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CS Health Score Planner

Calculate customer health scores and plan churn prevention actions. Enter values for instant results with step-by-step formulas.

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Worked Examples

Example 1: The Happy Customer

Problem: Logins 10, Usage 8, NPS 10, Pay 10, Support 9.

Solution: Weighted Sum = 9.3. Health Score = 93.

Result: Champion (Upsell)

Example 2: The Silent Sufferer

Problem: Logins 2, Usage 3, NPS 5, Pay 10, Support 10.

Solution: Weighted Sum = 4.2. Health Score = 42.

Result: Critical Risk

Frequently Asked Questions

What is a Customer Health Score?

A composite metric used by Customer Success teams to predict the likelihood of a customer renewing or churning. It combines quantitative data (usage) with qualitative data (relationship).

What do I do with a Red score?

Start a 'Get Well' plan. Identify the root cause (Technical issue? Stakeholder left?) and assign specific tasks to fix it within 30 days.

How do I score 'Support'?

Look at ticket sentiment and volume. High volume of 'How to' questions is good (engagement). High volume of 'Bug' reports is bad (frustration).

Should I show this score to the customer?

Generally, no. It's an internal risk metric. However, sharing usage data ('You are in the top 10% of users') is a great engagement tactic.

How do I interpret the result?

Results are displayed with a label and unit to help you understand the output. Many calculators include a short explanation or classification below the result (for example, a BMI category or risk level). Refer to the worked examples section on this page for real-world context.

Is my data stored or sent to a server?

No. All calculations run entirely in your browser using JavaScript. No data you enter is ever transmitted to any server or stored anywhere. Your inputs remain completely private.

Background & Theory

The Customer Success Health Score & Churn Prevention Planner applies the following established principles and formulas. Health and medicine calculators are grounded in validated physiological measurement methods established through decades of clinical research. Body Mass Index, or BMI, is calculated by dividing weight in kilograms by height in meters squared (kg/mยฒ), a formula originating from Adolphe Quetelet's 19th-century statistical work and later codified by the WHO into standard classifications: underweight below 18.5, normal weight 18.5 to 24.9, overweight 25 to 29.9, and obese at 30 and above. Basal Metabolic Rate quantifies the minimum energy required to sustain life at rest. The Mifflin-St Jeor equation, published in 1990 and widely regarded as the most accurate for most adults, calculates BMR as (10 ร— weight in kg) + (6.25 ร— height in cm) โˆ’ (5 ร— age) ยฑ sex adjustment. The older Harris-Benedict equations, revised in 1984 by Roza and Shizgal, remain in common use. Total Daily Energy Expenditure is derived by multiplying BMR by a physical activity factor ranging from 1.2 for sedentary individuals to 1.9 for extremely active ones, following the methodology validated by doubly labeled water studies. Body fat percentage can be estimated without laboratory equipment using the U.S. Navy circumference method, which uses neck, waist, and hip measurements, or via BMI-derived equations adjusted for age and sex. The Jackson-Pollock skinfold method offers higher precision with calipers. Blood pressure classification, according to the American College of Cardiology and the 2017 ACC/AHA guidelines, defines normal as below 120/80 mmHg, elevated as 120 to 129 systolic, and hypertension stage 1 as 130 to 139 systolic or 80 to 89 diastolic. Target heart rate zones for aerobic exercise are derived from maximum heart rate estimates, most commonly using the formula 220 minus age in years, with moderate-intensity training typically defined as 50 to 70 percent of maximum heart rate and vigorous intensity at 70 to 85 percent, consistent with CDC and American Heart Association guidelines. These thresholds guide safe and effective cardiovascular conditioning.

History

The history behind the Customer Success Health Score & Churn Prevention Planner traces back through the following developments. The history of health measurement stretches back to ancient Greece, where Hippocrates around 400 BCE laid the foundation for observational medicine by systematically recording patient symptoms, diet, and environment. His humoral theory, though scientifically superseded, established the principle that the body operates as an interconnected system subject to measurable imbalance. The transformation toward modern medicine accelerated in the 19th century. Louis Pasteur and Robert Koch developed germ theory in the 1860s and 1870s, identifying microorganisms as disease agents and enabling targeted interventions. Florence Nightingale, working during the Crimean War in the 1850s, introduced statistical analysis to nursing practice, demonstrating through data visualization that sanitation reduced mortality. Her work is foundational to evidence-based health measurement. The discovery of vitamins in the early 20th century, beginning with Casimir Funk's coinage of the term in 1912 and culminating in the isolation of vitamins A through K, created the field of nutritional science and gave rise to dietary reference intake frameworks. The World Health Organization, founded in 1948, subsequently established global standards for health metrics, disease classification through the International Classification of Diseases, and recommended daily allowances. The BMI as a clinical screening tool gained traction in the 1970s through Ancel Keys' large-scale epidemiological work, which validated Quetelet's index as a population-level obesity indicator. Through the 1980s and 1990s, the Framingham Heart Study produced landmark data linking cholesterol, blood pressure, and lifestyle factors to cardiovascular disease risk, directly shaping the numeric thresholds still used in health calculators. The evidence-based medicine movement, formalized by Gordon Guyatt and colleagues at McMaster University in the early 1990s, demanded that all health recommendations derive from systematically graded clinical evidence. The digital health era beginning in the 2000s brought these formulas to consumer devices, wearable sensors, and smartphone applications, expanding access to health self-monitoring on a global scale and enabling population-level data collection that continues to refine clinical reference ranges.

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