# Complete Online Course Blueprint ## Product Analytics for Customer Success **Using data to reduce churn, identify expansion opportunities, and demonstrate ROI to enterprise clients** **Audience:** Mid-level B2B SaaS Customer Success Managers with 2–5 years of experience **Format:** Fully asynchronous, self-paced, cohort starts every 8 weeks **Platform:** Teachable + Circle.so community **Duration:** 6 weeks, 6 instructional modules, 4–5 hours per week **Design model:** Adult learning, applied practice, social learning, spaced retrieval, project-based assessment Platform note: Teachable currently states that it is **partially conformant with WCAG 2.1 AA**, so the course build should include additional creator-controlled accessibility safeguards such as human-reviewed captions, downloadable transcripts, accessible documents, keyboard testing, and alternative formats. ([Teachable][1]) Teachable supports video subtitles through auto-generation or manual upload of `.srt` / `.vtt` files, which should be used for every video asset. ([Teachable Destek Merkezi][2]) Teachable also supports Course Compliance settings for completion requirements and integrates with Circle through single sign-on so learners can access community spaces from Teachable. ([Teachable Destek Merkezi][3]) --- # Section 1 — Course Architecture ## 1.1 Course-Level Learning Objectives By the end of this course, learners will be able to: 1. **Explain** the role of product analytics in customer success, churn prevention, expansion, and ROI storytelling as demonstrated by a written analytics use-case map for their own customer portfolio. **Bloom’s level:** Understand 2. **Identify** the product usage, adoption, engagement, health, and business outcome metrics most relevant to enterprise customer success as demonstrated by a metric selection worksheet. **Bloom’s level:** Remember / Understand 3. **Apply** basic product analytics methods to interpret customer behavior patterns as demonstrated by analysis of a sample SaaS usage dataset. **Bloom’s level:** Apply 4. **Analyze** product usage signals to detect churn risk, adoption gaps, and expansion opportunities as demonstrated by a customer health diagnostic assignment. **Bloom’s level:** Analyze 5. **Evaluate** the quality and usefulness of customer success dashboards and metric frameworks as demonstrated by a dashboard critique and peer review. **Bloom’s level:** Evaluate 6. **Create** a data-informed customer success action plan that connects usage insights to stakeholder communication, intervention strategy, and ROI narrative as demonstrated by a capstone portfolio project. **Bloom’s level:** Create 7. **Communicate** product analytics findings to executive stakeholders in clear, non-technical language as demonstrated by a concise executive insight memo. **Bloom’s level:** Apply / Create --- ## 1.2 Module Breakdown ## Module 0 — Getting Started: Tools, Expectations, and Community Setup **Estimated time:** 1–1.5 hours **Purpose:** Orientation before Week 1. **Objectives** * Navigate Teachable and Circle successfully. * Confirm technology, accessibility, and participation expectations. * Introduce professional context and learning goals to the cohort. **Topics** * Course roadmap and weekly rhythm * Teachable navigation * Circle discussion norms * Accessibility and support options * Syllabus quiz * Introductory community thread **Prerequisite knowledge:** None. **Connection thread:** Prepares learners to focus on applied analytics rather than platform friction. --- ## Module 1 — Product Analytics Foundations for Customer Success **Estimated time:** 4–5 hours **Module objectives** * **Explain** how product analytics supports churn prevention, expansion, and ROI conversations. * **Differentiate** activity, adoption, engagement, outcome, and business-impact metrics. * **Map** one customer success motion to relevant product analytics signals. **Topics covered** * Why product analytics matters for modern CSMs * Difference between CRM data, support data, product data, and business outcome data * Lagging vs. leading indicators * Churn, retention, expansion, and ROI as analytics use cases * Common metric traps: vanity metrics, averages, missing segmentation * Customer success analytics maturity model **Prerequisite knowledge** * Familiarity with CSM responsibilities, customer lifecycle, renewals, QBRs, and CRM basics. **Connection thread** * Establishes the “why” and vocabulary. Module 2 turns this vocabulary into a practical metric framework. --- ## Module 2 — Choosing the Right Metrics and Building a Customer Health Logic **Estimated time:** 4–5 hours **Module objectives** * **Select** meaningful product and customer success metrics for a defined customer segment. * **Construct** a basic customer health logic using product usage, relationship, support, and business indicators. * **Explain** why different customer segments require different success signals. **Topics covered** * North Star metrics vs. operational metrics * Product usage depth, breadth, frequency, and stickiness * Feature adoption and maturity models * Account segmentation: enterprise, mid-market, high-touch, tech-touch * Health scores: benefits, risks, and false confidence * Metric definitions and data dictionaries **Prerequisite knowledge** * Module 1 vocabulary: product data types, leading indicators, churn and expansion use cases. **Connection thread** * Learners move from “what analytics can do” to “which signals matter.” Module 3 teaches how to interpret those signals. --- ## Module 3 — Reading Product Usage Data Without Being a Data Analyst **Estimated time:** 4–5 hours **Module objectives** * **Interpret** basic usage trends, cohorts, funnels, and adoption patterns. * **Analyze** a sample customer usage dataset for meaningful behavior changes. * **Distinguish** signal from noise when reviewing product analytics outputs. **Topics covered** * Trend analysis for CSMs * Cohorts and customer segments * Funnels and drop-off points * Adoption curves and usage thresholds * Outliers, seasonality, and data quality concerns * Asking better questions of data teams **Prerequisite knowledge** * Ability to identify relevant metrics and define customer health signals from Module 2. **Connection thread** * Learners now understand what to measure and how to interpret patterns. Module 4 applies those patterns to churn risk. --- ## Module 4 — Using Product Signals to Reduce Churn **Estimated time:** 4–5 hours **Module objectives** * **Analyze** product behavior patterns that may indicate churn risk. * **Prioritize** customer interventions based on risk level, customer value, and confidence in the signal. * **Create** a churn-risk action plan using data-informed reasoning. **Topics covered** * Early warning indicators of churn * Declining usage, stalled adoption, champion loss, and support escalation * Product-qualified risk vs. relationship-qualified risk * Intervention prioritization matrix * Save plays and adoption recovery plays * Communicating risk internally **Prerequisite knowledge** * Ability to read trends, funnels, and adoption patterns from Module 3. **Connection thread** * Learners practice defensive analytics: preventing loss. Module 5 turns the same analytics skill toward growth and ROI. --- ## Module 5 — Finding Expansion Opportunities and Demonstrating ROI **Estimated time:** 4–5 hours **Module objectives** * **Analyze** product usage data to identify expansion readiness. * **Evaluate** whether an account has credible ROI evidence. * **Create** an executive-facing insight memo that connects product adoption to business value. **Topics covered** * Expansion signals: seat saturation, feature maturity, cross-team usage, workflow dependency * Product-qualified expansion opportunities * ROI evidence: efficiency, cost avoidance, risk reduction, revenue enablement * QBR storytelling with data * Executive-ready narratives * Ethical boundaries: avoiding overstated claims **Prerequisite knowledge** * Ability to diagnose churn risk and interpret product signals. **Connection thread** * Learners now use analytics for both retention and growth. Module 6 synthesizes everything into a capstone. --- ## Module 6 — Capstone: Customer Success Analytics Action Plan **Estimated time:** 4–6 hours **Module objectives** * **Create** a complete product analytics plan for one customer or segment. * **Justify** metric choices, risk signals, expansion indicators, and recommended interventions. * **Communicate** findings through a concise executive summary and peer-reviewed presentation artifact. **Topics covered** * Capstone project assembly * Metric framework * Customer health diagnosis * Churn and expansion action plan * ROI narrative * Peer review and revision * 30-day implementation commitment **Prerequisite knowledge** * Completion of Modules 1–5. **Connection thread** * Synthesizes all skills into a workplace-ready artifact learners can adapt for their own role. --- ## 1.3 Course Navigation Map ```text Module 0: Getting Started │ ├── Required: Platform tour, syllabus quiz, intro thread └── Optional: Accessibility tools walkthrough ↓ Module 1: Product Analytics Foundations │ ├── Required Path: │ ├── Watch: Product Analytics for CSMs │ ├── Read: Metrics That Matter │ ├── Quiz 1 │ ├── Discussion 1 │ └── Assignment: Analytics Use-Case Map │ ├── Foundational Support Path: │ └── Mini-lesson: Data Terms for Non-Analysts │ └── Advanced Stretch Path: └── Critique a real CS dashboard example ↓ Module 2: Metrics and Health Logic │ ├── Required: │ ├── Metric selection worksheet │ ├── Health logic mini-case │ └── Discussion 2 │ ├── Foundational: │ └── Metric glossary practice │ └── Advanced: └── Design segment-specific health logic ↓ Module 3: Reading Usage Data │ ├── Required: │ ├── Dataset walkthrough │ ├── Quiz 3 with spaced retrieval │ ├── Assignment: Usage Pattern Analysis │ └── Peer calibration exercise │ ├── Foundational: │ └── Spreadsheet basics refresher │ └── Advanced: └── Optional cohort/funnel interpretation challenge ↓ Module 4: Churn Risk Analytics │ ├── Required: │ ├── Branching churn-risk scenario │ ├── Discussion 4 │ └── Assignment: Churn-Risk Action Plan │ └── Advanced: └── Build a risk prioritization matrix for your own book of business ↓ Module 5: Expansion and ROI │ ├── Required: │ ├── Expansion signal case │ ├── ROI memo practice │ ├── Peer review │ └── Discussion 5 │ └── Advanced: └── Executive QBR storyline challenge ↓ Module 6: Capstone │ ├── Required: │ ├── Capstone draft │ ├── Peer review │ ├── Final submission │ └── Celebration thread │ └── Optional: └── Portfolio polish checklist ``` --- # Section 2 — Multimedia Integration Plan ## 2.1 Content-Type Matrix | Module # | Video Type | Video Length | Audio Notes | Interactive Element | Static Asset | Estimated Production Effort | | -------- | -------------------------------------------------------------------------- | -------------------------------------------: | -------------------------------------------------------------------- | -------------------------------------------------------- | -------------------------------------------------- | --------------------------- | | 0 | Screencast / demo walkthrough | 8–10 min | Calm navigation narration; define where to get help | Embedded knowledge check quiz | Course roadmap PDF, tech checklist | Low | | 1 | Talking-head lecture + animated explainer | 2 videos, 8–12 min each | Instructor credibility; explain why analytics matters to CSM careers | Drag-and-drop matching: metric type to CS use case | Analytics maturity model infographic | Medium | | 2 | Animated explainer + screencast | 2 videos, 10–12 min each | Emphasize “metrics serve decisions” | Concept mapping / mind map tool | Metric dictionary template, health score worksheet | Medium | | 3 | Screencast / demo walkthrough | 3 videos, 8–10 min each | Think-aloud interpretation of usage data | Virtual lab / simulation embed using sample SaaS dataset | Dataset guide, spreadsheet quick reference | High | | 4 | Scenario-based micro-video | 4 clips, 3–5 min each | Branching case: CSM chooses intervention | Branching scenario / case study simulation | Churn signal prioritization matrix | High | | 5 | Interview / expert panel + screencast | 1 expert video, 15 min; 1 demo, 8 min | Enterprise CS leader explains ROI storytelling | Collaborative annotation of QBR excerpt | Executive insight memo template | Medium–High | | 6 | Talking-head capstone briefing + learner-generated video response optional | 8 min briefing; optional 3-min learner video | Encourage synthesis and workplace transfer | Peer review workflow + capstone checklist | Capstone brief, rubric, portfolio template | Medium | --- ## 2.2 Multimedia Accessibility Checklist ## Video For every video: * { } Closed captions added using Teachable subtitle upload or auto-generation followed by human review. Teachable supports auto-generated subtitles and manual `.srt` / `.vtt` upload. ([Teachable Destek Merkezi][2]) * { } Captions reviewed to 99%+ accuracy, including product names, acronyms, and speaker identification. * { } Transcript published directly below the video lesson. * { } Descriptive audio or text-based visual description provided for videos with meaningful on-screen action. * { } No flickering or flashing content. * { } On-screen text contrast minimum 4.5:1. * { } Captions use high-contrast background and minimum 18pt equivalent. * { } Video player controls tested with keyboard: play, pause, seek, volume, captions, full screen. * { } Learners can download transcript and slide notes. * { } No instruction relies solely on “look at the red line” or “click the box on the right.” ## Images and Infographics * { } Every non-decorative image has concise alt text, ideally under 125 characters. * { } Complex diagrams include long descriptions below the image. * { } Decorative graphics use empty alt text or are hidden from assistive technology. * { } Color is never the only indicator of meaning. * { } Icons are paired with text labels. * { } Infographics are also available as structured text. * { } Charts include titles, axis labels, units, and key takeaway statements. ## Documents and PDFs * { } All PDFs are tagged and tested for logical reading order. * { } Heading hierarchy follows H1 → H2 → H3. * { } Tables use marked header cells. * { } Links are descriptive. * { } Body text contrast is at least 4.5:1. * { } Documents are also provided in editable `.docx` or HTML format when possible. * { } Worksheets have labeled form fields. * { } File names are descriptive, for example `Module-2-Customer-Health-Worksheet.pdf`. ## Interactive Elements * { } All interactions are keyboard accessible. * { } Visible focus indicators are present. * { } Error messages are descriptive and announced to screen readers where technically possible. * { } Time limits are removable or extendable. * { } No autoplay media. * { } Drag-and-drop activities have non-drag alternatives, such as dropdown or checklist formats. * { } Branching scenarios include text transcripts and a linear alternative path. * { } Embedded tools are tested with keyboard and screen reader before launch. ## Platform-Specific Accessibility Notes: Teachable + Circle **Teachable** * Teachable’s own accessibility statement says the platform is partially conformant with WCAG 2.1 AA, so the course team should not assume full platform-level compliance. Creator-controlled content must be remediated proactively. ([Teachable][1]) * Teachable supports video subtitles through auto-generation and manual subtitle file upload, so all videos should include reviewed captions and transcripts. ([Teachable Destek Merkezi][2]) * Use Teachable Course Compliance to require lesson completion, video completion where appropriate, and quiz completion before moving forward. ([Teachable Destek Merkezi][3]) * Keep custom HTML embeds minimal. When embedding simulations, test keyboard access, focus order, mobile responsiveness, and screen reader behavior. **Circle.so** * Teachable supports Circle integration via single sign-on, allowing students to access Circle spaces from the Teachable environment. ([Teachable Destek Merkezi][4]) * Circle spaces should be organized by module, with one required discussion thread per week to reduce navigation complexity. * For accessibility, avoid requiring image-only responses; every image upload should include a text explanation. * Provide a discussion participation alternative by email or LMS submission for learners who experience access barriers. --- # Section 3 — Asynchronous Discussion Facilitation ## 3.1 Discussion Architecture by Module General rule for every module: * Initial post: 200–300 words by Day 3 * Peer replies: minimum 2 substantive replies by Day 6 * Instructor response: Day 4 * Rubric: Content Depth 40%, Engagement with Peers 30%, Writing Quality 20%, Timeliness 10% --- ## Module 1 Discussion — “What Should CSMs Measure?” **Scenario / stimulus** A VP of Customer Success says: “Our team tracks logins, NPS, and renewal dates. That should be enough to understand account health.” **Primary prompt** 1. What is useful about these metrics, and what might they miss? 2. What product behavior would you want to see before calling an enterprise account “healthy”? 3. Share one customer situation where better product data would have changed the CS conversation. **Extension prompt** Design a better three-metric starter dashboard for this VP and explain why each metric matters. **Instructor Day 4 facilitation script** “I’m seeing a strong theme: many of you are distinguishing between activity and value. Let me challenge that further: when does high usage *not* mean health? Reply to one peer with a situation where usage could be misleading.” --- ## Module 2 Discussion — “Designing a Health Score You Can Trust” **Scenario / stimulus** A SaaS company gives every account a green, yellow, or red health score. The score is 50% login frequency, 25% support tickets, and 25% CSM sentiment. **Primary prompt** 1. What assumptions does this health score make? 2. Which customer segments might it work for, and where might it fail? 3. What would you add, remove, or weight differently? **Extension prompt** Create a segment-specific health score for high-touch enterprise accounts. **Instructor Day 4 facilitation script** “Several posts point out that the same metric can mean different things by segment. Consider this: should support tickets increase or decrease health? Build a case for both interpretations.” --- ## Module 3 Discussion — “Signal or Noise?” **Scenario / stimulus** A customer’s weekly active users dropped 35% over the last two weeks. The account is up for renewal in 90 days. **Primary prompt** 1. What questions would you ask before escalating this as churn risk? 2. What additional data would you want? 3. What would you say to the account owner or customer contact? **Extension prompt** Draft a Slack message to your internal account team that communicates concern without overclaiming. **Instructor Day 4 facilitation script** “I’m noticing careful thinking around seasonality and data quality. Let’s add urgency: renewal is 90 days away. What is the minimum evidence you need before acting?” --- ## Module 4 Discussion — “Prioritizing Churn Interventions” **Scenario / stimulus** You have three risky accounts: * Account A: high ARR, executive sponsor left, usage stable * Account B: medium ARR, usage down 45%, admin still engaged * Account C: low ARR, support complaints increasing, usage low since onboarding **Primary prompt** 1. Which account would you prioritize first and why? 2. What intervention would you choose? 3. What evidence would change your decision? **Extension prompt** Build a 2x2 prioritization matrix using risk confidence and business impact. **Instructor Day 4 facilitation script** “There is no single correct prioritization here. The strongest posts are naming both risk and confidence. Push each other: what hidden bias might influence which account gets attention?” --- ## Module 5 Discussion — “When Is an Account Ready for Expansion?” **Scenario / stimulus** An enterprise customer has 80% seat utilization, two teams using advanced features, and strong admin engagement. However, the executive sponsor has not attended the last two QBRs. **Primary prompt** 1. Is this an expansion opportunity? Why or why not? 2. What product data would strengthen the case? 3. How would you frame the ROI story for an executive audience? **Extension prompt** Write a 100-word executive insight paragraph that links adoption to value. **Instructor Day 4 facilitation script** “Many of you are separating product readiness from stakeholder readiness. That distinction is critical. Reply to one peer with one question you would ask before involving Sales.” --- ## Module 6 Discussion — “From Insight to Action” **Scenario / stimulus** You have completed your capstone analytics action plan. **Primary prompt** 1. What is the most important insight from your plan? 2. What action would you take in the next 30 days? 3. What risk or assumption should your team watch? **Extension prompt** Record a 3-minute video summary as if you were presenting your recommendation to your CS leader. **Instructor Day 4 facilitation script** “I’m seeing practical, workplace-ready plans. For the final stretch, help each other sharpen actionability: reply to a peer with one suggestion that would make their next step more specific, measurable, or stakeholder-ready.” --- ## 3.2 Community-Building Discussion Strategies ## Ritual 1 — Week 1 Introduction Thread: “Your Customer Success Data Moment” **Prompt** Post a short introduction that includes: 1. Your role, company type, and customer segment. 2. One moment when you wished you had better product or customer data. 3. One skill you want to build in this course. 4. Upload a workspace photo, dashboard screenshot with sensitive data removed, book on your desk, or other visual that represents how you work. Include a short text description of the image for accessibility. **Peer response requirement** Reply to two peers with either a shared challenge, a useful resource, or one clarifying question. --- ## Ritual 2 — Week 4 Mid-Course Check-In: “What’s Clicking, What’s Still Fuzzy?” **Prompt** * One idea that is clicking for me is… * One thing I am still confused about is… * One strategy I’m using to stay on track is… * One question I want the instructor or cohort to help me think through is… **Instructor role** Respond personally to each post within 48 hours. Identify common blockers and post a short “Week 4 Patterns I’m Seeing” summary. --- ## Ritual 3 — End-of-Course Celebration Thread: “Class Wisdom” **Prompt** * My single most important takeaway is… * In the next 30 days, I will apply it by… * The advice I would give a future learner is… **Instructor role** Synthesize posts into a “Class Wisdom” summary organized by themes: churn prevention, expansion, ROI storytelling, and confidence with data. --- ## 3.3 Preventing Common Async Discussion Failures | Failure Mode | Mitigation Strategy | | ------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Surface-level responses | Require replies to include one of three moves: “build,” “challenge,” or “connect.” Provide sentence starters: “I would add…,” “A risk in that approach is…,” “This connects to my context because…” | | Ghost learners | Automated Day 2 nudge, personal Day 4 instructor check-in, and low-stakes re-entry message: “You can still join with one late post this week.” | | Thread monopolization | Set a norm of one initial post and two to three replies before Day 6. Instructor privately thanks dominant posters and invites them to ask questions rather than answer every thread. | | Last-minute bunching | Initial posts due Day 3. Teachable completion requirement unlocks the next assignment only after initial post submission. | | Instructor over-presence | Instructor posts one synthesis/challenge on Day 4 and then responds selectively. Avoid replying first to every learner. | | Discussion fatigue | One required discussion per module only. Optional extension prompts are clearly marked as stretch activities. | --- # Section 4 — Engagement Scaffolding System ## 4.1 Cognitive Engagement Scaffolds ## Advance Organizers ## Module 1 — What You’re About to Learn and Why It Matters Customer Success teams often have more data than they know how to use. This week, you will learn how product analytics can help CSMs move from reactive account management to proactive value leadership. You will connect familiar CS goals—retention, adoption, expansion, and ROI—to product behavior signals. The goal is not to become a data analyst. The goal is to become a stronger business partner who can ask better questions, interpret key signals, and use data to guide customer conversations. ## Module 2 — What You’re About to Learn and Why It Matters Not every metric deserves attention. This week, you will learn how to choose metrics that connect to customer outcomes and business decisions. You will examine product usage, adoption, engagement, support, and relationship indicators, then combine them into a simple health logic. By the end of the module, you should be able to explain why a metric matters, when it can mislead, and how it should influence a CSM’s next action. ## Module 3 — What You’re About to Learn and Why It Matters Many CSMs hesitate when they see charts, funnels, or spreadsheets. This week is designed to build confidence. You will practice reading product usage data without advanced math or analytics training. You will learn how to interpret trends, compare segments, spot drop-offs, and ask follow-up questions. The focus is practical judgment: what does the pattern suggest, how confident are we, and what should we investigate next? ## Module 4 — What You’re About to Learn and Why It Matters Churn rarely appears out of nowhere. Customers usually show signs before renewal risk becomes visible. This week, you will learn how to use product behavior signals to identify risk earlier and prioritize interventions. You will practice distinguishing weak signals from strong signals, balancing urgency with evidence, and creating action plans that help CSMs respond before the customer relationship is damaged. ## Module 5 — What You’re About to Learn and Why It Matters Product analytics is not only for churn prevention. It can also reveal where customers are ready to grow. This week, you will examine expansion signals such as feature maturity, seat saturation, cross-team usage, and workflow dependency. You will also practice connecting product adoption to ROI stories executives care about. The goal is to communicate value clearly without exaggerating what the data proves. ## Module 6 — What You’re About to Learn and Why It Matters This week brings everything together. You will create a customer success analytics action plan that includes metrics, risk signals, expansion indicators, interventions, and an ROI narrative. Your capstone should be useful beyond this course: a template you can adapt for your own customer portfolio, team meeting, QBR preparation, or career portfolio. You will also give and receive peer feedback to sharpen your final recommendation. --- ## Worked Examples ## Worked Example 1 — Building a Health Logic **Problem** A CSM manages enterprise customers for a project management SaaS product. The company wants to know which accounts are healthy. **Think-aloud narration** “First, I need to avoid choosing metrics just because they are easy to count. Logins are useful, but they may not prove value. I’ll start by asking: what does successful adoption look like for this product? For project management software, healthy customers probably have multiple teams creating projects, recurring task activity, collaboration across roles, and executive reporting.” **Step-by-step** 1. Define the customer outcome: teams complete projects faster with fewer missed deadlines. 2. Select adoption metrics: active projects, active users by role, recurring weekly activity. 3. Select depth metrics: use of dependencies, templates, automations, reporting. 4. Select relationship indicators: sponsor engagement, QBR attendance, open success plan items. 5. Select risk modifiers: unresolved support issues, recent admin turnover, renewal date proximity. 6. Write health logic: “Healthy enterprise accounts show consistent weekly usage across at least two teams, adoption of two or more advanced workflow features, active admin engagement, and no unresolved critical support issues.” --- ## Worked Example 2 — Diagnosing Churn Risk from Usage Decline **Problem** An account’s active users dropped from 120 to 72 over four weeks. **Think-aloud narration** “My first reaction might be panic, but I need context. Is this seasonal? Did the customer complete a project? Was there a data tracking change? Is the drop concentrated in one team or across the whole account?” **Step-by-step** 1. Confirm the data: check whether tracking changed. 2. Segment the drop: team, role, region, feature, or license type. 3. Compare to history: is this unusual for this customer? 4. Look for related signals: support tickets, admin logins, champion engagement. 5. Assess urgency: renewal date, ARR, strategic importance. 6. Choose action: if signal is confirmed, schedule an adoption review with admin and champion. 7. Communicate carefully: “We noticed a recent usage shift and would like to understand whether this reflects a workflow change, team transition, or support need.” --- ## Worked Example 3 — Writing an ROI Insight **Problem** A customer has adopted automation features across three departments. **Think-aloud narration** “I should not claim ROI unless I can connect usage to business value. I need to describe the observed behavior, the likely business impact, and what evidence would strengthen the claim.” **Step-by-step** 1. Start with observed usage: “Automation usage increased from 18 to 74 workflows.” 2. Connect to workflow impact: “Three departments now use automated handoffs.” 3. Identify value hypothesis: “This likely reduces manual coordination and delays.” 4. Add evidence needed: “To quantify ROI, compare average cycle time before and after adoption.” 5. Write executive version: “Your teams have expanded automation from one department to three, suggesting the platform is becoming embedded in cross-functional operations. The next step is to quantify time saved by comparing cycle time before and after automation adoption.” --- ## Chunking Strategy * No reading block exceeds 2,500 words without an embedded activity. * Each module reading is divided into 3–5 sections of 600–1,000 words. * Every section ends with one of the following: * One-question reflection * “Choose the best metric” check * Mini scenario * Annotated example * Discussion seed question * Dense concepts use this sequence: 1. Plain-language explanation 2. Visual model 3. Worked example 4. Short practice 5. Job-context reflection --- ## Spaced Retrieval Quiz Schedule | Module | Quiz Includes | Question Types | | ------ | ------------------------------------------------------------------------- | ---------------------------------- | | 1 | Baseline quiz on metric types and CS use cases | Multiple choice, matching | | 2 | 3 new questions on metrics + 2 retrieval questions from Module 1 | Matching, scenario judgment | | 3 | 3 usage interpretation questions + 2 retrieval questions from Modules 1–2 | Chart interpretation, short answer | | 4 | 3 churn-risk questions + 2 retrieval questions from Modules 2–3 | Ranking, scenario analysis | | 5 | 3 expansion/ROI questions + 2 retrieval questions from Modules 3–4 | Memo critique, multiple select | | 6 | 5 cumulative capstone-readiness questions | Scenario diagnosis, short response | --- ## 4.2 Behavioral Engagement Scaffolds ## Weekly Action Plan Template ```text This week I will __________________________ by __________________________ using __________________________. I will know I succeeded when __________________________. The most likely obstacle is __________________________. My plan to handle that obstacle is __________________________. One person or resource I can ask for help is __________________________. ``` --- ## Progress Milestones | Milestone | Trigger | Recognition | | ----------------------------- | ------------------------------------------------ | --------------------------------------------------- | | Data-Ready Badge | Complete Module 0, syllabus quiz, and intro post | Automated badge + welcome comment | | Metric Builder Badge | Submit Module 2 metric framework | Instructor shout-out in weekly announcement | | Signal Detective Badge | Complete Module 3 usage analysis | Peer kudos prompt | | Churn Strategist Badge | Submit Module 4 churn-risk action plan | Badge + optional showcase | | Analytics Action Leader Badge | Submit final capstone | Completion certificate + celebration thread mention | --- ## Application Assignments ## Module 1 Assignment — Analytics Use-Case Map **Brief** Choose one customer success challenge from your role: churn prevention, adoption, expansion, onboarding, renewal, or QBR storytelling. Map the business question, relevant product signals, stakeholders, and action decisions. **Submission format** 1-page worksheet or 500-word response. **Sample excellent response** “My challenge is low adoption after onboarding for enterprise accounts. The business question is: which accounts are failing to form durable usage habits in the first 60 days? Relevant product signals include weekly active users, number of active teams, activation of core workflow features, and admin configuration completion. Stakeholders include the CSM, onboarding manager, admin, and executive sponsor. If signals show low breadth and low depth by Day 45, the action is a targeted adoption workshop focused on the customer’s highest-priority workflow.” --- ## Module 2 Assignment — Metric Selection and Health Logic **Brief** Create a metric framework for one customer segment. Include 5–7 metrics, definitions, rationale, and how each metric would influence action. **Submission format** Metric table + 250-word explanation. **Sample excellent response** “For enterprise customers, I would avoid a health score based only on login frequency. My framework includes active teams, core feature adoption, admin activity, executive engagement, unresolved critical tickets, renewal proximity, and business outcome evidence. These metrics work together because they show whether the product is embedded, supported, and tied to value.” --- ## Module 3 Assignment — Usage Pattern Analysis **Brief** Review a sample SaaS usage dataset and identify three meaningful patterns. For each pattern, state what it may mean, what additional data you need, and what action you recommend. **Submission format** Annotated spreadsheet or 750-word analysis memo. **Sample excellent response** “Pattern 1: Usage dropped 30% in the finance team but remained stable in operations. This suggests the issue may be team-specific rather than account-wide. I would check whether the finance admin changed roles or whether a workflow changed. Recommended action: contact the admin with a curious, non-alarmist message and offer a workflow review.” --- ## Module 4 Assignment — Churn-Risk Action Plan **Brief** Choose one risky account scenario. Diagnose the risk, assign confidence level, prioritize action, and draft an internal escalation message. **Submission format** Action plan template + internal message. **Sample excellent response** “This is a medium-high risk account because usage decline is broad, renewal is within 120 days, and the executive sponsor has disengaged. Confidence is moderate because we do not yet know whether usage decline is seasonal. My next action is to validate the data, ask the admin about workflow changes, and schedule an adoption recovery session.” --- ## Module 5 Assignment — Executive ROI Insight Memo **Brief** Write a one-page memo connecting product adoption to business value for an executive stakeholder. **Submission format** 500–700 word memo. **Sample excellent response** “Over the past quarter, your organization expanded automation usage from one team to four teams, and recurring workflow activity increased by 62%. This suggests the platform is becoming embedded in daily operations. The strongest ROI opportunity is to quantify cycle-time reduction in the approval workflow, where automation usage is highest.” --- ## Module 6 Assignment — Capstone Analytics Action Plan **Brief** Create a complete analytics action plan for a customer or segment. Include metric framework, usage interpretation, churn-risk diagnosis, expansion opportunity, ROI narrative, and 30-day action plan. **Submission format** Slide deck, memo, or worksheet portfolio. Optional 3-minute video summary. **Sample excellent response summary** A strong capstone clearly defines customer context, selects metrics tied to decisions, interprets usage patterns cautiously, recommends specific interventions, and communicates business value in executive-ready language. --- ## Peer Review Workflow **Calibration exercise** * Learners review a sample analytics memo. * They score it using the rubric. * Instructor provides model feedback so learners understand expectations. **Peer review protocol** 1. Read the submission once without commenting. 2. Identify the author’s main recommendation. 3. Use the rubric to score each criterion. 4. Leave at least three comments: * One strength * One question * One actionable suggestion 5. End with: “The one revision that would most improve this is…” **Sentence starters** * “Your strongest evidence is…” * “I was convinced by…” * “I need more context about…” * “One assumption to test is…” * “This would be more executive-ready if…” * “A clearer next action could be…” --- ## 4.3 Emotional Engagement Scaffolds ## Instructor Presence Plan | Week | Instructor Touchpoint | | ---- | ----------------------------------------------------------- | | 0 | Welcome announcement and platform orientation | | 1 | 3-minute welcome video + reply to intro posts | | 2 | Weekly synthesis post: “Metric patterns I’m seeing” | | 3 | Short encouragement post before data lab | | 4 | Mid-course video and personal replies to check-in | | 5 | Expert insight post on ROI storytelling | | 6 | Capstone encouragement, celebration thread, final synthesis | --- ## Week 1 Welcome Video Script “Welcome to Product Analytics for Customer Success. I’m glad you’re here. This course is designed for CSMs who want to use data more confidently without becoming full-time analysts. Over the next six weeks, you’ll learn how to choose better metrics, interpret product usage patterns, identify churn risk, spot expansion opportunities, and communicate ROI to enterprise stakeholders. You do not need a technical analytics background. What you do need is curiosity, a willingness to practice, and a real customer success challenge you want to improve. Start with Module 0, introduce yourself in Circle, and choose one work context you’ll use throughout the course.” --- ## Mid-Course Video Script Outline * Acknowledge Week 3–4 difficulty: “This is where the course gets more applied.” * Normalize confusion with data interpretation. * Reinforce that CSMs are not expected to be data scientists. * Highlight three common learner wins. * Preview churn-risk and ROI modules. * Invite learners to post one blocker in the mid-course thread. --- ## Final Message Template “Congratulations on completing the course. You now have a practical framework for using product analytics to improve customer success outcomes. Your capstone is more than an assignment; it is a workplace-ready artifact. In the next 30 days, choose one part of your plan to test with a real account, manager, or cross-functional partner. Thank you for the thoughtful discussions, peer feedback, and practical examples you brought to this cohort.” --- ## Learning Agreement This course is a practice space. You are not expected to know every analytics term or interpret every chart perfectly on the first try. Questions, uncertainty, and revision are part of the learning process. We will challenge ideas while respecting people. We will avoid sharing confidential customer data. We will give feedback that is specific, kind, and useful. We will assume positive intent and ask clarifying questions before judging. The goal is not to sound like a data scientist. The goal is to become a more confident, evidence-informed Customer Success professional. --- ## Motivation Architecture: Dropout-Risk Moments | Risk Moment | Why It Happens | Intervention | | ----------------------- | ---------------------------------------------------------- | ----------------------------------------------------------------------------------- | | Week 1 after enrollment | Learners feel busy and delay starting | Automated Day 2 welcome nudge + instructor comment on intro post | | Week 3 data lab | Learners with no analytics background may feel intimidated | “You don’t need to be a data analyst” video + optional spreadsheet refresher | | Week 5 before capstone | Learners may feel behind or overwhelmed | Capstone starter template + “minimum viable capstone” checklist + personal reminder | --- # Section 5 — Accessibility Compliance Audit ## 5.1 WCAG 2.1 AA Course-Level Checklist ## Perceivable | Requirement | Status | Risk / Note | | ------------------------------------------ | ------- | ----------------------------------------------- | | All non-text content has text alternatives | Planned | Must audit every infographic and screenshot | | Captions for prerecorded video | Planned | Use Teachable subtitle feature and human review | | Content can be presented in different ways | Partial | Provide HTML or DOCX alternatives to PDFs | | Minimum contrast ratios met | Planned | Test slide decks, PDFs, and embedded tools | ## Operable | Requirement | Status | Risk / Note | | ------------------------------------- | ------------------ | ----------------------------------------------------- | | All functionality keyboard accessible | Needs testing | Especially Circle, embeds, drag-and-drop, simulations | | No flashing content | Planned | Add media production rule | | Skip repeated navigation blocks | Platform-dependent | Teachable may limit creator control | | Clear page titles, focus order, links | Partial | Test custom pages and embedded tools | ## Understandable | Requirement | Status | Risk / Note | | ------------------------- | ------------------ | ---------------------------------------------------------------- | | Page language declared | Platform-dependent | Confirm in Teachable theme/settings | | Consistent navigation | Planned | Use identical module structure each week | | Input assistance | Partial | Ensure quizzes and worksheets have clear labels/errors | | Reading level appropriate | Planned | Target Flesch-Kincaid Grade 9–11 for professional adult learners | ## Robust | Requirement | Status | Risk / Note | | ---------------------------------- | ------------------------- | --------------------------------------------------- | | Compatible with assistive tech | Needs testing | Test with NVDA, VoiceOver, keyboard-only navigation | | ARIA roles for custom interactions | Needs vendor confirmation | Avoid inaccessible custom widgets | --- ## 5.2 UDL Alignment ## Multiple Means of Representation **Strengths** * Video, transcript, worksheet, infographic, dataset, discussion, and worked examples. * Complex concepts explained through plain language, visuals, and scenarios. **Gaps** * Infographics and dashboards may be inaccessible without long descriptions. * Data labs may disadvantage learners unfamiliar with spreadsheets. **Enhancements** * Provide text equivalents for every infographic. * Include spreadsheet refresher. * Offer narrated and written versions of dataset walkthroughs. --- ## Multiple Means of Action and Expression **Strengths** * Learners submit memos, worksheets, slide decks, or optional video summaries. * Capstone allows workplace personalization. **Gaps** * Peer review may privilege polished writing. * Video response could create inequity if treated as required. **Enhancements** * Keep learner video optional. * Grade reasoning and actionability over visual polish. * Allow accessible alternatives for every interactive task. --- ## Multiple Means of Engagement **Strengths** * Professional relevance, cohort rituals, badges, discussions, peer feedback. * Learners apply concepts to their own customer context. **Gaps** * Async courses can feel isolating. * Data anxiety may reduce persistence. **Enhancements** * Instructor weekly presence. * Mid-course metacognitive reflection. * Low-stakes practice before graded work. * “Minimum viable submission” guidance for overwhelmed learners. --- ## 5.3 Remediation Priority List | Remediation Item | WCAG Criterion | Priority | Estimated Effort | Recommended Solution | | ------------------------------------------------------ | -------------- | -------: | ---------------- | --------------------------------------------------------------- | | Human-reviewed captions and transcripts for all videos | 1.2.2, 1.2.3 | Critical | Medium | Upload reviewed `.srt` / `.vtt`; publish transcript below video | | Keyboard test all interactions and embeds | 2.1.1, 2.4.3 | Critical | Medium–High | Test Teachable lessons, Circle threads, quizzes, simulations | | Provide alternatives to drag-and-drop interactions | 2.1.1, 1.3.1 | High | Medium | Add dropdown, checklist, or text-based equivalent | | Tag all PDFs and provide DOCX/HTML alternatives | 1.3.1, 2.4.6 | High | Medium | Use tagged templates and accessibility checker | | Add long descriptions for complex infographics | 1.1.1 | High | Low–Medium | Place structured explanation below each image | | Verify color contrast in slides and worksheets | 1.4.3 | High | Low | Use contrast checker before publishing | | Make link text descriptive | 2.4.4 | Medium | Low | Replace “click here” with resource-specific labels | | Add plain-language instructions to assignments | 3.3.2 | Medium | Low | Use consistent assignment template | | Test mobile responsiveness | 1.4.10 | Medium | Medium | QA on desktop, tablet, and phone | --- # Section 6 — Platform Implementation Guide: Teachable + Circle ## 6.1 Course Setup Checklist ## Course Shell Configuration * Create course: **Product Analytics for Customer Success**. * Set visibility to private or published depending on launch stage. * Configure cohort start dates every 8 weeks. * Create enrollment tags by cohort, for example `PAC-2026-Cohort-01`. * Set course duration expectations in sales page and syllabus. * Configure completion certificate. * Add accessibility statement and support path to course welcome page. ## Module / Unit Structure Create these sections in Teachable: 1. Module 0 — Getting Started 2. Module 1 — Product Analytics Foundations 3. Module 2 — Metrics and Health Logic 4. Module 3 — Reading Usage Data 5. Module 4 — Churn Risk Analytics 6. Module 5 — Expansion and ROI 7. Module 6 — Capstone Each module uses consistent lesson order: 1. Start Here 2. Watch 3. Read 4. Practice 5. Discussion in Circle 6. Assignment 7. Retrieval Quiz 8. Wrap-Up Checklist ## Grade Book Configuration Suggested weights: * Knowledge checks: 10% * Discussions: 15% * Application assignments: 35% * Peer review: 10% * Capstone: 30% Set passing threshold: 70%. ## Communication Setup **Teachable** * Weekly announcement email. * Automated reminders for module opening, Day 3 discussion deadline, Day 6 reply deadline. * Completion nudges using Course Compliance. **Circle** * Create cohort-specific space. * Create module discussion spaces or topics: * `Start Here` * `Module 1 Discussion` * `Module 2 Discussion` * `Module 3 Discussion` * `Module 4 Discussion` * `Module 5 Discussion` * `Capstone Studio` * `Wins and Questions` * Pin community guidelines and accessibility note. Teachable supports Circle integration through SSO, so learners can move from the Teachable course into Circle spaces with reduced login friction. ([Teachable Destek Merkezi][4]) ## Third-Party Integrations | Tool | Purpose | Implementation | | -------------------------------- | ------------------------------------------------ | ------------------------------------------------------------------ | | Circle.so | Discussions and cohort community | Teachable Circle SSO integration | | Google Sheets or Airtable | Sample usage dataset lab | View-only link or embedded frame with accessible downloadable file | | Typeform / native Teachable quiz | Knowledge checks | Prefer native where possible for completion tracking | | Loom / Vimeo / Teachable video | Video delivery | Upload captions and transcripts | | Hypothesis or Perusall | Optional collaborative annotation | Use only if accessibility passes QA | | Zapier | Optional automation between Teachable and Circle | Trigger cohort space access and reminders | Teachable’s App Hub supports third-party integrations, including analytics and email tools, which can support course operations and learner communications. ([Teachable Destek Merkezi][5]) ## Accessibility Settings * Upload captions for every video. * Provide transcripts as lesson text and downloadable file. * Avoid image-only lesson content. * Use descriptive lesson titles. * Use consistent headings in lesson text. * Test all embedded tools with keyboard. * Provide non-embedded fallback links for every interactive tool. * Keep Circle discussions text-first and require descriptions for uploaded visuals. --- ## 6.2 Module 0: Getting Started ## Purpose Ensure learners can access the course, understand expectations, join the community, and complete basic technology checks before Module 1. ## Lessons 1. **Welcome to the Course** * 3-minute instructor welcome video * Transcript * Course promise and outcomes 2. **How This Course Works** * Weekly rhythm * Expected time commitment * Deadlines: Day 3 initial post, Day 6 replies * Instructor response window: 48 hours 3. **Technology Check** * Browser: current Chrome, Edge, Safari, or Firefox * Stable internet * Ability to view video and captions * Ability to download PDF/DOCX * Ability to open sample spreadsheet * Screen reader or keyboard navigation self-test if applicable 4. **Teachable Navigation Tour** * Course sidebar * Lesson completion buttons * Video controls * Downloads * Quizzes * Assignment submission * Certificate location 5. **Circle Community Tour** * Access via Teachable SSO * Find cohort space * Post and reply * Add image description * Mention instructor only when needed * Community norms 6. **Syllabus Quiz** * 5 questions: 1. When are initial discussion posts due? 2. How many peer replies are required? 3. What should you do if you fall behind? 4. What kind of customer data must not be shared? 5. Where do you go for platform support? 7. **Introduction Discussion** * Uses Week 1 icebreaker from Section 3.2. 8. **Support Paths** * Platform issues: Teachable support / course admin email * Circle access issues: community manager or admin * Content questions: Circle `Wins and Questions` thread * Accessibility accommodation: designated accessibility contact * Urgent account issue: email support with subject line `ACCESS ISSUE` --- ## 6.3 Instructor Dashboard and Analytics Guidance | Metric | Check Frequency | Warning Threshold | Intervention Action | | ------------------------ | --------------------- | ------------------------------- | -------------------------------------------------------------------------------- | | Module completion rate | Weekly | <70% by mid-module | Post reminder, identify stuck lesson, send targeted encouragement | | Discussion participation | Every 3 days | <60% initial post rate by Day 3 | Send cohort reminder and personal nudges to non-posters | | Grade distribution | After each assignment | >30% below 70% | Review instructions, post clarification, offer revision window | | Time-on-task per module | Weekly | <50% of estimated time | Check whether learners are skipping practice; send “how to use this module” note | | Login frequency | Daily | 3+ consecutive days absent | Send supportive re-entry email with next smallest action | | Quiz performance | Weekly | Average below 75% | Add explainer post or optional review activity | | Capstone draft progress | Weeks 5–6 | No draft by mid-Week 6 | Send minimum viable capstone checklist | --- # Section 7 — Assessment and Evaluation Framework ## 7.1 Assessment Blueprint | Assessment | Type | Module Alignment | Learning Objectives Assessed | Weight | Format | Submission Method | | ------------------------------ | ---------------------- | ---------------- | ---------------------------- | -----: | ----------------------------- | ------------------- | | Module knowledge checks | Formative / low-stakes | Modules 1–6 | LO 1–5 | 10% | 5-question quizzes | Teachable quiz | | Discussion participation | Ongoing | Modules 1–6 | LO 1, 4, 5, 7 | 15% | Initial posts + peer replies | Circle | | Analytics Use-Case Map | Application | Module 1 | LO 1 | 5% | Worksheet | Teachable upload | | Metric Framework | Application | Module 2 | LO 2, 5 | 10% | Table + explanation | Teachable upload | | Usage Pattern Analysis | Application | Module 3 | LO 3, 4 | 10% | Memo or annotated spreadsheet | Teachable upload | | Churn-Risk Action Plan | Application | Module 4 | LO 4, 7 | 10% | Action plan + message | Teachable upload | | ROI Insight Memo | Application | Module 5 | LO 5, 7 | 10% | Executive memo | Teachable upload | | Peer Review Exercise | Peer assessment | Modules 5–6 | LO 5, 7 | 10% | Rubric + comments | Circle or Teachable | | Capstone Analytics Action Plan | Summative | Module 6 | LO 1–7 | 30% | Portfolio, memo, or deck | Teachable upload | --- ## 7.2 Rubric Templates ## Rubric A — Application Assignments **Total: 100 points** | Criterion | Exceeds — 4 | Meets — 3 | Approaching — 2 | Below — 1 | Points | | --------------------------- | --------------------------------------------------------------------- | --------------------------------------------------- | ----------------------------------------------- | ---------------------------------- | -----: | | Relevance to CS Context | Uses a specific, realistic CS scenario with clear business stakes | Uses a relevant CS scenario | Scenario is vague or only partly relevant | Scenario is missing or unrealistic | 20 | | Metric / Evidence Selection | Selects strong, decision-oriented evidence and explains tradeoffs | Selects appropriate evidence and explains rationale | Uses some relevant evidence but lacks rationale | Uses vanity or unrelated metrics | 25 | | Analysis and Reasoning | Interprets evidence carefully, names assumptions, avoids overclaiming | Provides logical interpretation | Interpretation is incomplete or too general | Conclusions are unsupported | 25 | | Actionability | Recommends clear, practical next steps | Recommends reasonable next steps | Next steps are vague | No clear action | 20 | | Communication Quality | Clear, concise, professional, stakeholder-ready | Understandable and organized | Some clarity or organization issues | Difficult to follow | 10 | --- ## Rubric B — Discussion Participation **Total: 100 points** | Criterion | Exceeds | Meets | Approaching | Below | Points | | --------------------- | -------------------------------------------------------------- | ----------------------------------------- | ---------------------------------------- | ---------------------------------- | -----: | | Content Depth | Analyzes tradeoffs, uses examples, and connects to CS practice | Responds thoughtfully to all prompt parts | Mostly descriptive with limited analysis | Minimal or off-topic | 40 | | Engagement with Peers | Replies extend, challenge, or deepen peer ideas | Replies are relevant and substantive | Replies are polite but surface-level | Missing or non-substantive replies | 30 | | Writing Quality | Clear, concise, professional | Mostly clear | Some unclear points | Hard to understand | 20 | | Timeliness | Initial post by Day 3 and replies by Day 6 | One deadline missed by less than 24 hours | Late but completed | Missing or very late | 10 | --- ## Rubric C — Peer Review **Total: 100 points** | Criterion | Exceeds | Meets | Approaching | Below | Points | | ----------------- | ----------------------------------------------- | --------------------------- | ----------------------------------------- | --------------------------------- | -----: | | Rubric Accuracy | Scores align closely with criteria and evidence | Scores are mostly justified | Scores are inconsistent or underexplained | Scores are missing or arbitrary | 30 | | Specific Feedback | Gives precise, actionable comments | Gives useful comments | Comments are general | Comments are minimal or unhelpful | 35 | | Constructive Tone | Respectful, balanced, and growth-oriented | Respectful and appropriate | Somewhat blunt or vague | Dismissive or inappropriate | 20 | | Revision Support | Clearly identifies highest-value revision | Suggests a revision | Suggestion is vague | No revision guidance | 15 | --- ## Rubric D — Capstone Analytics Action Plan **Total: 100 points** | Criterion | Exceeds | Meets | Approaching | Below | Points | | -------------------------------- | ------------------------------------------------------------------------------ | ------------------------------------- | ----------------------------------------------- | ----------------------------- | -----: | | Customer / Segment Context | Clearly defines customer context, business stakes, and CS motion | Defines relevant context | Context is incomplete | Context missing | 15 | | Metric Framework | Metrics are decision-oriented, well-defined, and segment-appropriate | Metrics are appropriate and explained | Some metrics are weak or unclear | Metrics missing or misaligned | 20 | | Usage Analysis | Interprets patterns carefully, names assumptions, and identifies evidence gaps | Provides logical usage interpretation | Interpretation is surface-level | Unsupported conclusions | 20 | | Churn and Expansion Strategy | Balances risk, opportunity, priority, and action | Addresses both churn and expansion | Addresses one side more strongly than the other | Strategy unclear or missing | 20 | | ROI Narrative | Communicates value in executive-ready language without overclaiming | Clear business value story | Value story is vague | No ROI connection | 15 | | Presentation and Professionalism | Polished, concise, workplace-ready | Organized and understandable | Some organization issues | Difficult to follow | 10 | --- ## 7.3 Academic Integrity Strategy ## 1. Prevention * Use personalized assignments tied to the learner’s own customer context. * Require reflection on assumptions and evidence gaps. * Use sample datasets that require interpretation, not simple fact recall. * Require revision based on peer feedback. * Include a statement: “AI tools may be used for brainstorming or editing, but your analysis, customer context, and final recommendations must be your own.” ## 2. Detection * Compare submissions for generic, context-free responses. * Look for unsupported claims, invented metrics, or polished prose with weak reasoning. * Use Teachable submission history and timestamps. * Ask for a brief oral or written clarification when authenticity is uncertain. * Avoid relying solely on AI detection tools. ## 3. Response | Concern Level | Example | Response | | ------------- | ---------------------------------- | -------------------------------------------------------------------- | | Low | Overuse of generic AI phrasing | Ask learner to revise with personal context and evidence | | Moderate | Assignment lacks original analysis | Require resubmission and reflection on process | | High | Fabricated data or copied work | Follow institutional policy; grade penalty or failure for assignment | | Severe | Repeated violation | Escalate to program administrator | --- # Final Deliverables Summary ## Production Checklist by Role ## Instructional Designer * Final course map and module sequence * Learning objectives aligned to Bloom’s Taxonomy * Module lesson scripts and storyboards * Discussion prompts and facilitation guide * Assignment briefs and rubrics * Retrieval quiz bank * Peer review workflow * Accessibility design checklist * Module 0 orientation design * Instructor guide and communication templates * QA checklist ## Subject Matter Expert * Validate analytics concepts and terminology * Provide realistic CSM scenarios * Review metric framework examples * Create or validate sample SaaS dataset * Review churn and expansion case studies * Validate ROI memo examples * Record expert insights or approve scripts * Review capstone expectations ## Media Production Team * Record and edit videos * Produce captions and transcripts * Create animated explainers * Create screencasts and dataset walkthroughs * Design infographics and worksheets * Produce accessible slide templates * Export caption files and transcript files * Verify audio quality and visual contrast ## LMS Administrator * Build Teachable course shell * Configure modules, lessons, quizzes, and completion rules * Upload videos, captions, transcripts, and downloads * Configure gradebook * Set certificate rules * Configure cohort enrollment * Set up Circle integration and spaces * Configure announcement schedule * Test learner access and mobile experience ## Quality Assurance Reviewer * Review all content for broken links and missing files * Test captions, transcripts, and downloads * Run accessibility checks on PDFs, slides, and pages * Test keyboard navigation * Test screen reader experience on key pages * Confirm quiz scoring and gradebook weights * Confirm Circle discussion access * Review consistency of module structure * Complete full learner-path test before launch --- # Realistic Production Timeline **Team:** 3 people — Instructional Designer, SME, Media Producer **Capacity:** 20 total hours/week part-time **Recommended build time:** 10–12 weeks | Week | Focus | Outputs | | ---- | ------------------------------ | -------------------------------------------------------------- | | 1 | Discovery and design alignment | Final scope, learner personas, course outcomes | | 2 | Course architecture | Module outlines, assessment plan, discussion model | | 3 | Module 1–2 design | Scripts, readings, worksheets, quizzes | | 4 | Module 3–4 design | Dataset lab, churn scenario, assignments | | 5 | Module 5–6 design | ROI memo, capstone, peer review workflow | | 6 | SME review and revisions | Revised scripts, validated examples, final dataset | | 7 | Media production sprint 1 | Videos for Modules 0–2, captions started | | 8 | Media production sprint 2 | Videos for Modules 3–6, animations, screencasts | | 9 | LMS build | Teachable shell, Circle spaces, uploads, quizzes | | 10 | Accessibility remediation | Captions, transcripts, tagged PDFs, alt text, keyboard testing | | 11 | QA and pilot review | Full learner-path test, rubric test, discussion test | | 12 | Launch prep | Final fixes, instructor guide, cohort welcome messages | **Minimum viable production timeline:** 8 weeks if using simpler videos, fewer animations, and template-based worksheets. **Recommended quality timeline:** 12 weeks for a polished, accessible, production-ready course. [1]: https://www.teachable.com/legal/accessibility?utm_source=chatgpt.com "Accessibility Statement → Teachable" [2]: https://support.teachable.com/en/articles/11682456-video-subtitles?utm_source=chatgpt.com "Video Subtitles" [3]: https://support.teachable.com/en/articles/11682463-course-compliance?utm_source=chatgpt.com "Course Compliance" [4]: https://support.teachable.com/en/articles/11682607-circle-and-teachable?utm_source=chatgpt.com "Circle and Teachable" [5]: https://support.teachable.com/en/articles/11682584-use-third-party-apps-with-teachable?utm_source=chatgpt.com "Use third-party apps with Teachable"