Optimizing Product Lines with TURF: Go Beyond Traditional Analysis

Are you looking to maximize the impact and return of your product line? Traditional TURF analysis (Total Unduplicated Reach and Frequency) offers a starting point, but it often falls short when truly understanding customer interest and predicting sales. 

How is TURF a valuable research technique for a business? 

Imagine a company like Ben & Jerry’s trying to optimize its ice cream flavor lineup. 

They have 31 new flavors they are considering but want to determine the optimal combination of 5 flavors to offer in a new market. TURF is an ideal approach to help Ben & Jerry’s make a massive go-to-market decision. But, it overlooks the nuances of consumer behavior and can lead to flawed conclusions. 

This post introduces traditional TURF and TURF+, a probabilistic approach that addresses these limitations, offering more realistic reach calculations, improved line optimization, and enhanced volumetric forecasting.

What is TURF Analysis?

TURF, which stands for Total Unduplicated Reach and Frequency,  is a common advanced market research technique to help organizations understand reach to optimize a combination of options they’re facing. It aims to identify the optimal combination of these options that will reach the largest possible audience while minimizing redundancy.

Essentially, TURF helps answer the question: “Which combination of items will give me the greatest reach to my target market, without significant overlap?”  

While originally used for advertising campaign optimization, its applications have expanded to various areas, including product line optimization, feature prioritization, and even shelf placement decisions.  

The Challenge with Traditional TURF Analysis

A major weakness of traditional TURF analysis is its oversimplified approach to purchase intent.  

It often treats anyone expressing high interest (for example, selecting a 5 on a 5-point scale) as being 100% likely to buy. This assumption is rarely true and can significantly skew reach calculations by achieving maximum reach with relatively few items, leading to missed opportunities.  

The reason is that some consumers who don’t select the top rating (like a 3 or 4 on that same scale) will purchase an item, so any analysis that only counts the “top box” as reached, leaves these potential buyers effectively ignored. 

This can create a distorted view of the market and lead brands to make poor decisions about product selection, product lines, or even shelf placement.

Recommended Reading: How to Conduct Shelf Testing Market Research

The Best Way to Optimize Product Lines With TURF+

TURF+ takes TURF analysis to the next level by incorporating a probabilistic model of purchase intent using volumetric forecasting model learnings. 

Instead of simply assuming that anyone expressing high interest will definitely buy, TURF+ assigns purchase probabilities to each level of interest based on empirically-tested volumetric forecasting models to create a measure of trial potential called “Interested Universe.” This concept acknowledges that while “top box” interest is an excellent indicator, it doesn’t guarantee a purchase.

TURF+ utilizes these probabilities, in calculating reach, enabling more true-to-market results. This probabilistic approach allows our research team to determine the overall likelihood of a consumer actually purchasing an item within a line configuration, accounting for their stated purchase intent for all items in that line individually. 

As a result, our market research company can provide more nuanced insights related to flavors, SKUs, campaign optimization, pricing strategies, and overall product line optimization.

Benefits of TURF+ for Product Optimization

Traditional TURF analysis, with its simplified view of purchase intent, can sometimes lead to flawed conclusions. TURF+ addresses these limitations and offers significant advantages for product optimization, as detailed below.

This TURF+ probabilistic approach offers several key benefits:

• More Realistic Reach Calculations: TURF+ acknowledges that even top-box interest doesn’t guarantee a purchase. By incorporating probabilities, it provides a more accurate picture of actual reach.

• Improved Line Optimization: By understanding the likelihood of purchase for different combinations of products, features, flavors, items, or SKUs, TURF+ helps businesses create optimized product lines that maximize reach and sales potential.

• Enhanced Volumetric Forecasting: The probabilistic data generated by TURF+ can be directly used in sales forecasting, leading to more accurate predictions. Data can be easily integrated in a simulator and model for predictive analytics.

• Identification of Hero Products: TURF+ helps identify hero products that frequently emerge across top performing product line configurations. While these products may not stand out in a traditional TURF (that looks at products in isolation), TURF+ will help identify their consistent role and appearance in the most successful combinations of products. The role hero products play is crucial, and often hidden. TURF+ helps avoid inadvertently removing these valuable products from a line.
• Understanding Depth of Interest: TURF+ helps measure the average number of products customers are interested in, providing insights into potential upselling and cross-selling opportunities. The depth of interest provides a peek into the duplication aspect of robust line configurations by identifying when adding more items to the line is not expected to generate more sales. That is, even if you reach a limit of unique customers, there may be an increased chance of multiple purchases by adding new products, and increased protection against competition when certain items are out-of-stock.

TURF+ in Action: Real-World Applications

TURF+ has proven valuable in various scenarios in the market research and insights space, including:

• Line Optimization and Product Portfolio Optimization: Determining the optimal mix of products to maximize reach and sales. It dives into similarity mapping, which helps brands understand product appeal for customer segments. It is crucial for line optimization because it helps avoid including redundant products that cannibalize sales from each other.

• Planogram Optimization: Deciding which products to remove from shelves to make room for new ones, considering factors like redundancy and purchase intent.

• Volumetric Sales Forecasting: Predicting sales based on purchase intent data and planned marketing activities.

Overcoming the Complexity: TURF+ is Cultivated but Simple

While the underlying methodology of TURF+ is sophisticated, the input data required is simple: purchase intent. 

As complicated as it all sounds, the complication is managed by our expert team of market researchers, so you don’t have to sweat about design, setup, or analysis. It’s simply a matter of asking about the purchase intent of all the items, which is what you might be used to in a traditional TURF approach.

The beauty of TURF+ and experiencing more accurate and complete data is that the analysis can be run on purchase intent data you’ve already collected. The TURF+ analysis we complete for top brands often doesn’t require an entirely new survey. If your findings have shortcomings, we can likely take the survey data you collected from 3 months, 6 months, or a year ago and provide an updated perspective.

Our clients benefit from more realistic insights, improved line optimization, and enhanced sales forecasting. It’s a game-changer compared to the traditional, less accurate methods.

Contact Our TURF Market Research Company

Drive Research and Marketing Analysts have a history of fueling top brands with insightful and advanced research techniques going above and beyond what basic research services and insights are offered industry-wide. If you want to level up your research, a consultation call with our team can help.

Contact our TURF+ market researchers for a consultation.