In the digital era, data is ubiquitous, and as Product Managers, our role requires a solid grasp of data analytics. The project I undertook was not just about enhancing user engagement; it was about understanding it through the lens of data, every step of the journey providing valuable insights that could shape our product strategy.
The project commenced with the extraction of raw data — a large dataset of user comments stored in a CSV file. With Python and its powerful libraries at my disposal, I began the journey:
import pandas as pd
# Data extraction
comments = pd.read_csv('UScomments.csv', on_bad_lines='skip')
This simple yet powerful line of code was our starting gate. It pulled in millions of rows of data waiting to be deciphered.
Data in its raw form is often cluttered and incomplete. My next task was to cleanse it:
# Identifying missing values
comments.isnull().sum()
# Data cleansing by removing rows with missing values
comments.dropna(inplace=True)
This step was pivotal as it ensured the reliability of the analysis. It’s like preparing the canvas before painting — essential for the end result to truly shine.
Preparation involved more than cleaning; it meant transforming the data into a format ready for analysis:
from textblob import TextBlob
# Adding sentiment polarity to the dataframe
comments['polarity'] = comments['comment_text'].apply(lambda x: TextBlob(x).sentiment.polarity)
With the TextBlob library, I could calculate the sentiment polarity of each comment, assigning it a score that would later help segment the data into positive and negative sentiments.
Analysis without visualization is like a story without illustrations. So, I turned to seaborn and matplotlib to create visuals that could tell the story at a glance:
import seaborn as sns
import matplotlib.pyplot as plt
# Visualizing the overall engagement by category
plt.figure(figsize=(12,8))
sns.boxplot(x='category_name', y='likes', data=comments)
plt.xticks(rotation=90)
plt.title('User Engagement by Content Category')
plt.show()
Each plot was a brushstroke revealing different aspects of user engagement. The boxplot highlighted which content categories garnered the most likes, indicating areas where our users were most active.
# Plotting like rates against categories
sns.boxplot(x='category_name', y='like_rate', data=comments)
plt.xticks(rotation=90)
plt.title('Like Rate by Category')
plt.show()
This visualization focused on the ‘like rate,’ a key metric of user interaction, revealing not just the quantity but the quality of engagement.
# Examining the correlation between views and likes
sns.regplot(x='views', y='likes', data=comments)
plt.title('Correlation between Views and Likes')
plt.show()
Here, a regression plot unveiled the relationship between views and likes, offering insights into how visibility may translate into positive actions.
Sentiments are the emotions behind user interactions. I plotted word clouds to visualize the most frequent words in positive and negative comments:
from wordcloud import WordCloud, STOPWORDS
# Creating a word cloud for positive sentiment comments
wordcloud_pos = WordCloud(stopwords=set(STOPWORDS)).generate(' '.join(comments_pos['comment_text']))
plt.imshow(wordcloud_pos)
plt.axis('off')
plt.title('Positive Comments Word Cloud')
plt.show()
# And for negative sentiment comments
wordcloud_neg = WordCloud(stopwords=set(STOPWORDS)).generate(' '.join(comments_neg['comment_text']))
plt.imshow(wordcloud_neg)
plt.axis('off')
plt.title('Negative Comments Word Cloud')
plt.show()
These clouds provided a visual representation of user sentiment, with word size indicating frequency. It was like listening to our users’ voices without the noise, understanding their concerns and delights.
Through these steps, I transformed raw data into actionable insights. The visuals created a narrative of user engagement that was more compelling than numbers alone could ever be.
This project reaffirmed my belief in the power of data analytics in product management. The ability to not only analyze but also visualize data allowed me to grasp the nuances of user engagement and drive product decisions that were backed by solid evidence. It was a journey from data to decisions, from numbers to narratives.
From this journey, the key takeaway for any Product Manager is clear: Embrace data analytics. It’s not just about the numbers; it’s about the stories they tell and the decisions they inform. The blend of analytical skills and product intuition is what will define the next generation of successful product leaders.
