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Price is the moment of truth. It is where your product architecture and marketing strategy meet with your consumers’ perception of overall worth
Balancing market competitiveness with profitability means finding that pricing sweet spot. The point where consumers’ ultimate value equals what they are willing to pay for your products and ranges.
The right pricing strategy means you can maximize revenue in line with consumers’ value perceptions and continue to drive this value upwards.
Stop guessing. Know the impact before you act. Calculate your risks within a simulated test environment and seize the most rewarding outcome.
The Consumer Price Lab App is an always-accessible online application that makes it easy for you to test and draw practical actions from our unique combination of advanced conjoint, survey-based modeling science and sophisticated simulation and reporting tools. All this is underpinned by the expertise of our highly experienced marketing science, pricing and category teams. The conjoint data is calibrated to our gold-standard sales data, consumer panel data and/or other third-party sources, to enhance accuracy and reflect market realities. This pricing strategy testing lab allows you to calculate your risks within a simulated test environment to seize the most rewarding outcome.
GfK Consumer Price Lab looks beyond your current architecture to analyze the impact and cannibalization of intended product innovations, or changes to your current product offerings for which no historical data is available.
GfK Consumer Price Lab’s modeling capabilities can include hundreds of existing and innovation (new) SKUs, covering adjacent categories as well as the core category.
Conjoint is the leading methodology for understanding which elements of the proposition customers value and at which price points. It is an extremely powerful statistical tool that pulls out which elements of a proposition customers really value/trade off (price included), and that will influence the purchase decision, through a survey that mimics real-life shopping experiences and observes consumer choices. These shopping experiences are repeated multiple times so the shoppers’ purchase intentions can be measured at a range of price points and competitive contexts.
It offers considerable advantages over direct questioning techniques by teasing out which elements really matter to consumers, rather than taking what respondents state as being important at face value. It allows us to model any combination of features in a near-market like environment even though only a sub-set of propositions have been tested and to test scenarios that might not be present in the real market. Respondent-level data allows for subgroup analyses, consumer segments and contexts for the development of tailormade strategies for different groups of customers, channels and contexts.