Every product manager faces the same tension: you want a lineup broad enough to capture the full market, but lean enough to manage efficiently. Add too many SKUs and you drive up manufacturing, inventory, and distribution costs without proportional gains in reach. Cut too aggressively and you leave audience segments with no option that appeals to them. The question isn't "which products perform well individually?" — it's "which combination of products captures the most customers with the least redundancy?"
That's exactly the question TURF analysis was designed to answer. At Meraki Research, we apply TURF — Total Unduplicated Reach and Frequency — specifically to product line optimisation challenges. Our work helps businesses determine the ideal portfolio size and composition: which products to keep, which to retire, and which new product offerings to introduce for maximum market coverage.
We've optimised product lines for businesses across food and beverage, personal care, retail private label, financial services, and technology. Our clients come to us when the stakes are high — a reformulation, a line extension launch, a rationalization of underperforming SKUs — and they need data to back the decision rather than relying on sales momentum alone.
Our Approach: The Meraki Product Line TURF Methodology
Optimizing a product line with TURF requires more than running the analysis. It requires designing the right inputs, setting the right thresholds, and interpreting the output through the lens of operational reality. Our methodology moves through five phases built specifically for product portfolio decisions.
Phase 1: Portfolio Audit and Candidate Definition
We begin by mapping your current product line alongside candidate additions. This means understanding:
- Which existing products are in scope for potential rationalization
- Which new product concepts, flavors, variants, or features are being considered for launch
- What operational constraints exist (manufacturing capacity, minimum order quantities, distribution agreements)
- Whether there are "sacred cows" — items that must remain in the lineup regardless of TURF results
This audit produces the full candidate set: every item the TURF analysis will evaluate. We work closely with your product and market teams to ensure nothing is missing and constraints are documented upfront.
Phase 2: Consumer Research Design
The quality of a TURF optimisation depends on how well the input data reflects real consumer behaviour. We design custom surveys that measure genuine product appeal, not just awareness. For product line optimisation specifically, our preferred approach is:
- Present each product concept (with description, imagery, and relevant details) and ask respondents to indicate their purchase likelihood on a validated scale
- Set a "top box" threshold — typically "definitely would buy" or "probably/definitely would buy" — to define who counts as "reached" by each item
- Collect additional data on current purchase behaviour, brand perceptions, and demographics for profiling and segment-level analysis
We also build in measures of perceived overlap — asking respondents whether pairs of items feel interchangeable — to validate the statistical overlap patterns TURF reveals.
Phase 3: TURF Optimization
With clean data in hand, we run the core TURF analysis. For every possible portfolio size (best 2, best 3, best 4, and so on up to the full candidate set), we evaluate every possible combination and identify the one that maximizes total unduplicated reach — the percentage of people who would buy at least one item in the portfolio.
The key outputs at this phase include:
- The optimal portfolio at each size. For example: the best 5 out of 15 candidates reaches 72% of the audience, the best 6 reaches 78%, and the best 7 reaches 80%. Each additional item is identified specifically.
- Incremental reach of each item. The first item in the optimal set might reach 35% on its own. The second adds 15% new people. The fifth adds only 4%. This curve shows exactly where diminishing returns kick in.
- Cannibalization mapping. Which items in the portfolio appeal to the same customers? High overlap between two items means one may be redundant.
Phase 4: Scenario Modeling for Real-World Constraints
The mathematically optimal portfolio isn't always the operationally optimal one. We model constrained scenarios that reflect your business reality:
- Must-include scenarios: If your flagship SKU stays no matter what, how does the rest of the optimal set change?
- Category balance scenarios: If you need at least one item in each sub-category (e.g., one light option, one premium option), what's the best combination within those bounds?
- Segment-specific optimisation: The best portfolio for millennials may differ from the best for families. We run TURF separately for key audience segments and identify combinations that balance segment-level reach.
- Competitive substitution: If a competitor launches a new product, how does that affect the reach of your current and proposed lineup? We can model competitive scenarios by treating competitor items as fixed and optimising your portfolio around them.
These scenarios turn TURF from a one-answer tool into a decision framework. Your team can explore trade-offs interactively and build consensus around a portfolio strategy that's both data-driven and operationally sound.
Phase 5: Recommendations and Activation
We synthesize everything into a clear recommendation: here's the portfolio we'd build, here's why, and here's the reach you'll achieve. We also deliver a concise rationalization brief for any items we recommend cutting — showing the data behind why those products are redundant and what you lose (or don't) by removing them.
For product launches, we map each recommended new item to the audience segment it uniquely captures, giving your marketing team a head start on positioning and targeting. We also flag insights about unmet needs — individuals in the market who aren't well served by any item in the candidate set, representing potential whitespace for future innovation.
When Product Line Optimization with TURF Is the Right Approach
This service is the right fit when:
- You're rationalizing an overgrown portfolio. If your product line has expanded over the years and you suspect significant overlap, TURF identifies the leanest set that preserves maximum reach.
- You're launching new items and need to choose wisely. When you have more product concepts than launch slots, TURF tells you which combination captures the most new customers.
- You're entering a new market or channel. With limited shelf space or distribution capacity, you need to lead with the products that give you the broadest footprint from day one.
- You need to defend portfolio decisions to stakeholders. TURF provides quantitative evidence that's hard to argue with — it shows exactly what you gain or lose with each portfolio change.
When This Approach Is NOT the Right Fit
- You need to design the products themselves — determining the right combination of features, sizes, and price points within a single item. That's conjoint analysis territory.
- You want to set prices for your product line. Consider Gabor-Granger or Van Westendorp for pricing optimisation.
- Your lineup decisions are driven entirely by supply chain economics with no consumer input needed. TURF optimizes for demand-side reach; it doesn't account for unit-level profitability unless we layer in margin data as a secondary analysis.
- You only have two or three candidate items. TURF provides the most value when the candidate set is large enough that the optimal combination isn't obvious.
What We Deliver
- Optimal portfolio recommendations at each portfolio size, with specific items identified and total unduplicated reach quantified.
- Incremental reach analysis showing the added audience each item contributes and the point of diminishing returns.
- Overlap and cannibalization matrix showing which items compete for the same customers and which attract distinct individuals.
- Constrained scenario models reflecting your operational must-haves, category requirements, and segment priorities.
- Portfolio rationalization brief with data-backed recommendations for items to retain, retire, or replace.
- Whitespace identification — audience segments not adequately served by any item in the candidate set, representing product development opportunities.
- Executive presentation with visual summaries designed for cross-functional stakeholder alignment.
Timelines run 3-5 weeks for a full engagement including survey design, fieldwork, analysis, and reporting. If you already have suitable data from a recent concept test, we can run the optimisation in 1-2 weeks.
Industry Applications
Food & Beverage: Selecting the optimal flavor or variety lineup for shelf sets, foodservice menus, and direct-to-consumer subscription boxes. We've helped businesses reduce portfolios by 30% while maintaining 95%+ of their original reach.
Personal Care & Beauty: Optimizing shade ranges, fragrance portfolios, and formulation variants to maximize audience coverage. In categories where consumer preferences fragment rapidly, TURF keeps product lines efficient.
Retail Private Label: Determining which private-label SKUs to offer in each category to maximize the store brand's appeal across shopper segments without duplicating the assortment.
Financial Services: Designing product offerings — account types, insurance plans, investment packages — that collectively serve the broadest base of customers with minimal overlap between tiers.
Technology & SaaS: Selecting which features to bundle into different subscription tiers, ensuring each tier appeals to a distinct audience and the total lineup maximizes market coverage.
Media & Publishing: Choosing content themes, show concepts, or editorial verticals that collectively reach the widest audience with the least duplication.