Algorithmic ASO Keyword Portfolio Allocation Pipeline

Role: Lead App Store Optimization (ASO) Data Scientist

Context

• Raw Candidate Keywords Dataset: {{raw_keyword_dataset}}
• Competitor Metadata JSON: {{competitor_metadata_json}}
• Metadata Slot Constraints: {{metadata_slot_constraints}}
• Minimum Search Volume Threshold: {{min_search_volume_threshold}}
• Keyword Difficulty Decay Factor (Alpha): {{decay_factor_weight}}
• App Category Taxonomy: {{app_category_taxonomy}}

Task

Write a Python optimization script that processes {{raw_keyword_dataset}}, calculates keyword opportunity scores via non-linear utility functions factoring in {{decay_factor_weight}}, and solves a multi-choice knapsack optimization problem to maximize keyword reach within the exact string constraints in {{metadata_slot_constraints}}.

Method

1. Ingest candidate keywords from {{raw_keyword_dataset}} and filter entries below {{min_search_volume_threshold}}.
2. Extract competitor keyword frequency and positioning metrics from {{competitor_metadata_json}}.
3. Compute semantic token uniqueness and eliminate cross-field duplicates between Title, Subtitle, and Keyword Field.
4. Calculate an Opportunity Index for each token: Score = (Volume^1.0) / (Difficulty^alpha) using {{decay_factor_weight}}.
5. Formulate a 0/1 Integer Linear Programming (ILP) or greedy knapsack model constrained by {{metadata_slot_constraints}}.
6. Optimize character budget packing (e.g., Title: 30 chars, Subtitle: 30 chars, Keywords: 100 chars comma-separated).
7. Output the allocated metadata sets along with mathematical efficiency ratios and coverage diagnostics.

Constraints

• MUST use standard mathematical optimization libraries (e.g., PuLP or SciPy) for the allocation solver.
• MUST NOT exceed the character ceiling specified for any metadata field in {{metadata_slot_constraints}}.
• Repetitive words across different fields MUST be penalized to zero value to preserve byte budget.
• All text transformations MUST preserve international UTF-8 character length accuracy.

Output format

Fully commented Python 3 script structured into:

• Math formulation docstring defining the objective function and constraint equations
• Tokenization, cleaning, and de-duplication pipeline functions
• Linear Programming allocation solver class
• Reporting routine printing allocated slots, remaining character padding, and total predicted portfolio reach

Self-review

• Check that comma separators in the App Store keyword field are correctly counted in the 100-character budget.
• Verify that the decay parameter {{decay_factor_weight}} correctly penalizes high-difficulty keywords.
• Ensure non-ASCII string handling computes character counts rather than raw byte counts.