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Get Started Free →Apply STP (Segmentation, Targeting, Positioning) framework for market strategy. Use this skill when the user needs to define target customer segments, select which segments to pursue, or craft a positioning statement — even if they say 'who is our customer', 'which market should we focus on', or 'how should we position ourselves'.
.claude/skills/asgard-ai-platform-biz-stp/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -20% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 61% | 0% |
STP is the foundational marketing strategy framework: divide the market into segments (S), select which to serve (T), and define how to win in chosen segments (P). It must be done in sequence — you cannot position without first choosing a target, and you cannot target without first segmenting.
Trigger conditions:
When NOT to use:
IRON LAW: Sequential Execution — S Before T Before P
Segmentation FIRST, then Targeting, then Positioning. In that order.
Choosing a target without segmenting first means you're guessing.
Positioning without a clear target means you're positioning for everyone
(which means no one).IRON LAW: Segments Must Be MAMS
Every segment must satisfy all four criteria:
- Measurable: You can estimate the segment's size and purchasing power
- Accessible: You can reach the segment through available channels
- Material: The segment is large enough to be profitable
- Substantial: The segment is distinct enough to respond differently to
different marketing mixes
A "segment" that fails any criterion is not actionable.Use one or more segmentation bases:
| Base | Variables | Example | |------|-----------|---------| | Demographic | Age, gender, income, education, occupation, family size | "25-35 year old urban professionals earning >NT$60K/month" | | Geographic | Country, city, climate, urban/rural, region | "Northern Taiwan metropolitan areas" | | Psychographic | Lifestyle, values, personality, interests | "Health-conscious, willing to pay premium for organic" | | Behavioral | Usage rate, loyalty, benefits sought, purchase occasion | "Heavy users who buy weekly, price-insensitive, value convenience" |
Combine bases for sharper segments (e.g., demographic + behavioral).
Produce 3-6 distinct segments. Fewer than 3 means you haven't segmented; more than 6 is too fragmented to act on.
Evaluate each segment on:
| Criterion | Question | |-----------|----------| | Size & Growth | How large is the segment? Is it growing? | | Profitability | What margins can you achieve in this segment? | | Competition | How many competitors serve this segment? How strong? | | Fit | Does this segment align with your capabilities and brand? | | Accessibility | Can you reach this segment cost-effectively? |
Choose a targeting strategy:
Craft a positioning statement using this template:
For [target segment],
[brand/product] is the [category]
that [key benefit/differentiator]
because [reason to believe].Then validate positioning against three tests:
Plot brands on a 2D map using the two most important attributes for the target segment. This visualizes competitive positioning and identifies gaps.
markdown# STP Analysis: {Product/Brand} ## Segmentation | Segment | Profile | Size | Growth | Key Need | |---------|---------|------|--------|----------| | Seg A | {description} | {$X / N people} | {X%} | {primary need} | | Seg B | ... | ... | ... | ... | | Seg C | ... | ... | ... | ... | ## Targeting | Segment | Size | Profitability | Competition | Fit | Score | |---------|------|-------------|-------------|-----|-------| | Seg A | H/M/L | H/M/L | H/M/L | H/M/L | {total} | **Selected target(s):** {segment(s)} — {rationale} **Targeting strategy:** Concentrated / Differentiated ## Positioning **Statement:** For {target segment}, {brand} is the {category} that {benefit} because {reason to believe}. **Validation:** - Relevance: ✓/✗ — {evidence} - Differentiation: ✓/✗ — {evidence} - Credibility: ✓/✗ — {evidence}
Scenario: STP for a new plant-based protein bar in Taiwan
Segmentation (Behavioral + Demographic): | Segment | Profile | Size | Key Need | |---------|---------|------|----------| | Fitness Enthusiasts | 20-35, gym-goers, track macros | ~800K in Taiwan | High protein, clean label | | Health-Conscious Office Workers | 25-45, desk jobs, skip meals | ~1.5M | Convenient meal replacement, low sugar | | Vegan/Vegetarian Consumers | All ages, ethical/dietary choice | ~300K | Plant-based, no animal derivatives |
Targeting: Health-Conscious Office Workers — largest segment, underserved (most protein bars target gym-goers), high accessibility via convenience stores.
Positioning: "For busy professionals who skip meals, PlantBar is the plant-based protein bar that replaces a meal in 60 seconds because each bar has 20g protein, 5g fiber, and all essential vitamins — made entirely from whole food ingredients."
What went wrong:
references/perceptual-mapping.mdreferences/framework-comparison.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | fail→fail | 25,131 | 21,256 | -15% | 1 | 1 | 0% | 3,139 | 4,656 | +48% | 0 | 0 | — |
case-19 | pass→pass | 19,523 | 21,666 | +11% | 1 | 1 | 0% | 2,786 | 4,515 | +62% | 0 | 0 | — |
case-12 | fail→pass | 31,233 | 46,918 | +50% | 1 | 1 | 0% | 3,599 | 4,891 | +36% | 0 | 0 | — |
case-01 | fail→pass | 40,792 | 18,985 | -53% | 1 | 1 | 0% | 6,134 | 4,934 | -20% | 0 | 0 | — |
case-02 | fail→fail | 40,601 | 25,192 | -38% | 1 | 1 | 0% | 3,913 | 5,287 | +35% | 0 | 0 | — |
case-03 | fail→pass | 27,349 | 24,156 | -12% | 1 | 1 | 0% | 4,185 | 5,082 | +21% | 0 | 0 | — |
case-04 | pass→pass | 28,146 | 22,485 | -20% | 1 | 1 | 0% | 3,616 | 5,617 | +55% | 0 | 0 | — |
case-05 | pass→pass | 27,001 | 18,970 | -30% | 1 | 1 | 0% | 3,572 | 4,630 | +30% | 0 | 0 | — |
case-06 | pass→pass | 22,387 | 15,905 | -29% | 1 | 1 | 0% | 3,658 | 4,213 | +15% | 0 | 0 | — |
case-07 | fail→pass | 23,449 | 14,900 | -36% | 1 | 1 | 0% | 3,159 | 4,437 | +40% | 0 | 0 | — |
case-08 | pass→pass | 20,601 | 16,970 | -18% | 1 | 1 | 0% | 3,209 | 4,218 | +31% | 0 | 0 | — |
case-09 | pass→pass | 30,282 | 16,109 | -47% | 1 | 1 | 0% | 2,687 | 4,212 | +57% | 0 | 0 | — |
case-10 | fail→pass | 22,901 | 19,742 | -14% | 1 | 1 | 0% | 2,947 | 4,733 | +61% | 0 | 0 | — |
case-13 | fail→pass | 29,776 | 19,175 | -36% | 1 | 1 | 0% | 1,789 | 3,502 | +96% | 0 | 0 | — |
case-14 | fail→pass | 12,137 | 12,606 | +4% | 1 | 1 | 0% | 1,696 | 3,844 | +127% | 0 | 0 | — |
case-15 | fail→fail | 31,289 | 18,433 | -41% | 1 | 1 | 0% | 2,547 | 4,439 | +74% | 0 | 0 | — |
case-16 | pass→pass | 21,746 | 37,272 | +71% | 1 | 1 | 0% | 2,882 | 4,895 | +70% | 0 | 0 | — |
case-17 | fail→pass | 9,929 | 13,727 | +38% | 1 | 1 | 0% | 1,575 | 3,584 | +128% | 0 | 0 | — |
case-18 | pass→pass | 12,604 | 15,583 | +24% | 1 | 1 | 0% | 2,091 | 3,823 | +83% | 0 | 0 | — |
case-20 | fail→fail | 13,175 | 11,641 | -12% | 1 | 1 | 0% | 2,233 | 3,302 | +48% | 0 | 0 | — |
case-21 | pass→pass | 15,167 | 11,981 | -21% | 1 | 1 | 0% | 2,142 | 3,535 | +65% | 0 | 0 | — |
case-22 | pass→pass | 23,137 | 26,609 | +15% | 1 | 1 | 0% | 2,975 | 5,378 | +81% | 0 | 0 | — |
case-23 | pass→pass | 16,743 | 19,130 | +14% | 1 | 1 | 0% | 2,494 | 4,625 | +85% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +35 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.