Using Social Media to Boost Advertising Strategy

Aug 21, 2024

Data Analysis
NLP
Marketing
Social media image

www.github.com/d-pap/NLP-sentiment-analysis

www.medium.com/@dpapcodes

Summary

I analyzed 61,000 tweets about CS:GO and competitor games, built a 94.7% accurate sentiment analysis model, and used the insights to suggest targeted marketing strategies. The goal: help gaming companies attract and retain more players by understanding their frustrations and interests.

Insights

Approaching this as if I worked at Valve (makers of CS:GO), I found three key insights:

  1. Cheating frustrates CS:GO players the most
    Recommendation: Regularly highlight Valve’s anti-cheat actions publicly.

  2. Players love Twitch content and cosmetic items ("skins")
    Recommendation: Run more Twitch events and giveaways, similar to competitor successes (Apex, Valorant).

  3. Many competitor players (Call of Duty, PUBG, Fortnite, Rainbow Six) are highly frustrated
    Recommendation: Use targeted ads highlighting CS:GO’s smoother updates, strong stability, and availability in markets like India, where PUBG was banned.

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To see how I got this, check out my GitHub Repository or read my Medium article

Potential Business Impact

To see the financial benefit of implementing targeted sentiment-driven advertising, consider this scenario:

Audience. Suppose Valve targets social media users expressing frustration with competitors.

  • From roughly 32 million monthly active CS:GO users (ActivePlayer.io, 2024), a conservative assumption is that Valve’s marketing efforts could reach 5% (1.6 million) of users who are dissatisfied with competitor games.

Conversion assumption. With targeted and relevant ads, it's reasonable to assume 2% of these users could be converted into new or returning active CS:GO players each month.

  • Monthly new players gained: 1.6 million users x 2% conversion = 32,000 new players/month

Revenue impact (reasonable assumption). CS:GO primarily generates revenue through cosmetic items and keys, with estimated average revenue per paying user (ARPPU) around $5 per month (Newzoo Cosmetics Report, 2023).

  • Monthly revenue gain: 32,000 players × $5 ARPPU = $160,000 per month
  • Annual revenue gain: $160,000 × 12 months = $1.92 million annually

Even with conservative assumptions, leveraging sentiment analysis to better target frustrated competitor audiences can yield a significant revenue uplift.

How to Use It

  • Deploy sentiment analysis in daily social listening for real-time targeting.
  • Implement targeted ad tests to measure real-world campaign effectiveness (CTR, conversion rates).
  • Expand analysis to additional platforms (Reddit, YouTube) for richer audience insights.

Tools

  • Python, pandas, scikit-learn (data prep & NLP analysis)
  • TensorFlow, Keras (model building)
  • Matplotlib, Seaborn (visualizations)

Behind the Scenes

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I tested several NLP models (Logistic Regression, Gradient Boosting, MLP Neural Network) and selected an MLP model because it achieved the highest accuracy (94.7%). High accuracy means we reliably understand player sentiment and effectively target ads.

Why sentiment analysis?

  • Identifies real-time player frustrations.
  • Helps marketing create relevant, timely ads.
  • Significantly improves ad effectiveness (higher click rates, conversions).

Future steps could expand the analysis to platforms like Reddit and YouTube, adding richer insights.

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