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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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In the rapidly evolving landscape of industrial technology, digital twins have emerged as a game-changing innovation poised to revolutionize how industrial SaaS companies develop, deliver, and—most importantly—price their offerings. As manufacturing enterprises increasingly embrace Industrial IoT solutions, the strategic implications for software pricing models are profound and far-reaching.
What Are Digital Twins and Why Do They Matter Now?
Digital twins are virtual replicas of physical objects, processes, or systems that can be used for simulation, analysis, and optimization. These sophisticated technology models leverage real-time data from sensors and other sources to create living digital simulation environments that evolve and update as their physical counterparts change.
The market for digital twin technology is expanding at a remarkable pace. According to MarketsandMarkets, the digital twin market is projected to grow from $6.9 billion in 2022 to $73.5 billion by 2027, representing a compound annual growth rate of 60.6%. This explosive growth signals a fundamental shift in how industrial operations are managed, monitored, and monetized.
The Convergence of Digital Twins and Industrial SaaS
For industrial SaaS providers, digital twins represent both an opportunity and a challenge. The integration of digital twin capabilities within industrial software platforms is creating new possibilities for value creation and capture. However, this integration also necessitates a reconsideration of traditional pricing structures.
"Digital twins are becoming central to the industrial IoT ecosystem," says Dr. Carsten Bange, founder and CEO of BARC. "They're not just add-on features anymore—they're becoming the foundation of how industrial software delivers and demonstrates value."
Four Ways Digital Twins Will Transform SaaS Pricing Models
1. Enabling Outcome-Based Pricing
Perhaps the most significant impact of digital twins on industrial SaaS pricing is the enablement of outcome-based models. By providing accurate simulations of how software interventions will affect physical systems, digital twins allow vendors to confidently offer pricing tied to specific business outcomes.
For example, a predictive maintenance solution provider might use digital twin technology to simulate the effect of their software on equipment downtime. This allows them to price based on actual downtime reduction rather than charging a flat subscription fee.
2. Creating Tiered Value Propositions Based on Simulation Complexity
As digital twins become more sophisticated, SaaS providers can develop tiered pricing structures based on the complexity and fidelity of the simulation technology they offer.
Basic digital twins might provide simple visualizations and limited predictive capabilities at entry-level price points. Advanced twins offering high-fidelity simulations, predictive analytics, and scenario planning capabilities command premium prices, creating natural upsell pathways within product portfolios.
3. Facilitating Usage-Based Pricing Models
Digital twins generate enormous amounts of data about how industrial systems operate. This data provides unprecedented visibility into how customers actually use software solutions, enabling more sophisticated usage-based pricing models.
"Digital twin technology gives us visibility into patterns of value creation that simply weren't measurable before," explains Sarah Cohen, Principal Analyst at ARC Advisory Group. "This allows industrial software companies to align their pricing more precisely with the value customers actually derive."
Rather than charging based on simple metrics like user counts or data storage, SaaS providers can develop pricing models based on the specific manufacturing processes being optimized, the number of simulations run, or the complexity of the systems being modeled.
4. Enabling New Hybrid Pricing Structures
The rich data environment created by digital twins allows for innovative hybrid pricing models that combine elements of subscription, usage-based, and outcome-based approaches.
For instance, an industrial SaaS provider might offer:
- A base subscription fee for core platform access
- Usage-based pricing for simulation runs
- Performance bonuses tied to measurable outcomes like energy efficiency improvements
This flexibility allows vendors to tailor pricing structures to different customer segments while maintaining predictable revenue streams.
Implementation Challenges and Considerations
While digital twins offer transformative potential for industrial SaaS pricing, implementation presents several challenges:
Value Measurement Complexity: Determining how to measure and attribute value in complex industrial systems remains difficult, even with digital twin technology.
Data Quality Requirements: Effective digital twins require high-quality data inputs. Poor data can undermine the accuracy of simulations and thus the viability of advanced pricing models.
Customer Readiness: Not all industrial customers are prepared for sophisticated outcome-based or usage-based pricing models. SaaS providers must consider customer maturity in their pricing strategy.
Real-World Applications Emerging Today
Several pioneering industrial SaaS providers are already leveraging digital twin technology to transform their pricing models:
GE Digital uses digital twins in its Predix platform to offer performance-based pricing for asset optimization solutions. The company can confidently price based on efficiency gains because their digital twins accurately simulate how software interventions will affect physical assets.
Siemens has integrated digital twin capabilities across its software portfolio, enabling more precise value-based pricing for its manufacturing execution systems.
PTC leverages its ThingWorx platform to create digital twins that support subscriptions with performance-based components for its industrial IoT solutions.
The Future of Digital Twin-Enabled Pricing
Looking ahead, several trends will likely shape how digital twins influence industrial SaaS pricing:
AI-Driven Pricing Optimization: As artificial intelligence advances, digital twins will become more autonomous in suggesting optimal pricing structures based on simulated outcomes.
Ecosystem-Based Pricing: Digital twins that span multiple vendors' systems will enable ecosystem-based pricing models where value is shared across providers.
Risk-Sharing Models: Advanced simulation capabilities will make it possible for vendors to confidently enter into risk-sharing arrangements where they assume some of the downside if projected outcomes aren't achieved.
Conclusion: Preparing for the Transformation
Digital twins represent a paradigm shift for industrial SaaS pricing. By providing unprecedented visibility into how software interventions affect physical systems, they enable more sophisticated, value-aligned pricing models.
For industrial SaaS executives, the implications are clear: the competitive landscape is evolving rapidly. Companies that leverage digital twin technology to develop more sophisticated pricing strategies will gain significant advantages in customer acquisition, retention, and lifetime value optimization.
As you consider your company's approach to this transformation, begin by assessing your current digital twin capabilities and how they might support more advanced pricing models. Engage customers in discussions about value measurement and pricing preferences. And most importantly, recognize that this technology-driven pricing evolution is just beginning—the companies that experiment early will be best positioned to lead as the market matures.