User Retention Curve by Cohort — Seaborn

A line chart showing the percentage of retained users over time since signup, with separate curves for different cohorts. All curves start at 100% at time zero and typically exhibit exponential decay, revealing how well a product retains users over their lifecycle. By overlaying multiple cohorts, teams can visually compare whether retention is improving or degrading across signup periods.

User Retention Curve by Cohort rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
line-retention-cohort: User Retention Curve by Cohort
Library: seaborn 0.13.2 | Python 3.13.14
Quality: 90/100 | Updated: 2026-06-20
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns


# Theme tokens — Imprint palette
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

# Imprint categorical palette — canonical order, first series always #009E73
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]

# Data — 50 simulations per cohort so seaborn can draw 95% CI bands
np.random.seed(42)

cohorts = {"Jan 2025": 1245, "Feb 2025": 1380, "Mar 2025": 1510, "Apr 2025": 1420, "May 2025": 1605}

weeks = np.arange(0, 13)
decay_rates = [0.18, 0.16, 0.14, 0.12, 0.10]
floors = [8, 10, 14, 18, 22]
n_sims = 50

records = []
mean_final = {}

for (cohort_label, cohort_size), decay, floor in zip(cohorts.items(), decay_rates, floors, strict=True):
    label = f"{cohort_label} (n={cohort_size:,})"
    sim_finals = []
    for _ in range(n_sims):
        sim_decay = max(0.05, decay + np.random.normal(0, 0.018))
        retention = 100 * np.exp(-sim_decay * weeks) + floor * (1 - np.exp(-0.3 * weeks))
        retention[0] = 100.0
        noise = np.random.normal(0, 1.5, len(weeks))
        noise[0] = 0
        retention = np.clip(retention + noise, 0, 100)
        sim_finals.append(float(retention[-1]))
        for w, r in zip(weeks, retention, strict=True):
            records.append({"week": w, "retention": r, "cohort": label})
    mean_final[label] = float(np.mean(sim_finals))

df = pd.DataFrame(records)
cohort_labels = list(mean_final.keys())

oldest_mean = mean_final[cohort_labels[0]]
newest_mean = mean_final[cohort_labels[-1]]
delta_pp = newest_mean - oldest_mean

# Seaborn theme with Imprint chrome tokens
sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.12,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Canvas — 3200 × 1800 px (landscape, no bbox_inches to preserve exact size)
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Plot — errorbar=("ci", 95) is seaborn's built-in statistical CI band, drawn natively
sns.lineplot(
    data=df,
    x="week",
    y="retention",
    hue="cohort",
    hue_order=cohort_labels,
    palette=IMPRINT_PALETTE,
    linewidth=2.5,
    errorbar=("ci", 95),
    ax=ax,
)

# 20% retention target reference line
ax.axhline(y=20, color=INK_MUTED, linestyle="--", linewidth=0.9, alpha=0.7, zorder=1)
ax.text(0.3, 17, "20% target", fontsize=8, color=INK_MUTED, va="center", fontstyle="italic")

# +Xpp improvement annotation — replaces per-cohort endpoint labels (change request)
mid_y = (oldest_mean + newest_mean) / 2
ax.annotate(
    "", xy=(12, oldest_mean), xytext=(12, newest_mean), arrowprops={"arrowstyle": "<->", "color": INK_SOFT, "lw": 1.0}
)
ax.text(
    12.25,
    mid_y,
    f"+{delta_pp:.0f}pp\nimprovement",
    fontsize=8,
    fontweight="bold",
    color=INK_SOFT,
    va="center",
    ha="left",
    linespacing=1.3,
)

# Style
title = "line-retention-cohort · python · seaborn · anyplot.ai"
ax.set_title(title, fontsize=12, fontweight="medium", color=INK, pad=12)
ax.set_xlabel("Weeks Since Signup", fontsize=10, color=INK, labelpad=8)
ax.set_ylabel("Retained Users (%)", fontsize=10, color=INK, labelpad=8)
ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT)

ax.set_xlim(-0.3, 14.2)
ax.set_ylim(0, 108)
ax.set_xticks(weeks)

ax.yaxis.grid(True, alpha=0.12, linewidth=0.8, color=INK)
ax.xaxis.grid(False)

sns.despine(ax=ax, left=True, bottom=False)

# Legend with Imprint chrome tokens
legend = ax.legend(title="Signup Cohort", fontsize=8, title_fontsize=9, frameon=True, loc="upper right")
legend.get_frame().set_facecolor(ELEVATED_BG)
legend.get_frame().set_edgecolor(INK_SOFT)
legend.get_frame().set_linewidth(0.5)
legend.get_title().set_color(INK)
for text in legend.get_texts():
    text.set_color(INK_SOFT)

# Save — no bbox_inches to preserve exact 3200×1800 canvas
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
plt.close()

Part of User Retention Curve by Cohort on anyplot.ai.

User Retention Curve by Cohort — Seaborn

Python source (Seaborn)

Other implementations