When you start working as a data analyst, you expect your role to be clear-cut—transform raw data into actionable insights, build dashboards, and uncover trends that guide decision-making. What you don’t expect is just how closely you’ll end up working with MarTech and RevOps—until, little by little, you find yourself doing their job too.
At first, it seems like a separate world. MarTech is busy configuring automation platforms, integrating marketing campaigns, and monitoring lead tracking models, while RevOps ensures CRM accuracy, sales pipeline efficiency, and revenue reporting set up. But they are both backbone of business execution. If they’re overloaded, data breaks, marketing misfires, and sales struggles to close deals. It’s like planning the best vacation of your life, only to realize that your plane can’t take off. So as a data analyst, you quickly realize that your job isn’t just about insights—it’s also about helping MarTech & RevOps get that plane in the air. If they don’t have the capacity to fix a CRM issue, ensure proper attribution tracking, or automate reporting, you step in.
Martech & RevOps are responsible for constantly improving operational efficiency, quality and scalability in a B2B organization selling SaaS. To achieve this, they support and optimize Marketing and Sales execution [1], they foster collaboration within the functional teams [2], and they maintain and evolve data flows [3].
1. Support and Optimize Execution
Let’s begin by stating that MarTech & RevOps’ primary mission is to ensure the seamless execution of marketing and sales initiatives. Beyond execution, they also play a key role in ensuring the feasibility of strategic development plans each quarter. To meet the growing expectations of their increasingly challenging plans, they need to scale and enhance efficiency by continuously optimizing workflows and processes.
Provide real-time support for ongoing Marketing and Sales efforts. MarTech supports is primarily provided to plan, execute, and automate campaigns according to the marketers’ instructions. To do so, the Ops team ensures to connect the together the right audience, the tailored developed content, the selected channel communication strategy and its respective UTM parameters. These campaigns serve different purposes: they can either generate leads through programs such as cross-channel social marketing, referrals, networking, third-party lead sourcing, brand advocacy, PR, or loyalty; they can also convert leads via nurturing and engagement initiatives to move prospects through the funnel; and finally they can retain existing customers via other programs such as CSM, loyalty, renewals, upselling, cross-selling, win back, or customer support. This creates a complex customer journey, requiring Ops efforts across multiple areas. Their responsibilities range from negotiating with third-party service providers to outsource market research for instance, to cleaning and uploading the acquired lead into CRMs. Additionally, Ops must Quality Assess (QA) the campaigns before kick-off, connect the multi-touch attribution journey to the scoring models, flag strategic ABM or CSM accounts into the system, and manage the company’s website and search-engine (SEM) APIs. Finally, RevOps have an easier role throughout the quarter, with a focus on pipeline progression, optimizing outreach efforts and sales tactics, and ensuring smooth transitions from engaged prospects to deal closure. However, both teams will have to work together to configure marketing automation platforms, CRMs and sales tools for outreach and pipeline tracking.
Provide quarterly support for strategic development. This includes competitive intelligence, where efforts focus on updating the brand positioning and evaluating market entry feasibility to enhance strategic market presence. In strategic planning, support extends to bottom-up field target validation, resource allocation feasibility reports, and risk and contingency planning to mitigate potential challenges, along with sales territory assignments at the beginning of each year. A cumbersome task considering they can have up to 10,000 managers to allocate to a quota and a territory. Finally, for product development, the focus is on managing PLM (Product Lifecycle Management), setting up the product readiness and the GTM (Go-To-Market) strategy into the systems.
Optimise workflows and processes. To achieve this more efficiently, they automate repetitive tasks such as report generation or updates, minimizing efforts spent on non-value-added activities, reducing turnaround times, and allowing teams to focus on improving delivery quality. In addition, they assess and enhance the effectiveness of activities, ensuring that workflows and processes are continuously streamlined. This also includes identifying bottlenecks, investigating outages or data leakages, and recommending optimizations to improve overall efficiency.
2. Foster Collaboration and Requester Satisfaction
MarTech & RevOps play a crucial role in fostering collaboration across teams because they own the workflows and aim at enhancing their efficiency. Beyond execution, they focus on improving their collaborators’ satisfaction by clarifying expectations, defining timelines to delivery, and ensuring alignment with Service Level Agreements (SLA). To support this, they let function teams write requests for resolutions. They establish a structured ticketing system in Jira, Workfront or SharePoint with a criticality ranking model to prioritize requests effectively. Additionally, they gather team feedback to assess the usefulness of tools, insights, and processes, continuously refining operational support.
Issue Management Analytics. Real-time service tickets and resource utilization monitoring; QA (Quality Assessment) reports; productivity and efficiency improvement reports, process bottlenecks review, updates on workflows; cost savings from optimization summary, SLA (Service Level Agreement) compliance reports.
Issue Management Metrics. Ticket volume by tool and inquiry type; Tracking ownership with ticket owner, resolution rate, response time, TTR (Time to Resolution); SLA (Service Level Agreement) compliance rate, FCR (First Contact Resolution); RSAT (Requester Satisfaction) and RX (Requester Experience) KPIs; error rate reduction, outage rate and recovery time improvements.
3. Maintain, Scale and Improve Data Flows
In data-driven organizations, data quality, accessibility, and governance are the foundation of reliable insights and strategic decision-making. Poor-quality data can degrade insights for 1 to 5 years, leading to inaccurate forecasting and flawed decision-making. Even if the raw data is accurate, poor processing, fragmentation or improper stored can compromise decision-making for 6 months to a year, causing workflow interruptions and blocking future system evolution. To prevent these long-term consequences, rigorous data verification is essential before launching any report into production.
To uphold data integrity, MarTech & RevOps must properly set up systems to ensure an accurate data flow that directly impacts key business processes, such as lead progression in the CRM through scoring models. Additionally, they must maintain and refine data pipelines by formatting collected data , integrating and consolidating multiple warehouses, and ensuring data quality through regular data cleaning and validation processes. Finally, they must implement scalable solutions to support growing data and customer volumes, ensuring long-term sustainability.
Data Management Journey Analytics. Data management involves a structured process from data collection, to data integration (wrangling, lead data cleaning and CRM upload for instance), to cross-warehouse data consolidation (between CRMs, marketing automation or e-commerce platforms like Salesforce, Microsoft Dynamics, Marketo, Magento …), to pipeline dataflow management (quality standards, maintenance procedures).
Data Management Metrics. I have come up with a simple way to remember the key metrics for data quality which is “CUT VICE”, which stands for Completeness (no crital data missing) + Uniqueness (avoid duplicates) + Timeliness (update data when required) + Validity (follow standards) + Integrity (logical relationships across datasets)+ Consistency (alignment across sources)+ Exactitude (accuracy and low error rate); data latency (update speed or time to update across platforms), update frequency (batches), throughput (volume of data handled or processed), scalability, cost per unit data (processed data only), data utilization (% of data stored actually used).
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To read about other performance optimization missions, check this post My All Hands Rosetta Stone : Defining B2B Core Business Objectives & Missions.