# Retention Curve Analyzer MCP for AI Agents AI Agent Connect > Retention Curve Analyzer helps you see if your users are actually sticking around or just bouncing. It analyzes cohort retention sequences to tell you if your growth is healthy or if you're losing people too fast. You can check your D1 to D90 metrics against actual industry standards for SaaS, gaming, and e-commerce to see where you stand. It's designed for growth engineers who need to turn raw data into a clear status report on product stickiness. ## Overview - **Category:** product-management - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_ytt4XiNsbgtSylAxvrVj9SJRjpf3TqMNN7YDCZfU/ai-agent-connect - **Tags:** retention, cohorts, churn, benchmarks, analytics ## Description The Retention Curve Analyzer is a specialized tool for product managers and growth engineers who need to see the truth behind their user data. Stop guessing whether your user base is stabilizing or bleeding out. This tool gives you a clear look at how cohorts behave over time. Instead of staring at a spreadsheet of percentages, you can ask your agent to look at a sequence of numbers and tell you if the curve is flattening out or if it's in a tailspin. It's built for the kind of work that happens when you need to know if a new feature actually improved stickiness or just caused a temporary spike. You compare your actual performance against real-world benchmarks for specific industries like mobile gaming or e-commerce to see if you're hitting the right targets. When you're looking for a reliable way to manage these insights, Vinkius makes it easy to plug this directly into your workflow. It handles the heavy lifting of pattern recognition so you can focus on fixing the churn. You move past the gut feeling and start making decisions based on whether your users are actually finding value in the product. It looks at your D1, D7, and D30 numbers to tell you if you're on track to hit your long-term goals. ## Tools ### verify_benchmark_compliance Compares your D1, D7, D30, and D90 metrics against industry standards. Use this to see how you stack up against SaaS or gaming averages. ### analyze_retention_trend Tells you if your retention sequence is flattening or declining. Use this to see if user behavior is stabilizing over time. ### classify_curve_pattern Categorizes the specific shape of your retention curve. This helps you identify Smiling Curves versus high-churn patterns. ## Prompt Examples **Prompt:** ``` Is my retention curve flattening or declining? Here are the values: [100, 50, 30, 25, 22] ``` **Response:** ``` The retention curve is **Flattening**. The recent drop from 25 to 22 is minimal, indicating the curve has reached a plateau. ``` **Prompt:** ``` Check if my SaaS product meets D7 benchmarks. My D1 is 40% and D7 is 20%. ``` **Response:** ``` The compliance report shows that your **D7 metric failed** to meet the SaaS benchmark. ``` **Prompt:** ``` What pattern does this sequence represent: [100, 80, 60, 40, 20, 5]? ``` **Response:** ``` The pattern is classified as **Declining** because the sequence shows a continuous downward trend approaching zero. ``` ## Capabilities ### Identify flattening trends Determine if your user retention is reaching a stable plateau or continuing to drop. ### Classify curve patterns Categorize the specific shape of your cohort journey to spot healthy stickiness. ### Check industry compliance Compare your cohort metrics against hardcoded standards for SaaS, gaming, and e-commerce. ### Detect churn risks Spot declining sequences that indicate high risk of user loss in specific cohorts. ### Validate D7 and D30 metrics Get immediate feedback on whether your mid-term retention hits industry targets. ## Use Cases ### Validating a new onboarding flow A PM isn't sure if a new login flow worked. They give the agent 5 days of retention numbers and ask if the curve is flattening. ### Reporting to stakeholders A growth lead needs to report to the board. They use the tool to check if their D30 retention meets standard e-commerce benchmarks. ### Diagnosing a drop in active users A game dev sees a drop in active users and has the agent classify the curve pattern to see if it's a standard decline or a new churn risk. ### Checking SaaS health A SaaS founder wants to know their health and inputs their D1 and D7 metrics to see if they are hitting the healthy marks for their industry. ## Benefits - Stop guessing about churn. Use `analyze_retention_trend` to get an objective look at whether your user base is actually stabilizing. - Spot Smiling Curves early. `classify_curve_pattern` helps you identify healthy long-term retention patterns before they become invisible in large datasets. - Know your standing in the market. `verify_benchmark_compliance` lets you see exactly how your D7 or D30 metrics compare to SaaS or gaming standards. - Save hours on manual calculations. Your agent handles the math of comparing cohorts so you don't have to build complex spreadsheets. - Identify high-risk cohorts instantly. Use `analyze_retention_trend` to get immediate feedback on whether a specific user group is showing a dangerous declining trend. - Get instant pattern recognition. `classify_curve_pattern` tells you the difference between a healthy plateau and a steady decline without manual plotting. ## How It Works The bottom line is you get a clear, data-backed answer on whether your user retention is actually healthy or needs immediate attention. 1. Provide your agent with a sequence of retention percentages for a specific cohort. 2. Ask the agent to analyze the trend or compare it to a specific industry. 3. Get a clear classification of the curve pattern and a compliance report. ## Frequently Asked Questions **What does the Retention Curve Analyzer do for my product?** It analyzes your user retention sequences to tell you if your growth is healthy or if you're losing people too fast. It helps you identify whether your curve is stabilizing or in a tailspin. **Can I use Retention Curve Analyzer to see if my users are sticking around?** Yes, it specifically looks for patterns in your data to tell you if your users are finding enough value to stay or if they are likely to churn. **How does the Retention Curve Analyzer help with churn?** It identifies declining trends in your cohorts, allowing you to spot high-risk groups before they drop off completely. **Can Retention Curve Analyzer compare my data to other companies?** It compares your actual metrics against hardcoded industry standards for SaaS, gaming, and e-commerce so you can see how you stack up. **What kind of industries does Retention Curve Analyzer support?** It includes specific benchmarks for SaaS, Mobile Gaming, and E-commerce to give you a relevant reality check for your specific market. **How do I know if my retention is actually healthy?** The tool classifies your curve pattern and checks it against industry benchmarks to give you a clear status report on your growth health. **How does the tool determine if a curve is flattening?** The `analyze_retention_trend` tool examines the change between consecutive values in your retention sequence. If the decrease between recent intervals falls below a specified stability threshold, it is marked as Flattening. **What categories are supported for benchmarking?** The tool currently supports benchmarks for SaaS, Mobile Gaming, and E-commerce. **Can I provide my own stability threshold?** Yes, when using `analyze_retention_trend`, you can optionally provide a `stabilityThreshold` to define the maximum allowable drop between intervals for a trend to be considered stable.