Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
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Author: Frank Buckler, Ph.D.
Published on: June 14, 2022 * 3 min read
When companies who do not position as cost leaders do NPS studies, VOC studies, or alike, one theme pops up regularly: “Too expensive”.
The intuitive interpretation is that lowering the price will improve the customer experience, satisfaction, and loyalty. That’s obvious.
But at the same time the question arises: Does the loss in margin really pays off thru less churn and higher cross-selling?
At the resource section of cx-ai.com and CX.AI’s Master Course you can learn, how you can find out, how much bottom-line impact lower prices would bring.
But the big question “how exactly will be existing and new customers respond to price changes?”
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Update your Pricing Know-how
Because of those reasons, as a CX professional, you need to be aware of the critical role pricing has for every company. This article gives a perfect introduction:
Once you have acknowledged the pricing decision as your #1 lever to manage your profitability, you know it’s important to measure customers’ willingness to pay.
Here is an overview of classic methods to accomplish this:
Everyone knows Conjoint analysis. Many view it as the holy grail of price measurement. Here is a review of different Conjoint methods and a conclusion that it is by no means a holy grail.
The latest trends lead to methods that combine Neuroscience with Artificial Intelligence. The interesting part: it makes it simpler, easier and better. This article gives an overview.
https://supra.tools/implicit-intelligencetm-a-new-gold-standard-in-price-research
Whenever Pricing is an issue in your CX research…
Whenever “too expensive” is an issue in your CX research and stakeholders ask you what to do, you need to respond:
“I don’t know … yet.”
But what you know is this is your moment to research the #1 profit lever of your company: pricing.
Having updated yourself in pricing research methods you are now equipped to decide whether Garbor Granger type methods, Conjoint analysis, or Implicit Intelligence tools might be good for you.
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