Master Positioning LLC × Salterra Digital Services v1.0
Audience
Marty Marion's
Search Optimization Opportunity Score Model
A new approach to competitive keyword determination for Search Optimization · Built on the Salterra CDS platform
Methodology Edition May 2026 Production Ready
Project
Methodology · May 2026

A new approach to competitive keyword determination for Search Optimization

Conventional keyword scoring rewards activity: how big the search volume is, how hard the keyword is, how often it appears. The Opportunity Score rewards capture — where measurable demand, a measurable competitive gap, realistic ranking mobility, commercial intent, and a sensible effort profile line up at the same time.

Methodology by Marty Marion · Master Positioning LLC · Author, Advanced Brand Positioning and the De-Positioning Matrix · Tool by Salterra Digital Services · Phoenix, AZ
Methodology v1.0 · May 2026 · Next review Q4 2026 · MOSS (Marty's Opportunity Scoring System) — The Capture Equation™
01 · Transparent
Published formula, not a black box
Every score is reproducible from inputs. The full math, multipliers, and intent weights are documented on this page — defensible in a client meeting.
02 · Forecasted
Scores converted to revenue dollars
Calibration tab turns the opportunity index into directional traffic, conversion, and dollar projections per keyword — so the deck has a number, not just a rank.
03 · Deliverable
Client-ready exports in one click
CSV for the data team, co-branded PDF for the client, executive summary for the inbox. The artifact a strategist and a client can both read the same way.
See it in 30 seconds
Skip the data entry — score Marty's worked example now
One click loads 12 keywords in the iron-supplement vertical (the worked example from this methodology) and jumps you to Phase 2. See exactly what the deliverable looks like before you bring your own data.
From the source material
"The brands that win in search are not the ones chasing the biggest keywords — they're the ones executing where measurable demand and a measurable competitive gap line up at the same time."
Marty Marion · Advanced Brand Positioning and the De-Positioning Matrix
Methodology & speaking credentials
SEOSTConference speaker
SEO UniversityFaculty / speaker
Master Positioning LLCMethodology IP holder
Salterra Digital ServicesPhoenix, AZ · est. 2009
The practical business question this Master Positioning × Salterra tool answers
"Where can your brand most efficiently capture organic demand that competitors are currently winning?"
What this isn't
The Opportunity Score is a structured opportunity index, not a traffic forecast and not a revenue guarantee. Forecasting (clicks, conversions, dollars) is layered on top via the Calibration tab and is explicitly directional — see "From score to revenue" below for the bridge. Treat scores as a ranked execution queue, not a prediction.
What you get
Ranked, scored, revenue-sized — in one view
A live preview of the Results tab using the iron-supplement worked example. Edit any cell to recalculate; export to CSV or co-branded PDF.
Preview
9.7
safest iron supplement for kids
Commercial · diff 19 · pos 19→4
720
vol/mo
180
est clicks/mo
$3,420/mo
9.3
best iron supplement for athletes
Commercial · diff 35 · pos 18→4 · Marty's worked example
2,400
vol/mo
540
est clicks/mo
$10,260/mo
7.4
iron supplement vs ferrous sulfate
Commercial · diff 22 · pos 28→5
590
vol/mo
105
est clicks/mo
$1,995/mo
6.8
iron supplement for anemia
Commercial · diff 42 · pos 24→6
5,400
vol/mo
810
est clicks/mo
$15,390/mo
4.1
iron supplement side effects
Informational · diff 56 · pos 45→8 · discounted for intent
9,900
vol/mo
180
est clicks/mo
$540/mo
Static preview · 5 of 12 sample rows shown · click Try it now above to load the full dataset and explore the live, editable Results tab.
Built for
Who this tool is for
AG
SEO agencies selling to clients
Replace gut-feel keyword decks with a defensible, transparent ranking your client can read in 60 seconds.
Use it for: new-client onboarding audits, quarterly opportunity refreshes, and co-branded pitch deliverables.
IH
In-house SEO defending budget
Turn a list of keywords into a list of dollars. Show leadership why this content sprint, not the obvious one.
Use it for: roadmap prioritization, quarterly planning, and CFO-grade revenue-impact briefs.
SC
Solo consultants & strategists
A repeatable, branded methodology that scales your judgment — without spreadsheet gymnastics or per-seat tool fees.
Use it for: discovery calls, paid audits, and proof-of-value before a retainer conversation.

Versus the conventional approach

Most keyword tools score in isolation: how big, how hard, how trafficked. They produce a leaderboard of vanity terms — the keywords every competitor is also chasing, often because they're the most obvious, not because they're the most winnable. The Opportunity Score is built on the opposite premise.

Conventional keyword scoring
Optimizes for activity
  • →Ranks by volume, difficulty, or a generic composite
  • →Treats your current rank as informational, not strategic
  • →Ignores the competitor's position — the actual asset you're attacking
  • →Weights all intent equally, including navigational
  • →Surfaces ambitious vanity terms that look great in decks and never move
Opportunity Score · MOSS — Marty's Opportunity Scoring System
Optimizes for capture
  • ✦Ranks by the combination of demand, gap, proximity, intent, and difficulty
  • ✦Treats your current position as the launching point for realistic gain
  • ✦The competitor's position is the asset you're attacking — and the model says so
  • ✦Intent is weighted by revenue alignment; navigational is heavily discounted
  • ✦Surfaces the winnable middle: keywords where one well-targeted push moves the needle

Five principles

The model is built on five tenets. Each one was chosen in deliberate opposition to a common failure mode in conventional keyword prioritization.

Principle 01
Demand without contestability is theater.
Big volume on a keyword you can't realistically rank for is not an opportunity. It is a line item in a vanity report.
Principle 02
The gap is the asset.
Your competitor's lead over you is not a deficit — it is a measurable, attackable position. The bigger and more recent the gap, the clearer the target.
Principle 03
Proximity beats ambition.
Moving from page 2 to page 1 produces far more revenue per hour invested than chasing top-3 placement from nowhere. The model rewards reachability.
Principle 04
Intent is economics.
A transactional keyword and a navigational keyword are not comparable demand. The model treats them as economically different signals — because they are.
Principle 05
Multiplication enforces discipline.
Multiplying — not adding — guarantees that any one weak factor disqualifies the keyword. Strong on four dimensions and weak on one is still a no.

The formula

Opportunity Score =
log10(Search Volume + 1) × log10(1 + Rank Gap) × Proximity Multiplier × Intent Multiplier × Difficulty Multiplier

Each component plays a specific role in ensuring the prioritization is balanced, realistic, and aligned with business impact — because in organic channel optimization, competitive advantage is everything: for brands and clients, for search engine algorithms, and for AI and LLM inclusions and mentions.

The five components

01
Search Volume Component
log₁₀(Search Volume + 1)

Search volume measures how many times a keyword is searched each month on average. Higher search volume means more potential traffic — but raw search volume is misleading because click-through rates, conversion rates, and competition do not scale linearly with volume. The logarithmic transformation compresses extreme numbers so high-volume keywords stay important without overwhelming every other factor. Adding 1 prevents a math error when volume is zero.

02
Rank Gap Component
log₁₀(1 + Rank Gap)

Rank Gap = Your Brand Position − Competitor Position. If a competitor ranks at position 4 and your brand ranks at position 24, the gap is 20 — the competitor is 20 positions ahead, and that represents lost traffic and revenue opportunity. The log transform prevents extreme gaps (positions 80–100 down) from overpowering modest gaps that are actually winnable.

03
Proximity Multiplier
Ranking distance adjustment

Ranking movement is not linear. Moving from position 18 to position 9 is often achievable in weeks — Google already considers the page relevant. Moving from position 90 to position 9 requires structural improvements and significant effort. The Proximity Multiplier boosts the score when your brand sits between positions 11 and 30 with a competitor in the top 10, because that's where realistic near-term wins live.

04
Intent Multiplier
Revenue alignment adjustment

Not all searches are equal. The model uses the four traditional intent categories: Transactional, Commercial, Informational, and Navigational. Commercial and transactional keywords get boosted because they align directly with revenue. Informational stays neutral. Navigational is heavily discounted because the searcher already has a specific brand in mind.

05
Difficulty Multiplier
Effort-to-reward adjustment

Difficulty estimates how entrenched the competition is — strong authority domains, deep backlink profiles, stable ranking incumbents. Lower difficulty means faster ranking movement. The multiplier slightly favors lower-difficulty keywords without excluding ambitious targets. It balances ambition with realism.

Why multiply, not add

Design principle
The model multiplies rather than adds. If any one factor is weak, it meaningfully reduces the total score. High volume with low proximity won't dominate. Low difficulty with low demand won't rise to the top. This produces disciplined prioritization — not a leaderboard of vanity terms.

Compared to industry-standard scoring

Most SEO teams already use a keyword priority score from Ahrefs, SEMrush, or Moz. None of them are wrong — they answer a different question. This is where the Opportunity Score sits in that landscape:

At-a-glance feature comparison across keyword-prioritization scores
Capability Ahrefs
Traffic Potential		 SEMrush
KD %		 Moz
Priority Score		 MOSS
Opportunity Score
Ranks by raw demand (volume) ● ○ ● ●
Models keyword difficulty ◐ ● ● ●
Factors in your current rank position ○ ○ ○ ●
Models the competitor gap as the asset ○ ○ ○ ●
Weights by commercial intent ○ ○ ◐ ●
Produces revenue-dollar forecasts ○ ○ ○ ●
Transparent, published formula ○ ○ ◐ ●
Co-branded, client-ready PDF export ○ ○ ○ ●
● full · ◐ partial · ○ not modeled. Ahrefs/SEMrush/Moz remain the canonical sources for volume, difficulty, and position inputs — MOSS sits on top as the prioritization layer.
Comparison of common keyword-prioritization scores
ScoreWhat it optimizes forWhat it doesn't model
Ahrefs Traffic Potential Total topical traffic the #1 page captures across all related queries Your current gap; near-term winnability; intent quality
SEMrush Keyword Difficulty How hard the keyword is to rank for, expressed 0–100 Demand alignment; your position; revenue intent; opportunity ranking
Moz Priority Score Composite of volume, difficulty, and organic CTR potential Competitor-relative gap; proximity logic; multi-competitor overlap
Volume-only sort Where the audience is, in absolute terms Everything else. The dominant failure mode of vanity SEO strategy.
Opportunity Score (MOSS) Realistically capturable demand where a competitor is currently winning Acts as the priority layer above these tools — does not replace their inputs

In practice, agencies that use this model continue to pull volume from Ahrefs or SEMrush, continue to pull difficulty from those same providers, and then run the Opportunity Score on top to produce the actual execution sequence. The score is an opinionated prioritization layer, not a replacement for industry data sources.

Multiplier value tables

These are the default multiplier values built into the calculator. They are Marty Marion's documented baselines, calibrated against agency portfolios across consumer, B2B SaaS, healthcare, and direct-to-consumer commerce. They can be tuned per-category in the Calibration tab.

Proximity Multiplier — by your brand's current position
Your Brand PositionMultiplierLogic
Positions 1 – 100.8×Already on page one. Easy gain has been captured.
Positions 11 – 201.5×Page two. Most realistically movable. The sweet spot.
Positions 21 – 301.3×Within striking distance. Partial authority signals present.
Positions 31 – 501.0×Neutral. Movement possible but requires effort.
Positions 51 – 1000.6×Disproportionate effort required.
Not ranking (101+)0.3×Requires structural campaigns. Not near-term.
Intent Multiplier — by traditional search intent
Intent ClassificationMultiplierLogic
Transactional1.5×Closest to buying decision. Highest revenue alignment.
Commercial1.3×Active evaluation. Strong buyer signal.
Informational1.0×Neutral. Top-of-funnel research behavior.
Navigational0.4×Searcher already has a specific brand in mind.
Difficulty Multiplier — by keyword difficulty score (Ahrefs / SEMrush, 0 – 100)
Difficulty ScoreMultiplierLogic
0 – 201.4×Volatile category. Fast movement potential.
21 – 401.2×Modest competitive entrenchment.
41 – 601.0×Neutral. Standard competition.
61 – 800.8×Entrenched competition. Requires sustained effort.
81 – 1000.6×Authority-heavy SERP. Slow movement expected.

AI Overview & the modern CTR curve

The single largest change to organic search behavior between 2022 and 2026 has been Google's AI Overview rollout. AIO compresses click-through on positions 1–5 by an average of ~35 % across categories, with informational queries hit hardest and transactional queries comparatively spared. Any prioritization model that uses a pre-AIO CTR curve will systematically over-value top-of-page keywords. The Opportunity Score's revenue forecast (Calibration tab) ships with Salterra's 2026-calibrated CTR curve baked in.

CTR by SERP position — Pre-AIO baseline vs. Salterra 2026 calibration
CTR by SERP position — pre-AIO baseline declines from 30% at position 1 to 2% at position 10; Salterra 2026 post-AIO calibration declines from 22% at position 1 to 2.7% at positions 6-10. 30% 20% 10% 0% P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 SERP position
Pre-AIO (industry baseline, 2022)
Salterra 2026 calibration (post-AIO)
What this means: The position 1 CTR has fallen from ~30 % to ~22 % under AI Overview. A model that uses the old curve will inflate revenue forecasts on top-of-page wins by 30–40 %. The Opportunity Score's forecast tab uses the calibrated 2026 curve by default and is fully tunable in the Calibration tab.

What a typical score distribution looks like

A single Opportunity Score is meaningless in isolation — it is meaningful only relative to the rest of the keyword set. In a typical dataset of 400–1,200 keywords, the score distribution is heavily right-skewed: most keywords cluster in the low-to-middle range, and a small minority surface as genuine high-priority targets. That shape is intentional and is the disciplining force of the model.

Typical Opportunity Score distribution across a ~600-keyword dataset
Typical Opportunity Score distribution — right-skewed histogram across 8 buckets from 0 to 16, with the right tail (score 10+) representing the top 10% highest-priority keywords. 0–2 2–4 4–6 6–8 8–10 10–12 12–14 14–16 Opportunity Score bucket Top 10 % cutoff →
Bottom 50 % — low-yield
Middle 40 % — secondary
Top 10 % — highest priority
How to read this: The right tail is where the work is. The model's job is not to flatten that tail — it is to make sure the keywords in it are the ones worth ranking.

Limitations & edge cases

The model is opinionated and deliberately simple. The simplicity is what makes it defensible — but a few categories of keywords require care. A leading SEO team should know these going in, not discover them downstream.

Zero-volume long-tail

Keywords with reported volume 0 still produce a score of 0 (the log component collapses). Some of these are genuine intent signals. Use the long-tail discovery in Phase 1, then promote manually if needed.

Local SERPs with map packs

The model is built for the organic 10 blue links. Map pack and Local Pack categories warp CTR dramatically. For local-heavy verticals, calibrate the CTR curve aggressively and treat scores as directional.

Featured snippets & AI Overview

Position 1 with a featured snippet behaves very differently from position 1 without one. The model doesn't account for SERP feature presence per keyword. The default CTR curve assumes mixed-feature SERPs.

Difficulty-source variance

Keyword difficulty scores from Ahrefs, SEMrush, Moz, and Surfer disagree by 10–30 points on the same keyword. The model accepts whatever difficulty you provide — be consistent within a dataset.

Brand-vs-generic confusion at P1

If a competitor ranks #1 with their brand name embedded in the keyword, the gap is not contestable through standard optimization. Use the branded-strip filter in Phase 2 to clear these.

Time-to-rank variance

"Realistic near-term wins" assumes the brand has baseline domain authority. Sub-DR-20 sites should expect proximity wins to take 2–4× longer than the model implies; calibrate effort hours in the Calibration tab.

A worked example

Here is a sample keyword carried all the way through the model. The example uses two competitors (the typical Phase 2 input shape) and applies intent-depth weighting.

Worked example · 2-competitor case
"best iron supplement for athletes"
Search Volume 2,400
Your Position 18
Competitor A 4
Competitor B 7
Intent Commercial
Intent depth Deep
Difficulty 35
Rank Gap = 18 − 4 = 14
Volume Component = log₁₀(2,400 + 1) = 3.38
Rank Gap Component = log₁₀(1 + 14) = 1.18
Proximity Multiplier (position 18 → bucket 11–20) = 1.5
Intent Multiplier (Commercial) = 1.3
Difficulty Multiplier (35 → bucket 21–40) = 1.2
Opportunity Score
3.38 × 1.18 × 1.5 × 1.3 × 1.2
9.33

That keyword now sits in the prioritized list with a score of 9.33. Every other keyword in the dataset is calculated the same way, and the sort order tells you where to focus first.

Interpreting the scores

Opportunity Scores are relative, not absolute. A score of 9.33 is meaningless on its own — it is meaningful only compared to the rest of the list. In a typical dataset, scores range from roughly 0.5 at the bottom to 15 at the top, though this varies by category and dataset size.

Top 10%
Highest priority
Where optimization effort produces the highest return relative to effort. Focus here first.
Middle 40%
Secondary priorities
Worth pursuing once top-tier targets are in motion. Build content authority around these.
Bottom 50%
Low-yield targets
Typically does not justify dedicated investment unless it supports broader topical authority goals.

Calibration for your category

The default multiplier values above work reliably across most categories — but they can be adjusted based on the specific dynamics of your market. In a category where transactional keywords are unusually competitive, you may raise the Commercial multiplier slightly to surface mid-funnel opportunities. In a high-volatility category, you may raise the Proximity multiplier for positions 21 – 30 to capture more near-term movement potential.

Use the Calibration tab to tune the values directly. Calibration is a refinement, not a requirement — the default model produces a defensible, actionable priority list out of the box.

From score to revenue

Salterra forecasting extension
Opportunity Score → projected monthly revenue

The Opportunity Score answers "where should we focus first?" The Salterra forecasting layer answers "and what is that worth in dollars?" The two are deliberately separated. The score is defensible across categories and audiences; the forecast is directional and depends on calibration inputs that change per business.

The forecast multiplies projected click-gain at the target position by the configured conversion rate and value-per-conversion. All three of these inputs are tunable in the Calibration tab and are bounded explicitly so the projection cannot run away from reality.

Volume × ΔCTR (target − current) × Conversion rate × $ / conversion = Projected monthly $

What the Opportunity Score represents

The final score is not a traffic forecast. It is a structured opportunity index. Higher scores indicate strong demand, measurable competitive disadvantage, realistic ranking mobility, commercial relevance, and an efficient effort-to-reward ratio.

This framework ensures your brand does not chase vanity traffic, overreact to extreme rank gaps, overinvest in impractical battles, ignore revenue alignment, or misallocate SEO resources. Instead, it identifies where competitors are capturing category demand and ranks those opportunities by realistic, near-term impact.

In summary
It transforms competitive ranking data into a disciplined execution plan. And it works.
Brand profile
Phase 1 · Brand intake & demand mapping

Phase 1 is where you tell us the brand.
Phase 2 is where we tell you the opportunity.

A keyword universe built without positioning is a category search. This intake captures the brand's value proposition, what it is and isn't, who it serves, and at what stage of the buyer journey — then Our Proprietary System maps the competitive demand landscape that actually matches.

1. Project setup

Context that helps Our Proprietary System generate relevant keywords
Marty's Phase 1 Step 21 — any keyword containing one of these brand tokens is treated as branded navigation (not contestable through standard optimization) and filtered out. Also exposed as a one-click "Strip branded" bulk action in Phase 2.
Brand positioning · Marty Marion intake

The brand strategy lens

These five fields shape every keyword Our Proprietary System generates. Without them, you get a category map. With them, you get a category map filtered through what the brand actually stands for, who it is for, and what it is not.

What the brand promises, in one line. Drives "best for…" and "with…" keyword variants.
The single defensible thing competitors don't have.
Bias toward keyword language that matches.
Marty's de-positioning move. A premium brand should not be ranked for "cheap" — that's positioning suicide. Any keyword Our Proprietary System considers that matches a concept here is flagged as off-brand and excluded.
Marty's five-stage model. Choose the dominant stage you want to acquire — Our Proprietary System weights expansion accordingly. "Mixed" coverage is the default if no stage is selected; choose one to focus.

2. Keyword sources

Either provide seed keywords directly, or paste 1–5 competitor URLs — this tool will fetch each page server-side and let Our Proprietary System analyze the content to discover their ranking themes.
5 – 20 core terms, one per line. Our Proprietary System expands each into variants and modifier combinations.
0 seeds entered · 0 unique after normalization · Add seeds
Pro tip — Salterra workflow: if you have a Google Search Console export, an Ahrefs Organic Keywords CSV, or an SEMrush Positions export, paste the query / keyword column here as seeds. After Phase 2 scoring, the Enrich tab will rejoin the volume, position, and difficulty automatically.
Marty's Phase 1 Step 18 — discover true organic-overlap competitors from your project context. Fill in Project name, category, and description above first.
1–5 competitor URLs (homepage or category pages work best). This tool fetches each page on the server, extracts the readable content (title, headings, body), and Our Proprietary System expands it into 30–60 keyword opportunities you could realistically compete for. Note: JavaScript-rendered pages may yield less content than server-rendered HTML.

3. Run options

Control how aggressively Our Proprietary System expands the universe and what minimum-volume floor to enforce.
Marty's Step 14 floor. Our Proprietary System will avoid generating long-tail keywords whose likely volume falls below this.
Before you click — what Our Proprietary System will receive

Run preview

Project
— fill in project name & category above
Mode & sources
— add seeds or competitor URLs
Audience & locale
—
Buyer-journey stage
Solution-Aware
Positioning signal
— no value prop / differentiator yet
Exclusions
— no anti-keywords or competitor brands
Expected output: ~100 keywords across ~6 clusters · est. cost ~$0.04 · est. time ~30s
billed to Replit credits · roughly a few cents per run
One Our Proprietary System call expands your seeds (or competitor pages) into keyword variants, classifies each by search intent and intent depth, assigns each to a topical content cluster, and flags off-brand or off-category terms. You review every row before sending the qualified list to Phase 2.
Results will appear here
Each generated keyword arrives with the columns below. You can edit, bulk-reassign clusters, and drop flagged junk before promoting to Phase 2.
Keyword Intent Intent depth Cluster AI reason Junk flag
Phase 2 · The verdict

Where demand meets capture

Phase 1 mapped the landscape. This is where the math turns it into a ranked list of contestable opportunities — keywords your brand can actually win, sized by traffic, revenue, effort and time-to-rank. Edit any cell to recalculate live.

Loads 12 sample keywords in the iron-supplement vertical, including the worked example from the methodology. Fastest way to see how the model behaves across the full input range (low/high volume, on/off page one, all four intents, full difficulty range).

Paste CSV or tab-separated data from a spreadsheet. Required columns (any order, flexible header naming): keyword, search_volume, your_position, competitor_position, intent, difficulty. ↓ Download template

Add keywords one at a time. Every cell in the table below is fully editable — type or paste into any input. Click + Add row to grow the dataset, then Score the landscape when you're ready.

Upload a .csv file with headers. Same column requirements as paste. Drag-and-drop is supported.

If keywords are already in the table (from Phase 1 or manual entry), upload Ahrefs/SEMrush exports to fill in volume, your position, competitor position, and difficulty automatically. The tool matches by keyword text and only fills empty cells unless you opt to overwrite.

1

Your domain rankings

Paste or upload your Ahrefs/SEMrush "Organic Keywords" export. Required: a keyword column. Optional but recommended: volume, position, difficulty (KD). Column naming is flexible — common variants are auto-detected.
No data loaded
2

Competitor rankings

Add one or more competitor exports. Required: keyword + position columns. For each keyword, the tool will use the best (lowest) competitor position across all competitors — implementing Marty's step 27 ranking-across-domains logic.
Matches by keyword text (case-insensitive, whitespace-tolerant). Empty cells are filled; existing values are preserved unless you check the box below.

Input dataset

0 keywords loaded
Keyword Current URL Search Volume Your Position Competitor Position Intent Difficulty Tags Notes Live
Results · Master Positioning × Salterra

Where to compete, where to wait, where to walk

Your scored keyword universe, sorted by where capture is most contestable in the next 90 days. Three lenses on the same list: the headline strategic read, the operator's working board, and the cluster view for content planning.

Prioritized keywords

No keywords scored yet — load data and click Calculate in Phase 2.
# Keyword URL Score Tier Volume Your pos Target Traffic Δ Revenue Δ Effort Time Intent Diff State
No scored data yetGo to Phase 2 — Calculator, load or enter keywords, then click Calculate scores. Results will appear here automatically.

Snapshots

Save versioned snapshots of scored results · stored in this browser only. S15: name each snapshot and tag it (e.g. pre-launch, after May audit) for easy recall.
Powered by Salterra CDS
Want help capturing these priorities?
Salterra's CDS team builds the content, the technical SEO, and the AI authority infrastructure to capture the top opportunities from your scored list — combining Marty Marion's Opportunity Score Model with the De-Positioning Matrix for a complete competitive displacement system.
Talk to Salterra ▸
Methodology · Calibration

Tune the math behind your tiers

Calibration is a positioning act, not a settings page. The default multipliers are right for most markets; changing them tells the system your category's economics — what counts as Tier 1, what's a real revenue swing, what a "win" looks like. Preset → tune → see live impact → Apply.

Calibration preset

Proximity Multiplier

based on Your Position

Rewards keywords where your brand is within realistic striking distance of page one. Page-two keywords (positions 11–20) get the highest weight because they're the most movable.

What this controls
Proximity is the "how close to a win" dial. Pages already ranking 11–20 are the cheapest movable wins on the internet — one solid optimization pass can push them to page one. Pages ranking 51+ are valuable only if the search volume justifies the climb.
e.g. Local Service preset boosts tier1 (1–10) → 1.4, because in local-pack categories the position-1 click share is so dominant that defending #1 outranks chasing page-two keywords.
Positions 1 – 10 default 0.8
Positions 11 – 20 default 1.5
Positions 21 – 30 default 1.3
Positions 31 – 50 default 1.0
Positions 51 – 100 default 0.6
Not ranking (101+) default 0.3

Intent Multiplier

based on Intent Classification

Aligns scoring with revenue intent. Transactional and Commercial searches score higher because they correlate with buying behavior. Navigational is heavily discounted (the searcher already has a brand in mind).

What this controls
Intent weights tell the model what kind of traffic is worth chasing. Defaults assume a typical commercial site (transactional > commercial > informational > navigational). Two categories flip this: Publisher (informational matters most — page views are the product) and B2B SaaS (commercial / evaluation queries outrank raw transactional, because the buying cycle is research-heavy).
e.g. Publisher preset sets Informational → 1.6 and Transactional → 0.8, inverting the default.
Transactional default 1.5
Commercial default 1.3
Informational default 1.0
Navigational default 0.4

Difficulty Multiplier

based on Difficulty Score (0–100)

Balances ambition against realistic effort. Lower-difficulty keywords get boosted (faster wins), but high-difficulty keywords are not excluded — just slightly de-prioritized.

What this controls
Difficulty is the "how steep is the hill" dial. The curve is intentionally gentle — even an 81–100 difficulty keyword stays in play at 0.6× because raw difficulty doesn't tell you whether a category leader actually deserves the top slot. Don't flatten this curve to all 1.0 — that disables the multiplier entirely and lets impossibly hard keywords dominate the list.
e.g. B2B SaaS softens the high-end (tier5 → 0.7) because in enterprise software, "hard" keywords often have weak SERPs that defer to one PR-driven incumbent.
Difficulty 0 – 20 default 1.4
Difficulty 21 – 40 default 1.2
Difficulty 41 – 60 default 1.0
Difficulty 61 – 80 default 0.8
Difficulty 81 – 100 default 0.6

Forecasting & Business Value

Master Positioning × Salterra extension

Configures traffic and revenue projections per keyword. Search volume × position-based CTR estimates monthly traffic, then conversion rate × value-per-conversion produces revenue projections. Effort hours and time-to-rank are derived from current position + difficulty. All values are tunable per category.

What this controls
Forecasting converts ranking math into dollars. Conversion rate + value per conversion is the dollarization layer applied to projected traffic. The CTR curve is the single highest-leverage knob — even a small shift in pos-1 CTR can change every revenue forecast on the page. The target strategy defines where you're trying to land: matching the leader is safer; beating them is more ambitious. Min search volume filters noise — anything below the threshold is excluded from scoring entirely (Marty Phase 1 Step 14).
Conversion rate (%) default 1.5
Value per conversion ($) default $50
Target position strategy default: match
Min search volume default 0 (no filter)
Per-cluster CR & value overrides
Operator reality: branded queries convert at 5–10%, informational queries at 0.1–0.5%. One global conversion rate distorts every revenue forecast in mixed-intent portfolios. Set a per-cluster CR or value below to override the global default just for that cluster.
No clusters defined yet — assign keywords to clusters in Phase 1 or via the cluster: tag in Phase 2 to enable per-cluster overrides.

CTR curve — click-through rates by position. These are Master Positioning × Salterra 2026-calibrated baselines reflecting AI Overview impact and SERP feature saturation. Override for category-specific behavior (e.g. local pack categories have very different curves).

CTR curve · current shape
Pos 1 CTR (%) 22.0
Pos 2 CTR (%) 13.0
Pos 3 CTR (%) 9.0
Pos 4 CTR (%) 6.0
Pos 5 CTR (%) 4.5
Pos 6–10 avg (%) 2.7
Pos 11–20 avg (%) 0.5
Pos 21–100 avg (%) 0.15
Source: CTR baselines aggregate Advanced Web Ranking 2026 Q1 study, SISTRIX SERP-feature impact analysis (Mar 2026), and Salterra client-data adjustments for AI Overview deflation on informational queries (pos-1 deflated ~3pp vs. pre-AIO 2023 baselines).
Using documented defaults
About · Credits · IP notice

A published methodology, executed by a technical SEO veteran agency

Most keyword tools score with an opaque proprietary number. This one runs Marty Marion's documented Opportunity Score Model — formula, multipliers, and Phase 1 / Phase 2 framework published in full — engineered into a working tool by Salterra Digital Services, whose principals have spent decades on the technical SEO side of the same problem the model was designed to solve.

Methodology by
Marty Marion · Master Positioning LLC
Brand Positioning Strategist Author × 2 De-Positioning Matrix Intent Behind The Intent Opportunity Score Model
Built by
Terry Samuels · Co-founder, Salterra Digital Services
US Navy Veteran 30+ yrs software Technical SEO Schema & structured data Forensic SEO audits Site structure Franchise / multi-location SEOST Conference owner SEO University owner
Methodology
Marty Marion
Brand Positioning Strategist · Master Positioning LLC

The formula, the multiplier value tables, the Phase 1 / Phase 2 framework, and the worked example used throughout this tool are created and documented by Marty Marion. The methodology is fully published in the May 2026 edition of Marty Marion's SEO Opportunity Score Model paper — a transparent, defensible alternative to opaque proprietary scoring systems.

Marty's broader work positions companies and categories before they ever touch a SERP. The Opportunity Score Model is the search-layer expression of the same discipline: deciding which battles are worth fighting before spending a dollar on content or links.

Selected works
  • Advanced Brand Positioning and the De-Positioning Matrix — book
  • Marty Marion's SEO Opportunity Score Model — methodology paper (May 2026)
  • Intent Behind The Intent — search-intent framework extending beyond the four traditional categories
  • De-Positioning Matrix — competitive displacement framework

Email: marty@masterpositioning.com
Website: masterpositioning.com

Tool implementation
Terry Samuels
Co-founder · Salterra Digital Services · Phoenix, AZ

Built by Salterra Digital Services — a veteran-owned digital marketing agency co-founded by Terry & Elisabeth Samuels. Terry is a US Navy Veteran with 30+ years in software and computers and 10+ years running a digital marketing agency, specializing in technical SEO, schema markup, site structure, and forensic audits. He owns and hosts the SEOST Digital Marketing Conference and SEO University, where he teaches the same systems used in this tool.

The tool is part of the Salterra CDS platform — Competitive Displacement System — which integrates Marty's De-Positioning Matrix and Opportunity Score Model into an end-to-end workflow for local, national, and franchise clients.

SEOST Conference SEO University Speaker page

Phone: 602-641-9797
Email: terry@salterrasite.com
Web: salterrasite.com

Why this partnership

Methodology meets technical execution

A scoring model is only as useful as the workflow that operationalizes it. Marty defines the intellectual framework — what counts as opportunity, how to weight it, and why; Salterra brings the technical SEO depth and AI-pipeline engineering to turn that framework into a tool a working agency can run today. The result is a single defensible artifact that a strategist, an SEO lead, and a client can all read the same way.

Marty Marion provides
The methodology

Formula, multipliers, intent framework, Phase 1 / Phase 2 architecture, worked examples, calibration guidance.

Salterra provides
The execution layer

Tool engineering, AI-assisted Phase 1, forecasting math, calibration UI, client-ready exports, deployment.

How to cite

Academic / professional citation

When referencing the methodology in client deliverables, white-papers, or published research, please use:

APA-style
Marion, M. (2026). Marty Marion's SEO Opportunity Score Model (May 2026 ed.) [Methodology paper]. Master Positioning LLC. Implemented in the Salterra CDS Opportunity Score Calculator, v1.0, by Salterra Digital Services.

Reproduction or dissemination of the methodology itself (formula, multiplier tables, framework) requires written authorization — see the IP notice below.

Intellectual property notice

© Master Positioning LLC & Salterra Digital Services
All worldwide rights reserved

Authorized use: this tool is licensed for use in Salterra Digital Services client engagements and authorized partner deployments. The methodology may be referenced and cited using the citation block above.

The SEO Opportunity Score Model, the formula, the multiplier value tables, and the documented methodology are the intellectual property of Marty Marion and Master Positioning LLC. Dissemination or reproduction of the methodology in any medium is prohibited without written authorization of the author.

Tool specifications

v1.0 · May 2026

Methodology
SEO Opportunity Score Model (May 2026 edition)
AI model
Our Proprietary System (via Replit AI Integrations)
Architecture
Single-file HTML, Replit-hosted Node proxy
Data storage
localStorage only (calibration, mode preference, project drafts)
Data exfiltration
None — your data only leaves the browser when Phase 1 calls Our Proprietary System (via this site's proxy) or when scraping the competitor URLs you supply
Modes
Internal · Client · Public (lead-gen)
See the deliverable
Try it now with sample data
One click loads the iron-supplement worked example and jumps you straight to Phase 2 — no data entry, no signup, no friction.
Score 12 sample keywords →
Methodology & positioning
Reach Marty Marion
Licensing, custom calibration for verticals, strategic positioning engagements.
marty@masterpositioning.com →
Implementation & SEO
Schedule with Terry Samuels
Agency engagements, audits, white-label deployments, speaker bookings (SEOST & SEO University).
salterrasite.com — solution meeting →

Glossary

A — Z

Every term used in this tool defined in one place. Methodology terms come from Marty Marion's documented model; forecasting and tool terms are Salterra additions on top.

Methodology · Core Model
Opportunity Score
A dimensionless number representing the realistic value of pursuing a keyword. Computed as log₁₀(Volume+1) × log₁₀(1+RankGap) × Proximity × Intent × Difficulty. Higher = better leverage. Marty Marion's documented formula. Per Marty's paper, scores typically range from ~0.5 at the bottom to ~15 at the top, though this varies by category and dataset size — what matters is the relative ranking within your list, not the absolute number.
Rank Gap
The arithmetic difference between your current position and the leading competitor's position. A rank gap of 14 (you at 18, competitor at 4) means you have 14 positions to close. The log of this gap rewards opportunities where displacement is realistic.
Proximity Multiplier
A tier-based multiplier reflecting how movable your current position is. Page 2 (positions 11-20) gets the highest default value (1.5) because it's the realistic sweet spot for SEO wins. Positions beyond page 5 get progressively penalized.
Intent Multiplier
A weighting by traditional search intent: Transactional (1.5) for buy/sign-up queries, Commercial (1.3) for evaluation, Informational (1.0) for research, Navigational (0.4) for brand queries. Tuneable per vertical.
Difficulty Multiplier
A tier-based multiplier inverting the standard Keyword Difficulty (KD) score. Low-difficulty keywords (0-20) get a 1.4× boost; high-difficulty (81-100) get a 0.6× penalty. Reflects the realistic cost of competing.
Tier Top / Mid / Low
Top 10% = the top decile of scored keywords, Middle 40% = the next four deciles, Bottom 50% = the rest. Assigned automatically based on Opportunity Score ranking after Calculate.
Forecasting · Salterra Extension
CTR Curve
Click-through rates by SERP position. The default curve is calibrated for 2026 SERP behavior including AI Overview impact. Position 1 ≈ 22%, Position 5 ≈ 4.5%, Position 11-20 ≈ 0.5% average. Tunable in Calibration.
Target Position
The realistic SERP position to aim for. Determined by your target strategy setting: match leading competitor (default), beat by 1, aim for #1, or aim for top 3. Drives the traffic and revenue projections.
Traffic Gain (Δ)
Projected additional monthly visits at the target position vs. your current position. Computed as Volume × (CTR_target − CTR_current). Conservatively clamped at zero when you'd lose traffic.
Revenue Gain (Δ)
Projected additional monthly revenue at the target position. Computed as Traffic Gain × Conversion Rate × Value per Conversion. The currency of business decisions.
Effort Hours
Estimated content + technical hours to capture this keyword. Derived from current position (further = more work) and difficulty (harder = more authority signals needed). Displayed as a color-coded badge: low <25h, med 25-75h, high >75h.
Time to Rank
Estimated days from work-start to reaching the target position. Page-2 keywords typically need 90 days (×0.85 to ×1.3 depending on difficulty); not-ranking keywords need 365+ days. Aggregated as a score-weighted average across the universe.
Tool · Process & Features
Phase 1
AI-assisted keyword universe building. Either expand seed keywords into 60-150 variants, or use web-search to discover competitor ranking themes. Output includes intent classification and topical clustering.
Phase 2
Per-keyword scoring with the full data model: volume, your position, competitor position, intent, difficulty. Produces the Opportunity Score plus forecasting projections.
Content Cluster
A short topical label (e.g. "Iron for Athletes") grouping keywords that should target the same canonical piece of content. Auto-assigned by Phase 1; preserved as a tag through Phase 2; surfaced as cluster cards with suggested content types in Results.
Calibration Preset
A complete set of multiplier + forecasting values pre-tuned for a vertical. Seven built-in (Default, Local Service, E-commerce, B2B SaaS, Lead-Gen, Publisher, Healthcare). Custom presets can be saved.
Snapshot
A versioned save of scored results + calibration. Useful for tracking progress quarter-over-quarter or comparing alternative calibration scenarios. Stored locally, capped at 10 (FIFO).
Smart Fill / Enrich
The Enrich tab uses Our Proprietary System with web search to automatically populate missing fields (volume, position, competitor position, difficulty) for unscored rows. Optional — manual data entry is always supported.
Auto-Inclusion (★)
Per Marty's Phase 1 Step 16: any keyword where your domain already ranks between positions 1 and 30 is automatically included in the Priority Optimization List — the site has already demonstrated relevance and these can often be improved faster than no-rank keywords. The tool marks these rows with a ★ in the results.

Frequently Asked Questions

13 questions

The score multiplies five components: log₁₀(Volume+1) × log₁₀(1+RankGap) × Proximity × Intent × Difficulty. Volume uses log to dampen huge numbers; rank gap uses log to reward realistic-but-meaningful movement; proximity, intent, and difficulty are tier-based multipliers that can be calibrated per vertical.

For Marty Marion's worked example — "best iron supplement for athletes" with volume 2400, your position 18, competitor 4, Commercial intent, difficulty 35 — the score lands at 9.30 with default calibration.

Three common reasons: (1) you're already on page 1 — small rank gap means little room to grow, so the rank-gap component is low; (2) navigational or unrelated intent — the Intent multiplier penalizes branded or off-category traffic; (3) extreme difficulty — KD 81-100 cuts the score in half via the Difficulty multiplier.

That's the model working as designed: pure volume isn't an opportunity unless you can realistically capture meaningful additional traffic.

Rank Gap is the spatial distance between you and the leading competitor right now — a positional measurement. A 14-position gap means there's room to move.

Difficulty is the structural barrier to ranking at all — backlink authority requirements, content depth expectations, domain trust. A high-difficulty keyword can have a tiny rank gap (everyone's piled at the top) or a huge one (page 1 is locked down by giants).

The model uses both because they're independent: low-difficulty + meaningful rank gap = highest leverage.

They're directional, not predictive. The forecast multiplies volume by a CTR estimate (which has 50-100% real-world variance depending on SERP features), then by your conversion rate (assumed flat across keywords), then by value per conversion (assumed flat too).

Use the forecast for relative prioritization ("keyword A is worth 5× keyword B") and order-of-magnitude planning ("this campaign should add roughly $20K annually"). Don't put exact dollar figures in client contracts without calibrating against your historical conversion data first.

You can tune the CTR curve, conversion rate, and value per conversion in the Calibration tab to match your data.

Yes. In Phase 1 setup, choose your target locale from the dropdown — 13 options including UK/CA/AU/IE English, German, French, Spanish ES and LatAm, Italian, Dutch, Brazilian Portuguese, and Japanese. The AI will adjust spelling, terminology, idiom, and search modifiers for the target market.

The scoring formula itself is locale-agnostic. CTR curves may differ by market — adjust in Calibration if needed.

No. This deployment uses Replit AI Integrations to call Our Proprietary System on your behalf — no third-party account or API key is required. Phase 1 and Enrich both use Our Proprietary System.

Each Phase 1 run costs only a few cents, billed automatically to the Replit credits on the account hosting this tool. Competitor-URL mode adds a server-side scrape of each URL (free).

No. All calibrations, snapshots, custom presets, project state, and lead capture data live in your browser's localStorage only. Project files are saved as JSON downloads to your machine.

The only external network calls are: (1) Phase 1, which is routed through this site's own /api/anthropic endpoint to Our Proprietary System via Replit AI Integrations, and (2) in competitor-URL mode, this site's own /api/scrape endpoint fetches the URLs you supplied. No third-party tracking or analytics endpoint is contacted.

This tool is complementary, not a replacement. Ahrefs/SEMrush/Moz provide the raw input data (volume, positions, difficulty, competitor positions) via their crawlers — this tool can't crawl SERPs. You feed that data into Phase 2 and the model produces a prioritized list with traffic/revenue/effort/time projections.

What this tool adds: (1) Marty Marion's documented scoring formula with transparent multipliers vs. opaque proprietary scores, (2) forecasting in business currency (revenue, effort hours, time-to-rank), (3) vertical calibration presets for category realism, (4) topical clustering with content-type suggestions, (5) client-ready co-branded PDF reports.

Use position 101 to indicate "not in top 100." This caps the proximity multiplier at the lowest tier (0.3 by default), making the keyword a tougher win — which matches reality. If nobody is ranking, you have a green-field opportunity but the rank gap component is small (since competitor position 1 against your 101 gives gap=100, log of which is 2.0 — still meaningful).

For keywords with strong topical relevance but no current rankings, consider running Phase 1 with competitor URLs to discover what they're actually ranking for, then score those keywords explicitly.

Indirectly. Custom presets are stored in localStorage, so they don't auto-sync between browsers or users. To share a calibration: load it as the active calibration, save the entire project (Phase 2 → Save project) — the saved JSON includes the calibration block. Anyone who loads that project file will inherit the calibration.

For agency teams who need a shared canonical preset library, the practical workflow is: name the project file something like cal-preset_dental_v2.json and check it into your team's shared drive.

Two parties, one product. Marty Marion (Master Positioning LLC) authored the Opportunity Score Model — the formula, multipliers, intent framework, and Phase 1 / Phase 2 architecture, all documented in the May 2026 methodology paper. Salterra Digital Services (Terry Samuels, co-founder; US Navy Veteran; 30+ years in software; owner of the SEOST Digital Marketing Conference and SEO University) engineered the tool, the AI Phase 1 pipeline, the forecasting math, and the client-export layer.

The division of labor is deliberate: a scoring model is only as defensible as the agency that can operationalize it on real data, and a working tool is only as trustworthy as the methodology underneath it. Each party does what they do best, and the artifact is the same one a strategist, an SEO lead, and a client can all read the same way.

Yes. Terry is an active SEO speaker and educator — owner of the SEOST Digital Marketing Conference and SEO University — and books keynotes, hands-on workshops, executive briefings, and agency team training. Topics include forensic SEO (traffic and ranking drops), advanced schema, on-page optimization, site structure and internal linking, and franchise / multi-location SEO.

Schedule a conversation via salterrasite.com — solution meeting, or see the speaker page for full topic list and event formats.

Use the citation block at the top of this About tab — APA-style, copy-ready. Short form for inline use:

Marion, M. (2026). SEO Opportunity Score Model. Master Positioning LLC.

Reproducing the formula, multiplier tables, or methodology framework itself (as opposed to citing it) requires written authorization from the author — see the IP notice.

Privacy & Data Handling

Local-first by design

This tool is engineered as a local-first application. Your data stays on your machine. No part of this tool transmits keyword data, calibrations, project files, or contact information to Salterra, Master Positioning, or any third party.

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Local storage only

Calibrations, custom presets, snapshots, recent projects, mode preference, and lead capture data all live in your browser's localStorage. Cleared if you clear browser data.

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No API key needed

This deployment uses Replit AI Integrations — Our Proprietary System credentials are auto-injected on the server and never exposed in the browser. Phase 1 requests are proxied through this site's own /api/anthropic endpoint.

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No analytics, no tracking

This tool contains zero analytics scripts, no Google Analytics, no Mixpanel, no Sentry, no Hotjar, no pixel tags. Open the network inspector and verify — only /api/anthropic (Phase 1) and /api/scrape (competitor-URL mode) requests fire.

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Project files are yours

Save Project produces a JSON file downloaded to your machine. Load Project reads a JSON file you provide. No cloud sync, no Salterra-hosted backup. You own the artifacts.

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What Anthropic sees

In Phase 1: your project description + seed keywords (or scraped competitor page text) + the prompt. In Enrich: the keyword + project context. Requests are proxied through Replit AI Integrations to Anthropic — see anthropic.com/legal for upstream privacy terms.

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Lead capture (public mode only)

In public mode, a lead capture modal collects name + email before showing scored results. This data is stored only in your browser's localStorage. If you deploy this publicly, you're responsible for wiring it to your CRM via a server-side endpoint.

Keyboard Shortcuts

Power user
Shortcut Action
⌘ Enter or Ctrl+Enter Run calculation (when on Phase 2 tab)
⌘ S or Ctrl+S Save snapshot (after Calculate)
⌘ P or Ctrl+P Export PDF report
Esc Close open modal (custom presets, lead capture, shortcuts)
? Show keyboard shortcuts overlay (any tab)

Note: shortcuts don't trigger while you're typing inside an input, textarea, or select element — so they won't interfere with normal data entry.