Identified key drivers
Defined customer needs
Unveiled product attributes
Discovered growth opportunities
The challenge
Overcoming data complexity: Analyzing X for personality insights
A leading loyalty analytics company aimed to identify the personality traits of approximately 64K of its loyalty shoppers based on their X (Twitter) feeds. The goal was to use this data to better understand consumer behavior and improve targeted marketing efforts.
Key challenges
Only 5K out of 64K X (Twitter) followers were in the user base
Creating accurate auras for personality segments was challenging
Twitter's noisy data, with abbreviations and emoticons, complicated analysis
The solution
Personality segmentation and data processing
Personality segmentation
Built personality auras with taxonomy and resources
Extracted key phrases unsupervised
Used embeddings for accuracy
Data processing and analysis
Extracted Twitter data with Kafka
Cleaned text using dCrypt
Calculated similarity
Implementation approach
1
Data enrichment
Integrated DBPedia
Enhanced data insights
Compared terms to profiles
2
Trait identification
Identified traits for 3K followers
Recognized interests: movies, music
Uncovered actionable insights
3
Marketing insights
Tailored strategies with traits
Boosted shopper engagement
Identified campaign opportunities
The impact
Driving targeted engagement and growth
Targeted marketing
Boosted engagement
Delivered personalized offers
Enhanced marketing precision
Social media expansion
Analyzed more platforms
Reached a wider audience
Enhanced profiling
Enhanced offers
Boosted conversions
Strengthened connection
Targeted offers via handles
Looking ahead
Broaden insights
Expand analysis to include additional social media platforms for broader insights
Refine segmentation
Leverage customer insights to refine segmentation strategies and enhance personalization
Boost engagement
Enhance targeted marketing with AI-driven recommendations for greater engagement
