Enterprise Branding Automation

Enterprise Content Automation

Overview

This branding automation system transforms master training presentations into retailer-specific branded versions automatically. What previously took 20+ hours of manual work now completes in 5-10 minutes, enabling rapid deployment of training materials to 7+ retail partners.

The Problem

The client produces training materials for multiple retail partners, each requiring:

• Custom branding (logos, colors, partner names)
• Consistent quality across all materials
• Rapid turnaround for partner requests
• Multiple output formats (slides, Brainshark modules)

Manual rebranding was:

• Extremely time-consuming (20+ hours per partner)
• Error-prone (missed slides, inconsistent styling)
• Difficult to scale (new partners = repeat all work)

Solution Architecture

Master PPTX (292 slides)
         |
    Python Processor
         |
   ┌─────┴─────┐
   |     |     |
 Logo  Color  Text
Replace Match Replace
   |     |     |
   └─────┬─────┘
         |
    51 Output Files
    (Brainshark-ready)

Key Features

Dynamic Logo Replacement

Intelligent logo detection and replacement:

• Position-aware placement
• Automatic sizing to match original
• Transparency handling for complex logos

Fuzzy Color Matching

def match_brand_color(source_color: RGB, brand_palette: List[RGB]) -> RGB:
    """Find closest brand color using LAB color space"""
    source_lab = rgb_to_lab(source_color)
 
    best_match = min(
        brand_palette,
        key=lambda c: delta_e(source_lab, rgb_to_lab(c))
    )
 
    return best_match

Handles color variations in source files:

• Gradient colors
• Slight color variations
• Theme vs. explicit colors

Text Token Replacement

Partner-specific text replacement:

• Company names
• Contact information
• Custom messaging
• Slide-specific content

Batch Processing

Single execution processes entire presentation:

• 292 slides analyzed
• 51 output files generated
• Full audit log created

Technical Implementation

PPTX Processing

from pptx import Presentation
from pptx.util import Inches, Pt
 
def process_slide(slide, brand_config):
    # Process shapes
    for shape in slide.shapes:
        if shape.has_text_frame:
            replace_text(shape, brand_config.text_map)
 
        if is_logo(shape):
            replace_logo(shape, brand_config.logo_path)
 
        if has_fill_color(shape):
            remap_color(shape, brand_config.color_map)

Image Processing

Pillow for logo manipulation:

• Resize to match original dimensions
• Handle transparency (PNG with alpha)
• Color space conversion when needed

VBA Macro Injection

Brainshark-ready outputs include:

• Navigation macros
• Timing configuration
• Audio sync points

Results

The automation delivers:

| Metric | Before | After | |--------|--------|-------| | Time per partner | 20+ hours | 5-10 minutes | | Error rate | ~5% | Under 0.1% | | Partners supported | 3 | 7+ | | Scalability | Limited | Unlimited |

Efficiency Gain

97% reduction in processing time

Quality Improvement

• Consistent branding across all slides
• Zero missed replacements
• Audit trail for verification

Sample Output

For each retail partner, the system produces:

• 51 individual PPTX modules
• Named with partner prefix
• Brainshark macro-enabled
• Ready for immediate deployment

This project demonstrates file format mastery, image processing, and building automation that dramatically improves operational efficiency for enterprise clients.