Find what your best sequences do differently
Reply rates say which sequences win. The steps, timing and audience behind them say why, and that is what the rest of the team can copy.
The conversation
Sample accounts and numbers, for illustrationYouWhich of our sequences got the most positive and willing-to-meet replies in the last 90 days, and what do the top three do differently from the rest: number of steps, timing between them, channels, email length and who they were sent to?
Unify3 checks · sequences, replies and CRM
- Counted replies tagged Positive or Willing to meet per sequence over 90 days, leaving out OOO, opt-outs and automated replies.
- Read each sequence’s steps: count, days between them, channel of each step and the length of the first email.
- Matched enrolled people to Salesforce to see which sequences produced opportunities.
Twelve sequences ran in the last 90 days. Three account for most of the positive and willing-to-meet replies, and they share a shape.
- 1Support leaders · routing19 of 214 · 8.9%
- 2Ops leaders · migration14 of 180 · 7.8%
- 3Series B follow-up7 of 96 · 7.3%
- 4All others (9 sequences)31 of 1,140 · 2.7%
Reply rates here count Positive and Willing to meet only. Raw reply counts include out-of-office and opt-outs.
YouWhat do the top three do that the other nine don’t?
UnifyShorter first emails, fewer steps, and one call step in the middle. The audience is narrower too.
- 58 to 74
- words in the first email the other nine average 140
- 3 to 4
- steps over ten days the others run 6 to 8 over three weeks
- 1
- persona per sequence six of the others mix two or more
- 11 vs 9
- opportunities created from a fifth as many people
- Length
- The top three open with 58 to 74 words. The other nine average 140, and two of them exceed 200.
- Shape
- Three or four steps over ten days, with a phone call as the second or third step. The others run six to eight email-only steps over three weeks.
- Audience
- Each of the top three was enrolled from one persona. Six of the other nine mixed two or more titles in the same sequence.
- Outcome
- People reached by the top three became 11 opportunities in Salesforce. The other nine produced 9 from five times as many people.
YouWrite this up for the team and flag the two sequences we should look at.
UnifyThe pattern is written up with the numbers, and two sequences are flagged to their owners rather than paused.
What the best sequences share
First email under 75 words. Three or four steps over ten days with a call in the middle. One persona per sequence. People reached by these three became 11 of this quarter’s 20 outbound opportunities.
Set up
- Two sequences flagged “Enterprise · general” and “Q3 nurture” got 6 opt-outs each and no positive reply in 90 days. Flagged to their owners, not paused.
- Weekly rerun The same comparison runs every Monday, so a new sequence that beats the pattern shows up.
Pausing is left to the sequence owner. The report says what the numbers say and stops there.
Sequence reporting, with and without Unify
A reply rate is a score. The difference between sequences is the lesson.
| Without Unify | With Unify |
|---|---|
| Reply rate is one number per sequence, and it counts out-of-office and opt-outs the same as a yes | Replies are classified as they land, so sequences are ranked on positive and willing-to-meet replies only |
| Comparing two sequences means opening both and reading every step side by side | Unify lays the top three against the rest on steps, spacing, channels, email length and who was enrolled |
| The winning sequence is understood by the rep who wrote it, and by nobody else | The pattern is written down once, with the numbers behind it, for the whole team to use |
| Nobody can say whether the replies turned into anything | Enrolled people are matched to CRM opportunities, so a sequence that gets replies but no deals is visible |
Start from this prompt
Make it your own in Unify, then review the results.
Which of our sequences got the most positive and willing-to-meet replies in the last 90 days, and what do the top three do differently from the rest: number of steps, timing between them, channels, email length and who they were sent to?
What else you can ask for
The walkthrough is one path through this workflow. These are the turns people take most often.
- Reply ratesWhich of our sequences have the highest reply rates, and what do they do differently?Try in Unify
- One teamRank my team's sequences by positive replies this quarter and tell me what the top one does that the others don't.Try in Unify
- One personaWhich sequences aimed at heads of support got the most willing-to-meet replies, and how long were their first emails?Try in Unify
- Most opt-outsWhich sequences got the most opt-outs in the last 90 days, and what do they have in common?Try in Unify
How it changes by who you sell to
- Software
- Software sequences split on who they were sent to: a sequence for heads of support and one for CTOs should not be compared on one number. Ask for the ranking per persona, and for the first-email length of the winners.
- Financial services
- Compliance reviews slow replies at banks and insurers, so a sequence judged at 30 days looks worse than it is. Ask for a 90-day window and for willing-to-meet replies specifically, since positive replies there rarely commit.
- Healthcare
- Clinical and administrative buyers reply on different timelines, and a sequence with a call step often wins with the second group. Ask which sequences included a call and whether the meetings came from the email or the call.
- Manufacturing
- Plant and operations leaders reply to short emails and to phone calls, and rarely to a six-step nurture. Ask for the sequences with the fewest steps and the most willing-to-meet replies, and how long their first emails were.
- Professional services
- Partners forward more than they reply, so the reply that matters often comes from someone else at the firm. Ask Unify to count out-of-thread replies and referrals, and which sequences produced them.
Next in pipeline intelligence and coaching
+2Activity versus fitCheck whether reps work the right accountsActivity is easy to count. Whether it landed on accounts that fit the ICP and showed intent is the question, and the answer is different for every rep.
+2Coverage gapsFind pipeline coverage gapsCoverage by rep, segment and territory against the number, and the accounts that could fill each gap, so the fix is a list of names rather than a bigger target.- +2Pipeline driversFind what drives outbound pipelinePipeline broken down by signal, persona, segment, channel and rep, from the CRM and the outreach records, so the next quarter is planned on what worked rather than on what felt busy.
Connect what this workflow uses
Each guide covers the connection, what Unify reads and writes, and what to check before the first run.
- Connect GmailConnect Gmail to Unify for sequence emails and replies.
- Connect OutlookConnect Microsoft Outlook directly to Unify.
- Connect SalesforceAuthorize Salesforce in Unify, configure CRM field mappings, and choose when to enable writing.
- Connect HubSpotConnect HubSpot to Unify, map Company and Person fields, and plan CRM exclusions and supported write-back.
Questions about this workflow
Why not rank sequences by raw reply rate?
Because a raw count includes out-of-office, automated replies and opt-outs. Unify tags every reply, so the ranking here uses Positive and Willing to meet only. A sequence can have a high reply rate and nothing worth reading.
What does 'differently' actually compare?
The number of steps, the days between them, the channel of each step, the length of the first email, and which persona was enrolled. Those are the parts of a sequence the team can change, so those are what get compared.
Can it tell me which sequences produced pipeline?
It matches the people each sequence reached to opportunities in Salesforce or HubSpot and counts them. It does not claim closed revenue; it shows opportunities created from enrolled people, which is what the CRM can support.
How is this different from the best-reps report?
That report is about people: who calls, who writes and who books. This one is about the sequences themselves, whoever ran them. A great rep with a weak sequence and a weak rep with a great sequence show up differently in each.