How to Fix Failing GTM in 2026: Switch from Channel-First to Decision-First Marketing
- Abhi Yadav
- Sep 28
- 6 min read
Updated: Oct 7
Introducing Decision Intelligence beyond AI in Marketing over-simplification or thinking beyond content
The blunt truth
Most GTM misses aren't idea problems; they're decision execution problems. Studies suggest ~60–90% of strategies stumble in execution often because data, audiences, and decisions aren't wired into one loop. Meanwhile, 55% of campaigns fail to justify their investment, and half of media plans are under-funded by a median 50%, so results look worse than they should.
Take a AI SaaS company we worked with… They were spending $200K/month across DSPs, LinkedIn, Google, and events. Lists got uploaded, creative shipped, and CAC kept climbing. When we asked "Why this audience with this message?" the answer was "Because it worked last quarter."
But did it? Inside their stack, they were juggling ~275 SaaS apps while ~60–70% of B2B content went unused—a perfect recipe for waste. Budgets get parked in platforms. Lists get uploaded. Creative ships. Then we argue about attribution while CAC drifts up and "what worked" gets fuzzier. That's channel-first thinking pretending to be data-driven.
Decision-first marketing starts with who to move, what decision to trigger, when, and why—then chooses the channel as an implementation detail.
This isn't theory. We've implemented this system across B2B and D2C; the pattern is consistent: decision-first beats channel-first.
Channel-First vs Decision-First (reality check)
Channel-First
Plans = allocations ("$X to LinkedIn, $Y to CTV").
Audiences = static CSVs.
Creative = one-and-done bursts.
Measurement = proxy metrics and slide theater.
Decision-First
Plans = outcomes ("convert ICP-A evaluators within 14 days," "expand NRR in segment B").
Audiences = living graphs that re-score as reality changes.
Creative = human-directed, AI-assisted, matched to signals.
Measurement = causal reads and closed loops that retrain the system.
If you're honest, most orgs are still in column one.
What We Mean by Decision Intelligence
Decision Intelligence is your GTM brain getting smarter with every action.
Most companies have data. Many have automation. But Decision Intelligence is different - it's a system that learns what works and gets better at predicting what will work next.
Think of it this way: instead of guessing which audience to target or which message to send, you have a system that knows your customer signals so well it can tell you "Person X is 73% likely to convert in the next 14 days if you send Offer Y through Channel Z."
What it's NOT:
More dashboards or reports
Marketing automation on steroids
AI that replaces human judgment
Another tool in your stack
Another analytics or AI/ML model
What it IS: A continuous intelligence loop that:
Unifies identity & context — One complete view of each prospect/customer
Scores fit/intent/timing continuously — Real-time readiness signals, not monthly snapshots
Selects next-best actions with guardrails — AI recommends, humans approve, brand stays protected
Activates across any channel — Same intelligence, any platform
Measures causally so the system learns — What drove results (not just what happened)
The outcome? Every campaign gets smarter. Every audience gets more precise. Every dollar works harder.
With enterprises juggling ~275 SaaS apps, Decision Intelligence doesn't add to the chaos—it makes sense of it. It's the Sense → Orient → Decide → Act → Learn cycle built into your revenue engine.
The Method (no buzzwords, just work)
1) Build the unified data foundation
Identity fabric: One profile per person/account across CRM, marketing automation, web/app, commerce, and support.
Governed features: A shared feature store tracking the signals that matter—ICP fit, propensity, churn risk, eligibility, lifetime value—with real-time consistency.
Policy in code: Consent, data retention, brand guidelines, frequency caps, and suppressions. Actually enforced, not just documented.
Output: a substrate you can trust. Debates move from "whose number?" to "what decision?".
2) Make your audience dynamic
Static lists die fast. Model states (readiness, saturation, risk) and keep them fresh. When signals change, the audience changes—automatically.
Output: the system tells you who moved, who's close, who's over-messaged, and who's slipping.
3) Track the signals that matter
Fit: Company size, technology stack, and demographics against your ideal customer profile.
Intent: Consumption patterns, queries, product usage, evaluation behaviors.
Timing: Recency/frequency, seasonality, contract cliffs, pattern gaps.
Reachability & risk: Channel match, identity confidence, brand safety, compliance.
Output: each profile carries a living signal vector your plays can use.
4) Decide with guardrails (the brain)
Policies over vibes: "If Fit=A & Intent=B & Timing=C → Play=D," with budget/frequency/geo/brand constraints.
Explore vs exploit on purpose: Fixed exploration budget (5–10%); the rest follows evidence.
Explainability: Every action has a reason path ("we targeted Persona X with Offer Y because A/B/C crossed thresholds").
Output: fewer random acts of marketing; more repeatable wins.
5) Activate with a human-in-the-loop (where it matters)
Agents handle the grunt work: Enrich, dedupe, expand/suppress, QA, pacing, eligibility checks.
Humans set the voice and judgment: Narrative, offer, visuals, brand nuance.
Pre-flight "Audience Grader": Score each audience 0–100 (A–F) versus the goal; fix issues in one click (enrich, tighten eligibility, expand lookalikes, reduce frequency).
Output: speed without brand debt.
6) Measure causally, learn continuously
Truth sets: Always-on holdouts, geo tests, incrementality frameworks that survive privacy.
Attribution that matters: Blend lift with journey reads; stop worshipping CTR.
Closed-loop learning: Results update features, policies, and creative briefs.
Output: every cycle gets cheaper, faster, smarter.
A pragmatic ramp (90–180 days)
Phase 1 | Foundation Ship identity stitching, feature store, audience views, global suppressions, holdouts, match-rate baselines. Turn on Audience Grader.
Phase 2 | Activation Codify 2 decision policies (e.g., net-new acquisition + expansion/retention). Agents run hygiene and pacing; humans control briefs and brand.
Phase 3 | Optimization Causal reads drive budget shifts. Extend to secondary channels (LinkedIn + paid social + email/SMS for B2B; add retail/commerce media/CTV for D2C).
Phase 4 | Scale Richer models refine fit/intent/timing; add more agents and plays; central scorecard aligns GTM, product, finance.
What "good" looks like
Operational: Audience freshness < 24h; dupes < 2%; pre-flight violations caught before spend.
Decisioning: ≥70% of spend routed by policies; exploration budget enforced.
Creative: Briefs anchored in top signals; message–audience fit improves without channel whiplash.
Business: CAC bends down; lift/ROAS up; cycle time compresses; pipeline quality climbs.
The Results Speak for Themselves
Based on our client experience, companies using this decision-first approach typically see:
Within 60 days:
20-35% reduction in wasted ad spend
15-25% improvement in campaign performance
40% faster campaign setup and optimization
Within 6 months:
25-40% lower customer acquisition costs
20-30% increase in pipeline quality
35% reduction in time spent on manual audience management
Client example: A B2B software company reduced their CAC by 38% while increasing lead quality scores by 30%—all by switching from static lists to dynamic, signal-based audiences.
*Results may vary based on implementation and market conditions.
Start here
Publish the substrate: Identity + 5 core features + audience views.
Grade before you pay: Run Audience Grader on every activation.
Codify two plays: One acquisition, one expansion—with guardrails.
Wire truth: Holdouts by default; review lift weekly; move budget based on evidence.
Protect the brand: Keep humans in the loop for narrative and creative decisions.
Ready to Stop Guessing?
Fixing GTM isn't "more channels." It's a decision system that: unifies identity & context → keeps audiences live → acts on fit/intent/timing sig
nals → decides with guardrails → human-in-the-loop creative → measures causally and learns.
When you operate GTM as a loop—unified data → dynamic audiences → real signals → governed decisions → human-guided activation → causal learning—channels stop competing and start compounding.
The bottom line: Own your audience. Compound your growth.
Ready to replace guesswork with a decision engine? We'll help you install the system and prove ROI in weeks, not quarters. Because your competition is already making this shift, the question is whether you'll lead or follow.
Sources:
Strategy execution failure (~60–90%) — Harvard Business Review
55% of campaigns fail to justify investment — Gartner (covered Dec 2024; also referenced in 2025 budget coverage)
50% of media plans underinvested by a median 50% — Nielsen ROI Report 2022
~275 SaaS apps per enterprise; +9.3% YoY SaaS spend — Zylo 2025 Index
Martech landscape size (>14k tools) — Chiefmartec / CMSWire coverage
~60–70% of B2B content unused — Forrester/SiriusDecisions
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