April 24 by Brion Schweers
Historically, the only thing that Price Optimization and Management (PO&M) tools and Configure Price Quote (CPQ) tools had in common was the word “Price.” But over the past couple of years that has begun to change. Traditional PO&M vendors have been acquiring CPQ companies, while CPQ has evolved to incorporate price optimization capabilities with price execution and administration and enhancing their ability to consume optimized prices from various sources. The result of this market consolidation is a new breed of Quote-to-Cash solutions that contain embedded sales intelligence. With this blended solution, organizations can improve their ability to set price and deliver guidance to the user at point of sale. This is a key part of the Sales Empowerment model that I wrote about here.
Lower Cost of Ownership and Improved Time to Market
The introduction of Artificial Intelligence (AI) and Machine Learning (ML) embedded within CPQ enables Sales teams to maximize revenue. This unified solution has already begun to drive new market demand for companies that sell CPQ tools with embedded sales intelligence. The initial benefit that a business realizes with a cloud-based ML solution is a lower cost of ownership and shorter time to value. Gartner notes that innovation in this area “can reduce the cost of deploying price optimization and management (PO&M) solutions by as much as 80%1” when compared to legacy practices and products.
This is partially due to the fact that both tools rely on the same master data and PO&M tools also rely on the transactional data that the CPQ and/or ERP solutions produce. In a traditional deployment some form of integration is required, so that CPQ and PO&M are in sync. But in a merged solution, the data is consumed by both tools from a common data model. This implies that significantly less administration is required to maintain the solution and that transactional data from CPQ can be consumed by PO&M on a near real-time basis.
The other big change is the democratization of machine learning. The early systems relied heavily on Data Scientists to develop and tune complex proprietary algorithms. The modern cloud-based solutions provide configurable advanced intelligence that is immediately consumable by its users and can be set up and managed by pricing administrators, rather than data scientists.
New Users and New Use Cases
This convergence also opens the door for new types of sales intelligence including: territory and account white space analysis, upsell and cross-sell recommendations, quote conversion scoring, renewal and revenue risk guidance. The introduction of AI and ML embedded into CPQ has enabled this modernization and will continue to drive new market demand for companies that sell CPQ tools with embedded sales intelligence.
The focus of these types of tools is to provide real-time actionable advice to the CPQ user and learn from doing so. The other key point of these types of solutions is to minimize the reliance on data scientists, by providing democratized algorithms that can be tuned to meet a wide range of business needs. This approach is reducing the barriers to entry related to the legacy solutions and provides the user with world class price strategy development capabilities that are embedded within best in class CPQ price execution solutions.
Increased Market Demand
According to Gartner both CPQ and PO&M product demand is on the rise, and that price optimization “will be increasingly available as part of cloud CPQ solutions.” Moreover, Gartner estimates that as many as 10,000 B2B companies globally might benefit from a PO&M deployment.
The question that businesses must now ask is what mix of capabilities will solve their pricing problem. For the organization that wants to calculate prices and discounting thresholds in real time based upon factors such as the type of customer, the mix of products, historical win / loss, product costs, product availability, competitors, geography and corporate priorities, the hybrid approach of CPQ incorporating AI-driven price optimization is likely the right way to go. Our experience with the hybrid model is that customers benefit from a more practical and higher impact solution for optimizing price. The benefits of this approach include:
• Broader Focus – Sellers can view the deal holistically and maximize margin, rather than simply setting a price
• Broader Breadth of Data – With a unified data model the insights can be derived based on information captured at all levels of the funnel and customer lifecycle
• Dynamic Management – With a real-time approach, price setting occurs dynamically during the quote process allowing the solution to provide guidance based on ever-changing conditions, like updates in the shopping cart and the effect of other transactions.
Success in these three areas yield hard benefits. For example, with pricing, Gartner believes enterprises can target success metrics on growth, margins, process efficiencies and customer experience to drive business outcomes like:
• Increases in revenue of 1% to 5%.
• Increases in margins of 2% to 10%.
• Eliminating 80% of discount approvals.
• Increases in customer lifetime value of 20%.
In short, the CPQ and Price Optimization markets are evolving rapidly and converging, presenting new opportunities for driving optimal business outcomes and sustained competitive advantages through superior sales intelligence. From what we see, a key catalyst for these developments is the emergence of this hybrid model leveraging innovation in AI and ML, to expand pricing capabilities in CPQ to embed AI-powered price optimization with price execution, price administration, and product configuration, quoting and ordering.
These are exciting times. For more information, at Apttus’ upcoming Accelerate conference, these trends and developments will be analyzed in sessions, notably with: Execute a Winning Pricing Strategy with Artificial Intelligence (AI)