Building sequences is straightforward. Making them effective requires monitoring performance and iterating systematically. A sequence with a 35% open rate produces dramatically different results than one at 5%, for identical effort.

Where to look

The Sequences overview gives you a workspace-wide picture across all campaigns: performance at a glance, the outbound funnel, reply sentiment, and how many sends are going out today. Start here for your daily check.

Per-sequence analytics show the campaign's own funnel and per-step performance. Each step node also shows how many leads have reached it, so you can see exactly where contacts drop off. Filter by channel (Email, LinkedIn, WhatsApp) to compare.

The campaign metrics

Dalil tracks these statistics per campaign:

Lead in campaign: everyone enrolled
Leads launched: leads published and started
Leads reached: leads who received at least one message
Message sent / Message not sent: delivery attempts and skips (e.g., missing email or phone)
Delivered: messages that reached the contact
Opened rate: unique leads who opened: an email open, or a message marked read (WhatsApp/LinkedIn)
Clicked rate: leads who clicked a link
Replied rate: leads who replied
Positive replies: replies classified as positive (see lead stages)
Connection acceptance: LinkedIn connection requests accepted

Benchmarks to aim for

These are industry reference points for cold outreach, not Dalil guarantees:

Email

Metric	Poor	Good	Outstanding
Open rate	<15%	25–40%	50%+
Click rate	<3%	8–15%	25%+
Reply rate	<1%	3–6%	10%+

Metric	Poor	Outstanding
Connection acceptance	<20%	70%+
Message reply rate	<5%	35%+

Metric	Target
Delivery rate	95–100%
Reply rate	10–50%

Identifying bottlenecks

Low open rate (<15%) → Weak subject lines, or deliverability trouble. Test personalised subject alternatives; check your sender accounts and sending limits.

Many "Message not sent" → Data gaps. Add an Enrich step or Has Email Address / Has Phone Number conditions before send steps.

Low click/interaction rate → Content is irrelevant. Rewrite for specific pain points and add a clearer CTA.

Low reply rate (<2%) → CTA is too pushy. Replace "Book a call now" with "Open to a quick conversation?"

Replies but few positives → Targeting problem. Add the Qualify Lead AI step early so only fitting contacts proceed.

Optimisation strategies

Subject line testing

Compare: personalised ("quick thought on {{currentPerson.company.name}}"), question-based, curiosity-driven, and social proof formats.

Personalisation depth

Test: first-name only → company + industry insight → custom field reference → per-contact AI-generated messages. Deeper personalisation consistently drives higher reply rates.

CTA softening

Hard CTA: "Schedule a demo now": lowest reply rates
Soft CTA: "Open to a conversation?": noticeably better
Curious CTA: "Does this resonate with what you're seeing?": often best for cold audiences

Timing adjustments

Test morning vs. afternoon send windows, mid-week vs. end-of-week, and varying delays between touches, all configurable per sequence in Settings.

Audience segmentation

Tailor messaging by company size, industry, or buyer persona, either as separate sequences or with condition-based branches. Segmented outreach consistently beats generic sends.

AI cost awareness

Sequences using Qualify Lead or Generate AI Message consume AI credits per contact. Check token/credit consumption when comparing the ROI of AI-personalised campaigns against static ones. AI personalisation usually pays for itself on high-value audiences, less so on broad low-intent lists.

Best practices

Monitor the overview daily during a campaign's first week, then weekly
Test one variable at a time
Allow 5–7 days per test before drawing conclusions
Share successful discoveries team-wide
Pursue continuous iteration: small improvements compound over time

Monitoring & optimizing results