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Portfolio Modeling (TURF)

Portfolio Modeling, often known by the acronym TURF, stands for Total Unduplicated Reach and Frequency. It is an analytical technique that is used to determine the optimal combination of items that will reach the widest audience possible with the fewest items.  

Different products from the same brand

When is TURF useful?

Since this technique seeks to maximize reach (or market penetration) across a range of products, TURF can be useful anytime a company is offering more than one of anything. This includes products (SKUs), menu items, messages / claims or product features. 

  1. When you need to understand the optimal number of products in a line.

    Running a TURF will help you rationalize your line-up, answering questions like "what is the optimal number of products?" or "which products should be included or excluded?"

  2. When you need to understand the incremental reach of line additions.

    Doing portfolio modelling through TURF will help you answer questions like, "which innovations add the most incremental reach to my current portfolio?" and "which innovations cannibalize the existing lineup the most?".

  3. When you need to land on claims or messaging with the best potential for success.

    TURF will tell you the optimal number of claims or features to include in products or marketing, answering questions like "what messages / claims should be featured in a marketing campaign?" and "which channels should be used to reach the largest audience?".

The benefits of TURF


TURF can be used on a variety of survey question types (e.g., multi-select, scaled questions, Upsiide, modelled data from MaxDiff, Conjoint, etc.). 


In addition to providing the optimal combination of products/messages, TURF allows us to determine which SKUs or messages, within this optimal solution, are the ‘must haves’ or drivers of reach.

Advanced TURF

The ability to increase the reach threshold, force and constrain items when building the optimal solution allows us to develop solutions that fit within a given parameter.

Our Perspective on TURF

It is not uncommon to have multiple combinations of products that reach a similar-sized audience, so then, how do we determine which of these is the best solution? At Dig Insights, we believe TURF is most powerful when other sources of data are layered onto this analytical technique.

These other sources of data can come from both primary or from secondary data. Some examples include: 
  • Reach Threshold (i.e., how many offerings must be ‘liked’ before being ‘reached’). 

  • Increasing your reach threshold allows you to improve the breadth of your assortment, which has the potential to increase frequency, basket size and brand loyalty. 

  • Frequency (i.e., can be either the number of items liked or how often a consumer would buy a product).

  • A portfolio of products that reaches a similar-sized audience as another one, but has a higher frequency, will yield more sales overall. 

  • Price Point / Cost: Multiplying your reach and frequency by the price point allows us to optimize solutions for relative revenue and for margin if costs are provided. 

  • Operational feasibility (i.e., secondary data that allows us to determine the potential operational impact of any given assortment).  

  • Examples for a restaurant optimizing their menu might include how complex the dish is to create, what stations in the kitchen it touches and for how long, the storage space required for potential new ingredients, etc. 

 Incorporating other points of data that essentially serve as ‘tie-breakers’ when multiple combinations reach a similar-sized audience allows us to be more strategic in our determination of what is the optimal portfolio of offerings.