I am building a Campaign Reach Calculator for a community outreach competition page — two sliders (m...

I am building a Campaign Reach Calculator for a community outreach competition page — two sliders (monthly budget $500-$5,000 and community partners 2-15) driving four animated metric cards (monthly impressions, QR code scans, new enrollments, cost per enrollment). The calculator uses an additive power-law model: digital_reach = digital_base × (budget / $1,000)^0.6, community_reach = community_base × (partners / 5), total_impressions = digital + community. QR scans = community_reach × 3% × awareness_multiplier. Enrollments = impressions × 0.05% + partners × 1 referral. CPA = budget / enrollments, clamped $8-$200. The model coefficients are derived from Claude's AI-generated channel plan (parsed reach values classified as digital vs community by keyword matching), with a 30% minimum digital floor and 100,000 maximum total reach cap. The page theme is dark glassmorphism with accent color #8b5cf6. Real-world benchmark data for community health and food access outreach campaigns. I need SPECIFIC numbers to validate my model's output ranges: (a) What monthly impression count does a $1,000/month community outreach campaign in a mid-size US city (Kansas City, Wichita, Chicago) typically generate? Include both digital impressions (social media, Google ads) and offline impressions (flyer views, event attendees, partner newsletter reach). Sources from CDC campaigns, food bank reports, community health center marketing data, or published case studies. (b) What is the actual cost-per-enrollment or cost-per-participant for food access programs, health navigator programs, and food bank outreach specifically? I have seen ranges of $20-$100 for community programs and $200-$1,000+ for ACA navigator programs — can you provide more specific data points with sources? (c) What QR code scan rates do community health programs actually observe on flyers, bus stop posters, and partner-distributed materials? I have 1-5% from general marketing data but need community-health-specific data if available. (d) What percentage of people who see a community outreach ad or flyer actually enroll in the program? I'm using 0.05% (impression-to-enrollment) which assumes 1% click-through × 5% conversion — is this realistic for food access or health navigator programs specifically? How to handle the internal consistency problem between the channel plan table and the calculator model. The page shows a channel matrix table with Claude's per-channel reach values (e.g., 'Community Health Workers: 8,000/mo', 'Church Partners: 3,000'). These sum to the total base reach used in the calculator. But my 30% digital floor REDISTRIBUTES the internal split (e.g., moving 6,000 from community to digital) without changing the displayed channel values. A sharp judge could notice that dragging the budget slider increases impressions even though all listed channels are community-based. How should I handle this? Options: (a) Accept the inconsistency as a modeling simplification; (b) Add an explanatory note like 'Budget drives digital amplification of community reach'; (c) Normalize the base reach to a fixed target regardless of Claude's channel plan; (d) Add a 'Digital Amplification' or 'Paid Media' line to the channel table automatically. Whether to normalize base reach to a target range. Claude's channel plans generate wildly different total reach values between runs — sometimes 15,000, sometimes 80,000, sometimes 40,000,000. I cap at 100,000 and floor at 5,000, but within that range, the calculator's behavior varies significantly. At 15,000 base, the calculator shows modest numbers (15K-58K impressions across full slider range). At 80,000 base, it shows much larger numbers (80K-272K). Should I normalize ALL base reach values to a consistent target (e.g., 40,000-50,000) to ensure the calculator always produces numbers in a believable, research-backed range? What are the tradeoffs? The awareness multiplier behavior below baseline. My formula is: awareness = 1 + 0.3 × (budget/$1,000 - 1), floored at 0.5. At $500 budget, awareness = 0.85, meaning QR scans DECREASE 15% below the baseline rate. Is this behavior correct — does spending less than baseline on digital ads actually reduce the likelihood of QR code scans? Or should awareness floor at 1.0 (never decrease below baseline, only increase above it)? What does marketing science say about the relationship between digital ad spend and offline action rates? Whether to add a methodology disclosure section. The research from 'Building an interactive what-if financial simulator' recommends a collapsible '<details>' element showing the actual formulas. Example: 'Digital reach = base × (budget / $1,000)^0.6 | QR scans = community × 3% × awareness | CPA = budget / enrollments'. This would directly address AI Mastery scoring by proving the computation is deterministic Python math, not LLM-generated text. Should I add this? If so, what level of detail is appropriate — just the high-level formulas, or also the coefficient values (0.6 exponent, 3% scan rate, 0.05% conversion)? Delta indicators showing percentage change from baseline. The research recommends showing '▲ +45% from base' next to metrics when sliders differ from defaults. This helps judges instantly understand the magnitude of change. Should I add these to each metric card? If so, what format works best — absolute delta ('▲ +8,000'), percentage delta ('▲ +45%'), or both? Where should they be positioned relative to the main number — below it in smaller text, or to the right? Visual affordances that signal interactivity on a dark-themed dashboard. The calculator might not be obviously interactive to a judge scrolling past it. The research recommends: (a) a pulsing green dot near the sliders as a 'try it' indicator; (b) a subtle breathing glow on the panel border; (c) text like 'Drag the sliders to explore scenarios'. Which of these approaches is most effective without being distracting? Should the interactivity signal disappear after first interaction? Whether the enrollment formula should differentiate between digital and community conversion rates. Currently, all impressions convert at 0.05% regardless of source. In reality, community impressions (flyers at a church, word of mouth from a partner) likely convert at higher rates than digital impressions (a Facebook ad scroll-past). Should I split the conversion rate — e.g., 0.03% for digital impressions and 0.1% for community impressions? Would this make the model more credible or just more complex without adding clarity? Ensuring the calculator is robust across different Claude channel plan outputs. On different runs with different datasets (KC food access, Chicago healthcare, Wichita unknown), Claude generates very different channel plans. I need the calculator to produce credible numbers regardless of what Claude generates. What guardrails or normalizations should I apply beyond the current floor (5,000), cap (100,000), and digital ratio (30%)? Should I also normalize the community_base to ensure the partner slider always has a meaningful effect? What about capping individual channel reach values before summing? How competition judges evaluate interactive data tools in marketing submissions. At the NAIPC, five dimensions are scored 1-5: Problem Understanding, Solution Quality, Presentation & Polish, Adaptability, and AI Mastery. Which specific aspects of the calculator contribute to each dimension? What would a 5/5 calculator look like vs a 3/5? What common mistakes in interactive demos cause judges to score lower? Is there value in having the calculator produce numbers that EXACTLY match other parts of the page (e.g., the hero stat, the KPI targets), or is approximate consistency sufficient?