Moneyball for Marketers: The Conviction Layer
This is Part 7 in the 2026 Planning Series.
This is the step where planning moves from storytelling to judgment.
Why this step matters now
Within the few weeks, ChatGPT began introducing paid ad placements.
What makes this different is where the ads operate. Traditional ad marketplaces (Amazon, Walmart.com, Instacart) surface options after a problem is defined. AI defines the problem and the solution at the same time.
And because these placements are embedded directly into those answers (versus interrupting a feed or being clearly labeled as ads), they shape what gets considered before a consumer ever reaches a traditional funnel.
The consumer gets their answer—and often a recommendation—without clicking through to search, compare, or discover alternatives. Your brand can be bypassed entirely, even if you would have ranked first.
That’s the type of seismic shift most annual plans aren’t built for.
So six months from now, when a competitor (likely the category leader) starts paying for AI placement and your sales dip, "we chose to focus on retention" isn't enough. You need to know: Is this the –2% visibility gap we expected? Or a –5% demand shift that breaks the plan?
That clarity comes from sizing your bets before the pressure hits with this step.
Where this fits in the planning process
If you’re new to the series, here’s the arc so far:
Macro forces → the context your strategy must reflect
Consumer questions → what demand actually hinges on
Category & competition → how to see the landscape clearly
Audit Your 2025 → what worked, what didn’t, and why
Define the business challenge → what problem actually needs solving
OGS(P)T → what you’re choosing to focus on and what you’re explicitly not doing
THIS STEP: Risks, Bets & Opportunities → what each choice is worth and when you'd change course
👉 You can revisit the series here.
The Conviction Layer
This step exists to force numerical conviction before budgeting and before emotions show up. Here’s how to start:
1. External forces — sized, not described
Identify the forces that will affect your sales whether you act or not. Then force the next question: what does this mean for our numbers?
Examples:
AI-driven discovery shifts: –2% to –5% demand capture risk
Retail promo pressure: –1–2 pts margin compression
Customer acquisition cost inflation: –1-2 pts margin compression
Category tailwind: +2–4% baseline lift
Competitive intensity increase: –1% velocity risk
A common mistake: teams list ten forces, size none of them, and call it “risk planning.” If you can’t put a rough range on impact, you’re not planning — you’re documenting anxiety.
2. Bets with expected upside and time tolerance
Now layer in the levers you actually control.
A bet must answer:
If this works, how much does it move the year?
How long are we willing to let it look inefficient?
Examples:
New SKU format: +3–5% halo, protected for 2 quarters
Promo pulse period: +5–7% lift, short-term margin hit acceptable
Retention mechanics: +1–2% baseline, slow ramp allowed
3. Risks — quantified and accepted
Every opportunity has a shadow. Risk is not a footnote — it’s a counter-number.
Examples:
Cannibalization risk: –1–2% net if SKU over-rotates
Demand softness: –2–3% downside if macro worsens
Price elasticity miss: –1 pt margin if increases don’t hold
If you don’t size the downside, teams will treat every wobble as catastrophic.
4. Force tradeoffs with a sales bridge + triggers
Example:
Baseline growth: +5%
Category tailwind: +3%
New SKU opportunity: +3%
2 New Promo pulses : +6%
Retail margin pressure: –2%
Competitive risk: –1%
Decision triggers (pre-committed):
If AI discovery impact exceeds –3% for two consecutive months → activate lever X
If promo pressure exceeds –2 pts margin → revisit pricing, not innovation funding
If a bet underperforms for <90 days → do nothing
Net view: ~+14% potential with known pressure points
(You don't need a complex finance model for this. A simple spreadsheet is enough. The value isn't in the precision—it's in forcing the conversation before you're in the middle of Q2 chaos.)
Your outputs from this step:
Protected bets you don’t cut at the first sign of volatility
Named risks you monitor instead of react to
Press-go levers you can pull mid-year
Trigger thresholds that tell you when to act (and when to wait)
What comes next
👉 Next in the series: Measurement
👉 Need a second set of eyes on your 2026 plan?
I offer limited 1:1 Planning Gut-Check sessions to pressure-test bets, risks, and assumptions before budgets lock.
(If you want to see what I’m observing with AI discovery and how challenger brands can prepare, I wrote more about it here.)
