Marketing
Sales and Marketing Analyst
Last updated
Sales and Marketing Analysts build and maintain the end-to-end analytical view of the commercial funnel — from marketing-sourced leads through pipeline and closed revenue. The role serves both functions, providing demand generation teams with campaign attribution data and sales teams with pipeline and conversion analysis, and identifying where friction in the handoff between functions is costing the business.
Role at a glance
- Typical education
- Bachelor's degree in business, economics, statistics, mathematics, or marketing
- Typical experience
- 2-5 years
- Key certifications
- None typically required
- Top employer types
- B2B technology companies, RevOps-oriented organizations, marketing agencies
- Growth outlook
- Growing demand driven by the adoption of Revenue Operations (RevOps) and increased marketing accountability
- AI impact (through 2030)
- Augmentation — AI can automate routine data joining and dashboarding, but the role's value is shifting toward complex attribution modeling and resolving cross-functional data discrepancies that require human strategic judgment.
Duties and responsibilities
- Build and maintain dashboards tracking the full commercial funnel from marketing lead generation through pipeline and closed revenue
- Analyze marketing campaign performance including lead volume, quality, conversion rates, and cost per pipeline dollar
- Track sales pipeline health metrics including stage conversion rates, deal velocity, and coverage ratios by segment and territory
- Build multi-touch attribution models connecting marketing investment to revenue outcomes across channels
- Conduct cohort analysis on leads generated by different sources and campaigns to identify which acquisition investments yield the best long-term customers
- Support marketing budget allocation by modeling expected return from different channel investments based on historical conversion data
- Identify and report on friction points between marketing and sales: lead quality issues, follow-up gaps, stage definition problems
- Produce regular reporting for marketing and sales leadership including monthly performance reviews and quarterly business reviews
- Support forecasting by providing analytical inputs on pipeline trends, conversion rate changes, and marketing pipeline contribution
- Build ad hoc analyses for both marketing and sales leadership on specific questions about performance, competition, or opportunity
Overview
Sales and Marketing Analysts occupy the analytical space where two functions that often operate in silos need to be seen as a single system. The job is to build and maintain the data infrastructure that makes the full commercial funnel visible — from the first time a prospective customer sees a marketing message through the signed contract — and to use that view to identify where the organization is investing well and where it's not.
In practice, this means maintaining reporting that both teams trust. Marketing needs accurate attribution data that credits campaigns for the pipeline they create, not just the clicks they generate. Sales needs pipeline reporting that reflects deal reality, not optimistic CRM entries that haven't been cleaned. Building credibility with both functions requires being accurate even when the numbers are inconvenient, which is harder than it sounds when both teams have incentives to interpret data favorably.
The funnel gap analysis is where combined analysts create the most unique value. When marketing is generating significant lead volume but pipeline isn't growing proportionally, the question is where in the handoff the value is being lost. Is the lead quality poor? Are sales reps not following up on inbound leads quickly enough? Are leads entering the funnel at the wrong company size or vertical? Answering these questions requires data that spans both functions — something a siloed analyst can't produce.
Marketing budget support is another major function. When the CMO is planning the next quarter's channel allocation, the question is which investments have historically produced the best pipeline return per dollar. That question requires connecting media spend data (from advertising platforms) through to pipeline and revenue (from CRM), which is a data joining problem that most marketing-only tools solve poorly. The analyst who has built that connection provides genuine strategic input to budget decisions.
Qualifications
Education:
- Bachelor's degree in business, economics, statistics, mathematics, or marketing
- Quantitative orientation is important; this role requires comfort with numbers and data manipulation
Experience:
- 2–5 years in sales analytics, marketing analytics, business intelligence, or a RevOps function
- Experience working with both CRM (Salesforce or HubSpot) and marketing automation data is a strong qualification
- Prior exposure to marketing attribution or funnel reporting preferred
Technical skills:
- CRM: Salesforce or HubSpot — report building, data model understanding, basic SOQL or similar
- Marketing automation: Marketo, HubSpot, or Pardot — campaign performance data, lead flow reporting
- SQL: ability to join data from multiple sources; increasingly a baseline expectation at companies with centralized data infrastructure
- BI tools: Tableau, Looker, Power BI, or Salesforce/HubSpot native reporting for dashboard development
- Attribution: understanding multi-touch attribution models and their limitations; experience with tools like Bizible, Rockerbox, or Northbeam is a plus
Analytical competencies:
- Funnel analysis: stage-by-stage conversion rate tracking and diagnosis
- Cohort analysis: tracking lead-to-revenue conversion by acquisition source, campaign, and time period
- Marketing ROI calculation: connecting spend to pipeline and revenue with defensible methodology
- Pipeline analysis: coverage ratios, velocity, and forecast contribution by segment
Career outlook
The combined Sales and Marketing Analyst role has grown significantly with the adoption of Revenue Operations (RevOps) as an organizational model. RevOps treats sales, marketing, and customer success as a unified commercial system and requires analytical support that reflects that integration rather than siloed measurement. Companies that have adopted RevOps structures are actively hiring analysts who can work across function boundaries.
B2B technology companies have been the most active growth market for this combined role. The complexity of B2B buying cycles — long, multi-stakeholder, multi-channel journeys — makes funnel visibility particularly valuable, and the software infrastructure required to support combined analytics is more mature in tech than in most other industries.
The trend toward marketing accountability has also created demand. CMOs who historically reported on impressions and engagement are increasingly expected to demonstrate pipeline contribution and revenue influence. This creates demand for analysts who can produce attribution data that holds up to scrutiny, not just dashboards that tell a favorable story.
For analysts in this role, career paths typically lead toward Revenue Operations Manager, Director of Marketing Analytics, VP of Revenue Operations, or specialized roles in marketing data infrastructure or attribution modeling. The cross-functional perspective developed in a combined role is a competitive advantage when pursuing leadership roles in commercial operations.
The main risk in this role is getting caught between two functions that have conflicting interpretations of the data. Analysts who maintain methodological rigor and present numbers without political spin build the credibility that makes them effective; those who adjust analysis to make one team look better undermine the entire analytical function.
Sample cover letter
Dear Hiring Manager,
I'm applying for the Sales and Marketing Analyst position at [Company]. I've spent three years in a marketing analytics role that expanded into combined sales and marketing analytics as our company adopted a RevOps structure. Today I maintain the full-funnel dashboard that our CMO and VP of Sales use for their joint weekly review.
The work that I'm most proud of is the attribution rebuild I completed last year. Our previous attribution was last-touch only, which significantly underweighted our content and webinar programs in favor of the demo request form. I built a multi-touch model using our Salesforce and Marketo data joined in Snowflake, applied a time-decay weight to earlier touchpoints, and presented the results alongside the original model. The new model showed our mid-funnel content program was responsible for 28% of pipeline — a number that was invisible in the last-touch view — and directly influenced the Q3 budget reallocation.
I also rebuilt our MQL-to-SQL handoff analysis after our VP of Sales noted that lead follow-up times had been slipping. I built a dashboard that tracked time-to-first-contact by rep, lead source, and lead quality tier, which identified that our SDR team was deprioritizing content-sourced leads in favor of demo requests regardless of quality signals. The analysis led to a scoring and routing change that improved follow-up times on content-sourced leads by 60%.
I'm looking for a role where both functions treat data as a shared asset rather than competing scoreboards. [Company]'s RevOps structure suggests that's the environment you've built. I'd welcome the chance to discuss the role.
[Your Name]
Frequently asked questions
- Why does a combined Sales and Marketing Analyst role exist rather than separate analysts?
- Separate analysts optimized for each function tend to produce metrics that don't connect — marketing measures leads and clicks, sales measures pipeline and revenue, and neither team has a clear view of where the handoff breaks down. A combined analyst builds the end-to-end funnel view that identifies where investment creates value and where it doesn't. RevOps organizations increasingly centralize this analytical function specifically because funnel health requires a unified view.
- What is multi-touch attribution and why does it matter?
- Multi-touch attribution is a methodology that distributes credit for a closed deal across the multiple marketing touchpoints that influenced the buyer journey — first touch, last touch, email clicks, content downloads, webinar attendance. Simple attribution models (credit the last click) misallocate budget toward bottom-of-funnel tactics that close deals without recognizing the earlier touches that created the opportunity. Multi-touch models give marketing better information about which investments are actually driving pipeline.
- What data sources does a Sales and Marketing Analyst typically work with?
- CRM (Salesforce or HubSpot) is the primary sales data source. Marketing automation platforms (Marketo, HubSpot, Pardot) provide campaign performance and lead flow data. Web analytics (Google Analytics 4) provides website and content engagement data. Paid media platforms (Google Ads, LinkedIn, Meta) provide campaign spend and conversion data. Ideally, these sources feed into a central data warehouse where joins can be done across systems.
- How does the Sales and Marketing Analyst role interact with each team?
- With marketing: analyzing campaign performance, providing attribution data, supporting budget allocation decisions, and building the metrics that CMO and demand gen leadership track. With sales: building pipeline dashboards, supporting forecasting, and flagging data quality issues. The analyst serves both and maintains trust with both by being accurate and not using data politically — the role loses effectiveness when it becomes an advocate for one team's interests over the other.
- How is AI changing this analyst role?
- AI tools have automated parts of campaign performance reporting and lead scoring that previously required manual analysis. Revenue intelligence platforms (Gong, Clari) have added AI-driven pipeline analytics that are now part of the analytical landscape rather than analyst-built capabilities. The combined Sales and Marketing Analyst role is evolving toward higher-level interpretation, model validation, and strategic recommendation work as routine data assembly becomes more automated.
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