What is a decoy?
The decoy effect, also called the attraction or asymmetric dominance effect, concerns how adding an alternative can change the appeal of an existing option. Joel Huber, John W. Payne and Christopher Puto published a foundational marketing study in June 1982. The added alternative is dominated by one existing option but not another. Read the original study.
The idea at a glance
A comparison you can see
Imagine three hypothetical plans. A costs $30 and provides 10 units; B costs $50 and provides 25 units; C costs $50 and provides 20 units. Assume every other attribute is identical and more units are useful. B is at least as good as C on price and better on quantity. A is cheaper than C but includes less, so that relationship is a tradeoff.
| Option | Price | Included units | Comparison |
|---|---|---|---|
| A | $30 | 10 | Lower price, smaller quantity |
| B | $50 | 25 | Same price as C, more units |
| C | $50 | 20 | Dominated by B on these attributes |
This is an invented teaching example, not Maqtrix pricing or experimental data. It illustrates the comparison structure. It does not demonstrate how many people would choose each plan.
What the research established
The 1982 research showed that an added, asymmetrically dominated option could increase choice of the option that dominated it, challenging the assumption that adding an option cannot increase an existing option’s choice probability. Original evidence.
In 2014, the same authors discussed findings questioning the effect’s generality. They defended it under suitable conditions while acknowledging factors that amplify or reduce it. Numeric comparisons and the construction of the choice set matter; the effect is not a dependable result of simply displaying three packages. Boundary-condition discussion.
A higher package share is not automatically a better business
If the target plan gains share while total purchases fall, the business may be worse off. Customers may also notice an obviously pointless option and question the seller’s intentions. A plan selected through confusion can lead to refunds or poor use.
Do not confuse the decoy effect with a compromise effect, where a middle option feels attractive between extremes. Do not assume three tiers demonstrate either mechanism. Real services vary in quality, support, timing and suitability, making dominance less clear than in a two-attribute example.
How to apply it in your business
This is an original Maqtrix workflow to test. It is not a procedure proven by the cited study or a claim about results we have delivered.
1. Map the actual customer needs
List the jobs customers are trying to complete, not the package you most want to sell. Identify which differences change the usefulness of a plan. For a service, those might be implementation responsibility, response time or deliverables rather than arbitrary unit counts.
2. Build packages with a legitimate use
Each published option should make sense for someone. If you cannot explain who a package serves, reconsider it. The lesson from the experiment is that comparison context matters; it does not require selling a deliberately poor-value product.
3. Normalise the comparison
Use the same period, currency, feature names and price basis across plans. State optional charges and limits beside the relevant item. A visitor should not need to calculate three different billing conventions to compare the options.
4. Explain the meaningful tradeoff
Add a short “suited to” line and a limitation for each package. For example, a review-only service leaves implementation with the customer. This lets people choose by responsibility and need rather than interpreting a row of unexplained checkmarks.
5. Test comprehension with realistic tasks
Ask a person with a defined scenario to choose a package and explain why. Can they identify the least expensive suitable option? Can they explain what an upgrade adds? Confusion is a reason to redesign the table, not evidence of successful persuasion.
6. Measure the whole decision
If testing a revised comparison, hold the audience and underlying offer steady where possible. Track total qualified purchases as well as package mix. Observe refunds, support needs and delivery margin before expanding the new presentation.
A practical service-business example
Hypothetical agency application: offer an audit for a team that can implement, a defined implementation project for a team that cannot, and ongoing support for a business with continuing needs. These are genuine alternatives. The comparison table should make ownership and scope explicit.
A more expensive plan is not necessarily better for the customer. If a business only needs one broken enquiry form repaired, an ongoing campaign package may be unnecessary. A clear comparison earns its place by improving fit, even if some people choose the smaller option.
What to measure
| Measure | What to record |
|---|---|
| Total conversion | All suitable purchases or enquiries ÷ eligible visitors. |
| Package mix | Share selecting each plan; interpret alongside total volume. |
| Net value | Contribution after delivery, refunds and support—not headline revenue alone. |
| Decision quality | Correct understanding of inclusions, limits and the reason for choosing. |
Define the outcome and observation period before launching. Where volume allows, compare randomly assigned, comparable groups and keep other changes limited. With a small customer base, begin with a usability pilot and report the actual counts. A before-and-after change is useful to investigate, but it does not by itself prove causation.
Use the first-party measurement guide to plan the records, and the campaign brief template to document the test.
Common questions
Is every three-tier price table a decoy strategy?
No. It may present genuine differences or encourage a middle-option choice. A decoy requires a particular dominance relationship.
Should we create a bad option?
A more useful approach is to make real alternatives comparable. A pointless option may reduce trust and complicate the purchase.
Can we promise a percentage uplift?
No. The cited studies do not supply a transferable uplift for your audience, offer or pricing table.
Original sources and research notes
Researched October 7, 2026. Historical claims and findings are linked to the sources below. Company accounts are labelled as such. Business workflows, examples and interpretations are Maqtrix’s proposals, not independently established performance outcomes.
- Huber, Payne & Puto (1982), Adding Asymmetrically Dominated AlternativesOriginal Journal of Consumer Research paper; definition and choice-set experiments.
- Huber, Payne & Puto (2014), Let’s Be Honest About the Attraction EffectAuthors’ response to research on boundary conditions and practical applicability.
Named brands and researchers are discussed for educational analysis. No affiliation or endorsement is implied.
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