Beyond Traditional Segmentation: Using the Shiver Grid to Diagnose B2B Tech Adoption Readiness

The global economic turmoil means B2B solution providers face anxious buyers. Understanding the deeper motivations behind customer purchasing decisions is more crucial than ever for effective marketing. Traditional segmentation models often miss important nuances that drive decision-making, making the Shiver Grid approach invaluable.

First published during a project for Mimecast by Billy Hamilton-Stent and his team at what is now Publicis Pro, the Shiver Grid approach is named for its ability to diagnose how anxious clients are about future challenges versus immediate problems. Rather than creating a simple two-by-two matrix, it plots customers along two critical dimensions that reveal deeper motivational patterns.

What makes the Shiver Grid particularly powerful is its ability to identify hidden customer segments that traditional frameworks might miss. Moving beyond simplistic price sensitivity analyses, we discovered new customer segments with distinct needs and purchasing patterns.

Case Study: Applying the Shiver Grid to AI Software Adoption in Manufacturing

Let's apply this approach to a Berlin software company SageCircle worked with last year: an AI software provider targeting manufacturing clients facing two primary challenges:

1. Quality Challenges: Data integration issues and limited visibility preventing standardized quality management
2. Growth Challenges: Supply chain rigidity limiting responsiveness to market changes and expansion opportunities

For this case, our Shiver Grid plots two key dimensions:

• Vertical axis: Urgency of current operational pain (immediate vs. future-oriented concerns)
• Horizontal axis: Data readiness and integration capability (fragmented vs. integrated systems).

Understanding the Six Manufacturing Segments in the Shiver Grid

1. Crisis Managers (25% of market)

These manufacturers face immediate quality and compliance issues due to fragmented data systems. Their pain is acute – perhaps dealing with product recalls, major customer complaints, or regulatory violations. They need immediate solutions to consolidate quality data but lack the infrastructure to implement comprehensive AI systems.

Key Characteristics:

• Fighting fires with quality issues daily
• Data exists in disconnected silos
• Decision-making is reactive rather than proactive
• Limited IT resources for integration projects

2. Quality Focused (18% of market)

These companies have moderate data integration capabilities but face pressing quality challenges. They've made some investments in digital systems but struggle to connect them effectively. Their focus is on stabilizing quality before tackling growth initiatives.

Key Characteristics:

• Have implemented point solutions for quality
• Can't effectively connect quality data with other systems
• Willing to invest in targeted AI solutions with clear ROI
• Quality issues are constraining current operations

3. Growth Blockers (22% of market)

These manufacturers have achieved decent data integration but face immediate challenges with supply chain agility that directly impact growth initiatives. They need AI solutions that can help them respond faster to market changes.

Key Characteristics:

• Have established data connections but lack predictive capabilities
• Facing supply chain bottlenecks when trying to scale
• Have the budget for AI solutions with clear growth outcomes
• Want solutions that integrate with their existing infrastructure

4. Data Dreamers (15% of market)

These companies recognize future challenges but currently operate with highly fragmented systems. They're beginning digital transformation journeys and are interested in AI but need to build foundational data capabilities first.

Key Characteristics:

• Limited immediate pain, but aware of future competitive risks
• Data exists in legacy systems with minimal integration
• Leadership is exploring digital transformation roadmaps
• Need education and framework for AI adoption

5. The Preparers (12% of market)

These manufacturers have moderate data maturity and are focused on preparing for future market challenges. They're not experiencing acute pain today but see the strategic value of AI solutions for long-term competitiveness.

Key Characteristics:

• Have begun data modernization initiatives
• Generally stable operations with good quality management
• Forward-thinking leadership teams
• Want measured, phased approach to AI adoption

6. Innovation Leaders (8% of market)

With advanced data integration capabilities and a future-oriented mindset, these manufacturers are ready to leverage sophisticated AI solutions. They view technology as a competitive advantage rather than just operational support.

Key Characteristics:

• Well-established data governance and integration
• Strong IT-OT collaboration already in place
• Seeking AI for competitive differentiation
• Able to implement complex solutions at scale

The Value of the Shiver Grid Approach

Unlike traditional segmentation, the Shiver Grid reveals important insights that might otherwise be missed:

1. Hidden Opportunities: The "Growth Blockers" segment would be overlooked in typical frameworks but represents a significant opportunity (22% of the market) for AI vendors. These companies have data readiness but face urgent growth challenges that AI can address.
2. Solution Pathways: For "Quality Focused" clients, a tailored implementation approach is needed that addresses immediate quality pain while building toward broader integration.
3. Sales Messaging Alignment: Different segments require entirely different conversation approaches – "Crisis Managers" need rapid ROI discussions, while "Innovation Leaders" want strategic partnership conversations.
4. Product Development Guidance: The segmentation reveals that modular solutions with clear implementation pathways would serve most segments, while comprehensive platforms may only appeal to "Innovation Leaders."

Practical Applications for AI Software Providers

For AI software providers targeting manufacturing clients, this segmentation offers clear guidance:

1. For Crisis Managers: Focus on rapid-deployment modules that address specific quality pain points without requiring full system integration. Present clear 30/60/90-day implementation plans with measurable quality KPIs.
2. For Growth Blockers: Emphasize how AI solutions can help them identify supply chain constraints and optimize production scheduling to enable growth. Focus on integration with existing ERP and MES systems.
3. For Data Dreamers: Provide assessment tools and roadmaps rather than immediate implementation. Help them understand the foundational data requirements and develop phased plans.
4. For Innovation Leaders: Position your company as a strategic partner in their digital transformation journey, emphasizing the competitive advantages of advanced AI capabilities.

Conclusion

The Shiver Grid offers a more nuanced understanding of customer motivations than traditional segmentation approaches. By mapping both the urgency of current pain and data readiness, AI software providers can develop more targeted solutions, sales approaches, and implementation strategies.

This approach reveals important customer segments that might otherwise be overlooked and provides a framework for continuous refinement of go-to-market strategies. As with the original Mimecast application, the power of the Shiver Grid is in moving beyond simplified assumptions about customer motivations to uncover the true drivers of purchasing decisions.

For manufacturing clients navigating quality and growth challenges, this framework provides a clear path to identify where they stand today and what solutions will best meet their specific needs as they advance in their AI adoption journey.