Feature and claim selection and measuring willingness to pay for features for a single product.
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Brand-Price Trade-Off (BPTO) is a specialised tool that helps answer pricing questions for consumer goods in a competitive context, such as:
The methodology borrows from the ideas and algorithms of conjoint analysis and Gabor-Granger. It is a choice-based technique that reflects consumers' differing preferences for SKUs/brands as well as budget and psychological pricing constraints.
Using BPTO to find a new flavour with the highest revenue potential and preference share.View case study
The survey flow consists of four separate exercises to introduce respondents to our NPDs.Take example survey
Brand-Price Trade-Off surveys can be automatically translated to more than 30 languages.View translations
Bring your own respondents or buy quality-assured panel respondents from us.Get respondents
Through simulation, we can estimate Conjoint.ly's volume share following the introduction of our NPDs; performance is compared before and after adcepts are shown to the respondent. This helps to assess the effect that awareness/advertising has on the adoption of our NPDs.
The example study shows that NPD Lemon has the highest adoption rate, even before the adcept is displayed.
Movements in volume share show the increase and decrease in each of the current SKUs after introducing our NPDs. This helps to determine whether each NPD's increased volume share is derived from our existing SKUs or from competitors.
Our example study shows that each NPD’s market share sources different proportions from our competitors.
We're able to calculate the specific co-efficient of price elasticity of demand for our NPDs by selecting two points on our simulation chart (the approximate price elasticity of demand is automatically calculated and displayed in Conjoint.ly). This helps us to understand how market volume will perform at the different price points our NPDs were tested at.
The example study shows that for Conjoint.ly Kiwi, demand is elastic (i.e., an increase in price by 1% leads to more than 1% drop in volume). Revenue does not vary greatly, regardless of price point.
Through simulation, we can learn how our NPDs' revenue
and profitability will perform at different price points. Revenue projections are displayed in the simulation chart by default (this can be switched to profitability) and are calculated by
assuming 1000 units offered. Profitability is calculated using assumed fixed and variable costs through the formulas:
Revenue = price * share * 1000;
Profit = (price-$1) * share * 1000. Adjustments are also available by replacing market size with assumed 1000 units.
Our example study shows that for Conjoint.ly Lemon, Revenue does not vary greatly, regardless of price point. Demand is elastic.
Van Westendorp results will show us a psychologically acceptable range of price for our NPDs. It can be used as a diagnostic to validate the recommended price from the main conjoint exercise.
Raw data is available as an Excel output — confidence intervals are provided wherever possible, and all reports are segmentable by respondent characteristics such as information provided from panel profiling, respondents' answers to panel responses to additional questions, screening questions, and your uploaded variables.
These results will help us to answer the following (and similar) questions:
The tool works best when:
The tool works for pricing NPDs as well as re-pricing existing products.
The tool takes into account:
This tool, however, does not take into account:
.95): Monadic (“A/B”) price testing or Brand-Specific Conjoint is a better fit for this type of research questions.
The survey flow consists of four separate exercises, with the purpose of introducing both our existing SKUs and competitor SKUs before introducing respondents to our NPDs:
The new proposition is added to existing SKUs.
Adcepts explain the NPD as a picture board or video.
This can be followed by diagnostic questions, such as Van Westendorp Price Sensitivity Meter, as well as additional questions.
Using our example study for Conjoint.ly soft drinks, we explain how to set up your own BPTO study, step-by-step.
We are preparing to release a new product: a fruity soft drink can. The product will come in a 375mL can and we aim to price it higher than our 200mL can offering. However, the price is not set and needs to be assessed through pre-launch research.
To understand the pricing opportunities available, we'll the following research questions:
To begin, several inputs are required to provide sufficient information for your study:
This should be a clear category, such as “Soft Drink Can (fruit-flavoured)”.
You will also need an adcept if you want to measure the effect of awareness on NPD adoption.
Your study will need at least four current SKUs (including your SKUs and competitor SKUs) — you should ideally select SKUs that cover at least 80% of the market by volume. Also, you may want to include highly salient SKUs (emerging brands that you want to compete against). If there are currently more than 20 SKUs in your market, please consider limiting the SKU count to 20 to optimise the study's sample size.
Price points should be on a consistent scale (for example, “$XX.99 per 800g” should not be mixed with “£XX.50 per 1 litre”.
Packshot images need to be of consistent quality and size.
We recommend providing 5 to 10 potential price points for each SKU (including current your SKUs, current competitor, and the NPD). For example, if you provide 5 price points, you can insert:
SKU names and prices can be supplied in a table format, for example:
Competitor SKU 1 $20 per 1kg $22 per 1kg $25 per 1kg $27 per 1kg $30 per 1kg $32 per 1kg $35 per 1kg Competitor SKU 2 $20 per 1kg $22 per 1kg $25 per 1kg $27 per 1kg $30 per 1kg $32 per 1kg $35 per 1kg Our SKU 1 $20 per 1kg $22 per 1kg $25 per 1kg $27 per 1kg $30 per 1kg $32 per 1kg $35 per 1kg Our SKU 2 $22 per 1kg $25 per 1kg $27 per 1kg $30 per 1kg $32 per 1kg $35 per 1kg Our SKU 3 $27 per 1kg $30 per 1kg $32 per 1kg $35 per 1kg $37 per 1kg $40 per 1kg Our SKU 4 $30 per 1kg $32 per 1kg $35 per 1kg $37 per 1kg $40 per 1kg $42 per 1kg $45 per 1kg Our NPD 1 $30 per 1kg $32 per 1kg $35 per 1kg $37 per 1kg $40 per 1kg $42 per 1kg $45 per 1kg Our NPD 2 $30 per 1kg $32 per 1kg $35 per 1kg $37 per 1kg $40 per 1kg $42 per 1kg $45 per 1kg
Once all your inputs are added, it’s time to set up your study:
Copy and paste the SKUs and prices from Excel (as in above example). If you don't have them available in the format, skip to Step 3.
List all the SKUs for the test, including:
Insert visual stimuli: Click on the highlighted icon.
Drag and drop images into the pop-up window. Alternatively, you can copy them from the buffer or upload from your computer files)
Arrange SKUs on the set-up page into appropriate categories by selecting the highlighted icon and dragging them around.
Insert your NPD adcept/s. Adcepts bring your proposition to life — they can be either a picture board or a video that shows the product, with a short story, including benefits and reasons to believe. You can choose how long your respondents must view your adcept before continuing the survey.
Optionally, review the price map. This view helps you check the distribution of prices.
Note that the greater the number of SKUs and price points you test, the higher the required sample size. A recommended sample size will be automatically calculated.
There are three ways to choose respondents, dependent on targeting.