How often have you sat in meetings trying to answer seemingly basic questions about your business, only to realize you don’t have the data to answer them? Questions like: How many touches does it typically take to close a deal? Which lead sources are actually working? Why are some reps consistently outperforming others? If you’ve struggled to answer questions like these, you’re not alone. I recently sat down with Noah Burkhold, Director of RevOps at demandDrive, to learn how they tackled this exact problem. After merging three companies into one (no small feat), they realized they couldn’t answer crucial questions about their business because they weren’t tracking the right data points. What started as a push for better reporting turned into a complete transformation of how they track and use customer interaction data. Noah led the effort to track what he calls “micro-interactions”: a series of small, but important data points that allowed more precise targeting and personalization. This, in turn, allowed the business to maximize revenue by speaking to leads with the right message at the right time. Watch the interview Or listen to it on Spotify or Apple Podcasts. Start by tracking (almost) everything Before they could improve anything, Noah and the team needed to understand what was actually happening. So they started tracking nearly every interaction from emails sent, meetings held, calls made, and website visits, to booking rates, and noting whether people were responding to emails or not. But it wasn’t just about collecting data. They also implemented guardrails to ensure the data stayed clean and useful. For example, they prevented sales reps from arbitrarily moving deals backward in the pipeline, which would mess up their funnel reporting, and instead set up new processes to ensure every rep was inputting required information at each stage. Use the data to power better segmentation and personalization Once they had reliable data, the demandDrive team could segment their audience more effectively and personalize their outreach. They started: Routing leads to reps based on their strengths (some excel with SMBs, others with enterprise accounts) Creating engagement scores based on how prospects interact with their company Building “fit scores” based on company characteristics like revenue and employee count Using these scores to influence both sales outreach and marketing campaigns As a result, they could communicate very differently with highly engaged prospects versus those who hadn’t responded in months, leading to more relevant conversations and better conversion rates. Build visualizations for every level of the organization, working from the top-down Noah emphasized the importance of working “top-down” when implementing this kind of system. They started by asking: – What does the C-suite need to see to run the business? – What do managers need to support their teams? – What do individual reps need to be effective day-to-day? This helped to reveal gaps in their data collection processes. For example, they discovered they needed deal probabilities from reps to provide accurate forecasting for executives. This led to a discovery that reps weren’t entering probabilities because they were overwhelmed with manual data entry. Understanding these details and connections to the people using them helped them optimize the entire system. Using their newfound insights Once demandDrive had clean, consistent data flowing in, this allowed: Better forecasting at every level They implemented month-over-month revenue forecasting that actually worked They could now track both company-level projections and individual rep forecasts This helped them predict staffing needs for servicing new clients Clearer pipeline visibility They mapped their core stages: contact → sales accepted lead → opportunity → closed won Within each stage, they created “mini pipelines” to track granular progress They could now see exactly how long it took to move between stages They required deal probability updates at each stage to improve forecast accuracy Automation of manual tasks They eliminated time-consuming manual prospecting where reps were searching company websites and LinkedIn for contact details They used Clay for data enrichment instead, automatically pulling in contact information They automated lead routing based on rep strengths (enterprise vs. SMB expertise) Marketing and sales alignment They could now see which lead sources the sales team converted best This helped marketing adjust ad spend and campaign focus They could identify which types of leads each rep performed best with They used enrichment tools to spot companies that were hiring or recently funded, allowing for more targeted outreach By gathering and connecting all this data, they could answer crucial business questions like: How many touches does it typically take to get a response? How long does it take to convert a contact into a qualified lead? Which reps are best at re-engaging cold leads? What’s our true pipeline value based on deal probabilities? Advice for implementing your own tracking system If you want to improve how you track and use customer interaction data, Noah recommends: 1. Start with a flowchart before touching any tools. Map out your entire process: How do contacts enter your system? Who gets notified? How are they routed? What happens next? 2. Get buy-in from every team on how information should flow. This might take several iterations, but it’s crucial for adoption. 3. Create tailored views for different roles. Don’t overwhelm users with data they don’t need. A rep’s view should be different from an admin’s view. 4. Remember that change is hard. People have established ways of working, and new systems require new habits. Focus on showing how these changes will make their jobs easier. Most importantly, treat this as an ongoing process of optimization. Noah says it’s a never-ending circle of optimizing > getting better data > optimizing further > getting even better data > and repeating.