Opportunity Scoring Algorithm

How Scoring Works

Every opportunity goes through a multi-factor scoring process:

1. Base Score Calculation - Each source type (Search Console, Trends, Keyword Discovery, etc.) has its own formula based on relevant metrics
2. Source Weight Adjustment - High-value source types receive a multiplier boost
3. Brand Relevance Check - Keywords matching your brand or competitors are adjusted accordingly
4. Preference Learning - Your approval/rejection history personalizes future scores
5. Final Score - Normalized to 0-100 for easy comparison across all opportunity types

Scoring Factors

Relevance to Your Brand/Audience

• Core head term match: +20% boost when keyword contains your primary head term
• Brand keyword match: +10% boost for keywords matching your configured brand terms
• Competitor filtering: Keywords containing competitor brand names are automatically filtered out

Configure these in Settings > Brand Keywords and Settings > Competitor Brands.

Timing/Urgency

Opportunities with time sensitivity receive urgency multipliers:

Rising searches also decay over time - a "rising" trend from 60+ days ago receives reduced weight since the momentum has likely passed.

Competition Level (Keyword Difficulty)

Lower difficulty keywords score higher when all else is equal:

Estimated Effort (Content Complexity)

For keyword and content gap opportunities:

• Simple topics (low word count targets) receive a slight boost
• Complex topics requiring research-heavy content are scored based on their value potential

Potential Impact (Search Volume & Traffic)

Search volume contributes up to 50 points using a logarithmic scale:

The logarithmic scale prevents extremely high-volume keywords from completely dominating your feed.

Source Type Weights

Different opportunity sources have different base multipliers based on their strategic value:

Score Ranges and What They Mean

How to Filter by Score

In the Discover feed:

1. Sort by Score: Click the "Score" column header to sort highest-to-lowest or vice versa
2. Filter by Status: Use the status tabs (New, Snoozed, Accepted, Rejected) to focus on specific opportunities
3. Filter by Source: Click a source type badge to see only opportunities from that source
4. Filter by Intent: Narrow to informational, commercial, transactional, or navigational queries
5. Filter by Funnel: Focus on top, middle, or bottom of funnel opportunities

Why Some Opportunities Rank Higher

An opportunity with a score of 85 vs 65 typically has:

• Higher search volume - More potential traffic
• Lower keyword difficulty - Easier to rank
• Better brand alignment - Matches your configured keywords
• Time urgency - Event approaching or trend accelerating
• Source weight boost - From a high-value source like Content Gap or Arbitrage
• Entity alignment - Matches uncovered topics in your semantic map

Multiple factors compound. A trending keyword (1.10x) with high brand relevance (1.2x) and approaching deadline (1.3x) can see significant score amplification.

Preference Learning: How Your Actions Improve Scoring

ILLIXIS learns from your decisions to personalize future recommendations.

Signals That Train the Model

• Brief Approved - You want more like this
• Brief Rejected - You want fewer like this
• Planner Selected - Added to weekly plan
• Planner Rejected - Removed from weekly plan
• Content Published - Ultimately valuable enough to publish

When Learning Activates

The preference model requires at least 30 signals before it starts adjusting scores. After that threshold:

• Model retrains automatically as you make decisions
• Accuracy improves as more signals are collected
• Your preferences for volume vs. difficulty, trending vs. evergreen, etc. are encoded

What Gets Learned

The model tracks your preferences across 8 dimensions:

1. Keyword Volume - Do you prefer high-volume or niche keywords?
2. Keyword Difficulty - Do you tackle competitive topics or easy wins?
3. Opportunity Score - Base preference for high/low scoring opportunities
4. Trend Velocity - Fast-moving trends vs. stable topics
5. Competitor Count - Crowded vs. uncrowded SERPs
6. Content Gap Score - Priority for gap-filling content
7. Time Sensitivity - Urgent vs. evergreen content
8. Brand Relevance - On-brand vs. adjacent topics

Check your preference profile in Settings > Preference Learning to see what the model has learned about your content strategy.

Rejection Patterns

When you repeatedly reject certain opportunity types, ILLIXIS detects patterns and automatically applies penalties to similar opportunities. This includes:

• Keywords below a volume threshold you consistently reject
• Topics with difficulty scores you avoid
• Source types you rarely act on

Manual Score Adjustments

While ILLIXIS calculates scores automatically, the system respects your decisions:

• Snoozing an opportunity hides it temporarily without affecting its score
• Rejecting an opportunity archives it and contributes to rejection pattern learning
• Accepting an opportunity promotes it to brief creation

The underlying score remains calculated algorithmically. If you want to change how scores are calculated for your account:

1. Adjust your brand keywords to boost relevant topics
2. Add competitor brands to filter unwanted keywords
3. Continue making approval/rejection decisions to train the preference model
4. Review preference learning settings to see and understand your learned weights

Recalculating Scores

Opportunity scores are recalculated when:

• New metric data arrives (search volume updates, ranking changes)
• Your preference model is retrained
• You add/remove brand keywords or competitors
• Temporal factors change (event dates approaching)

Scores on existing opportunities update automatically. You do not need to take any action.