Retailers react: Pricers’ thoughts on conversational AI embedded in pricing

Conversational AI seems to be everywhere. It’s a constant presence in our day-to-day lives — on our phones, in the stores where we shop, on the workout machines we use in the gym, in the speakers and televisions in our living rooms, in our search engines and, ever-increasingly, in our cars. And as you might expect, we’re all getting more and more comfortable with how to have effective conversations with the AI embedded into our everyday routines.

How about in our work lives? Sure, most of us have used ChatGPT or Claude or Copilot or Gemini to help us craft an email or a presentation. And many of us have dabbled in AI image creation to help us get that exact combination of elements in the exact setting we want for a presentation or a proposal. And others of us have dabbled in conversational analytics with a data bot or two. But are we getting comfortable having conversations with the core enterprise applications and data that drive our businesses? The jury is still out on that question.

So we decided to try to find out for ourselves. We wanted to see how our clients feel about the conversational AI embedded in their enterprise pricing technology. We wanted to investigate just how comfortable our clients’ pricing teams are with conversational AI as an interface for our lifecycle pricing optimization solutions. While we’ve recently added significant conversational analytics capabilities to our solution, not every user in our client base has fully adopted the technology. We hosted a webinar for those new to the capabilities to share use cases ideally suited to conversational AI. Our goal was to help educate pricers who are new to the capabilities, gauge their reactions to the use cases we shared and answer their questions about how to take full advantage of the innovation.

And what we heard from them was fascinating: We did not hear any resistance or hesitation at all. We heard a willingness to explore and expand their use of tools that can help them deliver better results from their pricing strategies.

A case in point: During the webinar, several colleagues prepared demonstrations of how to use Revionics Conversational Analytics to accomplish a variety of pricing use cases. During the demonstration, Revionics Senior Price Strategist Lina Montoya presented a use case designed to help pricing teams explore ways to use conversational AI to ask Revionics to help adapt their price strategies to more closely align with shifting price elasticity on key products. She was on the hunt for opportunities to grow margins without sacrificing demand.

Lina asked Revionics’ Conversational Analytics to show her any top-selling items that had their price status shift from inelastic to elastic in the past year.

Revionics Conversational Analytics responded with a list of products ranging from cabbage to cilantro that have experienced significant elasticity shifts in the past twelve months. The tool reported that the elasticity on some products was more than twice what it had been just twelve months ago, and therefore those demand patterns had become very sensitive to price. Lina then asked Revionics Conversational Analytics to dig deeper and show her products where price changes had been made in the past six months alongside those shifts in elasticity.

From there, Lina described how to use those insights to make more-intelligent pricing recommendations based on shifts in price elasticity and recent price changes. She did all of this using Revionics’ Conversational Analytics as the primary interface with Revionics’ Pricing AI.

When the use case demonstrations were complete and we got to the Q&A section of the agenda, our clients had lots of follow-up questions. And despite their limited exposure to conversational AI embedded in their pricing optimization tools, they asked thoughtful questions about ways to expand and refine how they might use conversational AI to achieve their pricing objectives.

A few of their questions are highlighted here:

“Can I look for specific business opportunities that are different across categories (e.g., look for opportunities to increase profit in produce vs. increasing market share in dairy)?”

“Does the AI assistant have the ability to recommend SKUs that should be part of a price family?”

“How do I provide feedback to the model to improve future insights?”

Our team answered each of these questions to help attendees better understand the role of conversational AI within their own businesses. We offered advice and suggestions for getting started with Revionics Conversational Analytics. And as we reached the end of our allotted time, we walked away from the webinar convinced that the pricers we spoke to are not only ready for conversational AI but are in fact hungry for it to be part of their daily work lives.

And we can’t wait to see where the conversation leads.