AI Driven Customer Segmentation and Targeting Workflow Guide

AI-Driven Customer Segmentation and Targeting

1. Data Collection

1.1 Identify Data Sources

Gather data from various sources including:

• Customer Relationship Management (CRM) systems
• Website analytics (Google Analytics)
• Social media platforms
• Email marketing platforms

1.2 Data Integration

Utilize tools such as:

• Apache Kafka for real-time data integration
• Zapier for connecting web applications

2. Data Preprocessing

2.1 Data Cleaning

Remove duplicates and irrelevant data using:

• Pandas (Python library)
• OpenRefine

2.2 Data Transformation

Standardize data formats and normalize data using:

• DataRobot
• Alteryx

3. Customer Segmentation

3.1 Define Segmentation Criteria

Identify key attributes for segmentation such as:

• Demographics
• Behavioral patterns
• Purchase history

3.2 Implement AI Algorithms

Utilize machine learning algorithms for segmentation:

• K-means clustering
• Hierarchical clustering

Tools to implement these algorithms include:

• TensorFlow
• Scikit-learn

4. Targeting Strategy Development

4.1 Analyze Segmented Groups

Evaluate the characteristics and needs of each segment using:

• Tableau for data visualization
• Google Data Studio

4.2 Develop Targeted Campaigns

Create tailored marketing strategies for each segment:

• Email marketing with Mailchimp
• Social media advertising using Facebook Ads Manager

5. Campaign Execution

5.1 Automate Campaigns

Use AI-driven tools for automation:

• HubSpot for inbound marketing automation
• Marketo for lead management and engagement

5.2 Monitor Campaign Performance

Track key performance indicators (KPIs) using:

• Google Analytics for web traffic analysis
• Sprout Social for social media performance

6. Continuous Improvement

6.1 Analyze Results

Review campaign outcomes and customer feedback to assess effectiveness.

6.2 Refine Segmentation and Targeting

Utilize insights gained to improve future segmentation strategies and targeting methods.