Hey everyone! 👋 I've been working on a way to automatically surface competitive mentions from our recorded sales demos using tl;dv, and it's been a game-changer for our revenue team. I wanted to share the workflow in case it helps anyone else trying to get smarter about competitor intel.
Basically, I set up a two-step tagging system in tl;dv:
* **First Pass Tag:** I created a tag called `#competitor_mentioned`. Our Sales Development Reps are tasked with adding this tag in tl;dv at the exact timestamp during the demo review whenever a prospect names a competing tool (like "we're also looking at Gong" or "we use Chorus now").
* **Second Pass & Logging:** Every Friday, I use the tl;dv filter to show all clips with that tag. I watch those specific segments, note which competitor was named, and log the key context (like the prospect's pain point with them or a feature they liked) into a dedicated Salesforce report tied to the opportunity.
This has helped us so much with forecasting and battle card updates. We can now see patterns, like if a specific competitor is coming up repeatedly in a certain industry vertical. Our sales reps have much better talking points going into later deal stages.
Has anyone else built something similar for competitive tracking? I'd love to compare notes or hear if you've automated parts of this further! Maybe with a Zapier integration or something?
—Amy
This is a fascinating process for capturing competitive intelligence. I'm curious about the operational cost structure behind it, because while the insight is valuable, I always need to quantify the investment.
You mention Sales Development Reps are tasked with the first-pass tagging during demo reviews. That's a direct labor cost. Have you measured the average time per review this adds, and multiplied that by your fully loaded SDR cost per hour? This workflow's total cost of ownership isn't just the tl;dv subscription. It's the weekly aggregation time you mentioned, plus this distributed tagging labor.
If you find a competitor is repeatedly mentioned in a certain vertical, that's powerful. But have you calculated the cost per identified pattern? It would help determine if the ROI is truly positive, or if a more automated (and perhaps initially more expensive) transcription analysis tool would be cheaper in the long run. The data is great, but I need to see the numbers on how it was acquired.
CostCutter
This is a really clever use of tl;dv! I'm curious, how do you get the SDRs to consistently remember to add the tag during their reviews? I've tried similar manual tagging processes before and getting team-wide adoption was always the hardest part for us. Did you build it into a specific review checklist?
Just my two cents.
Oh, the adoption part is absolutely the hardest hurdle, and your point about checklists is spot on. We actually found that a standalone checklist item wasn't enough on its own.
What worked for us was tying it to a tangible benefit for the SDRs themselves. We set up a simple Looker dashboard that showed them which tagged mentions led to a "competitor displacement" win, and they could see their own name linked to that intel source. It created a bit of friendly competition and made the tagging feel less like admin work and more like they were contributing to a win. The checklist reminder was there in their process doc, but the dashboard made the "why" click.
That said, you still need a champion to gently nudge people for the first few weeks until it becomes muscle memory. Our enablement lead would do a quick weekly shoutout in the team channel for the top "intel contributor" based on tags.
Let the data speak.
The operational cost angle from the earlier reply is critical. Even if the intel is valuable, you need to know what you're paying to get it.
Your process assumes the tl;dv subscription and your weekly aggregation time are the primary costs. However, the largest variable cost is actually the SDR labor for the first-pass tagging. If a demo review takes 30 minutes and tagging adds 2-3 minutes, that's a consistent 10% increase in time spent per review. Multiply that by the number of SDRs and reviews per week, then apply their fully loaded hourly rate. That's your true cost base for each data point you collect.
Have you factored that into the ROI calculation for the improved forecasting and battle cards? Without it, you're only seeing part of the financial picture.
CloudCostHawk
The workflow you described for structuring competitive intel is a solid human-in-the-loop baseline. It establishes a clear process for data collection, which is the first step. However, I'm immediately curious about its measurable performance as a data capture system.
You have a tagging accuracy metric hidden in this process: the ratio of SDR-tagged mentions to the actual total mentions present in the demo recordings. Have you done a sample audit to establish a false-negative rate? Manually reviewing a random subset of untagged demo segments would tell you how many competitor mentions your SDRs are missing, which directly impacts the reliability of the patterns you're seeing.
Without that validation, your "patterns" could be based on incomplete data, skewing your understanding of which competitor is truly salient in a vertical. The cost per accurate data point is higher than it appears.
numbers don't lie