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return mapping.get(stage, ["pipeline velocity", "conversion rate"])
@staticmethod
def _recommend_opportunities(stage: str, focus: str) -> List[str]:
opportunities = {
"awareness": [
"Anchor thought-leadership content to prime the market",
"Deploy programmatic amplification across hero stories",
],
"consideration": [
"Introduce nurture sequences aligned to buyer pains",
"Layer intent scoring to prioritize engaged accounts",
],
"conversion": [
"Bundle conversion offers with social proof assets",
"Tighten sales enablement loops with marketing triggers",
],
"retention": [
"Launch customer marketing to reinforce product value",
"Pair success cadences with expansion playbooks",
],
}
base = opportunities.get(stage, [])
if focus.startswith("enterprise"):
base = base + ["Align executive-level narratives with ROI benchmarks"]
elif "startup" in focus:
base = base + ["Stand up scrappy experiments to validate fast wins"]
elif "consumer" in focus:
base = base + ["Personalize lifecycle messaging using behavioral cohorts"]
return base
@staticmethod
def _detect_risks(prompt: str) -> List[str]:
lowered = prompt.lower()
risks: List[str] = []
if "flat" in lowered or "stalled" in lowered or "plateau" in lowered:
risks.append("Performance plateau signaled")
if "limited budget" in lowered or "tight budget" in lowered:
risks.append("Budget constraints highlighted")
if "new market" in lowered or "unknown" in lowered:
risks.append("Market unfamiliarity risk")
if "churn" in lowered or "retention" in lowered:
risks.append("Retention fragility noted")
return risks
def diagnose(self, prompt: str) -> PromptDiagnostics:
tokens = self._keyword_set(prompt)
stage, stage_hits = self._infer_stage(tokens)
urgency = self._infer_urgency(prompt)
focus, focus_hits = self._infer_focus(tokens)
kpis = self._recommend_kpis(stage)
opportunities = self._recommend_opportunities(stage, focus)
risks = self._detect_risks(prompt)
keyword_summary = {
"intent": stage_hits,
"focus": focus_hits,
"tokens": tokens,
}
confidence_seed = len(stage_hits) + len(focus_hits)
confidence = max(0.35, min(0.95, 0.35 + 0.1 * confidence_seed))
return PromptDiagnostics(
stage=stage,
urgency=urgency,
focus=focus,
recommended_kpis=kpis,
opportunities=opportunities,
risk_signals=risks,
keyword_summary=keyword_summary,
confidence=round(confidence, 2),
)
def portfolio(self, prompt: str, campaigns: Sequence[CampaignScore]) -> CampaignMetrics:
if not campaigns:
raise ValueError("portfolio requires at least one campaign score")
roi = sum(entry.metrics.expected_roi for entry in campaigns) / len(campaigns)
ctr = sum(entry.metrics.expected_ctr for entry in campaigns) / len(campaigns)
confidence = sum(entry.metrics.confidence for entry in campaigns) / len(campaigns)
drivers: Dict[str, float] = {}
for entry in campaigns:
for feature, value in entry.metrics.drivers.items():
drivers[feature] = max(drivers.get(feature, 0.0), value)
return CampaignMetrics(
expected_roi=roi,
expected_ctr=ctr,
confidence=confidence,
drivers=drivers,
)
def keywords(self, prompt: str) -> List[str]:
return self._keyword_set(prompt)
def expansion_grid(self, prompt: str, campaigns: Sequence[CampaignScore]) -> List[GrowthExperiment]:
"""Design a set of Experiment Growth Grid (EGG) plays from campaign rankings."""
if not campaigns:
raise ValueError("expansion_grid requires at least one campaign score")
diagnostics = self.diagnose(prompt)
stage_recipes: Dict[str, List[Dict[str, str]]] = {
"awareness": [
{
"name": "Narrative Amplification Sprint",
"hypothesis": (
"If we spotlight {offer} through {channel}, "
"we will warm {audience} toward {objective}."
),
"play": "Launch hero content and amplify across paid/owned reach streams.",
"metric": "impressions",
},
{
"name": "Thought Leadership Cascade",
"hypothesis": (
"Positioning {audience} challenges alongside {offer} "
"via {channel} will expand top-of-funnel attention."
),
"play": "Sequence authoritative narratives across social and partner syndication.",
"metric": "share of voice",
},
],
"consideration": [
{
"name": "Intent Magnet Program",
"hypothesis": (
"If we orchestrate {offer} journeys through {channel}, "
"we can mature {audience} into evaluators for {objective}."
),
"play": "Build nurture modules triggered by behavioral intent signals.",
"metric": "marketing qualified leads",
},
{
"name": "Education Sprint",
"hypothesis": (
"Providing {audience} with {offer} across {channel} "
"will compress evaluation cycles."
),
"play": "Layer webinars, playbooks, and retargeting to reinforce learnings.",
"metric": "content engagement",
},
],
"conversion": [
{
"name": "Pipeline Acceleration Lab",
"hypothesis": (
"If {audience} experience {offer} via {channel}, "
"we can unlock {objective} faster."
),
"play": "Run multi-touch conversion blitz with calibrated social proof.",
"metric": "sales qualified leads",
},
{
"name": "Revenue Experiment Track",
"hypothesis": (
"Blending {offer} within {channel} for {audience} will uplift deal velocity."
),
"play": "Pair high-intent triggers with offer experimentation and CRO support.",
"metric": "conversion rate",
},
],
"retention": [
{
"name": "Value Expansion Studio",
"hypothesis": (
"If {audience} engage with {offer} programs via {channel}, "
"we will fortify {objective}."
),
"play": "Launch customer storytelling and advocacy loops to reinforce value.",
"metric": "renewal rate",
},
{
"name": "Lifecycle Momentum Cell",
"hypothesis": (
"Activating {offer} through {channel} for {audience} "
"will catalyze expansion outcomes."
),
"play": "Coordinate success-led campaigns with product usage nudges.",
"metric": "net revenue retention",
},
],
}
focus_overlays: Dict[str, Dict[str, str]] = {
"enterprise b2b": {
"play": "Map executive-specific proof points and ABM orchestration layers.",
"metric": "executive meeting volume",
},
"startup founders": {
"play": "Prototype scrappy experiments with rapid iteration cadences.",
"metric": "activation-to-demo ratio",
},
"consumer": {
"play": "Employ personalization matrices and lifecycle messaging bursts.",
"metric": "repeat purchase rate",
},
"developer": {
"play": "Enable technical champions with sandboxes, tutorials, and community proof.",
"metric": "trial-to-adoption rate",
},
"default": {
"play": "Instrument experiment tracking with weekly conversion retros.",
"metric": "conversion rate",
},
}
base_recipes = stage_recipes.get(diagnostics.stage, stage_recipes["conversion"])
overlay = focus_overlays.get(diagnostics.focus, focus_overlays["default"])
recommended = diagnostics.recommended_kpis or ["conversion rate"]
experiments: List[GrowthExperiment] = []
for index, scored in enumerate(campaigns[: max(1, len(campaigns))]):
recipe = base_recipes[index % len(base_recipes)]
name = f"{recipe['name']} via {scored.campaign.channel.title()}"
hypothesis = recipe["hypothesis"].format(
offer=scored.campaign.offer,
channel=scored.campaign.channel,
audience=scored.campaign.audience,
objective=scored.campaign.objective,
)
playbook = [
f"Activate {scored.campaign.channel} targeting {scored.campaign.audience}.",
recipe["play"],
overlay["play"],
f"Align offer '{scored.campaign.offer}' to accelerate {scored.campaign.objective} outcomes.",
]
primary_metric = recipe.get("metric", recommended[0])
secondary_candidates = set(recommended[1:])
secondary_candidates.add(overlay["metric"])
secondary_metrics = sorted(secondary_candidates)
confidence = (diagnostics.confidence + scored.metrics.confidence) / 2
experiments.append(
GrowthExperiment(
name=name,
hypothesis=hypothesis,
primary_metric=primary_metric,
secondary_metrics=secondary_metrics,
playbook=playbook,
confidence=round(max(0.2, min(0.99, confidence)), 2),
)
)
return experiments
def google_ads_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[GoogleCampaignPerformance]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._google_process.performance(prompt, scored, limit)
def cj_affiliate_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[CJAffiliateRecommendation]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._cj_process.offers(prompt, scored, limit)
def crypto_market_enrichment(
self,
prompt: str,
limit: int = 3,
) -> List[CryptoMarketSnapshot]:
return self._crypto_process.market_data(prompt, limit)
def stripe_checkout_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[StripeMonetizationPlan]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._stripe_process.plans(prompt, scored, limit)
def amazon_marketplace_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[AmazonProductRecommendation]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._amazon_process.listings(prompt, scored, limit)
def aws_cloud_enrichment(
self,
prompt: str,
campaigns: Sequence[CampaignScore] | None = None,
limit: int = 3,
) -> List[AWSRevenueRecommendation]:
scored = list(campaigns) if campaigns is not None else self.recommend_statistical(prompt, limit)
if not scored:
return []
return self._aws_process.plays(prompt, scored, limit)
@lru_cache(maxsize=1)
def default_marketing_engine() -> MarketingIntelligenceEngine:
return MarketingIntelligenceEngine.from_file()