Huni Analizi
Çok adımlı bir huninin her adımına kaç kullanıcının ulaştığını izleyin ve ardışık adımlar arasındaki dönüşüm oranlarını hesaplayın.
Huni Analizi, CoddyKit'te ücretsiz bir Pandas & NumPy Academy dersidir. Bu, 4 dersinin 2. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Pandas & NumPy Academy öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Pandas & NumPy Academy kursu toplamda 4 dersten oluşur.
Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.
What Is a Conversion Funnel?
A conversion funnel models a user journey through a sequence of required steps: for example, Homepage → Product Page → Add to Cart → Checkout → Purchase. At each step, some users drop off and never reach the next step. Funnel analysis quantifies these drop-offs so teams can prioritise which step to improve first. The output is a table showing user count and conversion rate at each step.
import pandas as pd
FUNNEL_STEPS = [
'homepage',
'product_page',
'add_to_cart',
'checkout',
'purchase'
]
df = pd.read_csv('clickstream.csv', parse_dates=['timestamp'])
print(df['event_type'].value_counts())Counting Unique Users at Each Step
For each funnel step, count the number of unique users who triggered that event at least once. Use df[df['event_type'] == step]['user_id'].nunique() inside a loop and collect the results into a dictionary. Converting to a Series makes subsequent percentage calculations straightforward with a single division.
step_counts = {}
for step in FUNNEL_STEPS:
users = df[df['event_type'] == step]['user_id'].nunique()
step_counts[step] = users
funnel = pd.Series(step_counts, name='users')
print(funnel)Computing Step-to-Step Conversion Rates
The step-to-step conversion rate shows what fraction of users who completed step N also completed step N+1. Compute it with funnel.shift(1) (which gives the previous step's count) and divide: funnel / funnel.shift(1) * 100. The first step has no prior step, so its rate is NaN or 100 % depending on how you define the baseline.
funnel_df = funnel.to_frame()
funnel_df['prev_users'] = funnel_df['users'].shift(1)
funnel_df['step_conversion_%'] = (funnel_df['users'] / funnel_df['prev_users'] * 100).round(1)
print(funnel_df)Computing Overall Conversion Rate
The overall conversion rate measures what fraction of all users who entered the funnel at step 1 eventually completed the final step. Divide the last step's user count by the first step's count. A 2–5 % overall conversion from homepage to purchase is typical for e-commerce; numbers below this signal a critical drop-off somewhere in the funnel.
overall_rate = funnel.iloc[-1] / funnel.iloc[0] * 100
print(f'Overall conversion rate: {overall_rate:.1f}%')
funnel_df['overall_conversion_%'] = (funnel_df['users'] / funnel_df['users'].iloc[0] * 100).round(1)
print(funnel_df[['users', 'overall_conversion_%']])Ordered Funnel: Requiring Step Sequence
A simple unique-user count treats users who jumped directly to checkout without visiting the product page as valid funnel entries, which inflates conversion at early steps. To enforce ordering, check that each user reached step N only after reaching step N-1. This requires comparing timestamps: the user must have a product_page event before their add_to_cart event.
# Get first occurrence of each step per user
first_events = df.groupby(['user_id', 'event_type'])['timestamp'].min().unstack('event_type')
# Enforce ordering: each step must come after the previous
first_events['valid_to_cart'] = first_events['add_to_cart'] > first_events['product_page']
print(first_events[['product_page', 'add_to_cart', 'valid_to_cart']].head())Funnel by Segment: Device Type
Different user segments may convert at very different rates. Run the same funnel calculation separately for desktop and mobile users using groupby('device_type'). If mobile users drop off heavily at the checkout step, the checkout page likely has a usability issue on small screens. Segmented funnels reveal targeted optimisation opportunities.
def funnel_for_group(group_df):
counts = {}
for step in FUNNEL_STEPS:
counts[step] = group_df[group_df['event_type'] == step]['user_id'].nunique()
return pd.Series(counts)
segmented_funnel = df.groupby('device_type').apply(funnel_for_group)
print(segmented_funnel)Identifying the Biggest Drop-Off Step
The step with the largest absolute user loss is the highest-priority optimisation target. Find it by computing the difference between consecutive step user counts with funnel.diff() — the most negative value corresponds to the biggest drop-off. Surface this insight in both numeric and visual form for stakeholders.
dropoffs = funnel.diff().abs()
biggest_drop = dropoffs.idxmax()
biggest_drop_count = dropoffs.max()
print(f'Biggest drop-off: {biggest_drop} ({biggest_drop_count:.0f} users lost)')Time Between Funnel Steps
The time users take to move from one step to the next reveals friction. If users wait hours between adding to cart and checking out, an abandoned-cart email could recover them. Compute the median time between each pair of steps using the first_events DataFrame by subtracting adjacent timestamp columns and taking the median across all users.
for i in range(1, len(FUNNEL_STEPS)):
prev = FUNNEL_STEPS[i-1]
curr = FUNNEL_STEPS[i]
delta = (first_events[curr] - first_events[prev]).dt.total_seconds() / 60
print(f'{prev} -> {curr}: median {delta.median():.1f} min, 90th pct {delta.quantile(0.9):.1f} min')Weekly Funnel Trend
Track whether conversion rates are improving or degrading week over week by computing the funnel for each week separately. Group the first-event table by week using first_events['homepage'].dt.isocalendar().week and compute the overall conversion rate per week. A sudden drop in a specific week may coincide with a product release or marketing change.
df['week'] = df['timestamp'].dt.isocalendar().week
weekly_conv = []
for week, wdf in df.groupby('week'):
top = wdf[wdf['event_type'] == FUNNEL_STEPS[0]]['user_id'].nunique()
bot = wdf[wdf['event_type'] == FUNNEL_STEPS[-1]]['user_id'].nunique()
weekly_conv.append({'week': week, 'conversion_%': bot/top*100 if top else 0})
print(pd.DataFrame(weekly_conv))Building the Funnel Summary DataFrame
Package all funnel metrics into a clean summary DataFrame that can be exported to a report or fed into a visualisation. Include columns for step name, user count, step-to-step conversion, overall conversion, and users lost. This single DataFrame should be the deliverable from funnel analysis, readable by both engineers and product managers.
summary = pd.DataFrame({
'step': FUNNEL_STEPS,
'users': [step_counts[s] for s in FUNNEL_STEPS]
})
summary['users_lost'] = summary['users'].shift(-1).rsub(summary['users'])
summary['step_conv_%'] = (summary['users'] / summary['users'].shift(1) * 100).round(1)
summary['overall_conv_%'] = (summary['users'] / summary['users'].iloc[0] * 100).round(1)
print(summary)Visualising the Funnel as a Bar Chart
Plot the funnel as a horizontal bar chart where each bar's length represents the user count at that step. A funnel shape emerges naturally when bars are sorted from top (most users) to bottom (fewest). Annotate each bar with the step-to-step conversion percentage so the chart is self-contained and does not require a separate data table to interpret.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(8, 5))
ax.barh(summary['step'][::-1], summary['users'][::-1], color='steelblue')
for i, row in summary[::-1].iterrows():
ax.text(row['users'], i - summary.index[0], f' {row['step_conv_%']}%', va='center')
ax.set_xlabel('Unique Users')
ax.set_title('Conversion Funnel')
plt.tight_layout()
plt.show()Quick Check
Test your understanding of Data Analysis concepts from this lesson.
Lesson Recap
In this lesson you learned: counting unique users at each funnel step, computing step-to-step and overall conversion rates, and segmenting funnels by device type and tracking weekly conversion trends. Next up we explore cohort retention tables to track how many users return week after week.
Sıkça Sorulan Sorular
“Huni Analizi” dersi ücretsiz mi?
Evet — “Huni Analizi” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Pandas & NumPy Academy kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Pandas & NumPy Academy kursu toplamda 4 dersten oluşur.
“Huni Analizi” dersinde ne öğreneceğim?
Çok adımlı bir huninin her adımına kaç kullanıcının ulaştığını izleyin ve ardışık adımlar arasındaki dönüşüm oranlarını hesaplayın. Pandas & NumPy Academy ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.
Pandas & NumPy Academy öğrenmeye başlamak için deneyim gerekli mi?
Önceden deneyim gerekmez. CoddyKit'te Pandas & NumPy Academy, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 2. dersidir.
“Huni Analizi” dersi ne kadar sürer?
Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.
Bu Pandas & NumPy Academy dersinde kod yazıp çalıştırabilir miyim?
Evet. Her Pandas & NumPy Academy dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.
Bu kursun tüm dersleri
- Oturumlaştırma ve Olay Sıralama
- Huni Analizi
- Kohort Elde Tutma Tablosu
- Kullanıcı Yolculuklarını Görselleştirme