TURF Analysis Examples: CPG, F&B, and Retail
Why Examples Matter for TURF
TURF analysis is conceptually simple (find the combination that covers the most people), but the implementation details vary significantly by industry. The acceptance data, portfolio constraints, and business trade-offs look different for a yogurt brand than for a SaaS feature set. These examples show how TURF works in practice across five contexts.
Example 1: Sparkling Water Flavor Launch
Context
A beverage company was launching a sparkling water line with retailer shelf allocation for 5 flavors. They'd developed 12 candidate flavors through internal R&D and qualitative taste testing. The question: which 5 flavors maximize the number of consumers who'd buy at least one?
Study Design
400 sparkling water category buyers (purchased sparkling water at least twice in the past 30 days) completed an online survey. For each of the 12 flavors, respondents saw a flavor name, brief description, and can mockup, then answered: "How likely would you be to buy this flavor?" (Definitely / Probably / Might or might not / Probably not / Definitely not).
Acceptance threshold: "Definitely" + "Probably" = accepted.
Results
Individual popularity ranking: Lime (68%), Lemon (61%), Peach (54%), Black Cherry (51%), Mango (48%), Grapefruit (43%), Watermelon (41%), Cucumber (31%), Hibiscus (28%), Lavender (22%), Ginger (19%), Elderflower (15%).
TURF optimal 5: Lime, Peach, Black Cherry, Mango, Cucumber. Reach: 89%
Popularity-based 5: Lime, Lemon, Peach, Black Cherry, Mango. Reach: 83%
The TURF selection replaced Lemon with Cucumber. Despite Lemon's 61% individual popularity (2nd highest), 89% of Lemon fans also liked Lime. Cucumber's fans (31% individual acceptance) were predominantly people who didn't select any of the top 4 flavors. Swapping Lemon for Cucumber gained 6 percentage points of reach.
Business Decision
The company launched with the TURF-optimized lineup. Cucumber became their unexpected hit with the health-conscious segment that other flavors didn't reach. Lemon was later added as a seasonal LTO, where it performed well as a variety option for existing customers (high frequency, low incremental reach).
Example 2: Quick-Service Restaurant Menu Reduction
Context
A QSR chain with 42 menu items wanted to reduce operational complexity. Kitchen throughput was suffering from too many items requiring different prep stations. They needed to identify the minimum menu that still gave 90%+ of guests their preferred order.
Study Design
650 regular guests (visited 2+ times per month) completed an in-app survey. Each guest saw all 42 items with photos and checked "Would you order this on a typical visit?" Binary Yes/No.
Results
| Menu Size | Optimal Reach | Items Cut |
|---|---|---|
| 42 (current) | 98% | 0 |
| 35 | 96% | 7 |
| 30 | 94% | 12 |
| 25 | 91% | 17 |
| 20 | 85% | 22 |
The inflection point sat at 28-30 items. Between 30 and 25, reach dropped only 3 points. Below 25, each cut had a steeper impact.
Key findings:
- 7 items could be removed with negligible reach impact (<2% total)
- The lowest-reach items were niche salads that appealed to a segment already well-served by 2 other salad options
- The chain's kids' menu items had near-zero overlap with adult items, meaning they couldn't be cut without losing families entirely
- Regional best-sellers in the Southeast (fried catfish, sweet tea chicken) had low national reach but high incremental reach in their markets
Business Decision
The chain standardized a 30-item national menu and allowed up to 4 regional additions per market. Total menu reduction: 8 items nationally. Operational impact: kitchen prep stations reduced from 6 to 5, average order fulfillment time decreased by 18 seconds, food waste decreased 12%.
Example 3: Yogurt Portfolio Optimization
Context
A mid-size yogurt brand carried 18 flavors across two formats (cups and tubes). Retail partners were pressuring them to reduce the line by 30% to free shelf space for a competitor's expansion. The brand needed to cut 6 SKUs while minimizing customer loss.
Study Design
500 yogurt category buyers rated all 18 SKUs (flavor x format combinations) on acceptability. TURF was run at portfolio size 12 (the retailer's target).
Results
The optimal 12-SKU lineup achieved 91% reach versus the current 18-SKU lineup's 94%. Only 3 percentage points of reach was lost by cutting a third of the product line.
The 6 items recommended for removal shared a pattern: each had high overlap with at least 2 remaining items. Blueberry cups and blueberry tubes were both on the cut list because strawberry and mixed berry cups already served 92% of blueberry buyers.
The TURF analysis also revealed that removing plain/unsweetened yogurt (only 18% individual acceptance) would drop reach by 5 points on its own. That item's fans had almost zero overlap with flavored yogurt buyers. Despite low individual popularity, it was the third most reach-efficient item in the portfolio.
Business Decision
The brand kept plain/unsweetened (protecting its unique audience segment) and cut 6 flavored SKUs with the highest overlap. The retailer accepted the 12-SKU plan. Six months later, per-SKU velocity had increased by 22% because the remaining items captured nearly the same demand with less shelf fragmentation.
Example 4: SaaS Free Tier Feature Selection
Context
A B2B project management tool was redesigning their pricing tiers. They needed to decide which 5 features from 18 candidates should comprise the free tier, maximizing the number of trial users who find the free product useful enough to engage.
Study Design
350 users on the existing free plan completed a MaxDiff study rating all 18 features. Individual-level HB utility scores were converted to binary acceptance (each user's top 6 features by utility = accepted). TURF was run on the acceptance matrix.
Results
TURF optimal 5: Task lists, Calendar view, File sharing, Basic reporting, Team chat. Reach: 94%
Internal team's proposed 5: Task lists, Calendar view, File sharing, Gantt charts, Basic reporting. Reach: 88%
The difference: Team chat replaced Gantt charts. Gantt chart users were a subset of the power users already served by task lists and calendar view. Team chat users were a distinct segment (small teams using the tool for coordination, not project planning) that no other free feature reached.
Business Decision
The product team implemented the TURF-recommended free tier. Free-to-paid conversion rates increased 11% in the first quarter, attributed to broader initial engagement across user types.
Example 5: Retail Category Assortment
Context
A grocery chain's category manager had 8 shelf facings for protein bars. The category had 25+ SKUs across 6 brands. They needed the 8-item assortment that attracted the most shoppers.
Study Design
POS data from 12 months across 150 stores. For each customer (identified by loyalty card), the analysis flagged which protein bar SKUs they'd purchased at least once. This behavioral data served as the acceptance matrix.
Results
The TURF-optimal 8 included items from 5 different brands rather than the current brand-concentrated assortment (3 brands, 8 items). Two niche brands entered the optimal set because their buyers had minimal overlap with mainstream brand buyers.
The current assortment's reach: 71%. TURF-optimal reach: 84%. A 13-point improvement from the same number of shelf facings.
Business Decision
The category manager restructured the planogram to include the TURF-recommended assortment. Category sales per linear foot increased 9% over the following quarter, driven by new shoppers entering the category who previously didn't find an appealing option.
Patterns Across Examples
Popular items aren't always the best portfolio items. High individual acceptance with high overlap adds less reach than moderate acceptance with low overlap.
Niche items are under-valued by popularity metrics. Cucumber sparkling water, plain yogurt, and team chat all had low individual scores but high incremental reach.
TURF-optimized portfolios are consistently smaller than existing ones. Every example achieved near-equivalent reach with fewer items, because existing portfolios accumulate redundant items over time.
Behavioral data produces different results than stated preference. When available, POS or usage data is more valid than survey-based acceptance. The protein bar example used behavioral data exclusively.
Frequently Asked Questions
Are these examples representative of typical TURF results?
The pattern of TURF recommending fewer items with comparable reach is extremely common. Most established portfolios have accumulated 20-40% redundant items. The specific numbers (reach percentages, items cut) vary by category, but the direction is consistent.
How much does a TURF study like these cost?
Survey-based TURF (examples 1-4) typically costs $8,000-$15,000 including sample, survey programming, and analysis. POS-based TURF (example 5) costs less if you already have the data, primarily analyst time for data preparation and analysis.
Can I replicate these examples with my own data?
Yes. If you have acceptance or purchase data at the individual level, you can run TURF analysis. Quali-Fi handles TURF as a built-in analysis module, or you can use R, Python, or dedicated TURF tools.
Related Guides
- TURF Analysis: Complete Guide -- Full methodology overview
- TURF for Product Development -- CPG and SaaS portfolio optimization
- TURF for Menu Optimization -- Restaurant applications
- How to Run TURF Analysis -- Step-by-step execution
- Conjoint Analysis Examples -- Comparison method case studies
- MaxDiff Analysis -- Complementary prioritization method
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