Why Market Volatility Makes Data a Top Priority

Semiconductor manufacturers are battling multiple disruptive forces that threaten revenue stability and business planning. The U.S.’s constantly shifting trade policies are roiling an already tumultuous global economic climate marked by international tensions, shifting market demands and tightening controls on critical raw materials and semiconductor exports. According to Model N’s 2025 State of Revenue Report, supply chain uncertainty is a top factor impacting high-tech revenue management in the year ahead. 

Adapting to the constant fluctuations will require chipmakers to rethink traditional approaches to contracts, pricing and forecasting. High-quality data, integrated analytics and emerging technologies, such as generative AI, will be critical to creating agility and optimizing revenue amid the extreme uncertainty.

Increasing supply chain transparency

To avoid regulatory violations, unexpected pricing impacts and unauthorized sales, semiconductor manufacturers must know where their materials come from and where their products are going.

Comprehensive channel data highlights supply chain vulnerabilities, such as overreliance on specific suppliers or limited geographic coverage. This information empowers chipmakers to proactively mitigate risks and quickly respond to disruptions or new regulations.

Supply chain transparency also uncovers compliance gaps and gray market activity, such as channel partners selling in unauthorized markets. By correlating sales data with geopolitical trends and restricted entity lists, companies can identify suspicious shipments, investigate unusual sales spikes in unexpected markets, and take corrective action. For example, one manufacturer I worked with noticed a sudden increase in orders from a low-volume reseller in Southeast Asia; after layering in external data, it found the products were headed to a flagged entity and stopped the shipments.

Additionally, clean, connected channel data reveals revenue management issues, like overpayments, incorrect channel pricing and inaccurate rebates, that lead to revenue leakage.

Adopting flexible contracts and pricing models

With trade policies changing quickly — sometimes even day-to-day — agile pricing strategies are imperative. Semiconductor manufacturers must be able to quickly assess the impact on their costs, then adjust list prices, price discounts and incentives accordingly.

Optimizing pricing requires real-time data on sales, contracts, channel performance, material costs, supply chain movement, regulations and market trends. Analyzing this data supports segmented, market-responsive pricing that captures maximum value. Based on AI insights, a manufacturer might shift from volume-based incentives to time-bound promotions for specific regions or customer segments to boost deal closings or improve margins.

With the current market volatility, the multi-year, fixed price contracts and static pricing strategies used by many semiconductor manufacturers pose a risk to revenue. Companies currently engaged in these rigid agreements are seeing their margins squeezed as their cost of materials increases.

Contingency-based contracts protect revenue by enabling flexibility. These structures embed performance-based incentives and pricing clauses that adjust according to market dynamics, regulatory shifts, supply chain fluctuations and customer orders. This format significantly reduces chipmakers’ financial exposure and helps them align interests with their customers by defining and sharing the market volatility risks.

Data supports agility

High-quality data is imperative for supply chain transparency, revenue optimization and organizational agility. According to the Model N survey, more than nine out of 10 surveyed executives expressed concern about the quality of their revenue data. Incomplete, inaccurate and outdated information ranked as the top issues.

Data quality directly affects day-to-day transactional processes such as pricing, contracting and compliance. For example, if a customer is misclassified into the wrong segment, they may be charged the discounted price or receive rebates they don’t qualify for. Similarly, if product attributes aren’t accurately maintained — such as specifications, regional restrictions or lifecycle status — it can lead to incorrect pricing, invalid quotes or compliance violations. Poor data creates operational inefficiencies that undermine agility and compromise profit margins.

To improve data quality, chipmakers must also invest in data integration and standardized data models. These actions ensure that information like product names and contract terms has the same meaning across all applications and departments, helping teams understand and use information consistently.

AI can enhance data accuracy by automating tasks, such as data standardization, enrichment and error detection. For example, an AI-powered platform can automatically detect and correct discrepancies in channel sales data, such as mismatched transaction records.

Once organizations have access to complete, accurate and timely information, they can leverage advanced analytics and AI-driven predictions to support agility. These technologies enable:

●      Demand forecasting: Advanced analytics can anticipate spikes or slowdowns in specific regions to support production plans and pricing strategies that align with real-time market realities.

●       Revenue forecasting: Manufacturers can rapidly recalibrate revenue projections based on evolving trade scenarios and adjust business strategies accordingly.

●       Pricing strategy optimization: Real-time data analysis allows for dynamic pricing adjustments in response to fluctuating import/export costs and other influences. AI tools can calculate complex, differentiated pricing rubrics that optimize deal and revenue opportunities.

●       Scenario modeling: Chipmakers can simulate the impact of potential trade policy changes, pricing and incentive adjustments, and other variables to inform business planning.

●       Deal evaluation: AI analyzes individual quotes and contracts for profitability, risk and strategic alignment.

●       Compliance monitoring: Automated systems keep tabs on trade laws and tariffs, alerting manufacturers to potential violations.

The benefits of technology in revenue management are widely recognized. The Model N survey found 91% of business leaders believe their technology innovation and investments have had a measurable impact on revenue management outcomes.

Nearly all companies (98%) use or plan to use new technology for revenue management activities. High-tech manufacturers are especially enthusiastic about GenAI, with 74% reporting that they have or will implement the technology in revenue operations.

Data will be a critical tool for navigating the turbulent semiconductor market in the years ahead. By investing in integrated systems that improve visibility across pricing, contracts and the channel, chipmakers can surface revenue risks sooner and identify new market opportunities. These insights will help companies make faster, more confident decisions that protect margins, improve compliance and maintain stability.