AI Pricing Tool: Developing Optimal Strategies
Inquiry Framework
Question Framework
Driving Question
The overarching question that guides the entire project.How can we design an AI-powered pricing tool that ethically optimizes revenue and profitability while adapting to market dynamics?Essential Questions
Supporting questions that break down major concepts.- How do different pricing strategies impact revenue and profitability?
- What data is needed to build an effective AI pricing model?
- How can AI algorithms be used to predict optimal prices?
- How do you evaluate the performance of a pricing model?
- What are the ethical considerations when using AI for pricing?
Standards & Learning Goals
Learning Goals
By the end of this project, students will be able to:- Understand the impact of different pricing strategies on revenue and profitability.
- Identify and collect the necessary data for building an AI pricing model.
- Develop AI algorithms to predict optimal prices.
- Evaluate the performance of a pricing model.
- Understand the ethical considerations when using AI for pricing.
- Design an AI-powered pricing tool that ethically optimizes revenue and profitability while adapting to market dynamics.
Entry Events
Events that will be used to introduce the project to studentsThe Virtual Marketplace
The class participates in a simulated online marketplace where they buy and sell virtual products. Initially, prices are set randomly, leading to chaos. Students then learn about and apply AI pricing strategies to optimize their profits in the marketplace. This gamified approach demonstrates the impact of pricing on market dynamics.The Pricing Negotiation
Students are divided into teams, each representing a different department (marketing, sales, finance) within a company. Each team has its own goals and biases regarding pricing. A negotiation simulation ensues, where they must use data and AI-driven insights to reach a pricing consensus that benefits the entire company. This activity emphasizes the collaborative nature of pricing decisions.The Desperate Plea
Students receive a cryptic email from a struggling local business owner, detailing plummeting sales and an inability to compete with larger companies. The email includes anonymized sales data and a plea for help, challenging students to use AI to analyze the data and develop a pricing strategy that could save the business.Pricing Catastrophes
Present students with examples of pricing failures from well-known companies (e.g., a product priced too low or too high, leading to losses). Ask them to analyze the factors that led to these failures and challenge them to develop an AI solution that could have prevented the missteps. This reverse-engineering approach highlights the importance of data-driven pricing.Portfolio Activities
Portfolio Activities
These activities progressively build towards your learning goals, with each submission contributing to the student's final portfolio.Pricing Strategy Explorer
Students research and analyze various pricing strategies (e.g., cost-plus, competitive, value-based, dynamic pricing). They will create a presentation that outlines the pros, cons, and impacts on revenue and profitability for each strategy.Steps
Here is some basic scaffolding to help students complete the activity.Final Product
What students will submit as the final product of the activityA presentation (PowerPoint, Google Slides, or Prezi) comparing different pricing strategies with a focus on their revenue and profitability implications.Alignment
How this activity aligns with the learning objectives & standardsLearning Goal: Understand the impact of different pricing strategies on revenue and profitability.Data Detective: Uncovering Pricing Clues
Students identify and collect relevant data types needed for an AI pricing model (e.g., historical sales data, competitor pricing, market trends, seasonality, customer demographics). They will create a data inventory document that details the data sources, types, and potential challenges in acquiring and cleaning the data.Steps
Here is some basic scaffolding to help students complete the activity.Final Product
What students will submit as the final product of the activityA data inventory document outlining the necessary data for an AI pricing model, including data sources, types, and challenges.Alignment
How this activity aligns with the learning objectives & standardsLearning Goal: Identify and collect the necessary data for building an AI pricing model.AI Price Predictor
Students will use a simplified AI tool or platform (e.g., Google AI Platform, Azure Machine Learning, or even a spreadsheet with advanced formulas) to build a basic pricing prediction model. They will input the collected data and experiment with different algorithms to predict optimal prices. This activity focuses on the practical application of AI in pricing.Steps
Here is some basic scaffolding to help students complete the activity.Final Product
What students will submit as the final product of the activityA basic AI pricing prediction model using a chosen platform, along with a report detailing the model's performance and limitations.Alignment
How this activity aligns with the learning objectives & standardsLearning Goal: Develop AI algorithms to predict optimal prices.Pricing Model Performance Analyst
Students learn and apply metrics to evaluate their pricing model (e.g. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or profitability lift). They will create a dashboard that shows the key performance indicators of their model, along with a reflection on how to improve the model's accuracy and effectiveness.Steps
Here is some basic scaffolding to help students complete the activity.Final Product
What students will submit as the final product of the activityA performance dashboard displaying key metrics (MAE, RMSE, profitability lift) and a reflection on model improvement strategies.Alignment
How this activity aligns with the learning objectives & standardsLearning Goal: Evaluate the performance of a pricing model.The Ethical AI Pricer
Students will explore ethical considerations related to AI pricing, such as price discrimination, transparency, and fairness. They will write a code of ethics for their AI pricing tool, ensuring it aligns with ethical principles and avoids potential biases.Steps
Here is some basic scaffolding to help students complete the activity.Final Product
What students will submit as the final product of the activityA code of ethics for their AI pricing tool, addressing price discrimination, transparency, and fairness.Alignment
How this activity aligns with the learning objectives & standardsLearning Goal: Understand the ethical considerations when using AI for pricing.AI Pricing Tool Architect
Students synthesize their learning to design a comprehensive AI-powered pricing tool. They will develop a user interface, integrate their AI model, incorporate ethical considerations, and create a presentation to showcase their final product.Steps
Here is some basic scaffolding to help students complete the activity.Final Product
What students will submit as the final product of the activityA presentation showcasing their AI-powered pricing tool, including the user interface, AI model, ethical considerations, and adaptation to market dynamics.Alignment
How this activity aligns with the learning objectives & standardsLearning Goal: Design an AI-powered pricing tool that ethically optimizes revenue and profitability while adapting to market dynamics.Rubric & Reflection
Portfolio Rubric
Grading criteria for assessing the overall project portfolioAI Pricing Tool Development Rubric
Understanding Pricing Strategies
Evaluates the student's understanding and analysis of various pricing strategies and their impact on revenue and profitability.Analysis of Strategies
Assesses the depth and accuracy of the student's analysis of different pricing strategies.
Exemplary
4 PointsProvides a comprehensive analysis of multiple pricing strategies with thorough consideration of their impacts on revenue and profitability, supported by detailed examples.
Proficient
3 PointsOffers a detailed analysis of several pricing strategies, correctly evaluating their impacts on revenue and profitability, supported by relevant examples.
Developing
2 PointsPresents a basic analysis of select pricing strategies, inconsistently evaluating their impacts on revenue and profitability, with limited examples.
Beginning
1 PointsAttempts an analysis of pricing strategies with minimal insight into their impacts on revenue and profitability, lacking supporting examples.
Data Identification and Collection
Assesses the ability to identify and gather relevant data necessary for building an AI pricing model.Data Inventory Quality
Evaluates the thoroughness and relevance of data sources identified and documented for AI model development.
Exemplary
4 PointsCreates an extensive and well-organized data inventory that identifies a variety of relevant data sources and thoroughly assesses potential challenges.
Proficient
3 PointsDevelops a detailed data inventory with a clear identification of key data sources and some assessment of potential challenges.
Developing
2 PointsPresents a basic data inventory with partial identification of necessary data sources and limited assessment of challenges.
Beginning
1 PointsLists minimal data sources with inadequate identification of necessary information and no assessment of challenges.
AI Model Development
Evaluates the creation and experimentation with AI algorithms to predict optimal prices.Algorithm Implementation and Performance Evaluation
Assesses the student's ability to implement AI algorithms and evaluate their performance.
Exemplary
4 PointsSkillfully applies multiple AI algorithms to develop a highly effective pricing model, thoroughly evaluating its performance with sophisticated metrics.
Proficient
3 PointsEffectively implements AI algorithms to create a functional pricing model and appropriately evaluates its performance using standard metrics.
Developing
2 PointsAttempts to use AI algorithms for a basic pricing model with inconsistent evaluation of its performance through limited metrics.
Beginning
1 PointsStruggles to apply AI algorithms effectively, with minimal evaluation of the model's performance.
Ethical Considerations and Implications
Evaluates understanding and integration of ethical considerations in AI pricing tools.Ethics Code Development
Assess students' ability to create an ethical code for their AI tool.
Exemplary
4 PointsDevelops a comprehensive and well-reasoned code of ethics that effectively addresses key ethical considerations in AI pricing.
Proficient
3 PointsCreates a detailed code of ethics that adequately addresses several key ethical issues in AI pricing.
Developing
2 PointsProvides a basic code of ethics with partial attention to ethical considerations in AI pricing.
Beginning
1 PointsIncludes minimal ethical considerations with limited understanding of their importance in AI pricing tools.
Design and Presentation of AI Tool
Assesses the design, integration, and presentation efforts of the AI-powered pricing tool.Tool Design and Integration
Evaluates the student's ability to design a user-friendly interface and integrate the AI model effectively.
Exemplary
4 PointsDesigns an innovative and user-centered interface, seamlessly integrating AI models and ethical considerations, showcased through a compelling presentation.
Proficient
3 PointsDesigns a functional interface with successful integration of the AI model and ethical considerations, presented effectively.
Developing
2 PointsCreates a basic interface with partial integration of AI models, with some ethical elements, in a presentation that outlines the core features.
Beginning
1 PointsPresents an incomplete interface with minimal AI model integration and ethical consideration, briefly outlined in presentation format.