Discovery Meeting Transcript
Participants:
- Sarah Chen (Data Scientist)
- Michael Roberts (Chief Marketing Officer)
- Jennifer Hayes (Director of Marketing)
- David Kim (Marketing Manager)
Sarah: Thank you all for meeting today. I’d like to understand the business context and specific challenges around your email campaigns to ensure our data science project delivers meaningful value. Could you start by describing the current situation?
Michael (CMO): Our email marketing costs are eating up too much of our budget. We’re paying per email sent through our vendor, ExactTarget, but our response rates are low. We need to be more strategic about who we’re targeting and when.
Sarah: Could you help me quantify the current situation? What are your current response rates and costs?
David (Marketing Manager): Our average open rate is around 15%, and click-through rates hover around 2%. We’re paying $0.008 per email sent, and we’re sending about 500,000 emails per month across all campaigns. That’s $4,000 monthly just in email costs.
Sarah: What defines a successful email campaign for your team? What are your key performance indicators?
Jennifer (Director): Ultimate success is conversion to sale, but we also track open rates, click-through rates, and unsubscribe rates. We have different goals for different campaign types - promotional campaigns aim for immediate sales, while nurture campaigns focus on engagement metrics.
Sarah: Could you break down the different types of campaigns you run and their specific goals?
Jennifer: We have three main types: 1. Promotional campaigns for sales and special offers 2. Nurture campaigns for leads who’ve shown interest 3. Newsletter campaigns for general audience engagement
Sarah: What are your target improvement goals? What would success look like for this project?
Michael: We’d like to reduce our monthly email spend by 30% while maintaining or improving our current revenue from email campaigns. That means we need to be much more precise about who we target.
Sarah: Do you have any constraints or requirements I should be aware of? For example, regulatory requirements or technical limitations?
David: We need to comply with GDPR and CAN-SPAM Act. Also, our ESP (ExactTarget) has some technical limitations on segmentation and personalization. We can only use the fields available in their system.
Sarah: What data do you currently have available about your customers and their email interactions?
David: We have:
- Customer demographics and purchase history
- Email interaction data (opens, clicks, unsubscribes)
- Website behavior for logged-in users
- Campaign history and performance metrics
- Customer service interaction records
Sarah: Are there any specific time constraints or deadlines for this project?
Michael: We’re approaching our busy season in Q4. We’d like to have a solution implemented by the end of Q3 to maximize impact during the holiday season.
Sarah: What’s currently working well in your email campaigns that we should make sure to preserve?
Jennifer: Our segmented promotional campaigns perform better than broad campaigns. When we target based on previous purchase behavior, we see about double the conversion rate.
Sarah: Are there any past attempts at solving this problem that I should know about?
David: We tried basic RFM (Recency, Frequency, Monetary) segmentation last year. It helped somewhat but wasn’t sophisticated enough to give us the precision we need.
Sarah: Who are the key stakeholders for this project, and how should we keep them informed?
Michael: The marketing team here, plus our CFO will want to see the cost implications. Our technical team will need to be involved for implementation. Monthly updates to this group should work, with ad-hoc updates for significant findings.
Sarah: Let me summarize the key points to ensure I’ve understood correctly:
- Primary goal: 30% reduction in email costs while maintaining revenue
- Current spend: $4,000/month on 500,000 emails
- Current performance: 15% open rate, 2% CTR
- Key constraints: GDPR compliance, ESP technical limitations
- Timeline: Solution needed by end of Q3
- Success metrics: Cost reduction and maintained revenue
- Available data: Customer demographics, email interactions, purchase history, website behavior
Is there anything I’ve missed or misunderstood?
Jennifer: That’s accurate. One additional point is that we’d like to understand which customer segments are most valuable for different types of campaigns. This could help us with content strategy as well as targeting.
Sarah: Thank you, that’s helpful. Last question: What risks should we be aware of?
Michael: Our biggest concern is maintaining revenue while reducing email volume. We can’t afford to cut off communication with valuable customers. Also, our email vendor contract renews in Q4, so any insights could help with negotiations.
Sarah: Thank you all for your time. I’ll document these requirements and come back with a project plan that addresses these goals and constraints. Would you like to schedule a follow-up meeting to review the project plan?
Michael: Yes, please. Set something up for next week. And make sure to include any additional data requirements you identify — we want to make sure you have everything you need to succeed.
Assignment
Review the following business understanding transcript.
Create a business understanding document that summarizes your understanding of the business problem and provides a purely hypothetical solution. We aren’t looking for a realistic (or even workable solution). We want to understand the process of creating this document, what it contains and why it is important to the overall CRISP-DM process.
Hospital Readmissions CRISP-DM Discovery Interview
Date: January 15, 2025 Participants:
- Sarah Chen, Lead Data Scientist
- Dr. James Morris, Chief Medical Officer
- Linda Thompson, Director of Quality Management
- Robert Garcia, Chief Financial Officer
- Dr. Emily Wong, Director of Care Management
Sarah: Thank you all for meeting today. I understand reducing readmissions is a key priority. Could you help me understand how this fits into the hospital’s broader strategic goals?
Dr. Morris: Absolutely. Our hospital’s strategic plan has three main pillars:
- Improving patient outcomes while reducing total cost of care
- Expanding our integrated care network
- Achieving top-quartile quality metrics nationally
Readmissions directly impact all three. We’re currently in the bottom 40th percentile for readmission rates compared to peer institutions.
Robert: The financial impact is significant. Last year, we faced $3.8 million in Medicare penalties due to excess readmissions. Plus, with value-based care contracts now representing 35% of our revenue, readmission rates directly affect our reimbursements.
Sarah: Could you walk me through the current readmission landscape? What are the key metrics you’re tracking?
Linda: Our overall 30-day readmission rate is 18.2%. For Medicare patients, it’s higher at 22.1%. We’re particularly concerned about:
- Heart failure: 24.8% readmission rate
- COPD: 21.3%
- Total joint replacement: 12.4%
- Pneumonia: 17.9%
We’ve seen a troubling trend of increased readmissions in the past 18 months, particularly among elderly patients with multiple chronic conditions.
Sarah: What initiatives are already in place to address readmissions?
Dr. Wong: We implemented several programs:
- Post-discharge phone calls within 48 hours
- Medication reconciliation program
- Care transition nurses for high-risk patients
- Primary care follow-up scheduling before discharge
However, we’re struggling to identify which patients need what level of intervention. Our risk stratification process is largely manual and based on clinical judgment.
Sarah: What specific goals would you like to achieve through this analytics project?
Dr. Morris: Primary objectives are:
- Reduce overall 30-day readmission rate to 15% or lower within 12 months
- Achieve top quartile performance in CMS Hospital Readmissions Reduction Program
- Reduce readmission rates for heart failure patients to below 20%
Robert: From a financial perspective, we need to:
- Reduce Medicare penalties by at least 50%
- Improve performance-based payments in our value-based contracts
- Optimize resource allocation for post-discharge interventions
Sarah: What data do we currently have available?
Linda: We have several relevant data sources:
- EHR clinical data going back 5 years
- Claims data from major payers
- Risk assessment scores from our case management system
- Patient satisfaction surveys
- Social determinants of health screening data for about 60% of patients
- Medication adherence data from our pharmacy system
Sarah: Are there any data quality concerns I should be aware of?
Dr. Wong: Yes, several:
- Social determinants data collection only started 18 months ago
- Medication adherence data is incomplete for patients using external pharmacies
- Risk scores are inconsistently documented
- Some clinical notes are unstructured and vary in detail level
Sarah: How will we measure the success of this analytics project?
Linda: Key success metrics should include:
- Reduction in overall readmission rate
- Improvement in risk prediction accuracy compared to current methods
- Rate of appropriate intervention deployment based on risk levels
- Cost savings from prevented readmissions
- Staff satisfaction with risk prediction tools
Dr. Morris: We also need to ensure:
- The solution integrates with existing clinical workflows
- Predictions are available in real-time during admission
- Risk factors are explainable to clinical staff
- The system accounts for social determinants of health
Sarah: What constraints should I be aware of?
Robert: Several key considerations:
- IT resources are limited due to concurrent EHR upgrade
- Care management staff is already at capacity
- Budget for new intervention programs is capped at $500K for this fiscal year
- Any new processes need to work within existing staffing levels
Sarah: Based on our discussion, I’ll focus on:
- Developing a detailed project plan
- Conducting initial data quality assessment
- Creating a baseline model using existing data
- Designing a validation approach with clinical stakeholders
Dr. Morris: That sounds good. We should also plan regular check-ins with the clinical leadership team to ensure we’re staying aligned with operational needs.
Sarah: What potential risks should we plan for?
Linda: Key risks include: - Data integration challenges across systems - Staff resistance to new predictive tools - Maintaining model accuracy as patient populations change - Ensuring interventions are cost-effective
Robert: We need to show meaningful progress before the next Joint Commission review in 8 months. Can we set some interim milestones?
Sarah: Yes, I’ll propose a detailed timeline in the project plan, but preliminarily:
- Month 1-2: Data assessment and preparation
- Month 3-4: Initial model development and validation
- Month 5-6: Pilot testing and refinement
- Month 7-8: Full implementation and monitoring