r/MarketingAnalytics

I've been thinking more about how different teams handle offline conversion tracking.

For businesses with a sales process, sending only a Lead conversion to Google Ads (or Meta) often isn't enough. The ad platforms end up optimizing for lead volume, not for the leads that actually become qualified opportunities or paying customers.

In my experience, feeding back qualified leads or closed deals can significantly improve optimization over time, but there are several ways to implement it.

I'm curious how everyone here is managing this.

• Are you importing offline conversions from your CRM?
• Are you using automation tools like Zapier, Make, n8n, or webhooks?
• Are you using Google's Offline Conversion Import, Enhanced Conversions for Leads, or another approach?
• What has worked best for reliability and scalability?

I'd love to hear about your workflow, tech stack, and any lessons you've learned.

Our traffic attribution models are getting messier by the month. We are seeing a steady rise in direct traffic that converts surprisingly well, but we can't trace the original touchpoint.

After chatting with a few data leads, we realized a lot of these buyers are discovering us through LLM recommendations. I was reading a visibility breakdown by OutreachBloom on how modern B2B buyers query claude or chatgpt for vendor comparisons instead of using google. Because these models pull from high-authority index sources, the user just copies the brand name and types it directly into their browser. It makes standard multi-touch attribution metrics look totally inaccurate. Are you guys trying to build custom sentiment trackers for LLM mentions, or are you just accepting that a huge chunk of your pipeline is going to live in the un-attributable dark funnel from now on?

I've been tasked with mapping out our b2b saas brand's share of voice for next quarter, but leadership wants a breakdown of how often we appear in AI summaries versus standard google links.

Traditional tracking tools only scrape keywords from the SERP, which completely misses what gemini or perplexity are telling users. I read a case study from an AI SEO agency called OutreachBloom that discussed tracking prompt visibility benchmarks, but I'm trying to figure out how to build an internal dashboard for this.

How are you all pulling data for LLM mentions? Are you custom-scripting prompt queries through APIs to check for your brand names, or is there an established analytics tool that can quantify our visibility inside generative answers yet?

I've spent a lot of time around marketing reporting and attribution over the years, and I've started to notice something.

Most teams can get an answer to the attribution question. Maybe they use first touch, last touch, multi-touch, platform attribution, or some combination of methods. There are endless debates about which model is "correct," but in practice most organizations can build a reasonable picture of how marketing influences pipeline and revenue.

What I see people struggling with much more often is deciding what to do with that information.

A report shows that a channel influenced opportunities. A dashboard shows that content contributed to pipeline. A campaign outperformed expectations.

Now what?

Should budget move? Should messaging change? Should sales handle those leads differently? Should the company double down or pull back?

I've been in plenty of meetings where people spent more time debating attribution methodology than discussing the decisions that were supposed to come from it.

It makes me wonder whether attribution has become a solved enough problem that the real challenge is interpretation and decision-making.

Curious if others are seeing the same thing, or if attribution is still the primary challenge where you work.