Instagram-Content-Creator-Analysis

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Instagram-Content-Creator-Analysis

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Instagram Creator Analytics Dashboard — An end-to-end data analytics project using Python, Pandas, and Power BI to analyze content performance, engagement, reach, and follower growth, providing actionable insights for improving content strategy.

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Instagram Creator Analytics Dashboard

Project Overview

This project analyzes approximately one year of Instagram content performance data for a content creator to understand what drives reach, engagement, and follower growth.

The project follows an end-to-end data analytics workflow:

Raw Instagram Data -> Python/Pandas -> Data Cleaning and EDA -> Power BI -> Interactive Dashboard

The main objective is to identify:

How content performance changes over time

Which days perform best

Which posting hours are most effective

How reach translates into engagement

Which types of audience interactions are strongest

Which posts contribute most to follower growth

Business Objective

The purpose of this project is to help an Instagram creator make data-driven content decisions instead of relying on assumptions.

The analysis focuses on six key performance indicators:

Total Reach

Engagement Rate

Total Follows

Follow Conversion Rate

Share Rate

Save Rate

The dashboard also analyzes posting frequency, monthly performance, day-of-week performance, posting hours, and individual post performance.

Dataset

The dataset contains Instagram content performance data covering approximately one year.

Dataset Coverage

Time Period: August 2025 - August 2026

Total Posts: 128

Instagram Reels: 122

Instagram Images: 4

Instagram Carousels: 2

Dataset Columns

Column

Description

Post ID

Unique identifier of the post

Account ID

Instagram account identifier

Account username

Creator's username

Account name

Creator's account name

Description

Caption or description of the post

Duration (sec)

Duration of the reel/video

Publish time

Date and time when the post was published

Permalink

URL of the Instagram post

Post type

Type of Instagram content

Data comment

Additional dataset field

Date

Post date

Views

Number of views

Reach

Number of accounts reached

Likes

Number of likes

Shares

Number of shares

Follows

Follows generated by the post

Comments

Number of comments

Saves

Number of saves

Python and Pandas Analysis

Python and Pandas were used for data inspection, cleaning, transformation, feature engineering, and exploratory data analysis.

Data Inspection

The dataset was inspected using:

shape

info()

describe()

Null-value analysis

Duplicate analysis

Data-type verification

Dataset Structure

Rows: 128

Columns: 18

Missing Values

The following missing values were identified:

Reach: 5 missing values

Follows: 5 missing values

Data comment: 128 missing values

The missing Reach and Follows values were retained as missing rather than being replaced with zero because a missing value does not necessarily mean that the actual performance was zero.

The Data comment field contained no useful values and was not used for analysis.

Duplicate Analysis

Duplicate records were checked during the cleaning process.

No problematic duplicate posts were identified.

Data Cleaning and Feature Engineering

The following transformations were performed using Pandas.

Date and Time Transformation

The original Publish time column was converted into a proper datetime format.

The following analytical columns were created:

Month

Month Number

Day of Week

Day Number

Posting Hour

These columns were created to analyze seasonal and timing-based performance.

Engagement

A new Engagement column was created:

Engagement = Likes + Comments + Shares + Saves

This provides a combined measure of audience interaction.

Overall Dataset Statistics

Metric

Mean

Minimum

Maximum

Views

588,173

3,137

4,795,750

Reach

404,167

2,494

3,116,254

Likes

65,331

112

612,617

Shares

10,411

10

122,337

Follows

1,579

3

17,252

Comments

374

2

2,633

Saves

4,844

0

49,781

KPI Calculations

The following KPIs were calculated and used in the Power BI dashboard.

1. Total Reach

Measures the total number of accounts reached by the content.

Total Reach = SUM(Reach)

2. Engagement Rate

Measures audience engagement relative to reach.

Engagement Rate = Total Engagement / Total Reach x 100

Where:

Total Engagement = Likes + Comments + Shares + Saves

3. Total Follows

Measures the total number of followers generated by the analyzed content.

Total Follows = SUM(Follows)

4. Follow Conversion Rate

Measures how effectively reached users converted into followers.

Follow Conversion Rate = Total Follows / Total Reach x 100

5. Share Rate

Measures how frequently users shared the content relative to reach.

Share Rate = Total Shares / Total Reach x 100

6. Save Rate

Measures how frequently users saved the content relative to reach.

Save Rate = Total Saves / Total Reach x 100

Monthly Analysis

Monthly performance was calculated for all 12 months.

Month

Posts

Total Reach

Total Engagement

Total Follows

Engagement Rate

Follow Conversion Rate

Share Rate

Save Rate

January

7

1,173,008

234,789

4,936

20.02%

0.42%

2.44%

1.09%

February

10

1,516,555

295,435

6,693

19.48%

0.44%

2.57%

1.24%

March

10

3,333,162

789,650

16,750

23.69%

0.50%

2.97%

1.23%

April

7

1,932,997

381,053

7,139

19.71%

0.37%

2.01%

1.11%

May

11

1,829,822

339,688

6,451

18.56%

0.35%

2.08%

1.05%

June

22

8,065,730

1,739,986

25,786

21.57%

0.32%

2.62%

1.30%

July

20

10,224,138

2,056,628

21,726

20.12%

0.21%

2.39%

1.51%

August

7

5,783,272

1,323,272

27,222

22.88%

0.47%

3.37%

1.18%

September

5

5,662,544

1,041,125

32,444

18.39%

0.57%

2.39%

1.10%

October

10

4,977,212

1,092,502

19,638

21.95%

0.39%

3.17%

1.10%

November

8

2,234,264

479,249

12,369

21.45%

0.55%

3.19%

1.18%

December

11

2,979,879

589,348

13,028

19.78%

0.44%

2.49%

1.24%

Monthly Insights

July recorded the highest total reach at approximately 10.22 million.

September generated the highest total follows at approximately 32.44 thousand.

March had the highest Engagement Rate at 23.69%.

August had the highest Share Rate at 3.37%.

July had the highest Save Rate at 1.51%.

September had the highest Follow Conversion Rate at 0.57%.

June and July had the highest posting volume with 22 and 20 posts respectively.

Day-of-Week Analysis

Day

Posts

Total Reach

Total Engagement

Total Follows

Engagement Rate

Follow Conversion Rate

Share Rate

Save Rate

Monday

13

6,990,975

1,476,712

20,927

21.12%

0.30%

2.75%

1.38%

Tuesday

19

11,610,330

2,400,002

47,804

20.67%

0.41%

2.63%

1.41%

Wednesday

11

2,448,953

471,541

10,364

19.25%

0.42%

2.64%

1.02%

Thursday

20

5,925,102

1,176,133

17,427

19.85%

0.29%

2.16%

1.23%

Friday

16

6,110,605

1,260,153

23,805

20.62%

0.39%

2.44%

1.17%

Saturday

28

6,945,195

1,319,795

28,645

19.00%

0.41%

2.38%

0.98%

Sunday

21

9,681,423

2,258,389

45,210

23.33%

0.47%

3.38%

1.27%

Day-of-Week Insights

Sunday recorded the highest Engagement Rate at 23.33%.

Sunday also had the highest Share Rate at 3.38%.

Sunday had the highest Follow Conversion Rate among the days at 0.47%.

Tuesday generated the highest total reach at approximately 11.61 million.

Saturday had the highest number of posts with 28 posts but the lowest Engagement Rate at 19.00%.

This suggests that posting frequency alone does not guarantee stronger performance.

Posting Hour Analysis

Posting hour was extracted from the original publish timestamp.

The analysis compares performance across different posting hours using:

Reach

Engagement

Engagement Rate

Follows

This helps identify time periods where content is more likely to generate strong audience interaction.

Power BI Dashboard

The final dashboard was developed in Microsoft Power BI.

Dashboard KPIs

The dashboard includes six primary KPIs:

Total Reach

Engagement Rate

Total Follows

Follow Conversion Rate

Share Rate

Save Rate

Interactive Month Slicer

A month slicer allows the user to dynamically filter the dashboard by month.

This makes it possible to compare individual months and understand how content performance changes over time.

Dashboard Visualizations

Monthly Reach and Engagement Rate

X-axis: Month

Measures: Total Reach and Engagement Rate

Monthly Engagement Rate

X-axis: Month

Y-axis: Engagement Rate

Day-of-Week Performance

X-axis: Day of Week

Y-axis: Engagement Rate

Posting Hour Performance

X-axis: Posting Hour

Y-axis: Engagement Rate

Reach vs Engagement

X-axis: Reach

Y-axis: Engagement

Used to identify high-performing content

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