Suzanne Valentine has been appointed as the Director of Pricing AI at Pricefx. In this role, she will oversee a team of pricing scientists, focusing on delivering customer value through innovative pricing strategies. Valentine has over 25 years of experience in enterprise software and AI-driven merchandising analytics. Her previous role was as Head of Data Science at Meta, where she led teams ranging from 30 to 100 data scientists, guiding strategy and evaluating the effectiveness of initiatives aimed at enhancing business adoption of Meta’s advertising products.
Pricefx provides AI-driven pricing management and optimization software designed to streamline pricing strategies, from base price setting to profit maximization. The platform enables businesses to improve profitability, enhance margins, and secure better deal outcomes.
Its “PricingAI” solution incorporates advanced generative AI technology, offering personalized pricing insights, an intuitive chat-based interface, and rapid optimization capabilities. Pricefx focuses on simplifying and modernizing pricing processes to help businesses stay competitive and achieve measurable success.
How is AI transforming the pricing landscape for large companies, and what unique opportunities does it offer for optimizing pricing strategies?
The discipline of pricing encompasses analyzing patterns of consumer demand, understanding competitors, personalizing and/or optimizing prices, and making pricing more dynamic via automation based on factors such as demand fluctuations and inventory levels. AI-fueled systems provide companies with the ability to synthesize, analyze, and leverage vast amounts of data (structured and unstructured) to make better business decisions faster. Many pricing decisions are made using at least some data today, but the routine use of AI not only makes analyses more comprehensive and scalable but also helps to uncover insights.
For example, a ubiquitous pricing need is to set prices in order to maximize demand (sales) while maintaining profit margins. Pricing AI starts by illuminating patterns in the data and quantifying the impact of both known and unknown factors that impact demand. With this foundation in place, it becomes possible to predict outcomes under various scenarios, which in turn enables true optimization to meet business goals.
Transparency is often essential for building user trust in AI-driven systems. How does Pricefx achieve this transparency, and why is it crucial for successful AI adoption in pricing?
Powerful AI-fueled data processing and analytics are crucial in today’s competitive environment. But without understanding of and visibility into how recommendations are being made, users likely won’t develop trust in pricing software, and may revert to their “old way” of making decisions. Ideally, transparency in Pricing AI encompasses both the techniques that are implemented and the user experience.
With regard to AI techniques: Pricefx employs a variety of AI approaches depending on the problem requirements, desired transparency of algorithms, and required granularity of results. For optimization, Pricefx typically relies on unique Reinforcement Learning (Multi-Agent AI) algorithms developed and improved over many years. With this approach, pricing objectives and constraints are defined in a user-friendly business framework, and subsequent recommendations are made clear to the user by transparently displaying interactions between those targets and various constraints.
Pricefx also has a “glass box” philosophy when it comes to user experience. Many analyses and summaries are provided as a default, but users can easily drill down as deep as they desire into source tables and even view and customize source code. The combination of intuitive AI and full transparency from raw data to recommendations builds the necessary confidence in business users that leads to trust in the recommendations.
With Pricefx allowing clients to use their own data science within its AI framework, how does this flexibility affect customer satisfaction and long-term ROI?
A challenge with adoption of any software solution is winning over users of an existing solution, and most of our potential and existing customers have made investments in AI people, processes, and tools. We want the Pricefx platform to become the primary destination for Pricing teams because it continually and securely synthesizes their key data… so we actually encourage our customers to “bring their own” science to the platform.
We see a wide range of what this looks like in practice – some customers start with an existing accelerator and customize it, some customers leverage “science bricks” that come with the platform (such as Multi-Factor Elasticity, Clustering, and Product Similarity), and some customers integrate existing code via our Model Class framework. We believe this approach can only enhance customer satisfaction and ROI, precisely because we can grow and evolve with an organization.
Pricefx suggests that clients may see returns of up to 70x ROI within the first year. What factors in the AI software contribute to reaching such high potential returns, and how is ROI typically measured in these cases?
First, ROI calculation is pretty straightforward: our customers look at the magnitude of their Gross Margin Improvements relative to the costs of the AI software implementation. Broadly, the ROI achieved via AI-fueled pricing software can be attributed to having comprehensive synthesis of relevant data, and harnessing it to make better pricing decisions.
But there are a number of ways this can manifest in practice — AI-driven decisions include changes such as increasing deal win rate by understanding what impacts price sensitivity by customer type, geography, and product line, and by improving margins by systematically identifying pricing outliers, complying with desired rules, and optimizing prices. Sometimes ROI improvements also come from efficiencies in the pricing process which reduce operational costs and enable fast simulation of the impact that changes in strategy will have. Indirectly, AI software can even improve customer lifetime value, by ensuring that pricing strategies are tailored to encourage long term relationships.
To ensure that our clients understand the value they are getting from AI software, Pricefx provides a combination of reporting and dashboards that provide necessary understanding and transparency.
In serving diverse sectors like manufacturing and energy, how does Pricefx’s AI adapt to meet specific industry requirements and address unique pricing challenges?
There are several common building blocks that benefit all industries, such having a flexible and scalable platform to capture not only internal data but crucial external signals, such as market fluctuations, technological advancements, and evolving consumer demand. And our investments in AI technologies are carefully chosen and tested to provide accuracy and stability for a wide range of business problems.
That said, there are clearly differences in pricing drivers across sectors and industries. Pricefx has cultivated a team of industry experts who have direct experience with the industries we serve – these experts span our Solution Strategy and Implementation teams, and work closely with our Product team to translate unique needs into specific requirements. The software is highly configurable, and we work with each customer to prescribe a solution that can be implemented quickly but is also a good fit for unique processes. Our partner ecosystem is also invaluable in bringing industry-specific solution design expertise to our clients.
An internal AI council was established to integrate AI across Pricefx’s operations. What role does this council play, and how does it align with the company’s product and business strategy?
As providers of AI software, it is important for us to keep pace with how industry technologies are advancing and evolving. We also want to ensure that our internal use of AI continues to fuel our innovation but also incorporates responsible practices, such as transparency, privacy, security, fairness, and sustainable use of resources. Our AI council brings together AI experts and leadership to have open discussions about the benefits and potential watchouts as we continue to embrace AI.
Pricefx CoPilot, which integrates GenAI for conversational data insights, aims to improve pricing decisions. What impact does this feature have on client decision-making, and what developments are planned for the future?
Pricefx Co-Pilot is a natural evolution for our product suite, building on the foundation of AI-based optimization that we provide to Pricing teams and leaders.
Once live, the Pricefx platform deploys predictive and prescriptive AI to continually surface insights. Power-users of Pricefx offerings become adept at leveraging these insights… and often their exploration is only limited by the person-hours available on their team. Our vision is that Co-Pilot will act as the very best analyst that could be trained on your data and business processes, allowing pricing professionals to query the platform with natural-language prompts such as “Who are my most and least profitable customers this quarter?” CoPilot will not only bring back answers quickly and comprehensively but also make suggestions on the next best action that they might take. This frees up pricing professionals to truly focus on more strategic elements of pricing while Co-Pilot handles routine tasks and acts as an interface to the data analyses. And leveraging Co-Pilot will not only create more bandwidth but ultimately speed up the decision-making process, thus improving the effectiveness of pricing strategies.
Two important things to understand about Pricefx Co-Pilot:
- Customer data is kept entirely private and secure. We have never (and will never) share data across our customers, and we are building our own in-house LLM capabilities specific to the Pricing space, not making calls to a publicly available GenAI.
- Investment in the data foundation is a crucial first step. As with all analytics and AI, insights will only be as good as the quality of the underlying data. Pricefx works with their customers to infuse their platform with the most relevant data first and bring up dashboards that highlight opportunities, both from a data integrity and a pricing practice perspective.
In terms of future developments: GenAI’s versatility is truly its strength. We expect to not only make Co-Pilot “smarter” by leveraging the recommendations from Optimization AI but also unlock additional capabilities throughout a company’s pricing ecosystem, from democratizing data and suggesting architectural improvements to optimizing pricing communications with various stakeholders.
While AI-powered automation offers significant advantages, where does Pricefx allow for human oversight to ensure alignment with strategic company goals?
Ultimately, our goal with Pricing AI is to give the pricing practitioner more control of their decision-making.
We design the workflow to allow visibility into each part of the process – a user starts by defining the scope of their analysis and immediately sees statistics that confirm what data will be analyzed. Some initial AI is run to provide insights into pricing drivers; the user reviews the outputs and sets parameters for the final optimization. Because the AI-fueled platform accelerates and automates complex data processing, this whole process can be run, reviewed, adjusted, and repeated multiple times in a day. And by not needing a “data scientist in the middle”, pricing practitioners can independently understand how various strategies can be used to meet company goals.
AI often emphasizes profit maximization, yet many companies also prioritize sustainable, long-term growth. How does Pricefx help clients balance these objectives?
Virtually all Pricefx customers are balancing multiple objectives – they want to increase their sales and “Win Rate” while protecting their margins, all while creating a pricing architecture that maintains key relationships and consistency. Pricefx Pricing AI solutions are designed to assist customers in reaching this balance and having the ability to understand the expected outcome of various strategies. One powerful example is our Price Waterfall Optimization, which leverages Multi-Agent AI (MAAI) to simultaneously optimize list prices, discounts, incentives, and other factors given a set of objectives and constraints.
What is Pricefx’s long-term vision for AI-driven pricing, and how does the company plan to evolve its strategies to meet emerging needs and technological advancements in the years ahead?
Our vision through the years has been to make Pricing AI science accessible to pricing practitioners who aren’t necessarily data science experts. We aim to research and invest in emerging AI technology pertaining to pricing, but also recognize that sophisticated science is most impactful when operated and consumed by those who understand pricing strategy.
Some examples of how we expect our strategy to evolve:
- Analyzing and improving user experience with rapid product design experimentation
- Evolving how data is ingested, validated, harmonized, and enhanced to maximize value
- Leveraging AI to supplement training, support, and consulting services by proactively identifying gaps
- And (of course) extending our GenAI Co-Pilot to comprehend a broader, more sophisticated set of pricing questions
But… we also recognize that opportunities for AI are limited only by our collective imagination – we look forward to collaborating with clients and partners to unlock the full potential of Pricing AI!
Thank you for the great interview, readers who wish to learn more should visit Pricefx.