fluctuations.

And the most interesting part - AMPE can integrate with existing systems and pricing tools, making it easier for product analysts to incorporate the AI-powered solution into their test and learn process. This seamless integration ensures a smooth transition and minimizes disruption to existing workflows.

Visit site to learn more about AMPE possibilities.