Time every step of your list assembly line, then batch, automate or delete each one until the first call happens before lunch.
Key takeaways
- Salesforce's State of Sales report, seventh edition, surveyed 4,050 sales professionals in August and September 2025. It finds that reps spend 40 percent of their time selling.
- Salesforce's 2023 edition, built on more than 7,700 sales professionals in 38 countries, had put selling time at 28 percent.
- Gartner reported on May 19, 2026 that AI saves sellers 4.8 hours a week on average. Yet 72 percent of sales organizations report low reinvestment of that time in high-value work.
- In Gartner's Seller Skills Survey of 1,026 B2B sellers (January to March 2024), 70 percent said they were overwhelmed by the number of technologies their work requires.
- In our illustration, a seven-step list assembly line takes about three hours of a Monday morning. Two of those steps exist only because the tools do not talk to each other.
Where does a rep's list-building morning actually go?
It goes into handoffs more than into research. A typical list passes through seven steps (export, dedupe, enrich, wait, re-import, patch and load) across three or four tools. In our illustration, those steps add up to about three hours, and less than half of that time involves any judgment about who to call.
Most reps have never timed the line. They know Monday disappears, but they cannot say where. So start with a week of timestamps, one per step, written down as you go.
The table below is our illustration of a common setup, where a database export is enriched in a second tool and loaded into a sequencer. The minutes are not a benchmark. Replace them with your own log after one week.
That comes to 180 minutes in our illustration. Two rows, wait and re-import, add 65 minutes of pure handoff. No prospect gets any closer during those minutes.
Which steps should you batch, automate or delete?
Apply one test per step. Delete a step that exists only because two tools cannot share a row. Automate a step that runs the same way every time, with no judgment. Batch a step that needs judgment but not every day. Anything left is selling, and it should start the morning.
Delete is for the handoffs. Waiting for a job and re-importing its output are artefacts of the stack, not of the work. If a step moves a file from one tool to another, the question is which tool can hold the whole row.
Automate is for repetition. Checking a row against the CRM, running an email through a verification waterfall and pushing a clean row into a sequence all follow fixed rules. A rule you apply by hand every Monday is a rule a tool should apply.
Batch is for judgment. Deciding the segment, reviewing the odd rows and fixing a title that looks wrong all need a human. They do not need one every day. One weekly slot, with a hard stop, contains them.
- Time each step for a week
- Delete the handoffs
- Automate the fixed rules
- Batch the judgment calls
- First call before lunch
Which of these situations are you in?
It depends on where your list comes from. The same audit gives a different answer for a rep shuttling CSV files, a rep hunting for a segment no filter can express, a rep whose lists bounce and a rep who rebuilds the same list every week. Find your situation below and apply its one change first.
Most reps sit in two of these at once. Start with the one that costs the most minutes in your log. Then run the audit again a week later, because removing one step usually exposes the next.
What if you export from one tool and enrich in another?
Then the handoff is your biggest cost. The fix is to delete the wait and the re-import by enriching where the rows already live. If your list must start as a file, upload it once to a tool that enriches, verifies and returns it ready to load, rather than bouncing it between tabs.
This is the CSV shuttle, and it is the most common setup. The export is fine. The damage happens after, when the file leaves one tool, waits in a second and comes back as a new version.
Every return trip creates a merge problem. Column names drift, a formula breaks, and someone loads the wrong version. The patch step then grows to cover mistakes the handoff created.
The change is structural. Pick one place where the row is found, enriched and verified. Our page on enriching a CSV of prospects shows that model applied to a file you already have. The one change for this situation is to delete the re-import.
What if no filter can express the list you need?
Then your time goes into the patch step, because you are researching by hand what the filters cannot select. Batch that research into one weekly slot. Write the segment as a sentence with one dated condition, and source that condition from where it becomes visible, instead of opening forty tabs.
A segment such as "teams hiring their first salesperson" has no checkbox. Reps compensate by exporting a broad list and reading company pages one by one. That is the patch step running at full cost on every row.
Writing the brief first changes the work. Our guide to building a list from a plain-language description splits a brief into attributes a filter holds and evidence only a live source shows. The one change for this situation is to batch research into one slot and stop patching row by row.
What if half the list bounces or is already in the CRM?
Then the list was built too early or checked too late. Automate the check against the CRM and the verification pass, and run them on the day you load, not on the day you export. A list that waits a week before loading has already started to decay.
Decay is not a rounding error. ZeroBounce, an email verification vendor, processed more than 11 billion addresses in 2025 for its 2026 decay report. It found that at least 23 percent of an email list decays within a year.
People move faster than lists do. Lusha, a sales-data vendor, detected 1,470,414 contacts changing companies between January 1 and June 1, 2026, about 13,600 every working day.
The checks themselves are covered elsewhere. Our guide on how to verify a B2B email list sets out the checks, the bounce thresholds and the cost per thousand. The one change for this situation is timing. Automate both checks and run them on load day.
What if you rebuild the same list every Monday?
Then the build itself should not be manual. If the segment definition has not changed since last week, only the new rows have. Automate the search on a schedule, let it deliver the new matches, and keep your Monday for the calls those rows deserve.
Many reps rebuild a territory list weekly from scratch. They reapply the same filters and re-export the same companies. Then they dedupe against last week's file to find the handful that are new.
That is a week of work to discover a delta. A saved search that runs on a schedule and returns only new matches does the same job with no one watching. The one change for this situation is to automate the build and review only the delta.
How much time do SDRs spend on list building, and how should a manager measure it?
No global survey isolates list building as its own line. The best published proxy is selling time. Salesforce's latest survey puts it at 40 percent of a rep's week. A manager gets a sharper number from the team's own step log and from the time of the first call each day.
Salesforce is a CRM vendor, so read its figures as that vendor's survey. The seventh edition of its State of Sales report surveyed 4,050 sales professionals in August and September 2025. It finds that reps spend 40 percent of their time selling. Its 2023 edition, with more than 7,700 respondents in 38 countries, had found 28 percent.
Tools do not fix the number on their own. In Gartner's Seller Skills Survey, 70 percent of 1,026 B2B sellers felt overwhelmed by the number of technologies required. Gartner's May 2026 survey of 210 sales leaders found AI saves sellers 4.8 hours a week. Yet 72 percent of organizations report low reinvestment of that time.
So measure two things. The first is minutes per step, from one week of logs across the team. The second is the median time of the first call each day. If the audit saves time and the first call does not move earlier, the time went somewhere else. Our guide on what prospecting data costs a sales team adds the cost side of the same decision.
What does a Monday look like after the audit?
It starts with calls. In our illustration, the build runs before the rep arrives, the checks run on load, and the only list work left is a capped review slot in the afternoon. The first call moves from early afternoon to the first hour of the day.
Here is the same morning, redrawn under the three rules. It is our illustration, not a measurement.
The new rows arrive from a scheduled search, already checked against the CRM and verified. The rep reads them for fifteen minutes and starts calling. The odd rows go into a weekly review slot of forty-five minutes. Nothing waits and nothing is re-imported.
The point is the order of the day. Selling comes first, and list work fits around it. Our page for SDRs and BDRs covers the rest of the prospecting week.
Where Freelvy fits
Freelvy is our product. It is an AI sales platform, used the way you use ChatGPT or Claude. You describe the customer you want in one prompt, and Freelvy searches 40+ live data sources at query time (maps, official company registries, job boards, storefronts, ad libraries). It enriches every row with verified contacts through provider waterfalls, 23 for email and 13 for phone. The waterfalls find and verify more than 92% of emails and find more than 90% of phone numbers. A row that fails verification is never delivered and never billed.
Search, enrichment and sequencing sit in one thread, which removes the wait and the re-import from the audit. Scheduled briefs rerun the same prompt and deliver new matches. Plans start at €49 a month for 1,500 credits, with unlimited team members and no commitment.
Free 7-day trial, no credit card, at freelvy.com.
How we verified this
We consulted every page listed under Sources on September 27, 2026. The step times in the audit table and the redrawn Monday are our own illustration, not a measurement. The 180-minute and 65-minute totals are our arithmetic on those illustrative times.
FAQ
How much time do SDRs spend building lists?
No global survey isolates list building as its own line. Salesforce's State of Sales, seventh edition, surveyed 4,050 sales professionals in August and September 2025 and found reps spend 40 percent of their time selling. The rest covers admin, data entry, research and list work. A one-week step log gives your own figure.
Which list-building steps should an SDR automate?
Automate every step that follows a fixed rule. Checking a row against the CRM, verifying an email and loading a clean row into a sequence are the usual candidates. A step you apply the same way every Monday, without any judgment, is a step a tool should run for you.
Which steps should be deleted outright?
Delete the steps that exist only because two tools cannot share a row. Waiting for an enrichment job and re-importing its output are the usual ones. In our illustration they cost 65 minutes of a 180-minute morning, and neither moves a prospect closer to a conversation.
When should I verify a list, at build or at load?
Verify on the day you load, not the day you export. ZeroBounce's 2026 report, built on more than 11 billion addresses processed in 2025, finds at least 23 percent of an email list decays within a year. A list that waits before loading has already started to lose contacts.
How does a manager measure list-building time?
Ask the team for one week of timestamps, one per step, then take minutes per step and the median time of the first call each day. Gartner reported in May 2026 that 72 percent of sales organizations reinvest little of the time AI saves, so track where saved minutes go.
Is it worth rebuilding the same territory list every week?
Rarely. If the segment definition has not changed, only the new rows have. A saved search that runs on a schedule and returns only new matches replaces the rebuild. Lusha counted about 13,600 contacts changing jobs every working day in early 2026, so the delta is what needs attention.
Sources and methodology
All pages below were consulted on September 27, 2026. Figures are attributed in the text to the organizations that published them.
- salesforce.com, stories state of sales report announcement 2026
- salesforce.com, state of sales sales statistics
- salesforce.com, sales salesforce state of sales report 2026
- salesforce.com, stories sales research 2023
- gartner.com, press releases 2026 05 19 gartner survey finds ai saves sellers nearly five hours per week yet seventy two percent of sales organizations fail to reinvest time in high value activities
- gartner.com, press releases 2024 12 18 gartner survey reveals only eleven percent of sales organizations are able to drive commercial success while executing a transformation
- zerobounce.net, email list decay
- lusha.com, blog b2b contact change report