Lesson 28 · Real-World Data Analytics

Python Data Analytics #28: Email Marketing Performance Analysis in Python

Video twenty-eight of the hundred-video real-world data analytics series. Real published email marketing benchmarks across forty-five real industries, from…

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CourseReal-World Data Analytics
Lesson28 of 100
FormatJupyter notebook · 30 code cells
Data1 dataset

pandas Matplotlib

What you'll learn

Real Benchmarks, Real Industries
Real Data Preview
Real Top Open Rate Industries
Real Bottom Open Rate Industries
Visualizing Real Open Rates
Real Click Rate Leaders
Real Open Rate vs Click Rate, Are They Related?
Visualizing the Real Weak Relationship

Datasets used in this lesson

Save these next to the notebook. In Google Colab, upload them with the 📁 icon on the left first.

email_marketing_benchmarks_2026.csv preview1.7 KB

📓 Full notebook

Download .ipynb

Data Analytics 100, Video 28: Email Marketing Performance Analysis#

Video twenty-eight of the hundred-video real-world data analytics series.
Real published email marketing benchmarks across forty-five real industries, from a real report covering 3.6 million real campaigns.
Let's get into it.

Part 1: Real Benchmarks, Real Industries#

import pandas as pd
import matplotlib.pyplot as plt
benchmarks = pd.read_csv('email_marketing_benchmarks_2026.csv')
benchmarks.shape

(45, 5)

Part 2: Real Data Preview#

benchmarks.head(5)
benchmarks.describe().round(2)

	Open_Rate_Pct	Click_Rate_Pct	Click_To_Open_Rate_Pct	Unsubscribe_Rate_Pct
count	45.00	45.00	45.00	45.00
mean	43.43	2.09	6.80	0.22
std	5.46	0.92	2.81	0.07
min	30.10	0.83	2.96	0.09
25%	39.95	1.39	4.84	0.16
50%	43.75	1.83	6.28	0.23
75%	47.19	2.66	7.96	0.27
max	55.71	4.90	14.82	0.40

Part 3: Real Top Open Rate Industries#

top_open_rate = benchmarks.sort_values('Open_Rate_Pct', ascending=False).head(10)
top_open_rate[['Industry', 'Open_Rate_Pct']]

	Industry	Open_Rate_Pct
38	Religion	55.71
20	Hobbies	53.25
29	Non-profit	52.38
3	Art gallery and museum	50.43
32	Photo and video	49.98
4	Artist	49.23
17	Government	48.52
9	Coaching	48.07
18	Health and fitness	47.81
13	Creative services/agency	47.69

Part 4: Real Bottom Open Rate Industries#

bottom_open_rate = benchmarks.sort_values('Open_Rate_Pct').head(10)
bottom_open_rate[['Industry', 'Open_Rate_Pct']]

	Industry	Open_Rate_Pct
44	Travel and transportation	30.10
14	e-commerce	32.67
35	Publishing company	34.24
10	Computers and electronics	35.29
43	Telecommunications	37.21
25	Marketing and advertising	37.23
24	Manufacturing	37.36
40	Retail	37.47
6	Beauty and personal care	38.40
21	Home and garden	39.18

Part 5: Visualizing Real Open Rates#

top10 = benchmarks.sort_values('Open_Rate_Pct', ascending=False).head(10)
plt.figure(figsize=(9, 6))
plt.barh(top10['Industry'], top10['Open_Rate_Pct'], color='steelblue')
plt.xlabel('Real Open Rate (%)')
plt.title('Real Top 10 Industries by Email Open Rate')
plt.gca().invert_yaxis()
plt.tight_layout()
plt.savefig('top_open_rates.png', dpi=120)
plt.close()

Part 6: Real Click Rate Leaders#

top_click_rate = benchmarks.sort_values('Click_Rate_Pct', ascending=False).head(10)
top_click_rate[['Industry', 'Click_Rate_Pct']]

	Industry	Click_Rate_Pct
23	Legal	4.90
24	Manufacturing	4.22
26	Media	4.10
11	Construction	3.53
20	Hobbies	3.30
17	Government	3.05
7	Blogger	3.00
38	Religion	2.95
29	Non-profit	2.90
35	Publishing company	2.82

open_click_corr = benchmarks['Open_Rate_Pct'].corr(benchmarks['Click_Rate_Pct'])
round(open_click_corr, 3)

np.float64(0.176)

Part 8: Visualizing the Real Weak Relationship#

plt.figure(figsize=(8, 6))
plt.scatter(benchmarks['Open_Rate_Pct'], benchmarks['Click_Rate_Pct'], color='darkorange', s=40)
plt.xlabel('Real Open Rate (%)')
plt.ylabel('Real Click Rate (%)')
plt.title('Real Open Rate vs Click Rate by Industry')
plt.tight_layout()
plt.savefig('open_vs_click_scatter.png', dpi=120)
plt.close()

Part 9: Real Click-to-Open Rate, The Content Signal#

top_ctor = benchmarks.sort_values('Click_To_Open_Rate_Pct', ascending=False).head(10)
top_ctor[['Industry', 'Click_To_Open_Rate_Pct']]

	Industry	Click_To_Open_Rate_Pct
24	Manufacturing	14.82
23	Legal	14.72
26	Media	12.92
11	Construction	12.38
16	Games	9.71
19	Higher education	9.15
35	Publishing company	9.12
7	Blogger	8.56
20	Hobbies	8.45
17	Government	8.44

Part 10: Real Low Open, High CTOR Industries#

median_open = benchmarks['Open_Rate_Pct'].median()
median_ctor = benchmarks['Click_To_Open_Rate_Pct'].median()
hidden_gems = benchmarks[(benchmarks['Open_Rate_Pct'] < median_open) & (benchmarks['Click_To_Open_Rate_Pct'] > median_ctor)]
hidden_gems[['Industry', 'Open_Rate_Pct', 'Click_To_Open_Rate_Pct']]

	Industry	Open_Rate_Pct	Click_To_Open_Rate_Pct
0	Agency	40.52	6.74
5	Author	43.14	7.81
7	Blogger	43.03	8.56
8	Business and finance	43.34	7.96
11	Construction	39.95	12.38
16	Games	41.35	9.71
21	Home and garden	39.18	7.05
23	Legal	42.58	14.72
24	Manufacturing	37.36	14.82
26	Media	42.97	12.92
35	Publishing company	34.24	9.12
36	Real estate	40.37	6.72
44	Travel and transportation	30.10	6.34

Part 11: Real Unsubscribe Rate Leaders#

top_unsub = benchmarks.sort_values('Unsubscribe_Rate_Pct', ascending=False).head(10)
top_unsub[['Industry', 'Unsubscribe_Rate_Pct']]

	Industry	Unsubscribe_Rate_Pct
32	Photo and video	0.40
39	Restaurants and cafes	0.39
43	Telecommunications	0.34
3	Art gallery and museum	0.33
5	Author	0.31
18	Health and fitness	0.30
9	Coaching	0.30
24	Manufacturing	0.30
13	Creative services/agency	0.29
4	Artist	0.27

Part 12: Real Lowest Unsubscribe Rate Industries#

bottom_unsub = benchmarks.sort_values('Unsubscribe_Rate_Pct').head(10)
bottom_unsub[['Industry', 'Unsubscribe_Rate_Pct']]

	Industry	Unsubscribe_Rate_Pct
23	Legal	0.09
19	Higher education	0.10
26	Media	0.10
38	Religion	0.13
44	Travel and transportation	0.13
34	Public relations	0.14
33	Politics	0.14
25	Marketing and advertising	0.15
7	Blogger	0.16
8	Business and finance	0.16

Part 13: Real Correlation, Open Rate and Unsubscribe Rate#

open_unsub_corr = benchmarks['Open_Rate_Pct'].corr(benchmarks['Unsubscribe_Rate_Pct'])
round(open_unsub_corr, 3)

np.float64(0.169)

Part 14: Real All-Metric Correlation Matrix#

metric_cols = ['Open_Rate_Pct', 'Click_Rate_Pct', 'Click_To_Open_Rate_Pct', 'Unsubscribe_Rate_Pct']
correlation_matrix = benchmarks[metric_cols].corr().round(3)
correlation_matrix

	Open_Rate_Pct	Click_Rate_Pct	Click_To_Open_Rate_Pct	Unsubscribe_Rate_Pct
Open_Rate_Pct	1.000	0.176	-0.059	0.169
Click_Rate_Pct	0.176	1.000	0.929	-0.199
Click_To_Open_Rate_Pct	-0.059	0.929	1.000	-0.267
Unsubscribe_Rate_Pct	0.169	-0.199	-0.267	1.000

Part 15: Real Overall Industry Benchmark#

overall_open = round(benchmarks['Open_Rate_Pct'].median(), 2)
overall_click = round(benchmarks['Click_Rate_Pct'].median(), 2)
overall_open, overall_click

(np.float64(43.75), np.float64(1.83))

Part 16: Real Campaign Benchmarking Function#

def benchmark_campaign(industry_name, my_open_rate, my_click_rate):
    row = benchmarks[benchmarks['Industry'] == industry_name].iloc[0]
    open_diff = round(my_open_rate - row['Open_Rate_Pct'], 2)
    click_diff = round(my_click_rate - row['Click_Rate_Pct'], 2)
    return {'open_rate_vs_benchmark': open_diff, 'click_rate_vs_benchmark': click_diff}
benchmark_campaign('e-commerce', 35.0, 1.5)

{'open_rate_vs_benchmark': np.float64(2.33),
 'click_rate_vs_benchmark': np.float64(0.43)}

Part 17: Real Testing the Function on Another Industry#

benchmark_campaign('Non-profit', 48.0, 3.5)

{'open_rate_vs_benchmark': np.float64(-4.38),
 'click_rate_vs_benchmark': np.float64(0.6)}

Part 18: Real Distribution of Open Rates#

plt.figure(figsize=(9, 5))
plt.hist(benchmarks['Open_Rate_Pct'], bins=15, color='mediumseagreen', edgecolor='white')
plt.axvline(overall_open, color='crimson', linestyle='--', label=f'Real Median: {overall_open}%')
plt.xlabel('Real Open Rate (%)')
plt.ylabel('Real Number of Industries')
plt.title('Real Distribution of Email Open Rates Across Industries')
plt.legend()
plt.tight_layout()
plt.savefig('open_rate_distribution.png', dpi=120)
plt.close()

Part 19: Real Range Check, Click Rate#

click_range = round(benchmarks['Click_Rate_Pct'].max() - benchmarks['Click_Rate_Pct'].min(), 2)
click_range

np.float64(4.07)

Part 20: Real Industries Above the Overall Median on Every Metric#

medians = benchmarks[metric_cols].median()
all_star = benchmarks[(benchmarks['Open_Rate_Pct'] > medians['Open_Rate_Pct']) & (benchmarks['Click_Rate_Pct'] > medians['Click_Rate_Pct']) & (benchmarks['Unsubscribe_Rate_Pct'] < medians['Unsubscribe_Rate_Pct'])]
all_star[['Industry']]

	Industry
17	Government
19	Higher education
29	Non-profit
31	Other
34	Public relations
38	Religion

Part 21: Real Worst-Performing Industries Overall#

struggling = benchmarks[(benchmarks['Open_Rate_Pct'] < medians['Open_Rate_Pct']) & (benchmarks['Click_Rate_Pct'] < medians['Click_Rate_Pct'])]
len(struggling), struggling['Industry'].tolist()[:10]

(12,
 ['Architecture and construction',
  'Beauty and personal care',
  'Computers and electronics',
  'e-commerce',
  'Home and garden',
  'Marketing and advertising',
  'Online courses',
  'Real estate',
  'Retail',
  'Software and web app'])

Part 22: Real Ranking by Composite Engagement Score#

benchmarks['engagement_score'] = (benchmarks['Open_Rate_Pct'] * 0.3 + benchmarks['Click_Rate_Pct'] * 10 * 0.4 + benchmarks['Click_To_Open_Rate_Pct'] * 0.3).round(2)
benchmarks.sort_values('engagement_score', ascending=False)[['Industry', 'engagement_score']].head(10)

	Industry	engagement_score
23	Legal	36.79
26	Media	33.17
24	Manufacturing	32.53
20	Hobbies	31.71
38	Religion	30.79
11	Construction	29.82
29	Non-profit	29.79
17	Government	29.29
3	Art gallery and museum	27.65
7	Blogger	27.48

Part 23: Real Bottom of the Composite Ranking#

benchmarks.sort_values('engagement_score')[['Industry', 'engagement_score']].head(10)

	Industry	engagement_score
14	e-commerce	15.28
6	Beauty and personal care	16.49
10	Computers and electronics	17.52
44	Travel and transportation	17.65
40	Retail	17.67
41	Software and web app	18.01
25	Marketing and advertising	18.20
33	Politics	18.36
43	Telecommunications	18.48
39	Restaurants and cafes	18.52

Part 24: Real Legal Industry Deep Dive#

legal_row = benchmarks[benchmarks['Industry'] == 'Legal'].iloc[0]
legal_row[metric_cols]

Open_Rate_Pct             42.58
Click_Rate_Pct              4.9
Click_To_Open_Rate_Pct    14.72
Unsubscribe_Rate_Pct       0.09
Name: 23, dtype: object

Part 25: Real Retail vs E-commerce Comparison#

retail_ecom = benchmarks[benchmarks['Industry'].isin(['Retail', 'e-commerce'])]
retail_ecom[['Industry'] + metric_cols]

	Industry	Open_Rate_Pct	Click_Rate_Pct	Click_To_Open_Rate_Pct	Unsubscribe_Rate_Pct
14	e-commerce	32.67	1.07	4.01	0.18
40	Retail	37.47	1.27	4.51	0.22

Part 26: Saving the Real Ranked Benchmark Table#

ranked_benchmarks = benchmarks.sort_values('engagement_score', ascending=False).reset_index(drop=True)
ranked_benchmarks.to_csv('ranked_email_benchmarks.csv', index=False)
reloaded_ranked = pd.read_csv('ranked_email_benchmarks.csv')
reloaded_ranked.shape == ranked_benchmarks.shape

True

Part 27: Real Sanity Check, Percentages in Range#

all((0 <= benchmarks['Open_Rate_Pct']) & (benchmarks['Open_Rate_Pct'] <= 100)) and all((0 <= benchmarks['Click_Rate_Pct']) & (benchmarks['Click_Rate_Pct'] <= 100))

True

Part 28: Real Sanity Check, No Duplicate Industries#

benchmarks['Industry'].is_unique

True

Part 29: Real Takeaway for a Hypothetical Campaign#

my_result = benchmark_campaign('Software and web app', 41.0, 1.3)
my_result

{'open_rate_vs_benchmark': np.float64(1.69),
 'click_rate_vs_benchmark': np.float64(0.15)}

Part 30: Real Recap Print#

top_industry = benchmarks.sort_values('engagement_score', ascending=False).iloc[0]['Industry']
print(f'Across {len(benchmarks)} real industries, the median open rate was {overall_open}% and median click rate was {overall_click}%, with {top_industry} posting the real strongest composite email performance.')

Across 45 real industries, the median open rate was 43.75% and median click rate was 1.83%, with Legal posting the real strongest composite email performance.

Wrap-Up: What You Learned#

Real published industry benchmarks give any marketer an honest yardstick, without one, a raw open or click rate number means very little.
Open rate and click rate turned out to be only weakly correlated across real industries, confirming they genuinely measure different things.
Click-to-open rate isolates real content quality specifically, separate from how many people opened the email in the first place.
A real simple reusable benchmarking function makes comparing any real campaign against its real industry standard fast and repeatable.
Combining several real metrics into one composite score is a real practical way to rank performance across many real categories at once.
Next video: real airline customer sentiment analysis, returning to real text analysis with a real single-brand deep dive.

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