You're planning a product line, a flavor portfolio, a subscription tier structure, or a media schedule — and the core question is deceptively simple: which combination of offerings will reach the most people? Add too many options and you drive up complexity and cost. Add too few and you leave audience segments unserved. Worse, many of the options you're considering appeal to the same customers, creating overlap that inflates your lineup without expanding your market.
TURF analysis — Total Unduplicated Reach and Frequency — is the analytical method we use at Meraki Research to solve this optimisation problem. It evaluates every possible combination of items from a defined set and identifies the combinations that maximize the total unduplicated reach — the number of unique individuals who find at least one option appealing. The result is a precise answer to the question: "If we can only offer five flavors (or plans, or messages, or products), which five will attract the largest possible audience?"
We've run TURF analysis for businesses across consumer goods, foodservice, media, financial services, and technology. Our approach goes well beyond selecting the winning combination. We quantify the incremental reach of every additional item, model the trade-offs between portfolio breadth and efficiency, and deliver insights your team can use to make confident portfolio decisions backed by data.
Our Approach: The Meraki Portfolio Optimization Framework
Our TURF analysis practise is built around a structured methodology that connects survey design to strategic portfolio decisions. We call it the Portfolio Optimization Framework, and it moves through five phases.
Phase 1: Defining the Consideration Set
The first step is determining what goes into the analysis. We work with your team to define the full set of items to evaluate — whether those are product SKUs, flavors, features, service tiers, content topics, or campaign messages. This set should include both current offerings and potential additions.
We also establish the constraints. How many items can your business realistically support? Are there must-have items that will be included regardless? Are there items that are mutually exclusive? These decisions shape the analysis and ensure the output is operationally feasible, not just mathematically optimal.
Phase 2: Survey Design and Data Collection
The quality of a TURF analysis depends entirely on the quality of the input data. We design survey instruments that capture genuine appeal or consideration, not just awareness. Depending on the context, we use:
- Purchase intent scales for product line optimisation
- Appeal ratings for concept or flavor testing
- "Would use" measures for features or service combinations
- Interest measures for content or media planning
We set clear thresholds for what counts as a "reached" individual. For example, only respondents who rate an item as "definitely" or "probably" would purchase are counted — not those who say "might consider." Setting the right threshold is critical. Too lenient, and the analysis overstates reach. Too strict, and you undercount genuine market potential.
Phase 3: TURF Computation and Optimization
This is the core computational step. For each possible portfolio size (e.g., best 3, best 4, best 5 items), we evaluate every possible combination and calculate the total unduplicated reach — the percentage of people who would find at least one item in the portfolio appealing.
The math is straightforward in concept but computationally intensive in practice. A set of 20 items evaluated in combinations of 5 produces over 15,000 possible portfolios. We use optimised algorithms to exhaustively evaluate all combinations rather than relying on shortcuts or greedy heuristics that can miss the true optimal set.
The output shows the winning combination at each portfolio size, along with the unduplicated reach each achieves. Equally important, we calculate the incremental reach of each additional item — showing the point of diminishing returns where adding another option to the lineup gains little new audience.
Phase 4: Overlap and Frequency Analysis
Reach is half the story. We also analyse the overlap structure — which items appeal to the same people and which attract distinct audiences. High overlap between two items means they're interchangeable from a reach perspective; you gain little by offering both.
Frequency analysis shows how many items in the portfolio each individual finds appealing. A portfolio where most people like only one option has broad reach but low engagement depth. A portfolio where most people like three or four options has strong engagement but may be missing audience segments that find nothing appealing. We help you understand and balance this trade-off.
Phase 5: Scenario Modeling and Strategic Recommendations
We don't just deliver the single "optimal" portfolio. We model multiple scenarios:
- What if you must include your current top seller?
- What if manufacturing constraints eliminate certain combinations?
- What if you want to optimise for a specific customer segment rather than the total market?
- What's the reach penalty for choosing the second-best portfolio for operational reasons?
These scenario models give your team the flexibility to make decisions that balance statistical optimality with real-world business constraints. We present findings as a decision framework, not a single answer.
When TURF Analysis Is the Right Approach
TURF analysis is the right tool when:
- You're selecting a fixed-size portfolio from a larger set. Whether it's five flavors from twelve candidates or three subscription tiers from eight concepts, TURF identifies the combination that maximizes unduplicated reach.
- You need to reduce overlap in your current lineup. If multiple products in your portfolio appeal to the same customers, TURF reveals which items are redundant and which are earning their place.
- You want to quantify the incremental value of line extensions. TURF shows exactly how many new people each additional item brings, helping you decide whether the added complexity is worth it.
- You're planning media, messaging, or content portfolios. TURF works for any context where you're choosing a set of items to maximize unduplicated reach across a target audience.
When TURF Analysis Is NOT the Right Fit
- You need to understand trade-offs between product attributes (e.g., flavor vs. size vs. price). That's conjoint analysis.
- You need to rank items by individual importance or preference. MaxDiff analysis is better suited.
- You want to set optimal prices. Consider Van Westendorp or Gabor-Granger pricing methods.
- Your product line decisions are driven primarily by margin or supply chain factors rather than demand-side reach. TURF optimizes for consumer appeal, not profitability per unit.
What We Deliver
Every TURF analysis engagement includes:
- Optimal portfolio recommendations at each portfolio size (e.g., best 3, best 4, best 5, best 6), with the total unduplicated reach for each.
- Incremental reach curves showing the point of diminishing returns — the portfolio size beyond which additional items add negligible new audience.
- Overlap matrices quantifying the audience shared between every pair of items, revealing redundancy and differentiation opportunities.
- Frequency distributions showing how many items each respondent finds appealing within each portfolio scenario.
- Scenario analyses for constrained portfolios, segment-specific optimisation, and competitive substitution.
- Executive presentation with clear visual summaries and strategic recommendations, plus a technical appendix documenting methodology and thresholds.
Turnaround is typically 2-3 weeks from survey completion. If survey design is included, add 1-2 weeks for questionnaire development and programming.
Industry Applications
Food & Beverage: Selecting the optimal set of flavors, varieties, or pack sizes to maximize reach within a target market while minimizing cannibalization across the portfolio. This is the classic TURF analysis use case, and we've run it for businesses ranging from craft beverage startups to global CPG brands.
Retail: Optimizing product assortments for shelf space constraints. When a retailer can stock 12 SKUs in a category, TURF identifies the 12 that appeal to the widest set of shoppers.
Media & Advertising: Selecting the message themes, creative concepts, or media placements that maximize unduplicated reach across a target audience. TURF ensures your campaign portfolio covers the broadest possible base of people.
Financial Services: Designing subscription tiers, product offerings, or service bundles that collectively appeal to the widest range of customers with minimal overlap in who each tier attracts.
Technology & SaaS: Determining which features to include in free vs. premium vs. enterprise plans to maximize the total audience reached across tiers.
Healthcare: Selecting patient education topics, wellness program features, or service offerings that collectively reach the broadest patient population.