Brand-Price Trade-Off (BPTO) is a specialised tool that helps answer pricing questions for consumer goods in a competitive context, such as:
- How will revenue, profitability, and market volume perform after launching a new product at a specific price point?
- How will revenue, profitability, and market volume perform after re-pricing an existing product?
- Where will the NPD source volume from (cannibalise your other products or take share from competition)?
- What is the effect of awareness and advertising on the adoption of new concepts?
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.
NPD volume share simulation
Through simulation, we can estimate Conjointly'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
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 Conjointly). 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 Conjointly 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 Conjointly Lemon, Revenue does not vary greatly, regardless of price point. Demand is elastic.
Van Westendorp Price Sensitivity Meter
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:
- Are the results significantly different with confidence intervals?
- What are the performances of different segments?
When to use Brand-Price Trade-Off
The tool works best when:
- You are investigating consumer goods (including durable goods, cosmetics, over-the-counter pharmaceuticals, and alcohol).
- You are only looking at SKUs and price points (no other features of the product).
- Price points are on a consistent scale (i.e. you are not comparing price per litre vs. price per kg).
- There is existing competition (at least 3 competitor SKUs).
The tool works for pricing NPDs as well as re-pricing existing products.
The tool takes into account:
- Price barriers,
- Differences in price sensitivity by SKU/brand,
- Effect of exposure to advertising,
- Differences in behaviour by type of consumer (users vs. considerers), and
- Individual differences in price sensitivity for each respondent.
This tool, however, does not take into account:
- Unique products or products in an entirely new category without existing competition. Gabor-Granger should be used instead.
- Psychological pricing (i.e. whether the price ends in
.95): Monadic (“A/B”) price testing or Brand-Specific Conjoint is a better fit for this type of research questions.
- Positive price elasticities (i.e. when consumers are willing to buy the product at a higher price point more than at a lower price point). Brand-Specific Conjoint suits such situations better.
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:
1. Conjoint exercise presents various brands along with current offering.
2. Conjoint exercise presents various brands along with NPD before adcepts.
The new proposition is added to existing SKUs.
3. Introduce NPDs through adcepts.
Adcepts explain the NPD as a picture board or video.
4. Conjoint exercise presents various brands along with NPD after adcepts.
This can be followed by diagnostic questions, such as Van Westendorp Price Sensitivity Meter, as well as additional questions.
Complete solution for pricing research
Fully-functional online survey tool with various question types, logic, randomisation, and reporting for unlimited number of responses and surveys.
Efficiently test up to 300 product claims on customer appeal, fit with brand, and diagnostic questions of your choice.
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.
Efficiently test product descriptions to identify the best one for your product
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.
Pricing, feature and claim selection in markets where product characteristics vary across brands, SKUs, or price tiers.
Test pricing of new and existing consumer goods in a competitive context using elasticity charts, revenue, and profitability projections.
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.
Efficiently evaluate potential business names to identify the best one to represent your brand
Efficiently test potential brand names to identify the best one to represent your business
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
Efficiently test potential business card designs to identify the best one for your business
Allowing advanced choice modellers to upload their own experimental designs and perform data collection on Conjointly.
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.
Conduct automated TURF analysis on results of any Conjointly experiment (or an outside dataset) using this user-friendly TURF analysis tool.