Main Goal for This Project

Interns will apply data analytics tools and techniques to real-world real estate and marketing challenges, generating data-driven insights to support decision-making. This includes creating interactive dashboards, performing sales and customer analysis, and improving internal operations through digital tools and structured reporting systems.

Learning Objectives

• Apply Data Analytics Tools: Utilize platforms such as Python, SQL, Power BI, Tableau, and Excel for data cleaning, querying, and visualization.
• Solve Organizational Problems: Address operational inefficiencies, lead conversion challenges, and customer behavior gaps with strategic, data-informed recommendations.
• Demonstrate Ethical Data Practices: Work with sensitivity when handling CRM, client, and marketing data.
• Evaluate Broader Impact: Consider how real estate data analysis affects client service quality, operational success, and market competitiveness.
• Collaborate and Communicate Effectively: Work closely with the founder and stakeholders to communicate technical insights in a clear and actionable way.

🔧 Tasks & Responsibilities

• Analyze real estate market data, customer behavior, and transaction history.
• Clean, organize, and synthesize data from diverse sources such as CRM platforms, ad campaigns, and Google Analytics.
• Build dashboards in Excel, Power BI, or Tableau to visualize sales, marketing performance, and lead activity.
• Track KPIs like conversion rate, average sales cycle length, cost per lead, and customer lifetime value.
• Present findings during weekly check-ins, offering insight into trends and anomalies.
• Translate complex analyses into strategies to optimize marketing spend, client retention, and listing performance.

📦 Sample Projects

1. Lead Conversion Analysis

• Analyze CRM and website traffic data to trace user journeys, identify bottlenecks, and recommend improvements in sales funnel design.

1. Marketing Performance Dashboard

• Build a unified view of social media, Google Ads, and newsletter performance. Calculate cost per acquisition and optimize channel spend.

1. Real Estate Market Insights Report

• Explore listing, pricing, and inventory data across neighborhoods. Deliver monthly updates to guide pricing and targeting strategies.

1. Client Retention & Engagement Model

• Use engagement data to build a model predicting which clients are most likely to convert again or refer others.

1. Forecasting Project

• Develop basic predictive models for revenue or home sales based on historical data and economic indicators.

Capstone Framework & Deliverables

Project Overview & Objective Statement

Clearly define the business problem (e.g., low conversion, poor visibility into marketing ROI) and outline the data-driven objective for addressing it.

Data Summary & Preprocessing

• Data Description: CRM exports, listing databases, ad platform reports, website traffic logs.
• Cleaning & Preparation: Remove duplicates, unify formats, address missing values.
• Preprocessing Notes: Document how raw data was structured into usable formats.

Analysis or Modeling Output

Depending on the project:

• Lead scoring model or conversion analysis
• Market segmentation using clustering
• Sales forecasting model
• Interactive marketing dashboard
• Customer journey visualization

Insights Report

• Key Findings: Conversion drop-off points, most effective marketing channels, top-performing property types, or client types most likely to convert.
• Trends & Anomalies: Seasonal sales trends, outlier properties, unexpected user behavior.

Recommendations

Based on insights:

• Marketing: Reallocate budget to high-performing channels
• Sales: Focus on lead types or zip codes with higher conversion rates
• Client Service: Increase touchpoints for high-LTV clients
• Listings: Adjust pricing strategies based on local competition

Stakeholder Presentation

Prepare a final presentation summarizing the problem, process, findings, and recommendations. This should be presented clearly for a non-technical audience (e.g., sales teams, marketing coordinators).

Reflection or Learning Report

Document what you learned, challenges you faced, and how your approach evolved. Reflect on tools, ethical considerations, communication strategies, and problem-solving growth.

Skills You Will Apply or Develop

• Technical Skills: SQL, Python, Excel, Power BI/Tableau, forecasting, statistical modeling
• Data Practices: ETL, wrangling, visualization, segmentation
• Soft Skills: Communication, stakeholder presentation, project management, business acumen
• Domain Exposure: Real estate market dynamics, marketing performance, client behavior analytics

Ideal Candidate Profile

• Enrolled in a Master of Data Analytics or related graduate program
• Proficient in SQL and/or Python
• Strong skills in Excel and familiarity with BI tools (Power BI, Tableau)
• Strong organizational and analytical thinking
• Able to present data to non-technical stakeholders
• Interest in real estate or marketing analytics is a plus