Consumption Pattern Analysis for Email Campaigns

We analyzed over 27 million cold emails. Here are key insights into mailbox activity and performance: 📊 open rates: - Across all non-agency campaigns globally, the average open rate is 26%. - Open rates remain stable across workdays but drop 6-9% on weekends. 📊 reply rates: - Smaller, highly targeted campaigns (1-50 prospects) have a reply rate nearly 3x higher than mass campaigns with 1001-5000 prospects. - Campaigns with a bounce rate under 2% enjoy a 60% higher reply rate than those with 20%+ bounce rates. - Initial emails have the highest reply rates (~1%), with subsequent follow-ups seeing diminishing returns (30% as effective by the 5th or 6th email). 📈 mailbox volume and performance: - Mailboxes sending 1-20 emails daily have reply rates four times higher than those sending 101-200 emails daily. - 21-50 daily emails see a balanced performance, with over twice the reply rates of high-volume senders. 🔑 key takeaways ↳ Personalized, concise, and well-segmented campaigns drive higher engagement. ↳ Maintaining a low bounce rate and optimizing email volume per mailbox is critical for maximizing results. ↳ Send no more than 3 messages in sequence. Have you noticed similar patterns in your campaigns?

The hidden reason 90% of outbound campaigns die after 30 days (and it's not what you think). It's not deliverability issues. It's not terrible offers. It's not bad copy. It's that most teams never build feedback loops. They launch a campaign, send it for a month, and when results plateau, they blame the list. Then they start over with new: Copy. Targeting. And sequences. And the cycle repeats itself. Here's what we learned after running outbound for 120+ companies: Your best-performing campaigns are hiding in your current data. You're just not listening to it. At ColdIQ, we treat every reply as intelligence. Prospects' feedback should be leveraged into better campaigns: 1. Tag Every Single Reply We use three categories in Instantly.ai: → Positive (interested, asking questions, booking calls) → Negative (unsubscribes, "not interested," objections) → Neutral (out of office, wrong person, timing issues) But we go deeper. For positive replies, we track: → Which email in the sequence hooked them → Which subject line did they respond to → Which value proposition resonated → Which persona/role they hold For negative replies, we track: → Budget concerns by role → Common objections by industry → And timing pushbacks by company size 2. Analyze Patterns Weekly Every Friday, we pull campaign data from Instantly and Clay. We look for: → Which industries respond best to specific messaging → Which angles get the most positive replies → Which CTAs drive the most meetings Example from last month: CTOs at Series A companies responded 40% better to efficiency messaging than to ROI messaging. So, we built a separate sequence just for that segment. 3. Build Iteration Workflows Based on weekly data, we create new email variations using Claude. But we don't rewrite entire campaigns. We test micro-improvements: → New subject lines for low open rates → Different pain points for cold segments → Alternative CTAs for warm prospects We use Instantly's A/B testing to run these variations against control groups. 4. Create Campaign Evolution Rules When a campaign hits certain thresholds, we automatically evolve it: → If positive reply rate drops below 2% after 500 sends, we test new angles → If objections cluster around budget, we add ROI-focused follow-ups → If timing pushbacks exceed 30%, we build nurture sequences 5. Feed Insights Back Into New Campaigns Every insight gets documented in our Clay database. When we build campaigns for new clients, we start with proven patterns: → Subject lines that work by industry → Pain points that resonate by role → CTAs that convert by company size We're not starting from scratch each time, but building on what already works. The result? Average positive reply rates improve 30-40% between month 1 and month 3. Feedback should guide your strategy. Treat outbound like a conversation where you actually listen and optimize accordingly. Questions? 👇

The most powerful email metrics aren’t the ones everyone talks about. Open rates. Click rates. Bounce rates. Unsubscribes. 📉 They’re fine—but they only scratch the surface. The real growth comes from lesser-known metrics most marketers never track. These hidden signals reveal which subscribers drive revenue, which channels produce the best customers, and where your strategy is leaking value. I've analyzed thousands of campaigns, and the marketers who consistently outperform their competition dig deeper. The real problem? You're missing the granular insights that actually drive revenue. Think about it - if you're only looking at overall open rates, you're treating your entire list as one homogeneous blob. But your subscribers aren't all the same, are they? So why are you measuring them that way? Here are 6 metrics the email pros track that you probably don't: ✅ Open & click rates BY SOURCE (Which acquisition channels bring your most engaged subscribers?) ✅ Revenue BY SOURCE (Where are your highest-value subscribers coming from?) ✅ Open & click rates BY DOMAIN (Are Gmail users more engaged than Outlook users?) ✅ Revenue BY DOMAIN (Which email providers host your best customers?) ✅ Revenue PER SUBSCRIBER (What's the actual value of each person on your list?) ✅ Open reach rate (What percentage of your list opens ANY email over time?) These metrics reveal the hidden patterns in your email program. And guess what? When you understand these patterns, you can make strategic decisions that actually move the needle. Your basic metrics aren't wrong - they're just incomplete. Start tracking these advanced metrics, and watch your email strategy transform from guesswork into precision. What other "hidden" email metrics have you found valuable? I'd love to hear in the comments.

We went from 1 positive reply every 500 emails to 1 every 90 emails, let me walk you through how we did it In this video, I walk through how we did it and more importantly, how we think about testing and improving campaigns. The core idea is we treat GTM like a scientific process not guesswork. Here’s the process we follow every time: 🔹Gather info 🔹Form a clear hypothesis (in this case: the pitch wasn’t landing) 🔹Design an experiment that isolates only the variables you want to test. 🔹Run it, gather results, and repeat For this campaign, that meant: 🔹Looking at the all the data we had already 🔹Hypothesis was that pitch was not specific enough to due to similar results for different ICPs, but much better results for leads that had previously engaged and were aware of the value prop. 🔹Next steps were to test new set of messages that tackled painful, specific problems — not just broad value props Some variants 5–6x’d performance. One segment hit ~1 in 90. Another ~1 in 80. But we didn’t just hit scale right away. Why? Because performance upstream doesn’t matter if it doesn’t align with how the client sells. So now we’re working with them to understand how those leads are converting and which messages are driving actual pipeline, not just replies. If you’re in the weeds optimizing GTM or just want a peek at how we think about cold email, the video might be worth a watch. And if you have Qs or want some help with your cold email campaigns, drop a comment or shoot me a DM.