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Shopper-Occasion Pack Matrix

Build the Consumer Decision Tree hierarchy for a frozen-pizza portfolio, score it against research-validated shopper behaviour, and watch the true competitive set reshape as you reorder the five decision gates. The same interactive model the full RGM Academy course uses for PPA Lesson 7 — no auth, no paywall.

Updated 23 April 2026Extracted from the Price Pack Architecture module, lesson 7: Consumer Decision Tree

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Scenario walkthrough

Tap any section to explore in detail

5.1Scenario setup

The starting SKU, market, and assumptions the model makes.

You are the Category Manager on a frozen-pizza portfolio heading into the annual PPA review with a major grocery chain. Sales have been flat for two quarters despite three line extensions and a price-pack redesign. Your internal framing of the category has been brand-first — DiGiorno vs Red Baron vs Freschetta vs Store Brand — and every commercial decision has been built on that assumption.

Shopper research just came back with a clear finding: frozen-pizza shoppers make the decision in a different order than your team assumed. They choose Type first (thin crust vs thick vs stuffed), then Brand, then Flavor, then Size, with Price as the final gate. Your brand-first framing is misidentifying the competitive set — you've been thinking your DiGiorno Thin Pepperoni competes with your DiGiorno Thick Pepperoni, when in fact the research says it competes with Red Baron Thin Pepperoni and Store Brand Thin Margherita.

Your job by Friday: run the Consumer Decision Tree under both hierarchies, quantify the shift in competitive set, and bring a CDT-aligned portfolio recommendation to the review.

Your objective

Use the CDT tool to compare the brand-first vs research-validated hierarchies, quantify the shift in the competitive set for each of the 6 focus products, and identify which SKUs are miscategorised under the current framing.

Key assumptions

The switching-probability model is coarse by design. Five bands [2%, 8%, 18%, 35%, 55%] indexed by count of shared top-of-tree attributes. Real conjoint studies produce continuous switching matrices with hundreds of cells; this teaching model uses 5 tiers to make the SHAPE of the competitive set legible at a glance. The teaching point is the hierarchy-driven reshape, not the exact switching dollar amounts.
Hierarchy accuracy is scored position-by-position against the research-validated order for frozen pizza (Type → Brand → Flavor → Size → Price). This order is category-specific — a cereal or yogurt CDT will have a different validated order, and applying the frozen-pizza order to another category would score as high accuracy but be operationally wrong. Always derive category-specific CDT from category-specific shopper research.
"Intra-brand" vs "cross-brand" top-competitor is the single most important signal on the page. An intra-brand top competitor means your own range is the biggest threat to a given SKU — classic cannibalisation, typically a signal that the hierarchy is putting Brand ahead of a more decisive gate. A cross-brand top competitor means you are competing in the true category; whether that is good depends on whether the current hierarchy matches research.
Default hierarchy is deliberately wrong for teaching. Most commercial teams arrive at the CDT tool already assuming Brand-first; the default [Brand → Type → Price → Flavor → Size] mirrors that common wrong framing, so the first slider move (Type to position 1) already delivers a meaningful teaching shift in the competitive set.
The sample is intentionally small (6 products, 4 brands, 3 types). Real frozen-pizza category assortments run 30-50 SKUs; this sandbox teaches the STRUCTURE and state-machine logic, not exhaustive category modelling. For portfolio-level work, extend the methodology to your full category assortment via scanner-data cross-elasticity analysis.

5.2Controls & toggles

Every input the calculator exposes, its range, and what it changes.

Control	Range	Default	What it changes
Up / Down reorder buttons (per level)	5 levels in any permutation (120 total orderings)	[Brand → Type → Price → Flavor → Size] (0% hierarchy accuracy — deliberately wrong)	Moves each decision gate up or down. Gate 1 explains the most decision variance (35-50% per published shopper research); gate 5 the least. Reordering reshapes the entire switching-probability matrix.
Focus Product dropdown	6 products (DiGiorno Thin Pepperoni/Thick Pepperoni/Thin Margherita, Red Baron Thin Pepperoni, Freschetta Stuffed Meat Feast, Store Brand Thin Margherita)	p1 DiGiorno Thin Pepperoni 350g	Sets the focus product for switching-probability analysis. The chart shows how likely a shopper leaving this product is to pick each of the other 5 competitors under the current hierarchy.
Hierarchy Accuracy tile	0%, 20%, 40%, 60%, 80%, or 100% (one step per correct position)	0% at default (deliberately wrong teaching default)	Percentage of positions matching the research-validated correct order for frozen pizza (Type → Brand → Flavor → Size → Price). Green ≥80%, amber 40-79%, red <40%.
Top Competitor readout	Any of the 5 non-focus products	p6 DiGiorno Thin Margherita 200g at 35% under default (INTRA-brand signal)	Single highest-probability competitor. Cross-brand dominance = real category competition; intra-brand dominance = cannibalisation artefact of hierarchy misordering.
Switching probability chart	5 bars per focus product, 2-55% each	Gold ≥30%, amber 10-29%, grey <10% at default (one gold, one amber, three grey)	Full competitor ranking. Colour shifts as you reorder gates; the bar pattern reshape tells you which gates are load-bearing for this focus product.

5.3Step-by-step exploration

7-step guided exploration of the scenario.

Read the default (deliberately wrong) hierarchy
Leave every control at default. Read the hierarchy [Brand → Type → Price → Flavor → Size], the accuracy score (0%), and the top-competitor readout.
Expected outcome: Hierarchy accuracy 0% — not a single position matches the research-validated Type → Brand → Flavor → Size → Price order. Focus p1 top competitor: p6 DiGiorno Thin Margherita 200g at 35%. The signal: under a brand-first framing, your own DiGiorno Thin Margherita is identified as the biggest threat to your DiGiorno Thin Pepperoni. That's textbook intra-brand cannibalisation — but it's an artefact of the wrong hierarchy, not a real category signal.
Fix gate 1 — move Type to the top
Click the ▲ Up button on the Type / Segment row until it is at position 1. New hierarchy: [Type → Brand → Price → Flavor → Size].
Expected outcome: Hierarchy accuracy 20% (1 of 5 positions match — Type at position 1). Focus p1 switching probabilities partially reshape: p3 Red Baron Thin Pepperoni climbs from 2% to 8% (Type✓ Brand differs → shared 1) and p4 Store Brand Thin Margherita climbs from 2% to 8% — cross-brand same-type competitors emerge. But p6 DiGiorno Thin Margherita 200g STAYS at 35%: at hierarchy [Type, Brand, Price, Flavor, Size], p1 and p6 match Type✓ Brand✓ Price✓ (both $4-$6) → shared 3 → 35%. Teaching point: moving Type to gate 1 is NECESSARY but not SUFFICIENT — you need to finish the reordering (push Flavor ahead of Price) to see the intra-brand cannibalisation signal drop.
Complete the correct hierarchy
Continue reordering until the hierarchy reads Type → Brand → Flavor → Size → Price (move Flavor up, Size up, Price all the way down).
Expected outcome: Hierarchy accuracy 100%. Focus p1 top competitor: p6 DiGiorno Thin Margherita 200g at 18% (down from 35% at default). The switching shift from 35% → 18% is the single most important number on the page: under the research-validated hierarchy, p6 and p1 share Type✓ Brand✓ but Flavor differs at gate 3 → shared 2 → 18%. The cannibalisation risk that looked dominant at the default just lost half its weight. Meanwhile p3 Red Baron Thin Pepperoni sits at 8% (Type✓ Brand differs at gate 2 → shared 1 → 8%) and p4 Store Brand Thin Margherita at 8% (same) — real cross-brand competitors, small but non-zero.
Test the price-first hypothesis
Reset, then reorder to [Price → Brand → Type → Flavor → Size].
Expected outcome: Hierarchy accuracy 0%. Focus p1 top competitor: p6 DiGiorno Thin Margherita 200g at 35% AGAIN — but now because p1 and p6 share Price✓ Brand✓ Type✓ (all three match at gates 1, 2, 3). The same 35% number as default hierarchy, different underlying attribute composition. Price-first diagnoses frozen pizza as a 'price bucket shop' category (shoppers pick price band first, then everything else) — research consistently shows this is NOT how frozen pizza shoppers behave. Lesson: the SAME top competitor number can come from very different hierarchies; the accuracy score is what anchors interpretation to reality.
Switch focus to p5 — the orphan branch
Reset to the correct hierarchy [Type → Brand → Flavor → Size → Price]. Change focus product to p5 Freschetta Stuffed Meat Feast 500g.
Expected outcome: Every other product scores 2% switching probability — because p5 is the ONLY Stuffed Crust product in the assortment, every other product's Type gate (gate 1) differs from p5's Stuffed Crust. Shared = 0 → base probability 2% across the board. The signal: under the correct hierarchy, p5 has NO direct competitors in the modelled assortment. Two possible readings: (a) blue-ocean position — Freschetta owns the Stuffed Crust branch; protect and grow it; (b) strategic orphan — the range is missing the rest of the Stuffed Crust branch (an in-house Thick/Stuffed Pepperoni, for example) that would compete with p5 but also expand the Stuffed sub-category. CDT separates the two readings by asking: does the shopper research show Stuffed Crust as a significant share of category occasions? If yes, expand the branch; if no, Freschetta is a niche play and mass-market investment in Stuffed is wasted.
Compare two focus products under the same hierarchy
Keep the correct hierarchy. Switch focus back and forth between p1 (DiGiorno Thin Pepperoni) and p3 (Red Baron Thin Pepperoni).
Expected outcome: p1 top competitor under correct hierarchy: p6 (DiGiorno Thin Margherita 200g) at 18%. p3 top competitor: p1 at 8% (shared Type only; brand differs at gate 2). Asymmetry read: DiGiorno's intra-brand cannibalisation (p1 ↔ p6) is higher than its cross-brand competition with Red Baron (p1 ↔ p3). For Red Baron, p1 IS the biggest threat at 8% — the brand-level competitive intensity is different FROM each side. This kind of asymmetric switching matrix is a normal finding in CDT research; single-number cross-price elasticities hide it.
Map back to PPA tools and the rest of the portfolio architecture
Open the related-concept links (OBPPC Framework, Pack Roles Framework, Pack-Size Elasticity, Good/Better/Best). Cross-reference the [OBPPC Matrix Builder](/tools/obppc-matrix-builder) and the [Pack-Size Elasticity Calculator](/tools/pack-size-elasticity-calculator).
Expected outcome: Understanding that the CDT is the DEMAND-SIDE root of every PPA decision. PPA L4 OBPPC (the [OBPPC Matrix Builder](/tools/obppc-matrix-builder)) assumes a CDT hierarchy when it maps Occasion × Channel cells to pack choices; PPA L3 Incentive Curve (the [Pack-Size Elasticity Calculator](/tools/pack-size-elasticity-calculator)) operates WITHIN a CDT branch (the 70-85 RSP/kg target is meaningless if the packs being compared are in different CDT branches); PPA L2 Pack Roles slot into the Size/Format gate of the CDT. All downstream PPA tools assume a specified CDT hierarchy; running any of them with the wrong hierarchy produces technically-correct numbers that mean the wrong thing. Always run the CDT FIRST.

5.4Reading the output

Every KPI, the formula behind it, and how to interpret a positive or negative value.

KPI	Formula	How to read it
Hierarchy Accuracy	positions matching correct-order × 100 / 5	Green ≥80%, amber 40-79%, red <40%. Your ordering scored against the research-validated order for frozen pizza (Type → Brand → Flavor → Size → Price). Category-specific; this score is only valid for frozen pizza.
Top Competitor	argmax over non-focus switching probabilities	The single highest-probability switch target from the focus product. Intra-brand = cannibalisation signal (often a hierarchy artefact); cross-brand = real category competition.
Switching Probability (per competitor)	[0.02, 0.08, 0.18, 0.35, 0.55][shared-gate count]	5-tier coarse model indexed by how many top-of-tree gates the focus + competitor share before the first mismatch. The bar colour + length reshape as you reorder gates — that reshape IS the teaching point.
Competitive Set Shape	bar pattern across all 5 competitors	The full bar chart. One gold bar + one amber + three grey = tightly-clustered competitive set dominated by one threat. All amber = diffuse / many moderate threats. All grey = isolated product in its own CDT branch (blue ocean OR strategic orphan).

Read Hierarchy Accuracy first — everything else is interpretable only in context of whether the current hierarchy matches reality. Then read Top Competitor and check intra-brand vs cross-brand. A high-accuracy-hierarchy cross-brand top competitor is the normal, defensible finding; a low-accuracy-hierarchy intra-brand top competitor is almost always a cannibalisation artefact that disappears when you fix the hierarchy. The full competitive set shape (bar chart colour distribution) gives you the texture of the category: dominated-by-one, diffuse-competition, or isolated-branch.

5.55 common mistakes to avoid

Diagnostic patterns that catch most misuse of this calculator in practice.

Mistake 1Applying the frozen-pizza research order to a different category
Symptom: Hierarchy accuracy reads 100% but shopper behaviour in the actual category (yogurt, cereal, ice cream) doesn't match the model's predictions.
Fix: The Type → Brand → Flavor → Size → Price order is FROZEN PIZZA SPECIFIC. Yogurt research often shows Flavor first; ice cream often shows Brand first with Type close second; cereal often shows Benefit / Dietary (gluten-free, high-protein) as the first gate. Always derive the correct hierarchy from category-specific shopper research — commission the study if you don't have it. A wrong hierarchy with a 100% accuracy score against a wrong benchmark is the most dangerous possible outcome.
Mistake 2Treating the intra-brand dominance as real cannibalisation instead of a hierarchy artefact
Symptom: A portfolio decision (delist the 'cannibalising' SKU, consolidate to one size) is made based on the default hierarchy's top-competitor readout.
Fix: Before any delist / consolidate decision, run the CDT at the research-validated hierarchy. If the intra-brand dominance PERSISTS at the correct hierarchy, the cannibalisation is real and a portfolio decision is defensible. If the intra-brand dominance DISAPPEARS at the correct hierarchy (as happens with p1 ↔ p6 in the default scenario: 35% → 18%), the cannibalisation was always an artefact of the wrong framing; delisting would remove a legitimate shopper choice.
Mistake 3Assuming the 6-product model is a complete category representation
Symptom: A CDT-based strategy recommendation concludes "the category has only 6 meaningful SKUs" or "Freschetta Stuffed Meat Feast has no competitors."
Fix: The 6 products are a teaching sample. Real frozen-pizza category assortments run 30-50 SKUs. The p5 Freschetta "no competitors at 2% across the board" finding is informative (Stuffed Crust branch is under-populated in this sample) but NOT a portfolio recommendation on its own. Extend the CDT analysis to the full category assortment via scanner-data pairwise cross-elasticity estimation before recommending any branch-expansion investment.
Mistake 4Running PPA tools (OBPPC, Incentive Curve, Pack Roles) before the CDT
Symptom: PPA analysis recommends a mid-tier pack gap that, after CDT research, turns out to sit in a branch shoppers don't actually navigate to; the new SKU launches and immediately underperforms.
Fix: CDT is the DEMAND-SIDE root of every PPA decision. PPA L2 Pack Roles assumes the Size/Format gate; PPA L3 Incentive Curve operates WITHIN a CDT branch; PPA L4 OBPPC assumes a CDT hierarchy per cell. Always run the CDT first, then the PPA tools. The published PPA Lesson 7 Module Arc literally says "CDT is the demand-side root of all PPA work."
Mistake 5Confusing 'cross-price elasticity' with 'CDT switching probability'
Symptom: Scanner-data cross-XED analysis shows a 0.15 cross-elasticity between p1 and p3; CDT model shows 8% switching probability. Analyst concludes "the numbers don't match" and dismisses one or both models.
Fix: They're different constructs. Cross-price elasticity measures volume response to PRICE changes; CDT switching probability measures the PICK rate under a free-choice decision. They correlate (products with high cross-XED usually have high CDT switching too) but they are not the same measurement. Use cross-XED for promo-priced competitive modelling (Pricing Lesson 6); use CDT for category structure / assortment / OBPPC decisions.

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