AI & CPQ: The Future of Rules Development & Automation
Authored By: Hillary Schwallie
In the future, CPQ (configure, price, quote) systems are expected to leverage artificial intelligence (AI) in several ways to enhance their capabilities. Here are 5 ways AI could be leveraged in CPQ systems:
Intelligent Product Recommendations: AI algorithms can analyze customer data, preferences, and historical purchasing behavior to provide personalized and intelligent product recommendations. By understanding customer needs and preferences, CPQ systems can suggest the most relevant and suitable products, options, and configurations, increasing the likelihood of upselling and cross-selling.
Automated Configuration: AI can automate the configuration process by utilizing machine learning algorithms to analyze and understand complex product configurations. It can learn from historical data to predict the most optimal configurations based on customer requirements and constraints, saving time and reducing errors.
Voice-Enabled CPQ: With advancements in voice recognition technology, AI-powered CPQ systems can enable voice commands and interactions. Users can verbally specify their requirements, ask questions, or make changes to configurations, making the process more intuitive and convenient.
Sales Analytics and Predictive Insights: AI can analyze sales data and patterns to provide valuable insights and predictions for sales teams. It can identify trends, customer behaviors, and market opportunities, enabling sales representatives to make data-driven decisions and optimize their quoting and selling strategies.
Dynamic Pricing Optimization: AI-powered algorithms can analyze various factors such as market demand, competitive pricing, customer profiles, and historical sales data to optimize pricing strategies. CPQ systems can use AI to dynamically adjust prices based on real-time market conditions, customer segmentation, and business objectives, ultimately maximizing revenue and profitability.
As artificial intelligence use grows people will start to ask: Does AI have the potential to assist in writing CPQ rules? While it is unlikely to completely replace the need for human involvement in rule creation and management, AI can be leveraged to analyze large datasets, identify patterns, and make recommendations for rule configurations. It can also automate certain aspects of rule generation based on predefined criteria. This can save time and provide insights to rule designers, helping them make more informed decisions. Artificial intelligence will become a valuable tool for the future of CPQ software’s rules development and data insight.