Insights

Product & pricing architecture

Informing product & pricing architecture

The brief

Our client was looking to make decisions regarding their product and pricing architecture to unlock potential market expansion opportunities.

With such decisions, however, come real financial trade-offs with respect to CapEx.
Moreover, there was a need to understand how to make such product and pricing architecture decisions work harder through claims optimisation, and marketing channel optimisation.

Approach

We designed a robust study speaking to buyers and potential buyers alike, with a shelf component and choice modelling at the core.

The shelf component was critical to ensure any changes in packaging didn't make it harder for existing buyers to find the product.

We then moved into choice modelling to help unpack product and pricing architecture.
Choice modelling (conjoint analysis) is a powerful technique wherein consumers are asked (over a series of screens), to choose products and/or services in various configurations. In this study , several attributes were varied (product format, pricing, promotion), and consumers made shopping decisions each time. The end result was a powerful simulator which allowed us to vary attributes and understand the impact on shares; this simulator even allowed varying competitor prices and promotions to understand activity and counter-activity.

Finally, there was a need to understand what claims would resonate most.
To approach claims, we used a technique known as best-worst scaling or Maximum Differential (MaxDiff). This was super simple for consumers - they simply indicated from just a handful of claims what they found most appealing, and least appealing. This exercise repeated several times. For consumers, it was nothing more than two decisions each time (what's most appealing, what's least appealing) - far easier cognitively than ranking dozens of statements, or trying to decide which of two very similar statements they like best. And for the insights-slinger, we got great grip on which claims worked best, and the magnitude of their strength. Furthermore, we used an analysis technique known as TURF to understand what combination of claims can work hardest to reach as many unique people as possible.

The outcome

Through the use of choice modelling, we were able to model the impact of making product and pricing decisions, helping inform CapEx decisions.

With a carefully considered experimental design, we were able to provide an indication of nuanced pricing decisions for our client - even as these came to light long after the research took place.
This was further enhanced by being able to model out the impact of price points with and without promotional stickers, and how the market might shift as competitors respond with their own promotions.

Claims testing allowed us to inform what claims, in what combination, could be deployed to maximise market resonance - including tweaks to language, to make claims and on-pack messages work harder.

 

Please reach out if you'd like to understand how to optimise your product (including packaging) and price architecture, as well as claims optimisation.
Whether it's a fast-moving consumer good, a mobile phone bundle or private health insurance product - choice modelling is a fantastic technique to maximise market potential.

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