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What is the Data Model?

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Unfortunately, in many organizations, sales, marketing, and customer success operate in a silo. Each team tracks different goals, uses different tech, and follows different data standards.

To create GTM synergy, teams must develop these four key components:

  1. A central framework for GTM
  2. A central view of the customer and the impact they experience
  3. KPIs for your customer journey
  4. Coordinated execution across the entire team

Enter: The Data Model

What is the Data Model?

The Data Model for recurring revenue companies, often represented by a bowtie, is a model that enables functional unison across the revenue team because it ensures everyone is marching to the same destination. While other methodologies and models cover parts of the customer journey, this Data Model covers the entire journey, from prospect to lifetime value.

The Data Model was originally conceived by Jacco van der Kooij of Winning by Design and is taught in Pavilion’s Revenue Architecture course. Customer-centric at its core, the recurring revenue Data Model maps specific stages of the customer journey.

To achieve a common language, each of the nine stages has clearly defined customer objectives and exit/entry criteria, creating milestones for each stage. These milestones ensure you can then build the right people, processes, and data signals to support the outcome that must be achieved.

There are then three primary metrics the model tracks:
  1. Volume: How many customers are needed in each stage of the customer journey to hit your revenue target
  2. Conversion rate: How frequently customers convert at each stage of the journey
  3. Time: How long it takes for the customer to move from one stage to another

Together, these three metrics provide a formulaic yet flexible methodology for driving customer value at all stages of revenue generation.


1. How can the Data Model specifically help enhance our sales strategy given our company size and revenue dynamics?

The Data Model is particularly effective for companies with 50-1000employees and $10M-$100M in revenue, as it offers a structured approach to align sales, marketing, and customer success around a common goal.

By mapping out each stage of the customer journey with clearly defined objectives and metrics, such as volume, conversion rate, and time, your sales strategy can become more focused and efficient.

This alignment ensures that efforts across all teams are directed towards maximizing customer value and driving revenue growth, leveraging your existing digital sales tools and customer base of 50+ with ASP ranging from $10K-$100K.

2. Given our operational setup with CRM, MAS,CX, and Product Analytics tools, how does RevAmp integrate with these systems to optimize the Data Model implementation?

RevAmp is designed to quickly add value by integrating seamlessly with your existing tech stack, including CRM, MAS, CX, and Product Analytics tools. This integration facilitates the collection and analysis of data across the entire GTM bowtie, enabling your organization to monitor and optimize each stage of the customer journey in real-time.

For B2B companies with a recurring revenue model, this means you can leverage RevAmp to gain insights into customer behavior, improve engagement strategies, and drive efficient, predictable growth, all while making data-driven decisions directly tied to your business objectives.

3. How can our demand generation strategies – and the role of BDRs – benefit from the Data Model for improved lead generation and conversion?

The Data Model enriches your demand generation strategy by offering a systematic way to measure and enhance lead generation and conversion metrics.

By leveraging RevAmp’s integration with your digital channels and sales tools, you gain deeper insights into the effectiveness of your BDRs and the impact of your content.

This data-driven perspective allows you to refine strategies, so your demand generation efforts are both targeted and aligned with your overall GTM strategy, ultimately leading to higher conversion rates and more effective resource allocation.

4. How does the Data Model aid the integration and optimization of our current tech stack, including CRM, MAS, CS, and Product Analytics?

The Data Model acts as a strategic framework that harmonizes with your existing CRM, MAS, and Product Analytics tools, enabling a seamless flow of actionable data across your go-to-market operations.

This integration lets your organization accurately track and analyze key metrics such as customer volume, conversion rates, and journey duration.

For RevOps professionals, this means enhanced visibility into each stage of the customer lifecycle, allowing for data-driven optimizations that propel revenue growth and operational efficiency.

5. How can RevOps leverage the Data Model to streamline processes and improve decision-making?

The Data Model provides a comprehensive blueprint for mapping out the customer journey and defining clear metrics and objectives at each stage.

By adopting this model, RevOps can streamline operational processes by ensuring that data collection and analysis are focused on key performance indicators that directly impact revenue growth.

This structured approach allows for quicker, more informed decision-making, reducing the reliance on ad-hoc analyses and enabling a more agile response to market dynamics and customer behavior patterns.

6. How does the Data Model address customer retention and expansion?

The Data Model emphasizes a holistic view of the customer journey, extending beyond acquisition to include retention and expansion stages.

For RevOps professionals in B2B recurring revenue companies, this means the model offers a strategic framework for identifying opportunities to enhance customer lifetime value.

By tracking metrics such as conversion rates and time to conversion across the entire journey, RevOps can pinpoint areas for improvement in customer engagement strategies, ultimately driving higher retention rates and identifying expansion opportunities within the existing customer base.

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