Thaweesakdhi Suvagondha
In the era of rapid technological innovation and digital transformation, Artificial Intelligence (AI) is reshaping the entire lifecycle of marketing strategies, particularly for patented products. Patented products offer a limited monopoly period, during which companies must maximize their return on investment before competitors enter the market. The use of AI provides a strategic advantage throughout the three core phases of the patent lifecycle: planning and development of new products, managing active patent stages, and handling post-expiry scenarios.
This article is divided into two parts. Part I covers Phase I and Phase II—AI’s role in the early planning, development, and management of patented products. Part II will focus on managing expired patents and strategies to sustain competitive advantages.
Phase I: AI in Planning and Development of New Products
1. Marketing Research
Every successful patented product begins with meticulous research.
AI plays a transformative role in marketing research by enabling data-driven decision-making. Through predictive analytics, natural language processing, and machine learning algorithms, businesses can gain precise insights into various aspects, such as:
Identifying the target market
Predicting market potential
Mapping competitors
Revealing hidden trends
AI provides a comprehensive, data-driven picture of the opportunity, enabling businesses to assess preferences, track market shifts, and evaluate competition effectively. Whether it’s assessing market trends, evaluating competition, or making strategic decisions, AI serves as a valuable strategic advisor.
Market Potential and Customer Preferences: AI tools like sentiment analysis and predictive modeling uncover unmet needs and forecast future trends based on vast amounts of data.
Market Structure and Environment: Algorithms analyze demographic, geographic, psychographic, and behavioral data to categorize market segments effectively.
Competition: AI compares competitors’ pricing, branding, and customer engagement through web scraping and competitor analysis tools.
AI automates data collection from various digital sources (social media, forums, databases), providing real-time insights that minimize risks and enhance product-market fit.
2. Marketing Operation Planning
Marketing Mix Planning (4Ps):
Product Development: AI-powered tools such as generative design and consumer behavior modeling contribute to innovative product and packaging designs that resonate with the target market. AI also assists in branding, including designing logos and optimizing trademarks, often protected by copyright and trademark laws.
Pricing: AI-based dynamic pricing models analyze demand elasticity, competition, and production costs to suggest optimal pricing that ensures market competitiveness while justifying return on investment.
Promotion: AI personalizes advertising through predictive targeting. Chatbots, programmatic ad placements, and AI-generated content improve ROI by delivering the right message to the right audience at the right time.
Place (Distribution Channels): AI optimizes distribution logistics and recommends the most accessible and profitable channels by analyzing customer behavior and preferences, enhancing omnichannel strategies.
Launching Campaigns:
AI supports the execution of targeted product launch campaigns by:
Identifying ideal customer personas
Timing the launch for maximum impact
Using machine learning to predict campaign performance and adjust strategies dynamically
Key Performance Indicators (KPIs) and Metrics:
AI systems set and monitor key performance indicators (KPIs), such as customer acquisition cost, campaign ROI, conversion rates, and customer lifetime value, providing real-time dashboards for decision-makers.
3. Sales Planning
Sales Forecasting: AI-enhanced sales forecasting models integrate historical sales data, current market trends, and external variables (like economic conditions) to produce highly accurate sales forecasts. These models adapt and improve with continuous data input.
Salesforce Readiness: AI plays a crucial role in preparing the salesforce for the new product. It delivers personalized learning paths based on individual strengths and weaknesses, utilizing virtual assistants and simulated selling scenarios for experiential learning. AI tracks individual and team performance against set targets, enabling proactive interventions. Additionally, it can help design performance-based incentives and recognize patterns leading to high productivity and engagement.
Phase II: AI in Management of Active Patent Stage
The management of patented products during their active lifecycle is crucial. This phase typically involves intensive marketing and sales strategies aimed at maximizing market penetration and customer loyalty before the patent expires.
Introduction Stage: During product launch and early market entry, AI-supported launch campaigns deliver high-intensity promotional efforts using behavioral segmentation and personalized content. AI tools enhance public relations efforts, monitor media mentions, and engage with customers across various platforms. AI-enabled customer feedback systems help refine product features post-launch.
Growth Stage: In this stage, the focus shifts to sustaining momentum and scaling. AI-driven CRM systems and marketing automation maintain customer engagement, identify upselling/cross-selling opportunities, and effectively prevent churn.
Continuous Salesforce Development: AI facilitates ongoing training through learning analytics, tracks sales patterns, and recommends personalized support.
Market Expansion Strategies: AI helps identify new customer segments and market geographies based on evolving data.
Approaching Expiry Period (3–5 Years Before Expiry)
As the patent nears its expiration date, companies must shift their strategies to preserve market share:
Customer Loyalty Campaigns: AI helps personalize retention programs based on individual customer data.
Reinforcement of Trademark and Brand Equity: Deep learning models evaluate brand perception and guide branding strategies to reinforce recognition and preference.
Emphasis on Trade Secrets: AI aids in identifying and protecting non-patented proprietary knowledge or methods, often embedded in service quality or production efficiency.
Portfolio Strategy: AI helps identify opportunities to file continuation patents or related trademarks to extend the lifecycle of competitive advantage.
Conclusion of Part I
AI is not just an optional enhancement; it is now a core enabler of marketing success in patented product management. From product ideation to managing its growth in the marketplace, AI empowers organizations to be faster, more accurate, and customer-centric. Part II of this article will explore how companies can leverage AI to navigate the challenges of expired patents, maintain market share, and prepare for future innovations.
On behalf of our creator, who served as an associate justice at the Central Intellectual Property and International Trade Court in Thailand from 2001 to 2006, I would like to express my gratitude for your attention. Please consider liking, sharing, and subscribing for more insights on intellectual property, marketing, and innovation.
For more resources and the full article, visit pathsinstitute.com. As always, listen twice, reflect, and stay ahead in your business practices.