AMA: Engine VP of Revenue Operations, Mollie Bodensteiner on RevOps Reporting

Good Revenue Operations reporting depends on clean data. Also, remember that data is fluid, so you must ensure it is consistently top of mind vs set and forget. Here are ways to maintain data quality: Data Standards * Use consistent naming across systems * Set required fields with validation rules * Document acceptable values for each field Governance Structure * Assign clear data ownership * Form a cross-functional data quality team * Create processes for making data changes System Integration * Align data definitions between platforms * Map fields correctly during integration * Add validation at connection points * Consider middleware for complex setups Regular Maintenance * Run automated quality checks * Build dashboards to monitor data health * Implement regular cleansing routines * Focus cleaning efforts where it matters most Technical Controls * Replace free text with dropdown options when possible * Add validation rules at entry points * Set up duplicate detection * Create alerts for unusual patterns Ongoing Improvement * Track data quality metrics over time * Get feedback from report users * Update rules regularly * Include data quality in team goals

Strong Revenue Operations reporting connects directly to what each team needs. Start by bringing sales, marketing, and customer success together to identify shared goals and unique requirements. Build dashboards/reports that give each team their relevant metrics while maintaining consistent calculations underneath. This consistency is crucial - when sales talks about "qualified leads," marketing should use the exact same definition. Document these shared metrics in a central place and review them regularly to prevent drift. Without this alignment, you'll end up with teams arguing about numbers instead of taking action. The most effective Revenue Operations teams go beyond just delivering insights - they create clear action plans based on what the data reveals. Establish a regular cadence where teams review together and commit to specific initiatives based on the findings. Did you discover that deals with custom terms take 30% longer to close? Build a playbook for handling earlier in the sales cycle and measure the impact. Found that certain customer onboarding approaches lead to faster expansion? Document the process and track adoption across the customer success team. Don't just deliver data and walk away - create regular feedback loops to learn what's working and what isn't. The real value comes when your reporting shows clear connections between team activities and revenue outcomes. Implement a consistent methodology for measuring the impact of changes, whether it's through A/B testing, cohort analysis, or before-and-after comparisons. When marketing can see how campaigns affect sales velocity or customer success understands which approaches drive expansion, you've created something truly valuable. Focus on making your reporting actionable rather than just informative, and regularly adapt as business priorities shift. Consistent metrics create trust across departments and enable meaningful cross-functional planning. The best Revenue Operations reporting feels like a competitive advantage, not administrative overhead.

After trying various approaches, I've found that blending multiple forecasting models works best. No single method gives you the full picture - you need different tools for different situations. All of this depends on your business model, sales cycle, etc. For the current quarter, weighted pipeline models deliver the most reliable results. But don't just use generic industry percentages - analyze your historical data to set accurate probabilities. Monitor your historicals and understand where seasonality factors might come into play. Looking further out, combining historical trend analysis with some machine learning elements has worked well. Basic time series models are fine for stable markets, but adding ML helps catch patterns that aren't immediately obvious. The real game-changer has been scoring individual deals based on specific signals rather than just their stage. Look into different engagement patterns, stakeholder involvement, technical validations, and competitive factors. This can help to spot which seemingly solid opportunities are actually at risk much earlier. One critical lesson: keep your new business and expansion revenue forecasts separate. They follow different patterns, and lumping them together only makes your predictions less accurate.

Moving sales leaders from gut decisions to data-driven thinking isn't about forcing spreadsheets on them - it's about making data useful for their day-to-day challenges. Start with their specific pain points. If they're worried about deals slipping, build a simple report that flags opportunities at risk before they fall apart. If they're concerned about rep performance, show which behaviors correlate with closed business. Keep everything simple and actionable. Sales leaders don't have time to become data scientists - they need clear insights they can act on immediately. Instead of complex dashboards, give them straightforward reports with obvious next steps. Focus on one small area first where data can deliver a quick win. Once they see it working, they'll want more. Leverage alerting as well that escalates the red flag vs showing a complex dashboard without remediation. Don't position data as replacing their expertise - frame it as enhancing their instincts. The best approach combines their years of sales knowledge with analytical insights. When data confirms their hunches, it builds confidence in the system. When it contradicts them, treat it as an interesting opportunity to explore rather than proof they're wrong. Connect everything back to what they care about most - hitting targets and maximizing commission. When they see how data directly impacts their wallet, you won't have to convince them anymore - they'll be asking for more.

Data security isn't just an IT problem - it's a critical Revenue Operations responsibility. First, lock down access tightly. Use role-based permissions so team members only see what they need. Apply field-level security for sensitive information and create custom report types that automatically mask PII. When someone leaves the team, have a clear process to immediately revoke their access. Be smart about report distribution. Instead of sending full customer datasets to the entire company, use scheduled reports with just the necessary data. Add watermarks to sensitive exports, set expiration dates for shared reports, and create clear rules about who can access what. Technical controls matter. Make sure your data is encrypted both at rest and in transit. Consider IP restrictions for accessing your reporting platforms, implement SSO and MFA across all tools, and enable detailed audit logging so you can track who's accessing what. Only collect what you actually need. Regularly purge outdated information and consider using tokenization where possible. Create aggregate reports that show trends without exposing individual customer details. Know your compliance requirements. Map your data handling to relevant regulations like GDPR and CCPA, conduct regular audits, and document your processes clearly. Establish retention policies that balance business needs with compliance requirements. Don't forget about vendor management. Assess the security practices of all your reporting tools, review data processing agreements regularly, and maintain an inventory of every third party with access to your customer data. Most importantly, train your team. Create clear guidelines, run occasional simulated breach scenarios, and make security part of your regular discussions. The best technology controls mean nothing if your team isn't following good practices day-to-day.