Shopper-Occasion Pack Matrix
Build the Consumer Decision Tree hierarchy for a chocolate-tablet portfolio, score it against an illustrative target hierarchy, 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 sign-in, no paywall.
Explore the tool, from setup to common mistakes
Five short sections explain the scenario, what each control does, how to read the output, and the mistakes to avoid. Open whichever helps. The tool above works without them.
5.1Scenario setupThe starting SKU, market, and assumptions the model makes.
The starting SKU, market, and assumptions the model makes.
You are the Category Manager on a chocolate‑tablet portfolio heading into the annual PPA review with a major grocery chain. (The category, brands, and numbers in this walkthrough are illustrative.) 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 (Corvane vs Brindel vs Aventra vs Store Brand), and every commercial decision has been built on that assumption.
In this scenario, shopper research has just come back with a clear finding: shoppers make the decision in a different order than your team assumed. They choose Type first (milk vs dark vs filled), then Brand, then Flavor, then Pack Size, with Price as the final gate. Your brand‑first framing is misidentifying the competitive set: it makes your own Corvane Milk Plain look like a 35% threat to your Corvane Milk Hazelnut, when under the scenario's research order that reading falls to 18% and Brindel Milk Hazelnut and Store Brand Milk Plain move up.
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.
Use the CDT tool to compare the brand‑first order with the Type‑first order, measure how the competitive set changes for each of the 6 products, and find which SKUs the brand‑first view pairs with the wrong competitors.
The switching‑probability model is coarse by design. Five bands [2%, 8%, 18%, 35%, 55%] indexed by count of shared top‑of‑tree attributes. A real conjoint study gives a switching figure for every pair of products, often hundreds of them; this teaching model uses 5 bands so you can see the SHAPE of the competitive set 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 illustrative target order for this chocolate‑tablet example (Type > Brand > Flavor > Size > Price). The target is an example chosen for teaching, not a published research finding, and every real category has its own order: a cereal or yogurt CDT is likely to put its gates in a different sequence. Apply this example's order to another category and the tool would still show high accuracy, but the decisions built on it would be wrong. Always derive category‑specific CDT from category‑specific shopper research.
"Intra‑brand" vs "cross‑brand" top competitor is the first signal to read on the page. An intra‑brand top competitor means your own range is the biggest threat to a given SKU: classic cannibalization. How strong it reads depends on the order of the gates: here the same Corvane pair reads 35% at the opening order, where Price sits above Flavor, and 18% at the target order. 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. The default [Brand > Type > Price > Flavor > Size] starts Brand‑first on purpose, so the first move (Type to the top) already delivers a meaningful teaching shift in the competitive set.
The sample is intentionally small (6 products, 4 brands, 3 types). Real chocolate‑tablet assortments run to dozens of SKUs in a large store; this sandbox teaches the STRUCTURE and state‑machine logic, not exhaustive category modeling. For portfolio‑level work, extend the methodology to your full category assortment via scanner‑data cross‑elasticity analysis.
5.2Controls & togglesEvery input the calculator exposes, its range, and what it changes.
Every input the calculator exposes, its range, and what it changes.
| Control | Range | Default | What it changes |
|---|---|---|---|
| Up and down arrows (one pair 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 (an illustrative 35 to 50%); gate 5 the least. Reordering reshapes the entire switching‑probability matrix. |
| Focus product dropdown | 6 products (Corvane Milk Hazelnut/Dark Hazelnut/Milk Plain, Brindel Milk Hazelnut, Aventra Filled Fruit & Nut, Store Brand Milk Plain) | Corvane Milk Hazelnut 100g | 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 vs. target order | 0%, 20%, 40%, 60%, or 100% (steps of 20 per correct position; 80% is unreachable, since fixing four of five positions forces the fifth) | 0% at default (deliberately wrong teaching default) | Percentage of positions matching the illustrative target order for this chocolate‑tablet example (Type > Brand > Flavor > Size > Price). Green 80% and above, amber 40 to 79%, red below 40%. |
| Top competitor figure | Any of the 5 non-focus products, or a count when several share the top figure | Corvane Milk Plain 45g at 35% under default (intra-brand signal) | The highest‑probability competitor; when two or more products share the top figure, the card says how many tie instead of naming one. A top competitor from another brand means real category competition; one from your own brand means cannibalization, and how strong it reads depends on the order of the gates (here 35% at the opening order and 18% at the target order). |
| Switching probability chart | 5 bars per focus product, 2 to 55% each | Red 40% and above, amber 20 to 39%, grey below 20% at default (one amber at 35%, four grey) | Full competitor ranking. The bar colors change as you reorder the gates, and the way the pattern changes shows which gates matter most for this focus product. |
5.3Step-by-step exploration7-step guided exploration of the scenario.
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 figure.
Expected outcome: Hierarchy accuracy 0%. Not a single position matches the illustrative target Type > Brand > Flavor > Size > Price order. Top competitor for Corvane Milk Hazelnut 100g: Corvane Milk Plain 45g at 35%. Your own Corvane Milk Plain reads as the biggest threat to your Corvane Milk Hazelnut. That is textbook intra‑brand cannibalization, and the wrong hierarchy inflates it: the two match on brand, type and price band, and Price sits above Flavor, the first gate where they differ. - Fix gate 1, move Type to the top
Click the up arrow on the Type / Segment row once, so it sits at the top. New hierarchy: [Type > Brand > Price > Flavor > Size].
Expected outcome: Hierarchy accuracy 40% (2 of 5 positions match: Type at position 1 and Brand at position 2). Some of Corvane Milk Hazelnut 100g's switching probabilities change: Brindel Milk Hazelnut 100g climbs from 2% to 8% (Type matches, Brand differs, so 1 shared) and Store Brand Milk Plain 100g climbs from 2% to 8%. Cross‑brand same‑type competitors emerge. But Corvane Milk Plain 45g STAYS at 35%: at hierarchy [Type, Brand, Price, Flavor, Size], the two Corvane products match Type, Brand and Price (both $2-$3), so 3 shared, so 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 cannibalization signal drop. - Complete the target 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%. Top competitor for Corvane Milk Hazelnut 100g: Corvane Milk Plain 45g at 18% (down from 35% at default). The drop in switching from 35% to 18% is the number to take away: under the target hierarchy, the two Corvane products share Type and Brand but differ on Flavor at gate 3, so 2 shared, so 18%. The cannibalization risk that looked dominant at the default just lost half its weight. Meanwhile Brindel Milk Hazelnut 100g sits at 8% (Type matches, Brand differs at gate 2, so 1 shared, so 8%), and so does Store Brand Milk Plain 100g, for the same reason. Both are real competitors from other brands: small, but not zero. - Test the price‑first hypothesis
Reset, then reorder to [Price > Brand > Type > Flavor > Size].
Expected outcome: Hierarchy accuracy 20% (Brand lands in its target slot at gate 2). Top competitor for Corvane Milk Hazelnut 100g: Corvane Milk Plain 45g at 35% AGAIN, because the two share Price, Brand and Type at gates 1, 2 and 3. The same 35% as the default hierarchy, from the same three matches in a different order. A price‑first order treats the category as a 'price bucket shop', where shoppers pick a price band first and everything else after; this example's illustrative order says chocolate‑tablet shoppers do not shop that way. The same top competitor number can come from very different hierarchies, so read the accuracy score first: it tells you whether the order behind the number is one you can trust. - Switch focus to Aventra Filled Fruit & Nut 180g, the orphan branch
Go back to the target hierarchy [Type > Brand > Flavor > Size > Price]. Change the focus product to Aventra Filled Fruit & Nut 180g.
Expected outcome: Every other product scores 2% switching probability, because the Aventra tablet is the ONLY Filled product in the assortment, every other product's Type gate (gate 1) differs from its Filled type. Shared = 0, so base probability 2% across the board. The signal: under the target hierarchy, the Aventra tablet has NO direct competitors in the modeled assortment. Two possible readings: (a) a blue‑ocean position: Aventra has the Filled branch to itself, so protect and grow it; or (b) a strategic orphan: the range is missing the rest of the Filled branch (a Filled Caramel line, for example), which would compete with it but also grow the Filled sub‑category. CDT separates the two readings by asking: does shopper research show Filled tablets as a significant share of category occasions? If yes, expand the branch; if no, Aventra is a niche play and mass‑market investment in Filled is wasted. - Compare two focus products under the same hierarchy
Keep the target hierarchy. Switch focus back and forth between Corvane Milk Hazelnut 100g and Brindel Milk Hazelnut 100g.
Expected outcome: Corvane Milk Hazelnut 100g's top competitor under the target hierarchy: Corvane Milk Plain 45g at 18%. Brindel Milk Hazelnut 100g's top competitor figure reads 8%, and the card says three products tie at it: Corvane Milk Hazelnut 100g, Store Brand Milk Plain 100g and Corvane Milk Plain 45g (each shares Type only; brand differs at gate 2). Read the asymmetry: Corvane's intra‑brand cannibalization (Milk Hazelnut against Milk Plain, 18%) is higher than its cross‑brand competition with Brindel (8%). For Brindel, the Corvane Milk Hazelnut is one of three equal threats at 8%, so the competitive intensity looks different from each side. This kind of asymmetric competitive picture 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 and the Pack-Size Elasticity Calculator.
Expected outcome: Understanding that the CDT is the DEMAND‑SIDE root of every PPA decision. PPA Lesson 4 OBPPC (the OBPPC Matrix Builder) assumes a CDT hierarchy when it maps each Occasion by Channel demand space to pack choices. PPA Lesson 3 Incentive Curve (the Pack-Size Elasticity Calculator) works WITHIN one CDT branch: its RSP/kg target of 70 to 85 tells you nothing when the packs being compared sit in different branches. PPA Lesson 2 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. Run the CDT first, then the PPA tools.
5.4Reading the outputEvery KPI, the formula behind it, and how to interpret a positive or negative value.
Every KPI, the formula behind it, and how to interpret a positive or negative value.
| KPI | Formula | How to read it |
|---|---|---|
| Hierarchy accuracy vs. target order | positions matching correct‑order x 100 / 5 | Green 80% and above, amber 40 to 79%, red below 40%. Your ordering scored against the illustrative target order for this chocolate‑tablet example (Type > Brand > Flavor > Size > Price). Any real order is category‑specific and comes from category‑specific shopper research; this score is only valid inside this example. |
| Top competitor | argmax over non‑focus switching probabilities | The highest‑probability switch target from the focus product, or the number of products that tie for it. Intra‑brand means a cannibalization signal (often inflated by the order of the gates); cross‑brand means real category competition. |
| Switching probability (per competitor) | [0.02, 0.08, 0.18, 0.35, 0.55][shared‑gate count] | A coarse 5‑band model: the probability depends on how many gates, counted from the top of the tree, the focus product and the competitor share before they first differ. The bar color and length reshape as you reorder gates, and that reshape IS the teaching point. |
| Competitive set shape | bar pattern across all 5 competitors | The full bar chart. One warm bar (amber, or red at the top tier) over mostly grey means a tightly‑clustered set dominated by one threat. Several amber or red bars mean a diffuse set with many real threats. All grey means an 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 the top competitor and check intra‑brand vs cross‑brand. When the hierarchy scores high and the top competitor is another brand, that is the normal finding, and you can defend it. When the hierarchy scores low and the top competitor is your own brand, it is often an inflated cannibalization signal that shrinks once you fix the hierarchy, here from 35% to 18%. The full competitive set shape (bar chart color distribution) gives you the texture of the category: dominated‑by‑one, diffuse‑competition, or isolated‑branch.
5.55 common mistakes to avoidDiagnostic patterns that catch the most common misuse of this calculator.
Diagnostic patterns that catch the most common misuse of this calculator.
- Mistake 1Applying this example's order to a different categorySymptom: Hierarchy accuracy reads 100% but shopper behavior in the actual category (yogurt, cereal, coffee) doesn't match the model's predictions.Fix: The Type > Brand > Flavor > Size > Price order is an ILLUSTRATIVE anchor for this chocolate‑tablet example, and any real order is CATEGORY SPECIFIC. Yogurt research often shows Flavor first; coffee often shows Brand 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, and 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 reading at the wrong hierarchy as the true size of the cannibalizationSymptom: A portfolio decision (delist the 'cannibalizing' SKU, consolidate to one size) is made based on the default hierarchy's top competitor figure.Fix: Before any delist / consolidate decision, run the CDT at the hierarchy your category research supports (here, the illustrative target order). If the intra‑brand reading HOLDS at the same strength at that hierarchy, the cannibalization is as large as it looked and a portfolio decision is defensible. If it SHRINKS at that hierarchy (as it does for Corvane Milk Hazelnut 100g and Corvane Milk Plain 45g in this scenario, from 35% down to 18%), the wrong framing inflated it, and a delist sized on the inflated figure would remove a choice shoppers really make.
- Mistake 3Assuming the 6‑product model is a complete category representationSymptom: A CDT‑based strategy recommendation concludes "the category has only 6 meaningful SKUs" or "Aventra Filled Fruit and Nut has no competitors."Fix: The 6 products are a teaching sample. Real chocolate‑tablet assortments run to dozens of SKUs in a large store. The Aventra Filled Fruit & Nut 180g "no competitors at 2% across the board" finding is informative (the Filled 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 CDTSymptom: PPA analysis recommends filling a mid‑tier pack gap; the new SKU launches and sells below plan from the start, and CDT research done afterwards shows the gap sits in a branch of the tree that shoppers don't actually go down.Fix: CDT is the DEMAND‑SIDE root of every PPA decision. PPA Lesson 2 Pack Roles works at the Size/Format gate; PPA Lesson 3 Incentive Curve works WITHIN one CDT branch; PPA Lesson 4 OBPPC assumes a CDT hierarchy for each demand space. 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 analysis shows a cross‑price elasticity of 0.15 between Corvane Milk Hazelnut 100g and Brindel Milk Hazelnut 100g, while the CDT model shows an 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 how much one product's sales move when another product's PRICE changes; CDT switching probability measures how often a shopper leaving one product PICKS the other, with no price change involved. 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 modeling (Pricing Lesson 6); use CDT for category structure / assortment / OBPPC decisions.
Go deeper on the theory
- Price Pack ArchitectureThe Four Pack Roles Frameworkpack roles framework FMCG
- Price Pack ArchitectureOBPPC FrameworkOBPPC framework
- Price Pack ArchitecturePrice Tier Laddersprice tier ladder FMCG
- Price Pack ArchitecturePack-Size Elasticitypack size elasticity
- Price Pack ArchitectureGood-Better-Best Pricinggood better best pricing
- PricingCross-Price Elasticity (XED)cross price elasticity
- Trade Promotion OptimizationSource of Volumesource of volume promotion
- PricingWillingness to Pay (WTP)willingness to pay measurement
Continue with the lessonsGo further inside Price Pack Architecture
This calculator is the sandbox slice of Lesson 7: Consumer Decision Tree. Each of the other 6 Price Pack Architecture lessons teaches a complementary concept that sharpens how you read the output above.
Go further inside Price Pack Architecture
This calculator is the sandbox slice of Lesson 7: Consumer Decision Tree. Each of the other 6 Price Pack Architecture lessons teaches a complementary concept that sharpens how you read the output above.
- Price Pack Architecture · Lesson 1Part of the coursePrice Tiers and LaddersThe price layers in any FMCG category, and which layer holds the volume vs. which holds the margin.Unlock the lesson
- Price Pack Architecture · Lesson 2Part of the coursePack RolesGiving every pack a clear job (entry, weekly shop, upsize, premium), so your range has no gaps and no overlaps.Unlock the lesson
- Price Pack Architecture · Lesson 3Part of the coursePack Ladder PricingHow to price your pack ladder so shoppers want to trade up to bigger packs, without throwing away your margin.Unlock the lesson
- Price Pack Architecture · Lesson 4Part of the courseOBPPC FrameworkThe Occasion, Brand, Pack, Price, Channel grid that every category review should land on.Unlock the lesson
- Price Pack Architecture · Lesson 5Part of the coursePack Size and Price MatrixA two-axis view of your pack range that shows where the maths is broken and the trade-up logic is off.Unlock the lesson
- Price Pack Architecture · Lesson 6Part of the courseGood, Better, BestDesigning a Good-Better-Best ladder so your premium pack lifts the middle one without breaking the base.Unlock the lesson
See Shopper‑Occasion Pack Matrix inside the full lesson
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