TURF Analysis Simulator
Conjointly provides the ability to conduct TURF analysis on data from any of your experiments or a custom dataset of your own. TURF stands for Total Unduplicated Reach and Frequency. It is a technique that came into prominence during the 1950s in the space of media planning and is used extensively in range optimisation. This page guides you how to use Conjointly’s modern TURF analysis tool – the best and easiest such tool in existence that helps you digest and check your data and even prepare PowerPoint presentations of your findings:
Number of items in combinationslider.
Prioritised sequence of launching SKUs (the ladder)
Table of reach by item
Performance of each item separately
Exports into PowerPoint and Excel
- You can export the output of all tabs into either Excel or Powerpoint by clicking on the
Export into Excelor
Export into PowerPointbutton.
- Alternatively, you can export tabs individually using the
CSVbutton at the bottom.
- Clicking on
Copywill copy the data into your clipboard so you can paste it into the program of your choice.
How to access the TURF Analysis Simulator
Method 1: Access the tool using data from your experiment
Certain outputs in Conjointly experiments will allow you to go to the TURF analysis tool with the data from that experiment preloaded. Look for the
Run TURF button on the top right corner of the output panel. Conjointly outputs that can be used to run TURF include:
- Generic Conjoint: levels per attribute
- Brand-Specific Conjoint: levels by attribute by brand
- Claims Test: Preference scores (if fewer than 51 claims tested)
- Product Variant Selector: Preference scores (if fewer than 51 variants tested)
- Survey questions: multiple choice, star ratings, and constant sum
It is not available for Brand-Price Trade-Off preference scores.
Method 2: Upload your own dataset
When you open the TURF analysis tool, you will see three preloaded example datasets. To upload a custom dataset into the app, click on the
Upload a new dataset button:
The dataset should be in a .csv file where each row represents a respondent and each column represents an item ( Example dataset).
TURF analysis simulator settings
You can adjust the number of items to include in combinations tested by moving the slider:
As different data sources will have different representation of how respondents show preference, the TURF analysis tool allows you to choose how respondents are determined to be activated by combinations of items. You can specify how activated respondents are chosen using the
Reach method drop down. TURF analysis tool supports these Reach methods:
- Among top options for a person: respondents are activated by items if the score for that item is in the top N items by score. The number of top options can be set by changing the threshold value. This reach method can be used where higher scores indicate stronger preference for a particular item. For most applications, we recommend using this method, setting
N = 1.
- Among bottom options for a person: respondents are activated by items if the score for those items is among the bottom N items by score. The number of bottom options can be set by changing the threshold value. This reach method should be used with data where lower scores represent higher preferences.
- Greater than: respondents are activated by items if the score for that item is greater than the threshold value. This reach method can be used with data where scores above a certain value indicate preference.
- Greater than or equal to: respondents are activated by an item if the score for that item is greater or equal to than the threshold value. This reach method can be used with data where scores above a certain value indicate preference.
- Equal to: respondents are activated by an item if the score for that item is equal to the threshold value. This reach method is best used when scores of a specific value represents activation, such as data from a multiple choice question.
- Less than: respondents are activated by items if the score for that item is less than the threshold value. This reach method can be used with data where scores below a certain value indicate preference.
- Less than or equal: respondents are activated by items if the score for that item is less than or equal to the threshold. This reach method can be used with data where scores below a certain value indicate preference.
- Utils: Utils calculates the average probability an item in a combination will reach the respondent if placed among a set of other combinations. This reach method is better at considering strong secondary preferences and is best used for utility scores from conjoints and MaxDiffs. It can also be used for any data where the scores are mean centred (the mean is 0).
You can also modify options such as the threshold the app uses for each of these reach methods by changing the threshold value, force inclusion of certain options or force the exclusion of certain options.
- Changing the threshold value: This option is only available for certain reach types that uses a threshold value (see section on reach methods). In general, changing this value will affect how many options are activated for each respondent
- Ensure each combination has at least one high-ranking item: This option will filter combinations so that they always will contain at least one of the top N rated items. You can change how many top items are included in the top items by adjusting the option labelled
Specifically, at least one item that is among top
- Always include all of these: Items selected in this option will always be included in combinations in the output. Use this option when looking for combinations that always include certain items. For example, if an item has already been released and therefore must be included
- Always exclude all of these: Items selected in this option will never appear in combinations tested in the output. Use this option when looking for combinations that will never include certain options. For example, if an item has been determined to be underperforming
Complete solution for product research
Fully-functional online survey tool with various question types, logic, randomisation, and reporting for unlimited number of responses and surveys.
Feature and claim selection and measuring willingness to pay for features for a single product.
MaxDiff (aka Maximum Difference Scaling or Best–Worst Scaling) is a statistical technique that creates a robust ranking of different items, such as product features.
Efficiently test up to 300 product claims on customer appeal, fit with brand, and diagnostic questions of your choice.
Pricing, feature and claim selection in markets where product characteristics vary across brands, SKUs, or price tiers.
Identify winning product variants from up to 300 different ideas (e.g., designs, materials, bundle options) on customer appeal, fit with brand, and diagnostic questions of your choice.
Test pricing of new and existing consumer goods in a competitive context using elasticity charts, revenue, and profitability projections.
Determine price elasticity for a single product and identify revenue-maximising price level.
The Price Sensitivity Meter helps determine psychologically acceptable range of prices for a single product and approximately estimate price elasticity.
Perform focussed comparisons between two items to determine which performs better.
Ask respondents to evaluate product concepts and digital assets one-by-one to get a read of their preferences and perceptions with various question types.
Test the effectiveness of your advertisements using a comprehensive research method
Test the effectiveness of your digital advertisements in an online environment
Efficiently test potential brand names to identify the best one to represent your business
Efficiently evaluate potential business names to identify the best one to represent your brand
Efficiently test potential domain names to identify the perfect new home for your brand
Efficiently test potential product names to identify the best one to reflect your brand
Conduct automated TURF analysis on results of any Conjointly experiment (or an outside dataset) using this user-friendly TURF analysis tool.
Allowing advanced choice modellers to upload their own experimental designs and perform data collection on Conjointly.
Ensure product-market fit and maximise your user acquisition and expansion, by differentiating software features according to your users' needs.