The cost of CRM data work

1. 01

   A list from someone else costs about $266 of staff time when someone new imports it.

   That is 5.7 hours, including fixing failed imports. An experienced person takes 3.5 hours. A list someone builds themselves takes 1.5 hours. PractitionerEstimate

2. 02

   At about 10 lists a week, someone new falls behind.

   Almost every list from someone else needs all nine steps. Outbound companies of 25 to 100 people handle 10 to 40 lists a week and usually give them to one person. Past 10 a week, something gives: lists wait, or checks are skipped. PractitionerEstimate

3. 03

   The checks nobody sees are the first to be skipped.

   People notice duplicates, so they fix them. Nobody notices a missing email or phone number, so verification and enrichment get skipped and the data provider takes the blame. Practitioner

4. 04

   On a list of average quality, two-thirds of the time is spent moving data.

   That means sending it to data providers, merging the results back and importing it. On messy lists, cleaning takes the larger share. PractitionerEstimate

5. 05

   Where the list came from matters more than its size.

   The same steps apply to 500 rows and 50,000. Lists from someone else often arrive messy, and a messy list takes about 6 times as long as a clean one. In the lead author's agency work, half or more arrived messy. PractitionerEstimate

6. 06

   Building lists at the source saves about as much as experience, and is easier to change.

   For an experienced person, a list they built takes less than half the time of one from someone else. Someone new takes about 1.6 times as long and gets about 1 in 3 imports wrong in their first year, against about 1 in 8 for an experienced person. PractitionerEstimate

7. 07

   Business data is messy even where it is meant to be clean.

   In 1.97 million records from a US healthcare registry that organizations are expected to keep current, 45% of names ending in "Inc" do not use the most common form, and 41% of suite numbers are written in a non-standard way. Our analysis

8. 08

   People make far more mistakes when they have to judge than when they copy.

   In clinical and lab studies, people copying data get between 1 in 350 and 1 in 100 fields wrong. People deciding what goes in a field from a messy source get about 1 in 15 wrong. Sourced

Everyone's trying to build the best data or the best CRM. Hardly anyone owns the job of getting the data into the CRM.

Other fields measure this kind of work as a cost per unit. Our unit is one list, from the moment it arrives to the moment it is in the CRM. We built the figure from four kinds of evidence and combined them in a simple model. Each claim carries a label saying where it comes from.

• Practitioner. Daniel Pank spent seven years in commercial and operations roles at a B2B outbound agency with UK and US clients, and rose to lead both teams. At his busiest he handled up to 50 lists and 100,000 rows a month. From that experience, he estimated how long each step takes, how often imports fail and what state lists arrive in.
• Sourced. US government data on pay, employer costs, job tenure and employment, and peer-reviewed studies of data entry errors and of other fields that already measure this kind of work.
• Our analysis. All 1.97 million organization records in a public US healthcare registry, to measure how messy real business records are.
• Vendor. Surveys and documents from software and data companies. Used only where nothing independent exists, and always named.
• Estimate. Our model and arithmetic, which combine the four above. The model and its inputs are in the tables under each chart.

The unit of this work is the list: an event list, an export someone else pulled from a data provider, a client's CRM export, a partner's spreadsheet. Most people count rows. People who do the work count lists, because every list goes through the same steps. Practitioner

The steps

A list that needs filling in goes through nine steps: look it over, size up the gaps, check for duplicates, remove them (deduplication), review the remaining near-duplicates by eye, send it to a data provider to fill the gaps (enrichment), merge the results back, reshape it for the CRM's fields (field mapping), and import it. For a 500-row list, that takes an experienced person 2 to 3.3 hours. Practitioner

The state it arrives in decides the time

The lead author judges a list as a whole, not on any one thing, and some things count far more than others. Columns that match your import are a small part. A list with no email addresses is messy however neat its columns are. Practitioner

• Clean. Yes, or close to yes, on every check. It goes through all nine steps, but each one is quick because few gaps need filling, and it is still verified before import.
• Average. The key information is mostly there, with gaps a data provider can fill and some fixes to formats, duplicates or columns.
• Messy. Key information is missing or bad across much of the list. It needs the full process: cut it down, pull out the company names, enrich, merge back, remove duplicates, fix formats and combine scattered notes into one field. An 80-field export from a client's own CRM is a typical example.

At least 9 in 10 lists from someone else go through all nine steps, and only 1 to 3 in 100 can go straight in after a quick check. Practitioner Our model times clean lists at about 45 minutes with no enrichment, so it leans low. Estimate

The people sending you lists don't share your priorities. So you check every list, wherever it came from.

Size adds time in only two places. Reviewing duplicates by eye takes minutes on 500 rows and days on 50,000. And a data provider that takes 1,000 companies at a time turns a list of 50,000 companies into 50 batches. Practitioner

What that costs

Someone new takes about 1.6 times as long as an experienced person and gets more imports wrong. Weighted by the mix above, a list from someone else takes them about 5.7 hours, including fixing failed imports. At $47 an hour all-in, our middle estimate for the mix of US roles that do this work (chapter 6), that is about $266 per list. Estimate

Not all of that $266 is necessary. Two things drive most of it: where the list came from, and who does the work. A company can usually change the first more easily than the second.

About 40% of the time ($104 a list) is saved if someone who has done the work for more than a year does it. Up to about 75% ($196) is saved if that person also built the list. The 75% is an upper bound, not a target. Event lists, inbound leads and client exports will always come from someone else, and we found no measure of how many lists could have been built at the source. Estimate

In cash, the saving is smaller. Experienced people earn more: Salesforce administrators with 3 to 5 years' experience earn about 18% more than those with up to 2 years (Salesforce Ben, US figures). At that premium, the experienced person's list costs $192 rather than $162, and the cash saving is about 28% rather than about 40%. The saving from building the list at the source rises to about $108, so the upper bound falls only to about 69% rather than about 75%. VendorEstimate

What that means for one company

A company of 25 to 100 people that sells outbound handles 10 to 40 lists a week. Practitioner We assume about 85% of them come from someone else, and a working week of 37.5 hours. Estimate

These figures count the work a company would need to pay for to keep up. With one person on lists, most of this would not show up as cash saved. The time would go back into the checks now being skipped (chapter 3). PractitionerEstimate

The figures also leave out the costs that land later, such as bounced emails, sellers' time and leads that go cold while they wait (chapter 7). And they price experienced people at the same rate as new ones. At the 18% premium, the saving from experience falls to about $33k, $83k and $132k a year, and the upper bound to about $81k, $202k and $324k. Estimate

Where the saving comes from

• Who built the list. When someone else built the list, the person importing it has to fix the builder's work before doing their own. Only 1 or 2 in 10 companies the lead author worked with had one person sourcing and importing, and they were the smaller ones. Practitioner
• Inexperience. On one list it costs about as much as fixing someone else's work, and it comes back with every new hire. Where sales development reps (SDRs) do list work, they stay in the role about 1.9 years on average (Bridge Group 2025), so about half their time in the role falls in their first year, when about 1 in 3 imports go wrong. PractitionerVendorEstimate
• Failed imports. Part of the saving from experience. A failed import is often a column deleted or missed during mapping, found only after import when a field arrives blank. Fixing one takes anywhere from 30 minutes to a full redo. Practitioner
• Handoffs. Not in the figures above. Each team a list passes through adds a queue (chapter 3). That costs speed more than staff hours: leads wait longer before anyone contacts them. Practitioner

When someone hands you a file, you're fixing their half before you can finish yours. Build it yourself and you've only got the finishing to do.

The figures above are for one kind of company. Put in your own numbers and the same model gives your result. It runs in your browser, and we do not save what you type.

The model uses the most likely times from chapter 1. An experienced person takes about 45 minutes for a clean list, 2.5 hours for an average one and 4.5 hours for a messy one. Someone new takes 1.6 times as long. About 1 in 3 of their imports go wrong, against about 1 in 8 for an experienced person (1 in 10 on a normal day, more when busy), and each fix takes about an hour. A list built at the source takes 1.5 hours, however experienced the builder. The calculator holds these fixed. If any is wrong, the answer moves, which is why we list the measurements still needed at the end. PractitionerEstimate

Most outbound companies of 25 to 100 people have one person handling lists. Practitioner At about 10 lists a week, someone new to the work falls behind, and even an experienced person is close to full. Above that, one person cannot keep up. Estimate

The queue

Queuing research gives three rules. If lists arrive faster than one person can clear them, the wait keeps growing until something gives. Below that point, waits still climb steeply as the person nears full load (Kingman 1961). And the average wait is the backlog divided by how fast it is cleared, so a backlog of 30 lists, cleared at 10 a week, means the average list waits about 3 weeks (Little 1961). Sourced

The lead author saw mid-sized companies where one person owned list work turn a list around in a day. At large companies lists took weeks to months, with extra sign-offs at each step. One large company imported only 7 columns from each list, so nothing else on it reached the CRM. Lists his agency sent every Friday took 1 to 2 months to get through some clients' CRMs, and the sales teams thought that was normal. Practitioner

Who goes first

Everyone thinks their list is the priority. Queues get ordered by arrival, by seniority, or by whoever is friendliest with the person doing the work, the way people get their laptop fixed first by being nice to IT. The lead author ordered by deadline, weighed against how much work each list needs. A messy list due Wednesday starts before a 45-minute list due Monday. Practitioner

What gets cut

People will put up with average data, but not late data.

When the queue builds, verification and enrichment go first, because nobody sees them. Duplicates and wrong rows get noticed, so people fix them. Missing details do not. Practitioner

Nobody misses a phone number that was never there. The gap gets blamed on the data provider, and everyone moves on.

The cost lands later, on someone else. Unchecked emails bounce and damage the sending domain's reputation, and sellers spend their own time finding the missing details (chapter 7). Estimate

Cutting verification saves almost nothing. It takes a few minutes per list Practitioner and costs up to 2 cents per email at Bouncer's published prices, often well under 1 cent. Vendor The lead author thinks skipping these checks is why almost everyone says their CRM is full of bad data. Practitioner In Validity's 2025 survey of 602 CRM users, 76% said less than half their CRM data is accurate and complete. Vendor

To see how messy real business records are, we analyzed all 1.97 million organization records in a public US registry of healthcare providers (CMS NPPES, downloaded October 2, 2026). Each organization keeps its own record up to date, or is meant to, so the registry should be cleaner than most lists. Our analysis

The 21% is not a duplicate rate. The registry gives one organization several ID numbers, one per site or service, and chains share names. The 21% shows how often a simple match on name and place would put two records side by side for someone to judge. Tidying case, punctuation and suffixes finds about 1.2 times as many matches as exact matching. We got the 3 to 4% two ways: from a year of update dates, and from two weeks of updates scaled up to a year. Our analysis

Data decay: people go stale faster than companies

Companies change their details slowly. People change jobs fast: 20.6% of US workers had been with their employer a year or less in January 2026 (BLS). Leaving out people new to work, job changes alone put just under a fifth of B2B contacts out of date each year. SourcedEstimate Checking every email in a 40,000-contact CRM costs about $250 at Bouncer's published prices. Vendor That catches some job moves but not all, and finding the person's new details is the real work. Practitioner

Why each field takes time

Job titles and company names need judgment, which is where rule-based software is weakest and people disagree. Addresses and countries follow rules, so they should be the cheapest to automate. Estimate

The data providers' blind spot

Data providers compete on how many records they have. We found no independent test of their accuracy, and no measure of how much of a bought list lands correctly in the buyer's CRM. So a buyer cannot tell which gaps the provider never filled and which were lost in matching and importing. In one client's weekly list, 5,000 rows became about 3,000 usable contacts with phone numbers. The lead author often had to buy from 3 to 5 providers to get enough usable contacts. Practitioner

A bigger list goes through the same steps but carries more risk: it has more rows to get wrong, and each row gets less attention. Practitioner

So manual data entry error rates run from about 0.3% of fields when people copy from a clean source to about 6.6% when they decide what goes in each field from a messy one. Sourced

Checking misses a lot. Beginners re-checking their own work cut errors by only about 14%. Entering everything twice removes most errors but takes about 66% longer (Barchard and Pace 2011). Across spreadsheet inspection studies, reviewers caught about 60% of planted errors on average, and a team of three caught 83% (Panko 2015). Sourced

Lose 50 contacts from a 500-row list and someone notices. Lose 5,000 from 50,000 and often nobody does. Practitioner

How often imports go wrong

Imports go wrong when a column is missed in mapping or existing records get overwritten. How often depends on who does the import and how busy they are. Practitioner For the errors each CRM reports, see our how-tos on HubSpot import errors and Salesforce import errors.

Source: lead author. A fix takes from 30 minutes to a full redo. Without an import preview, one import overwrote about 100 existing accounts, which then had to be found and restored.

Outside large companies, no job title is built around this work. It lands on whoever is nearest: the founder at a small company, or a marketing, sales or operations manager at a mid-sized one. Large companies have a team of CRM administrators. Practitioner

The cheap role is disappearing

The number of US data entry clerks, the cheapest people who could do this work, fell from 234,700 in 2010 to 131,800 in 2025, and the government expects another 25.5% fall by 2035. Market research analyst and marketing specialist jobs more than tripled in the same period. Government data does not track tasks, so we cannot prove the work moved from one to the other. Part of the clerk decline is probably web forms doing the typing instead. But where list work lands on a marketing specialist, each hour costs about 1.9 times as much as a clerk's. SourcedEstimate

Too few people in the middle

The work tends to go to either someone new or someone very senior. Experienced people in between, who work fast and get fewer imports wrong, are hard to find, so hiring one is a slow fix. Practitioner Industry surveys point the same way (MarketingOps.com 2025; Validity 2025): marketing operations teams are shrinking, often to one person, and 57% of CRM users say their company does manual cleaning while cutting spending on dedicated data quality staff. Vendor

Beyond staff time, list work puts sellers' time, new leads, your sending domain, legal compliance and your CRM bill at risk. Most of the evidence here comes from vendors, and we label it that way.

Sellers' time

Sales professionals say they spend 13% of their week entering data by hand (Salesforce, 4,050 people worldwide, 2026; all data entry, not only lists). An SDR spending 4 hours a week on list work gives up about 1 held meeting a month, or up to 2.5 if only selling hours count: 10 to 25% of a typical quota (Bridge Group 2025). Account executives carry quotas about 4.6 times their pay (Bridge Group 2026), so an hour they spend on data is worth more in selling time than it costs in pay. Only 48% of them hit quota, so not all of that time would turn into sales. VendorEstimate

Leads that wait

In one vendor's logs of 5.7 million leads, replies within 5 minutes converted 8 times as often as replies that took 6 minutes or more (InsideSales, 2018 to 2020, not only B2B). Vendor We found no measure of how long a list takes to reach the CRM, or of what the wait costs. In the lead author's experience it takes a day at best and months at worst. Practitioner

Your sending domain

HubSpot suspends a customer's email sending if more than 5% of emails hard-bounce in a month (its own policy). Sending to unverified lists is a common way to cross that limit. VendorEstimate

The law

This is a summary of published US law, not legal advice. CAN-SPAM applies to business email: unsubscribes must be honored within 10 business days, and the maximum penalty is $53,088 per email for knowing violations. Actual penalties are far lower. Verkada paid $2.95 million in 2024, the largest the FTC has obtained under CAN-SPAM, which works out to under 10 cents per email sent. One way to break the unsubscribe rule is to re-import an old list without its unsubscribe flags. For calls, the FTC's Do Not Call rules exempt most business-to-business calls, but the FCC's rules under the TCPA and state laws can still apply, especially to cell phone numbers. SourcedEstimate

The CRM bill

Some CRMs charge by the number of contacts, including ones you cannot use. HubSpot's Marketing Hub Professional plan bills marketing contacts in blocks of 5,000 at $250 a month. Clearing out dead and duplicate contacts saves nothing unless it drops you into a lower block, and then up to about $3,000 a year for each block dropped, starting at your next renewal. That is small next to the staff time. VendorEstimate

Many organizations doubt their data is ready for AI. Gartner found 63% either lack the right data practices for AI or are unsure whether they have them (1,203 data leaders, 2025). Vendor

AI is good at matching records: the best models score 76 to 98 out of 100 on matching tests without worked examples. In 2022 tests, GPT-3 was weak at spotting errors without examples. Newer studies find models change their answers when a question is worded differently, and can fill gaps by making values up. None of these tests used real CRM lists. Sourced

We expect AI to cut the time spent finding and merging duplicates. It does not remove the round trip to data providers, the limits of email verification, or contacts going stale as people change jobs. Some of the time it saves goes back into checking its work. Estimate

US healthcare has measured this kind of work for years. The CAQH Index compares the staff cost of the same task done by hand and electronically. Sourced

• Skilled people doing data entry. Doctors spent about 2 hours on computer and desk work for every hour with patients (Sinsky 2016), and 44% of their computer time was clerical (Arndt 2017). This is the closest parallel we found to sellers and marketers doing list work. Sourced
• Tools added to a manual process saved little. In a trial, one AI note-taker saved 9.5% of note time and another made no real difference (UCLA 2025). In mortgages, lenders estimated automation cut staff time by only 2 to 5%, while cost per loan rose for many reasons, including falling loan volumes (Freddie Mac 2024). Legal document review is the exception: software matched or beat people reading every page (Grossman and Cormack 2011). Sourced

In these fields, costs fell most when one standard covered the whole task, and little when a tool was added to one step. We have not tested this for CRM lists, but it fits chapter 2, where the cheapest list is one built by the person who imports it, with no one else's work to fix first. Estimate

Each of these follows from a finding above, where its figures are labeled. None needs a new hire.

1. 01

   Build lists at the source where you can.

   Set the ideal customer profile, company sizes and fields in the data provider before exporting. A list built this way takes an experienced person about 1.5 hours, against 3.5 hours for one from someone else.

2. 02

   Give one person the whole job, with one written process.

   Lists that pass between teams wait in each queue. Mid-sized companies where one person owned the work turned lists around in a day, and large ones with sign-offs at each step took weeks to months. A written process also helps someone new, who takes about 1.6 times as long and gets about 1 in 3 imports wrong in their first year.

3. 03

   Test every import on a few rows first.

   Import 10 rows, check that every column landed, then import the rest. A common failure is a column missed in mapping and found too late.

4. 04

   Never skip verification.

   It takes minutes and costs up to 2 cents per email, often well under 1 cent. Skipping it risks bounced emails and a damaged sending domain.

5. 05

   Carry unsubscribe and suppression flags through every import.

   Re-importing an old list without them is one way companies end up emailing people who opted out.

6. 06

   Order the queue by deadline and effort, and say so.

   First come, first served makes quick lists wait behind slow ones. A visible order should also keep the queue from going to whoever asks loudest.

Companies spend heavily on both ends of this work. Worldwide spending on CRM software was about $128 billion in 2024 (Gartner). Vendor ZoomInfo, one of the largest B2B contact data providers, took in about $1.25 billion in 2025 (its annual results). Sourced Far less attention goes to the work in between: getting the data from the provider into the CRM.

We also built a rougher top-down model. It puts the yearly US staff cost of CRM data work at about $10 billion, with a range of $5 billion to $17 billion, for B2B companies with 20 or more staff that use a CRM. Because it rests on assumptions about how many companies are B2B, how many use a CRM and how much of each week is spent on this work, we do not treat it as a finding. The figures usually quoted for what bad data costs rest on even less, as we explain in our post on cleaning data. The top-down model also covers ongoing CRM upkeep, not only lists, so do not apply the 40 to 75% from chapter 2 to it. Estimate

Much of this work is a necessary cost of running sales and marketing, in the same way that payroll is. A company can find its own cost by counting its lists, timing a few and pricing those hours.

• A survey. The time estimates come from one person. A survey of the people who do this work, with its questions and data published, will test them.
• A timed test. Outside people will clean three realistic messy lists by hand and with several tools, including free ones, and we will publish the lists and answers.
• How companies keep up. Our time estimates imply 0.8 to 5.4 full-time people at 10 to 40 lists a week, yet the lead author saw mid-sized companies where one person owned the work turn lists around in a day. This report argues they kept up by skipping checks (chapter 3). They may also have handled fewer lists, or our times may be too high. The survey will test which.
• The list mix. The $266 uses the mix of lists the lead author's agency received, which is probably messier than most companies see. With 30% clean, 45% average and 25% messy, someone new would take about 4.3 hours, or about $200 a list. Estimate
• The wait. How long a list takes to go from arrival to the CRM to first contact. We found no published measure.

To cite this report: Pank, D. (2026). The cost of CRM data work. Operelio. https://operelio.com/research/cost-of-crm-data

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