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Minds

July 31, 2026·Use-case·Minds Team

# **Run TURF Analysis for Product Portfolios**

Minds executes registered TURF analysis to evaluate combinations of product flavors, message variants, or feature bundles. The workflow calculates deterministic reach, incremental reach, and overlap across defined synthetic audiences to optimize portfolio decisions before live fieldwork.

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

TURF analysis in Minds evaluates candidate lists of flavors, message variants, or feature bundles to identify combinations that yield the highest total unduplicated reach. By calculating incremental reach and overlap across defined synthetic audiences, teams eliminate redundant options and focus portfolio investments on high-impact combinations.

## The decision TURF analysis supports

Portfolio decisions frequently fail when teams select candidate options based solely on individual preference scores. Choosing the top three items from a standard ranking study often results in selecting products or features that appeal to the exact same group of consumers, leaving broader audience segments unaddressed.

Total Unduplicated Reach and Frequency (TURF) analysis solves this cannibalization problem. The method identifies the combination of items that maximizes overall reach within a fixed budget, shelf space, or menu constraint.

Marketing, portfolio, and innovation teams use TURF analysis in Minds to answer specific allocation questions:

Which three claim variants should appear on retail packaging to capture the widest audience without repeating the same value proposition?

Which four product flavors should constitute a limited seasonal launch to ensure maximum consumer trial across distinct preferences?

Which subset of software features delivers the highest total utility across target buyer personas when package size is constrained?

By evaluating how candidate items perform in combination, TURF analysis shifts decision making from popularity contests to portfolio efficiency. The result is a clear directive on which items to keep, which to combine, and which to eliminate.

## Configure the study

Executing a TURF study in Minds requires specific inputs configured before running the method pipeline.

First, define the candidate items. All candidates must belong to the same decision domain so synthetic respondents can make consistent evaluation choices. Examples include eight consumer claim statements, ten beverage flavors, or six feature enhancements. Items should be distinct, clear, and presented in consistent text formats.

Second, select the target audience to establish respondent reach sets. Build or select an audience composed of grounded AI Minds that reflect target buyer segments, category users, or regional demographics. The choice of audience ensures that preference variations across segments directly inform the reach calculations.

Third, define the requested portfolio size. Specify the combination limit, such as selecting a top three or top five item bundle, along with the operational goal that the final set must support.

Unlike simple ranking tools, TURF analysis requires a clear threshold for what constitutes reach, such as an item being ranked in a persona top preference tier. Once these inputs are set, the registered method executes across the defined audience.

## How Minds runs the method

Minds executes TURF analysis through a deterministic calculation pipeline based on items, respondent reach sets, and requested portfolio size.

Stage 1: Structured response collection and reach mapping. Minds presents the candidate item list to the selected audience. Each grounded AI Mind evaluates the items through structured preference tasks. These evaluations establish the respondent reach sets across the full candidate set.

Stage 2: Deterministic TURF calculation. The platform aggregates individual reach data into a calculation pipeline. The calculator uses exhaustive search only when the combination count is at most 5,000; otherwise it uses a greedy method. The system calculates the reach metrics and stops adding items when incremental reach is zero.

Stage 3: Ranked evidence synthesis. Minds processes the quantitative matrix to produce structured output. The pipeline outputs sample size, requested and actual portfolio size, method, best combination, reach, reach percent, frequency, and incremental-reach steps.

This systematic execution ensures that reach calculations remain mathematically consistent, reproducible, and aligned with your configured audience structure.

## Interpret the output

The output of a Minds TURF study provides quantitative metrics paired with directional evidence to evaluate portfolio strategy.

Sample size displays the total count of synthetic respondents included in the reach calculations. Requested and actual portfolio size show the targeted combination count alongside the final count of items included before incremental reach reached zero.

Method indicates whether the calculator evaluated combinations via exhaustive search or the greedy method based on the overall combination count threshold. Best combination identifies the specific set of items yielding the highest evaluated performance.

Reach and reach percent show the absolute count and proportion of synthetic respondents covered by at least one item in the selected set. Frequency measures the average number of times reached respondents are covered by items in the portfolio.

Incremental-reach steps break down the exact sequence of additions, showing the precise net audience gained at each step until incremental reach reaches zero.

Teams review these outputs to construct efficient product line-ups, trim underperforming extensions, and eliminate redundant messaging variants.

## Workflow for marketing, portfolio, and innovation teams

Minds enables rapid, iterative TURF analysis across the product development cycle, serving distinct workflows across cross-functional teams.

Innovation teams apply TURF analysis during initial concept screening. When facing twenty prospective product concepts, innovation managers run a TURF study across target synthetic audiences to downselect to a high-reach shortlist of four or five concepts. This screens out redundant ideas before engineering or formulation resources are committed.

Portfolio managers utilize the workflow for line-up optimization. When managing physical shelf space, seasonal SKUs, or menu items, portfolio teams run TURF studies to identify combinations that achieve broad total category reach while minimizing inventory complexity.

Marketing teams integrate TURF analysis into message and claim testing. Before running advertising campaigns, brand managers evaluate sets of advertising headlines or value propositions. The workflow identifies the leanest set of claims that collectively address core customer motivations without redundant messaging spend.

Once optimal combinations are identified in Minds, teams export the structured evidence to inform trade presentations, finalize creative briefs, or structure validation studies.

## Limits and validation

TURF analysis in Minds provides directional, synthetic-audience evidence designed to improve early decision quality and speed up exploratory research workflows. Synthetic evidence allows teams to screen broad candidate lists, test hypotheses, and identify structural cannibalization risk early in the planning stage.

Synthetic-audience evidence is directional. It does not provide statistical market truth, demographic representativeness, universal accuracy, causal proof, or complete replacement of human consumers. Minds does not replace recruited human respondents for final stage gate approvals, capital investments, or regulatory submissions.

Instead, Minds complements real fieldwork. By using Minds to screen large pools of candidate items down to optimal combinations, teams optimize their research investments.

When high financial or strategic risk requires empirical validation, teams use the optimal combinations discovered in Minds to design focused human fieldwork instruments. This targeted approach reduces recruited panel fatigue, cuts survey fielding time, and focuses human research budget strictly on validating high-consequence decisions.

## **Frequently asked questions**

### **Can Minds run this method end to end?**

Minds executes registered TURF analysis end to end within the platform. Users configure candidate items and target audiences, after which Minds collects structured preferences, executes a deterministic calculation pipeline, and synthesizes ranked evidence.

### **When should a team use it?**

Teams run TURF analysis when selecting a limited set of flavors, claim variants, or features from a larger candidate pool. It resolves cannibalization by measuring incremental audience reach rather than simple individual preference.

### **What inputs are required?**

The method requires a comparable item list, a selected audience composed of grounded AI Minds, and a specific portfolio decision context that defines the combination limit.

### **Does it replace recruited research?**

Minds provides directional synthetic-audience evidence to refine candidate pools and screen hypotheses. It complements human fieldwork, focusing recruited panel studies on high-consequence portfolio decisions.