---
title: "Run Conjoint Analysis for SaaS Pricing with AI | Minds"
canonical_url: "https://getminds.ai/use-cases/conjoint-for-saas-pricing-research"
last_updated: "2026-08-25T03:53:19.307Z"
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  description: "Test SaaS pricing and packaging with an end-to-end AI conjoint workflow, including choice design, part-worth estimation, validation, and simulation."
  "og:description": "Test SaaS pricing and packaging with an end-to-end AI conjoint workflow, including choice design, part-worth estimation, validation, and simulation."
  "og:title": "Run Conjoint Analysis for SaaS Pricing with AI | Minds"
  "twitter:description": "Test SaaS pricing and packaging with an end-to-end AI conjoint workflow, including choice design, part-worth estimation, validation, and simulation."
  "twitter:title": "Run Conjoint Analysis for SaaS Pricing with AI | Minds"
---

Minds

July 31, 2026·Use-case·Minds Team

# **Run Conjoint Analysis for SaaS Pricing with AI**

Minds runs choice-based conjoint from attributes and levels through a server-built choice design, AI-audience collection, conditional-logit estimation, holdout validation, and share simulation. SaaS teams can compare packaging hypotheses quickly, then validate high-stakes pricing decisions with recruited buyers.

[Run a conjoint study](https://getminds.ai/?register=true)

Minds can execute choice-based conjoint for SaaS pricing and packaging inside one Study. A team defines attributes and levels, the server builds complete product configurations, the AI audience makes forced choices, and the workflow estimates part-worths, checks holdouts, and simulates preference across offers. It is designed for trade-offs, not for asking whether every feature sounds valuable.

## When to use it

Use conjoint when a buyer’s choice depends on a bundle of conditions. A SaaS plan is rarely only a price. It can combine usage limits, collaboration, integrations, support, security, contract term, and a set of product capabilities. Asking separate importance questions cannot reliably show whether a buyer would trade a higher price for more capacity or give up premium support for a shorter commitment.

Conjoint is therefore useful for product marketing, monetization, growth, and insights teams deciding how to package an offer. It also gives agencies a structured way to test client packaging hypotheses before recommending a launch architecture. If the team only needs a priority order for one list of features, run MaxDiff feature prioritization. If the central question is a price ladder for one fixed offer, the Minds pricing research methods guide explains when Gabor-Granger or Van Westendorp may be simpler.

## Questions and configuration

Start with the smallest credible model of the offer. Minds requires attributes, levels, and a choice-task design target. At least two attributes need at least two levels each, because a one-attribute study contains no cross-attribute trade-off. A practical SaaS pilot often begins with three to six attributes and two to four levels per attribute.

Good attributes describe a decision dimension, such as monthly price, seats, usage allowance, support response, or integration access. Good levels are concrete and comparable. Avoid levels such as “basic,” “better,” and “best” unless the actual differences are specified. Add constraints only when a combination is impossible or logically required. The planner can propose a configuration, but the team should confirm that every resulting offer could be understood and, ideally, sold.

Before execution, review:

1. Audience: include the buyer, administrator, or end-user perspective relevant to the packaging decision.
2. Attribute independence: do not encode the same benefit in multiple fields.
3. Level realism: use plausible prices and capabilities without pretending they are already validated.
4. Task burden: every extra level increases the design space and the number of choices needed.

## How Minds fits the workflow

The server builds a deterministic D-optimal choice design rather than asking an author to hand-write configuration pairs. Respondents choose between complete offers. The registered estimator then fits a conditional logit to calculate per-level part-worths and attribute importance. Holdout tasks provide validation diagnostics, and the simulator applies the estimated utilities to candidate configurations to produce preference-share evidence.

This creates a traceable sequence of design, responses, estimate, diagnostics, and simulation. The product refuses to expand a design whose part-worths would not be separately estimable, which is more useful than returning a polished but unidentified model. Method calculations remain authoritative artifacts for the narrative summary, so the explanation can use the registered values without inventing a second calculation.

A useful SaaS workflow is to compare a small number of credible package architectures, examine which attributes drive the synthetic audience’s choices, and use the simulator to pressure-test a new configuration. Take the strongest hypotheses into message testing, sales interviews, or a recruited buyer study. The Minds pricing research guide provides the broader sequence around method choice.

The workflow is most useful when every artifact answers a different review question. The design shows whether levels appear with enough balance to estimate their effects. The raw choices show what the configured audience actually selected. Part-worths show the direction and relative strength of each level within the tested design. Attribute importance summarizes how much each dimension contributed to choice variation, while holdouts show whether the fitted model can recover choices it did not estimate against. The simulator then answers a narrower scenario question: how the tested configurations compare under the fitted utilities. Keeping these artifacts separate prevents a persuasive narrative from hiding a weak design or an unstable estimate.

Teams should also decide in advance what would change after the result. Product leaders may use the study to remove an implausible package, adjust a level, or select two architectures for buyer interviews. Marketing teams may use it to clarify how plan differences should be explained. Researchers may use it to refine the final questionnaire and calculate the recruited sample needed for the decision. A study is less useful when every possible ranking leads to the same launch plan. Writing the decision rule before execution makes the output easier to challenge and reduces post-hoc interpretation.

## Limits and validation

Conjoint is sensitive to design quality. Part-worths are conditional on the selected audience, attributes, levels, constraints, and tasks. Leaving out an important attribute can distort the apparent value of the attributes that remain. Unrealistic combinations can teach respondents to ignore the exercise, while too many levels can create a task burden that exceeds the available evidence.

Synthetic-audience results should be treated as directional. Do not turn simulated preference share into a revenue forecast or claim population representativeness without recruited buyers and a defensible sampling plan. Validate pricing when the decision changes contracts, revenue expectations, public claims, or a large go-to-market investment. Compare the synthetic and human evidence explicitly instead of blending them into one number.

Pricing studies also need commercial feasibility checks outside the estimator. Finance should confirm that tested prices and discounts are economically possible. Sales and customer-success teams should flag contract structures or service promises that buyers would interpret differently from the research labels. Legal or procurement specialists may need to review regulated terms. Conjoint quantifies trade-offs among the options it receives; it does not certify that those options can be delivered or contracted. Record those checks alongside the model diagnostics so the final recommendation reflects both preference evidence and operating constraints.

## Starter template

- Audience: SaaS buyers and administrators in the intended company-size segment.
- Decision: which packaging architecture should proceed to buyer validation.
- Attributes: price, usage allowance, support, integrations, and one differentiating capability.
- Levels: two to four concrete, feasible values for each attribute.
- Method: choice-based conjoint with holdout validation.
- Output: part-worths, attribute importance, diagnostics, and simulated preference across candidate plans.

## Next step

Start a Minds Study, select conjoint, and enter the attributes and levels for a small, credible set of plans. Treat the first design as a model audit: if stakeholders cannot agree on realistic levels or feasible combinations, resolve that product question before asking any audience to choose.

## **Frequently asked questions**

### **Can Minds run conjoint analysis end to end?**

Yes. Conjoint is an available research method in Minds. The server builds a choice design, collects configuration choices from the selected AI audience, estimates part-worths with a conditional logit, validates the model with holdout tasks, and produces simulation evidence.

### **What inputs does a SaaS conjoint study need?**

Define at least two attributes with at least two levels each, such as price, usage limit, support, contract term, or a product capability. Use levels that are realistic, mutually understandable, and feasible to combine into complete offers.

### **Should I use conjoint or Gabor-Granger for pricing?**

Use conjoint when price must be evaluated with other package attributes and the team needs trade-offs between complete configurations. Use Gabor-Granger for a more focused read across explicit price points for one offer, and Van Westendorp for perceived price thresholds.

### **Does AI conjoint replace research with real SaaS buyers?**

No universal replacement claim is appropriate. Minds provides directional evidence from the configured synthetic audience. Validate consequential pricing, forecasting, and revenue decisions with recruited buyers and a sampling plan that matches the target market.