What it is
TURF identifies combinations of items that together cover the largest possible portion of the audience.
Overview notes
Why it is useful
TURF becomes valuable when what set covers most people is the real portfolio question.
Decision guide
When to use it
- When assortment or offer combinations are the decision
- When reach coverage matters more than average liking
When not to use it
- When depth of preference is more important than breadth of reach
Inputs required
- Item-level selection or preference indicators
Typical outputs
- Reach by combination size
- Recommended portfolios
Simple example
Find which three flavors together appeal to the widest proportion of category buyers.
Strengths
- Clear portfolio optimization framing
Limitations
- Ignores some nuances of preference strength
Common mistakes
- Using it where share or utility not reach is the real question
How I use it in practice
I use TURF when the output needs to be a portfolio recommendation rather than a simple rank order.
What is outputted
- Reach-maximizing item sets
How to interpret the output
- Focus on diminishing returns as items are added
How to communicate to clients
- Explain that reach is not the same as intensity of preference
Displayr / Q implementation notes
- Prepare binary inputs cleanly before analysis
Visual placeholder
Reach combination placeholder
Add a small reach curve screenshot for 1-item 2-item and 3-item combinations.
Recommended placeholder: chart screenshot, process diagram, output interpretation notes, and one short caption on what to inspect first.