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WHAT ARE THE PREREQUISITES FOR ENROLLING IN THE WHARTON BUSINESS ANALYTICS CAPSTONE COURSE

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The Wharton Business Analytics Capstone course at the University of Pennsylvania is typically taken during a student’s final semester before graduating with their Bachelor of Science in Economics degree from Wharton. As the culminating course in Wharton’s Business Analytics concentration, the capstone aims to provide students hands-on experience in integrating the various business analytics skills and techniques they have learned throughout their prior coursework.

Given its advanced role in the business analytics curriculum, several prerequisites must be fulfilled before a student can enroll in the capstone course. Chief among these is the completion of the introductory and core business analytics classes. Students are required to have successfully finished the following four courses:

STAT 101 – Introduction to Statistics and Data Analysis
This entry-level course introduces students to core statistical concepts and methods used for business analytics. Key topics covered include probability distributions, statistical inference, regression analysis, and experimental design. Successful completion of STAT 101 demonstrates a student has obtained foundational statistical literacy.

OPIM 210 – Introduction to Marketing and Supply Chain Analytics
As a follow-up to STAT 101, OPIM 210 provides an overview of marketing and supply chain analytics applications. Students learn how to synthesize and analyze customer data, optimize inventory levels, and predict product demand using statistical techniques. Completing this course verifies students can apply statistics in business contexts.

OPIM 303 – Introduction to Analytics Modeling
OPIM 303 delves into predictive modeling methodologies central to business analytics such as logistic regression, decision trees, and time series forecasting. Students gain hands-on experience building models in R and interpreting results. Passing this class confirms a student’s proficiency with analytics modeling workflows.

OPIM 475 – Data Analysis and Prediction
The capstone’s direct prerequisite, OPIM 475 explores advanced analytics topics like unsupervised learning, recommender systems, and machine learning algorithms. Students apply their knowledge to a major semester-long business case requiring data wrangling, exploratory analysis, and model development. Passing this course demonstrates a student’s readiness for the capstone.

In addition to the core analytics course prerequisites, students must also have completed the associated lab sections that accompany STAT 101, OPIM 210, and OPIM 303. These half-credit labs give students supplementary practice implementing analytic methods in software like R, Python, SQL, and Tableau. Completing the labs ensures students have experience using analytics tools that will be heavily relied upon in the capstone.

To gain the full benefit of the project-focused capstone experience, students are recommended to have completed additional courses from Wharton’s business curriculum covering functions like finance, accounting, marketing, and operations. Exposure to these business domains helps students apply their analytics skills to solving real-world management problems. While no specific business courses beyond the core are mandatory, exposure is encouraged.

The culminating capstone course challenges students to integrate their business analytics training through a large team-based consulting project with a corporate partner. Students must also have senior standing, meaning they need to have accumulated at least 90 credits, to ensure sufficient time remains after the capstone to complete their degree. This senior standing prerequisite not only guarantees students’ availability to devote significant effort to the semester-long project but also verifies their general readiness to transition into industry upon graduation.

Once all the prerequisite coursework and senior standing are confirmed, student admission into the capstone is still not guaranteed, as spots are limited each semester to facilitate close faculty supervision of projects. Students must apply during the preceding semester by submitting their academic transcripts, resumes, and statements of interests. Admission is competitive based on prior academic performance in the core analytics classes. A minimum cumulative 3.3 GPA is also usually required to ensure students have demonstrated excellent analytical skills and problem-solving abilities.

To enroll in Wharton’s Business Analytics Capstone course, students must fulfill several prerequisites demonstrating their extensive training and high proficiency in the business analytics concentration. The core coursework requirements in statistics, predictive modeling, and data analysis provide theoretical foundations. Additional labs and business exposure offer practical tools and contexts. And senior standing verifies availability to fully engage in the multifaceted capstone consulting project experience. These comprehensive prerequisites ensure students enter the capstone well-equipped to excel and gain tremendous hands-on value from applying their analytics skills to solve real business problems.

WHAT ARE SOME EXAMPLES OF BUSINESS ANALYTICS CAPSTONE PROJECTS

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Customer churn prediction and prevention: For this project, you would analyze a company’s customer transaction and demographic data to build predictive models to identify customers who are most likely to cancel their services or accounts. The goal would be to predict churn with reasonable accuracy. You would then make recommendations on how to prevent churn, such as targeted marketing, incentives to stay, or improving customer service. Some key steps would involve data collection, data cleaning, EDA, feature engineering, model building using techniques like logistic regression, random forests, exploring different predictive variables and their impacts, and recommending a prevention strategy.

Customer segmentation: For a retail company, you could analyze past transaction and demographic data to group major customer types into meaningful segments based on their spending patterns, purchase behaviors, product preferences. Common clustering techniques used include k-Means clustering, hierarchical clustering etc. You would need to select appropriate variables, preprocess the data, find the optimal number of clusters, label and describe each segment, their characteristics and differences. Recommend a customized marketing strategy for each segment. For example, discounts, loyalty programs etc. targeted to each customer group.

predicting movie box office revenues: For a movie studio, collect data on variables like movie budget, genre, ratings, critics reviews, social media buzz, cast, director etc. for past movies. Build predictive models to forecast the box office revenues for upcoming movies based on similar independent variables. Models like multiple regression, decison trees can be used. Also analyze factors influencing success and failure. Recommend data-driven strategies for marketing budget planning and movie development decisions.

Market basket analysis for online retailers: Analyze past purchase transaction data to determine which products are frequently bought together. Identify affinity patterns using association rule mining techniques. Provide insights on related/complementary products to showcase together to increase average order value and cross-sell opportunities. Recommend new product bundles or packages for marketing based on the analysis. For instance, showing snacks together with beverages or batteries along with electronic devices.

Predicting customer churn for a telecom operator: Collect customer data like demographics, usage patterns, payment history, services subscribed, complaints etc. Build predictive models to identify customers who are most likely to switch operators in the next few months. Techniques like logistic regression, random forests can be employed. Understand driver attributes for churn like pricing plan dissatisfaction, network quality issues etc. Recommend targeted retention strategies like loyalty programs, bundled discounts, network upgrades in probable churn areas. Regularly rerun models on new data to catch drifting behavior over time.

Predicting risks of credit card/loan defaults: Partner with a bank to analyze past loan application and repayment data. Develop predictive models to assess the risk level associated with approving new applications. Consider applicant factors like income levels, existing debts, credit history, collateral etc. Recommend risk-based pricing, underwriting criteria refinement and loan rejection guidelines to optimize portfolio quality vs volume. Models like decision trees, neural networks can be used. Evaluate model performance on new data batches.

Sales forecasting for retail stores: Obtain point of sales, item attributes, store attributes, promotions, seasonal data for chains of outlets. Build forecasting models at item/product, store and aggregate chain levels using statistical/machine learning techniques. Recommend inventory replenishment strategies, optimize allocation of fast-moving vs slow-moving products. Suggest test promotion strategies based on predicted lift in sales. Evaluate accuracy and refine models over time as new data comes in.

Predicting tech support ticket volumes: For an IT company, analyze historical support tickets, system logs, downtimes, software release notes to identify patterns. Develop predictive models using time series/deep learning methods to forecast probable weekly/monthly ticket volumes segmented by type/priority. Recommend optimal staffing levels and training requirements based on the forecasts. Suggest process improvements and preventive actions based on driving factors identified. Regularly retrain models.

These are just some potential ideas to get started with for an analytics capstone project. The key is to find meaningful business problems where analytics can create value, obtain reliable structured or unstructured data, apply appropriate techniques to gain insights and make actionable recommendations backed by data and analysis. Regular evaluations on metric tracking and model performance over time is also important. With in-depth execution, any of these projects have potential to exceed 15,000 characters in the final report. Let me know if you need any clarifications or have additional questions.

HOW CAN STUDENTS ENSURE THAT THEIR FINTECH CAPSTONE PROJECTS ARE FOCUSED ON USER AND BUSINESS NEEDS

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Conduct user research to understand pain points and identify opportunities. Students should speak to potential target users through surveys, interviews, focus groups or usability tests to understand what problems are most pressing in their daily tasks or workflows. User research helps uncover unmet needs and pain points that a solution could address. It’s important to get input from multiple users with different backgrounds and perspectives to find common themes.

Perform competitive analysis and gap analysis. Students should research what existing solutions are currently available on the market and how those solutions are meeting or not meeting user needs. A gap analysis evaluates the strengths and weaknesses of competitors while also identifying white spaces of unmet needs. This allows students to design a solution that fills gaps rather than duplicating what already exists. It’s important for projects to provide unique value.

Develop personas. Based on user research findings, students can create user personas – fictional representations of the target users. Personas put a human face to abstract user groups and help students understand the motivations, frustrations and characteristics of different types of users. Well-developed personas keep the solution focused on empathizing with and solving problems for specific user types throughout the design and development process.

Understand the business model and value proposition. Students must clarify how their proposed solution would generate revenue and provide value for both users and the business. Questions to consider include: What problem is being solved? Who is the customer? What direct and indirect needs are being addressed? How will customers pay and what is in it for them? How will the business make money? How does the value proposition differ from competitors? Having well-defined business model helps ensure technical solutions are developed with commercialization and profitability in mind.

Create user journeys and flows. Students should map out the step-by-step process a user would take to accomplish tasks within the proposed solution. User journeys identify touchpoints, potential frustrations, and opportunities for improvement. Mapping the before-and-after workflows helps validate whether the solution will provide a seamless, efficient experience and achieve the desired outcomes for users. User journeys also give insight into how functionality and features should be prioritized or developed.

Build prototypes. Low to high fidelity prototypes allow users to interact with and provide feedback on early versions of the concept. paper prototyping, interactive prototypes, or wireframes give students a chance to test design ideas and learn where the design succeeds or fails in meeting user needs before significant development effort is expended. Iterative prototyping helps students incorporate user feedback to refine the solution design in a user-centered manner.

Conduct iterative user testing. Students should test prototype versions of the solution with target users to uncover usability issues, comprehension problems, and ensure tasks can be completed as expected. User testing early and often prevents larger reworks later and helps keep the student focused on designing for real user needs and behaviors. Each round of user research, prototyping and testing allows for ongoing refinement to the solution and business model based on learning what is most effective and valued by potential customers.

Consult with industry mentors. Seeking guidance from industry mentors – such as accomplished alumni, executives, or potential customers – gives students an outside perspective on whether their proposed solution aligns with market opportunities and realities. Consulting experienced professionals in the target domain helps validate business assumptions, get early customer interest and feedback, and ensures the technical vision considers practical implementation challenges. Mentor input helps reduce risk and strengthen customer-centric aspects of the solution design.

Present to target users. Students should organize a stakeholder presentation to demonstrate prototypes or concepts to potential target users and customer organizations. Presentations mimic real-world customer validation opportunities and allow students to observe user reactions firsthand and answer questions. Students gain valuable insights into how well non-technical audiences understand value propositions and whether interests are captured as intended. Stakeholder feedback during final validation is crucial for fine-tuning the pitch before capstone conclusions are drawn.

By conducting iterative user research, developing personas, mapping workflows, building prototypes, testing with users, consulting mentors and stakeholders, students can have high confidence their capstone projects address authentic needs that are important and valuable to its intended users and target organizations. This user-centered mindset is imperative for developing commercially-viable fintech solutions and ensures the technical work produces maximum impact and benefit outside of academic requirements. Targeting real-world problems leads to more compelling demonstrations of how technology can enhance financial services, processes and experiences.

WHAT ARE SOME EXAMPLES OF CAPSTONE PROJECTS IN SPECIFIC FIELDS LIKE ENGINEERING OR BUSINESS?

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Engineering Capstone Projects:

Mechanical Engineering: Design and build a prototype of a robotic arm – Students would have to learn mechanical design principles, apply physics concepts like torque and forces, design electrical circuits to control motors, and write code for the robotic arm functionality. They would produce technical documentation, conduct stress analysis, and demonstrate a working prototype.

Civil Engineering: Design and simulate a long span bridge structure – Students research different bridge types, select a design, conduct load and stress analysis using structural engineering software, optimize the design, produce construction plans, and present the virtual bridge model. Factors like material selection, sustainment of loads, minimizing costs are considered.

Electrical Engineering: Develop an IoT-based home automation system – Students develop circuits with sensors and microcontrollers, write code to detect triggers like motion/sound and automate functions like switching lights/appliances. They design apps for remote monitoring/control over wifi/bluetooth. Areas like embedded systems, device networking, and user interface design are applied.

Computer Engineering: Build an artificial intelligence chatbot – Students research natural language processing techniques, train machine learning models on conversation datasets, and develop a conversational agent that can understand commands and answer questions on chosen topics. Evaluation metrics consider accuracy, response relevance and coherency of replies.

Business Capstone Projects:

Management: Launch a startup business plan – Students ideate a product/service idea, conduct market research to validate customer needs, analyze competition, and develop a comprehensive 1-2 year startup business plan covering all functional areas. Financial projections, funding strategies, scalability plans and risk assessments are key components.

Marketing: Develop an integrated marketing campaign – Students select a brand, identify target segments, and plan a holistic 12 month campaign strategy across different channels like print, digital, events. Tactics may comprise branding, advertising, public relations, influencer marketing, promotions etc. Campaign effectiveness metrics are proposed.

Finance: Simulate investment portfolio and wealth management strategies – Students research asset classes, develop customized model portfolios using stocks, bonds, funds, allocate proportions to maximize returns for different risk profiles. Financial analysis tools, fundamental analysis, economic factors and portfolio rebalancing rules over time are applied.

Human Resource Management: Create an employee training and development program – Students identify competency gaps for selected jobs, design modular training content mapped to job roles using various tools, propose methods for ongoing skills assessments and professional growth opportunities. Implementation plan, schedules and feedback processes are outlined.

Healthcare Administration Capstone Projects:

Healthcare Management: Plan a hospital or clinic facility expansion – Starting with current capacity constraints, strategic objectives and demand forecasts, students develop blueprints of expanded infrastructure, estimate costs, propose financing options, and create project schedules and risk mitigation strategies for building, certifications and operations.

Public Health: Conduct a community health needs assessment and develop intervention strategies – Students define target communities, research their demographics, design health surveys, conduct primary data collection, analyze key health issues, rank needs by severity and economic impact. Evidence-based pilot programs addressing priority issues like access, chronic diseases, awareness etc are proposed.

Healthcare Informatics: Build an electronic health records system – Students research data privacy regulations, design secure database architecture and interface templates for various entities. Programmers implement modules for patient registration, provider and staff access, billing/payments, scheduling, medical charts, prescription management, analytics and reporting. Usability is emphasized.

This covers detailed examples of the types of extensive, real-world capstone projects implemented across different disciplines like engineering, business and healthcare to fulfill degree requirements. Capstones allow students to synthesize and apply skills/concepts gained, work on open-ended problems, and produce impactful outcomes assessed via demonstratable final deliverables, technical evaluation and oral defenses.

HOW DID YOU MEASURE THE BUSINESS IMPACT OF YOUR MODEL ON CUSTOMER RETENTION?

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Customer retention is one of the most important metrics for any business to track, as acquiring new customers can be far more expensive than keeping existing ones satisfied. With the development of our new AI-powered customer service model, one of our primary goals was to see if it could help improve retention rates compared to our previous non-AI systems.

To properly evaluate the model’s impact, we designed a controlled A/B test where half of our customer service interactions were randomly assigned to the AI model, while the other half continued with our old methods. This allowed us to directly compare retention between the two groups while keeping other variables consistent. We tracked retention over a 6 month period to account for both short and longer-term effects.

Some of the specific metrics we measured included:

Monthly churn rates – The percentage of customers who stopped engaging with our business in a given month. Tracking this over time let us see if churn decreased more for the AI group.

Repeat purchase rates – The percentage of past customers who made additional purchases. Higher repeat rates suggest stronger customer loyalty.

Net Promoter Score (NPS) – Customer satisfaction and likelihood to recommend scores provided insights into customer experience improvements.

Reasons for churn/cancellations – Qualitative feedback from customers who stopped helped uncover if the AI changed common complaint areas.

Customer effort score (CES) – A measure of how easy customers found it to get their needs met. Lower effort signals a better experience.

First call/message resolution rates – Did the AI help resolve more inquiries in the initial contact versus additional follow ups required?

Average handling time per inquiry – Faster resolutions free up capacity and improve perceived agent efficiency.

To analyze the results, we performed multivariate time series analysis to account for seasonality and other time based factors. We also conducted logistic and linear regressions to isolate the independent impact of the AI while controlling for things like customer demographics.

The initial results were very promising. Over the first 3 months, monthly churn for the AI group was 8% lower on average compared to the control. Repeat purchase rates also saw a small but statistically significant lift of 2-3% each month.

Qualitatively, customer feedback revealed the AI handled common questions more quickly and comprehensively. It could leverage its vast knowledge base to find answers the first agent may have missed. CES and first contact resolution rates mirrored this trend, coming in 10-15% better for AI-assisted inquiries.

After 6 months, the cumulative impact on retention was clear. The percentage of original AI customers who remained active clients was 5% higher than those in the control group. Extrapolating this to our full customer base, that translates to retaining hundreds of additional customers each month.

Some questions remained. We noticed the gap between the groups began to narrow after the initial 3 months. To better understand this, we analyzed individual customer longitudinal data. What we found was the initial AI “wow factor” started to wear off over repeated exposures. Customers became accustomed to the enhanced experience and it no longer stood out as much.

This reinforced the need to continuously update and enhance the AI model. By expanding its capabilities, personalizing responses more, and incorporating ongoing customer feedback, we could maintain that “newness” effect and keep customers surprised and delighted. It also highlighted how critical the human agents remained – they needed to leverage the insights from AI but still showcase empathy, problem solving skills, and personal touches to form lasting relationships.

In subsequent tests, we integrated the AI more deeply into our broader customer journey – from acquisition to ongoing support to advocacy. This yielded even greater retention gains of 7-10% after a year. The model was truly becoming a strategic asset able to understand customers holistically and enhance their end-to-end experience.

By carefully measuring key customer retention metrics through controlled experiments, we were able to definitively prove our AI model improved loyalty and decreased churn versus our past approaches. Some initial effects faded over time, but through continuous learning and smarter integration, the technology became a long term driver of higher retention, increased lifetime customer value, and overall business growth. Its impact far outweighed the investment required to deploy such a solution.