Turning a Technology Stack into a Revenue Engine
Turning a Technology Stack into a Revenue Engine
Rapid Intro to Revenue Operations
Revenue Operations has become indispensable for modern businesses looking to optimize revenue growth.
At its core, RevOps aims to align technology, processes, and people to streamline operations and supercharge sales and revenue. With the rapid pace of digital transformation, having the right technology in place is now more critical than ever for unlocking the full potential of RevOps.
The three P’s of Revenue Operations are:
People
Process
Platform
Platform = technology
In this post we provide an in-depth look at the technology landscape powering modern RevOps. We will discuss the essential tools needed in a RevOps tech stack, the importance of high-quality revenue intelligence data, evaluating technology options, and how different components work together seamlessly.
Whether you are new to RevOps or looking to maximize your existing tech stack, this guide will offer valuable insights and considerations.
Let's get started!
Overview of the Technology Landscape in RevOps
The technology landscape enabling RevOps has expanded exponentially in recent years. As businesses embrace digital-first strategies, having robust tools to manage each step of the revenue cycle is a must.
Here are some key trends shaping the RevOps technology ecosystem today:
Proliferation of specialized RevOps platforms: Rather than patchwork solutions, businesses are investing in integrated RevOps platforms like Gainsight. These provide end-to-end functionality for revenue optimization processes.
Rising adoption of revenue intelligence: With data analytics playing a bigger role, revenue intelligence tools like Birst and RevSystems are gaining traction. These generate insights from revenue systems and data assets (customer database, product utilization, website visitors, etc..) to inform strategy.
Omnichannel customer engagement: Customers today use multiple channels, so technology needs to deliver seamless omnichannel experiences. This means integration between marketing, sales and service touchpoints.
Everything-as-a-Service: There is a shift towards cloud-based SaaS models rather than on-premise software across the RevOps tech stack. This enables scalability, flexibility and mobility.
Customer-centricity through CRM: CRM is shifting from sales-focused to becoming the customer hub across teams, providing a 360-degree customer view.
As technology dismantles data and process silos, RevOps is transitioning from tactical to strategic - aligning sales, marketing, customer success and revenue optimization.
The tech landscape is evolving to support this holistic approach.
The rate of change appears to be accelerating… and many are calling for a wave of consolidation among technologies and solution providers.
Essential Tools in the RevOps Technology Stack
While needs vary for each business, most RevOps tech stacks rely on four key systems working together seamlessly:
Customer Relationship Management
Customer Success Platform
Marketing Automation
Revenue Intelligence
CRM (Customer Relationship Management)
The CRM is the pivotal component, serving as the hub for capturing customer interactions and sales processes. Key features include:
Central database of prospect and customer information
Sales tracking and pipeline management
Integration of marketing automation data with sales processes
Analytics on customer lifetime value and sales team performance
The CRM is the launch pad for account-based marketing.
With 360-degree customer data in one platform, sales, marketing and customer success teams can optimize engagement. Leading CRM tools include Salesforce, HubSpot CRM and Zoho CRM.
Customer Success Platforms
These tools focus on driving customer satisfaction, boosting retention and minimizing churn. Key features include:
Tracking of customer health scores and product usage
Automated or trigger-based customer engagement
Self-service help portals and knowledge bases
Analytics on retention metrics
CSPs are vital extensions of your typical CRM. They focus on all the dimensions of your team’s engagements with the Client — they often map out stakeholders in each Account.
Multi-channel + Multi-thread engagement is the holy grail.
Top platforms are Gainsight, Totango, and ChurnZero.
Marketing Automation
Marketing automation systems help nurture prospects and leads through personalized messaging at scale. Typical capabilities:
Lead scoring based on customer engagement
Dynamic contact lists and email campaigns
Landing pages and forms
Integration with CRM, sales enablement, and other martech
Some popular options are Pardot, Marketo, and Ontraport.
Revenue Intelligence Tools
Providing accurate revenue analytics and insights is the main role of these tools.
Examples of functionality:
Data extraction, transformation and loading from multiple sources
Advanced reporting on performance trends, forecasts, and predictions
Drill-downs to granular deal and account details
Leading options are RevSystems, EverString, and Mintigo.
RevSystems delivers real-time insights into revenue engine efficiency, focusing on predictive revenue intelligence and optimizing sales effectiveness. Their cutting-edge approach lies in deploying sensors into CRM systems, enabling early problem detection and real-time monitoring, adding power and predictability to the revenue engine.
Together, these four pillars provide end-to-end revenue optimization, while also generating high-quality customer intelligence. But the data itself forms the foundation.
Importance of High-Grade Revenue Intelligence Data
The most powerful RevOps tech stack is useless without the right fuel - high-grade revenue intelligence data.
This means data that is:
Complete: Full view of the customer across touchpoints, with no critical gaps.
Accurate: Precise and consistent data, without errors or ambiguities.
Well-structured: Standardized and compatible across systems and tools.
Timely: Right data at the right time to drive decisions and actions.
Secure: Protected against breaches, with proper access controls.
With robust revenue intelligence data, businesses can:
Identify revenue risks and opportunities. For example, detection of dissatisfied customers or cross-sell prospects.
Understand sales performance at granular levels. Like which products, regions and channels deliver the highest ROI.
Optimize prices and contract structures. Based on customer usage and willingness-to-pay.
Accurately forecast revenues. Essential for strategic planning and allocation of resources.
In short, quality data serves as the fuel for powering strategic RevOps with impactful decisions and outcomes.
The Need for a Great Data Model
To maximize the value of revenue intelligence data, organizations need a strong data model as the foundation. What is a data model? It is the structure for capturing, organizing, and standardizing data from different systems and channels. The key elements of a robust RevOps data model are:
A unified customer profile bringing together data from CRM, marketing automation, and other sources to create a “Golden Record” for each customer.
Standardized taxonomy and schema, with consistent definitions and attributes for accounts, contacts, products, regions etc.
Linkages between related entities like customers, orders, and service cases.
Metadata definitions and hierarchical relationships between data fields.
Master data management principles to maintain consistency.
A reliable data model aligns with business processes. It ensures clarity for reports and analytics. Manual data manipulation is minimized without compromising information integrity.
For RevOps success, the mantra is “Better Data Definitions, Better Decisions.”
Integration and the Importance of Seamless Data Flow
An effective RevOps tech stack manages data fluidly across diverse systems. This requires extensive integration. When data flows seamlessly, the result is a frictionless customer revenue lifecycle.
Common integration challenges and solutions:
Fragmented customer data: API-based connectors, ETL tools, and cloud data warehouses to pull data into unified reports and analytics.
Complex legacy systems: Modern integration platforms with pre-built connectors, like Boomi, Workato, and Jitterbit.
Siloed applications: Integration hub or middleware to centralize and distribute data across solutions.
Security: Role-based access controls and encryption to protect data during transfers.
With integrated RevOps technology, sales reps have immediate access to prospect interactions from marketing campaigns. Customer service agents see the entire purchase history. Forecasters get a real-time data feed. This 360-degree view drives contextual engagement across the customer journey.
Working with time series data and event streams takes deep expertise, even with AI Co-Pilots and assistance.
Great tooling can help bridge that skill gap.
Evaluating and Choosing the Right Tools
With the extensive variety of RevOps solutions available, evaluation is vital for choosing the right tools aligned to business needs and capabilities. Some key considerations for the assessment process:
User experience: Intuitive and easy to use? Steep learning curve? Contextual help and assistance? User satisfaction scores from reviews and case studies can reveal UX quality.
Core functionality vs. customization: Out-of-the-box features that match requirements vs. the need for costly custom development and maintenance.
Data and integration capabilities: API availability? Pre-built connectors? Ease of importing and exporting data?
Scalability: Ability to support current and projected volumes of users, datasets, transactions etc. without performance lags. Cloud-based options often scale better.
Total cost of ownership: Upfront licensing, ongoing fees, maintenance needs, change requests and training costs. Project ROI over 3-5 years.
Vendor support & community: Quality of customer support, training resources/documentation, user community engagement, and product innovation pace.
Evaluating tools thoroughly ensures RevOps technology delivers effectively on its promise. But maximizing outcomes requires interconnectedness across the stack.
The Interplay Between Data, Data Models, and the Technology Stack
A RevOps technology stack on its own cannot guarantee results. The components need to work together, powered by robust data and data models. Here are critical interdependencies:
Marketing automation feeds lead scoring data into the CRM sales workflow. But for holistic lead views, the CRM data model must contain unified prospect profiles from both systems.
Revenue intelligence tools crunch CRM data for forecasts and metrics. But inaccurate or incomplete CRM records result in unreliable analytics.
Customer success platforms track product usage data. But deriving consumer insights requires compatible data schemas between usage tracking and CRM databases.
In essence, high-grade revenue data flows across the tech stack. Meanwhile, the data model acts as the robust framework organizing this data for efficiency. Together, they unlock the stack's full potential.
An optimized RevOps technology stack is now an enterprise imperative.
Modern tools help manage each facet of the revenue cycle. But technology alone cannot guarantee RevOps success.
Companies need an end-to-end data model and high-quality customer and revenue intelligence data integrated seamlessly across solutions. With clean data flowing freely through a robust stack, businesses can derive powerful insights and execute revenue strategies with precision.
As the landscape evolves, evaluating new tools and keeping data models dynamic will maximize value. With technology, processes and people working synergistically, the potential of RevOps shines through.
There has never been a better time than now to leverage it and accelerate revenue growth.