CRM Statistics Updated November 2026

Human Written & Fact Checked · Updated

100 CRM Data Quality Statistics for 2026

Can you trust the customer record behind your next decision? CRM adoption tells you whether a system exists. Data-quality research asks a different question: whether the information people act on is complete, current, consistent and usable.

This page separates direct CRM research from the wider enterprise and marketing-data environment. The latest CRM findings sit alongside clearly dated historical evidence and broader studies of trust, fragmentation and governance. Reported losses and confidence scores are survey responses, not an audit of every company's records.

Headline figures

  • 21% rated CRM data very well prepared for AI. (Validity, Aug 2026)
  • 62% reported revenue loss from poor CRM data. (Validity, Aug 2026)
  • Admins saying less than half their CRM data was accurate and complete: 24%. (Validity, Apr 2024)
  • Duplicate records among respondents with CRM quality difficulties: 53%. (Validity, Apr 2024)
  • Admins reporting poor data cost at least one-fifth of annual revenue: 31%. (Validity, Apr 2024)
  • Respondents reporting no full-time employee dedicated to CRM data quality: 55%. (Validity, Apr 2024)
  • Technical leaders' estimated share of organisational data that was untrustworthy: 26%. (Salesforce, Nov 2025)
  • Business leaders missing a deadline because of data problems: 27%. (Salesforce, Nov 2025)
  • Technical leaders agreeing unified data was main to meeting customer expectations: 87%. (Salesforce, Nov 2025)
  • Technical leaders reporting a severe impact on a unified customer view due to data silos: 36%. (Salesforce, Nov 2025)
  • CMOs' mean estimate of marketing decision data that was incomplete, inaccurate or outdated: 45%. (Adverity, Mar 2026)
  • CMOs choosing monitoring as their leading governance improvement priority: 24%. (Adverity, Mar 2026)

What does the latest CRM research say about unreliable data?

A CRM record can influence a decision long before anyone checks whether it is correct. Validity's August 2026 release describes reported consequences and leadership pressures, not independently audited financial losses.

Which roles reported acting on questionable AI recommendations?

  • Nearly 78% of C-suite respondents acted on suspected bad-data AI recommendations. (Validity, Aug 2026)
  • 92% of SVP/VP respondents acted on suspected bad-data AI recommendations. (Validity, Aug 2026)
  • 41% of individual contributors acted on suspected bad-data AI recommendations. (Validity, Aug 2026)
  • Nearly 60% of C-suite respondents felt pressure to deploy AI even with unready data. (Validity, Aug 2026)
  • 52% of SVP/VP respondents felt pressure to deploy AI even with unready data. (Validity, Aug 2026)

Senior and frontline respondents reported different experiences of questionable recommendations and deployment pressure. Retain the role labels: these comparisons do not estimate the behaviour of every marketer. The underlying survey asked respondents about their own experience and perceptions of organisational data readiness. Use the finding to decide which process to investigate, then verify that process against actual campaign records.

How do campaign reporting and operational consequences appear?

  • 67% of C-suite respondents acknowledged occasional campaign-data manipulation. (Validity, Aug 2026)
  • 38% reported campaign-data manipulation across organizations. (Validity, Aug 2026)
  • 63% reported compliance exposure from poor CRM data. (Validity, Aug 2026)
  • 67% reported delayed or abandoned campaigns from poor CRM data. (Validity, Aug 2026)

Respondents described both manipulation of campaign reporting and consequences linked to poor CRM information. The survey is evidence of reported exposure, not a causal experiment showing how much money a particular software change would recover. The numerical observations below refer to the same recent report. Use the finding to decide which process to investigate, then verify that process against actual campaign records. The report does not identify a generally effective remedy or establish that a particular technology caused an observed outcome.

Which safeguards and improvements did respondents identify?

  • 41% reported a dedicated data-governance team or owner for regulatory/privacy risk. (Validity, Aug 2026)
  • 39% chose continuous automated monitoring as the leading confidence-building capability. (Validity, Aug 2026)
  • 23% chose platform consolidation as the leading confidence-building capability. (Validity, Aug 2026)

Dedicated responsibility and continuous monitoring address different parts of the problem. The ownership observation describes a reported organisational arrangement, while the other figures concern preferred capabilities. A preference for consolidation is not evidence that a company has consolidated its platforms or that consolidation alone resolves data errors. Use the finding to decide which process to investigate, then verify that process against actual campaign records. The report does not identify a generally effective remedy or establish that a particular technology caused an observed outcome.

Which CRM quality problems were already visible in the historical baseline?

The earlier survey makes recurring operational problems concrete. Its fieldwork ended in April 2024; these figures remain a historical baseline and should not be described as current prevalence.

What defects and maintenance problems did CRM users report?

  • Respondents reporting company CRM data-quality difficulties: 40%. (Validity, Apr 2024)
  • Incomplete data among respondents with CRM quality difficulties: 68%. (Validity, Apr 2024)
  • Missing data among respondents with CRM quality difficulties: 65%. (Validity, Apr 2024)
  • Incorrect data among respondents with CRM quality difficulties: 61%. (Validity, Apr 2024)
  • Expired data among respondents with CRM quality difficulties: 49%. (Validity, Apr 2024)
  • Respondents reporting difficulty maintaining CRM cleanliness: 58%. (Validity, Apr 2024)
  • Respondents reporting difficulty using CRM data in everyday work: 46%. (Validity, Apr 2024)
  • Respondents reporting difficulty collecting CRM data: 38%. (Validity, Apr 2024)
  • Respondents reporting difficulty storing CRM data: 28%. (Validity, Apr 2024)

Missing, incomplete and incorrect records are separate response options in this survey. The defect questions apply to respondents who already reported CRM quality difficulties, while the maintenance questions use the broader survey framing.

What costs, delays and ownership gaps did admins report?

  • Admins saying data collection and storage created unnecessary costs: 37%. (Validity, Apr 2024)
  • Companies reporting halted useful initiatives due to low-quality CRM data: 41%. (Validity, Apr 2024)
  • Mean initiatives delayed or halted per quarter among affected companies: 6 initiatives. (Validity, Apr 2024)
  • Admins noticing faster customer-data decay in the preceding year: 48%. (Validity, Apr 2024)
  • Respondents unsure who owned data accuracy: 35%. (Validity, Apr 2024)
  • Respondents reporting organisational data silos: 50%. (Validity, Apr 2024)

The earlier survey linked poor information with costs and delayed work through respondents' own assessments. These are reported business consequences, not audited amounts. The ownership questions identify organisational gaps instead of measuring the accuracy of individual records. This historical survey is useful for identifying recurring categories to inspect.

What were the reported causes of CRM silos and cleaning work?

  • Incompatible tools cited as a cause of data silos: 51%. (Validity, Apr 2024)
  • Hard-to-integrate legacy systems cited as a cause of data silos: 51%. (Validity, Apr 2024)
  • Resistance to information sharing cited as a cause of data silos: 46%. (Validity, Apr 2024)
  • Missing governance policies cited as a cause of data silos: 43%. (Validity, Apr 2024)
  • Departmental competition cited as a cause of data silos: 27%. (Validity, Apr 2024)
  • Respondents reporting manual cleaning to improve CRM data quality: 53%. (Validity, Apr 2024)

The silo responses combine technical and organisational explanations. Respondents could select multiple reasons, so the categories overlap and should not be added into a total. Manual cleaning describes a reported practice, not a comparison proving manual or automated cleaning superior.

How does confidence in enterprise data affect business decisions?

Broader enterprise-data research helps explain the environment surrounding a CRM. These Salesforce observations concern business and technical leaders across their organisations, so they are not CRM-only defect rates.

How did confidence in accuracy, reliability and relevance compare?

  • Business leaders saying data strategy did not fully match business objectives: 42%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data accuracy, 2023 baseline: 43%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data accuracy, 2025 survey: 47%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data reliability, 2023 baseline: 49%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data reliability, 2025 survey: 47%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data relevance, 2023 baseline: 46%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data relevance, 2025 survey: 47%. (Salesforce, Nov 2025)

Business leaders reported their confidence in several distinct properties of data. Confidence is a perception, not a test of correctness. The historical and newer observations are labelled separately; their publication in one report does not make both measurements current.

What did leaders say about timeliness, completeness and uncertain reporting?

  • Business leaders fully confident in data timeliness, 2023 baseline: 45%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data timeliness, 2025 survey: 44%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data completeness, 2023 baseline: 45%. (Salesforce, Nov 2025)
  • Business leaders fully confident in data completeness, 2025 survey: 43%. (Salesforce, Nov 2025)
  • Business leaders giving a range because data was unreliable or inaccessible: 39%. (Salesforce, Nov 2025)
  • Business leaders supplying a figure different from a colleague's because of data problems: 36%. (Salesforce, Nov 2025)

A dataset can be reliable yet arrive too late, or be timely yet omit something important. The survey treats those concerns separately and also asks about reporting actions taken when information is unreliable or inaccessible. Keep the reported action distinct from the underlying quality dimension.

What decision-making problems did the broader survey reveal?

  • Business leaders omitting a questionable data point because of data problems: 32%. (Salesforce, Nov 2025)
  • Business leaders making gut-based decisions because of data problems: 32%. (Salesforce, Nov 2025)
  • Technical leaders reporting occasional or frequent conclusions based on misunderstood business context: 49%. (Salesforce, Nov 2025)
  • Technical leaders saying their company struggled to advance business priorities with data: 63%. (Salesforce, Nov 2025)

The remaining observations concern respondents' behaviour and technical leaders' interpretation of their organisations. These survey cohorts are separate. They support a discussion about confidence and context, while they do not quantify the proportion of customer records containing a verified error or identify a particular CRM product. The relevant distinction is between a respondent's confidence and an independent quality assessment. A team can be confident in data that still contains errors, or cautious about information that is correct.

How do fragmented data environments obstruct a customer view?

Correct records can still be difficult to use when they are scattered across systems. These enterprise-wide observations describe fragmentation and its perceived consequences around customer data, instead of the number of CRM licences or platforms in every company.

How many kinds of data environments did enterprises report?

  • Mean spreadsheet applications per enterprise: 26 environments. (Salesforce, Nov 2025)
  • Mean cloud storage services per enterprise: 21 environments. (Salesforce, Nov 2025)
  • Mean operational databases per enterprise: 21 environments. (Salesforce, Nov 2025)
  • Mean data warehouses per enterprise: 17 environments. (Salesforce, Nov 2025)
  • Mean data lakes per enterprise: 16 environments. (Salesforce, Nov 2025)
  • Mean customer data platforms per enterprise: 15 environments. (Salesforce, Nov 2025)
  • Technical leaders' estimated share of company data that was trapped: 19%. (Salesforce, Nov 2025)

The report lists average counts of distinct environment types across enterprises. A spreadsheet application, a warehouse and a customer data platform are different units. These counts cannot be added to estimate CRM installations, nor do they show how many systems are active for a particular business team.

Which severe consequences were attributed to silos?

  • Technical leaders reporting a severe impact on security and system access due to data silos: 42%. (Salesforce, Nov 2025)
  • Technical leaders reporting a severe impact on AI capabilities due to data silos: 40%. (Salesforce, Nov 2025)
  • Technical leaders reporting a severe impact on compliance due to data silos: 38%. (Salesforce, Nov 2025)
  • Technical leaders reporting a severe impact on revenue opportunities due to data silos: 38%. (Salesforce, Nov 2025)
  • Technical leaders reporting a severe impact on decision-making due to data silos: 37%. (Salesforce, Nov 2025)
  • Technical leaders reporting a severe impact on customer experience due to data silos: 37%. (Salesforce, Nov 2025)

Technical leaders reported several kinds of severe impact from trapped data. These results identify perceived consequences, not measured probabilities that a company will experience each event. Multiple effects can coexist within the same organisation, and a revenue opportunity is not the same thing as audited revenue loss.

How do customer visibility and integration appear in the survey?

  • Technical leaders reporting a severe impact on cross-platform findings due to data silos: 36%. (Salesforce, Nov 2025)
  • Technical leaders reporting a severe impact on a unified business view due to data silos: 35%. (Salesforce, Nov 2025)
  • Technical leaders reporting a severe impact on personalisation due to data silos: 33%. (Salesforce, Nov 2025)
  • Technical leaders at zero-copy organisations reporting fully connected customer data sources: 73%. (Salesforce, Nov 2025)
  • Technical leaders at organisations without zero-copy reporting fully connected customer data sources: 51%. (Salesforce, Nov 2025)

Customer visibility, business visibility and personalisation are related but distinct outcomes. The final comparison contrasts organisations with and without zero-copy integration. It is observational: differences in company resources, maturity or operating model can influence both integration choices and reported connectivity.

What does marketing research reveal about quality and automation?

Marketing information reaches the CRM through a wider reporting and data pipeline. Adverity's research concerns marketing data generally, and its automation groups describe different operating setups instead of a controlled test of automation.

Which overall quality problems did CMOs identify?

  • CMOs saying they trusted marketing-data quality and completeness: 85%. (Adverity, Mar 2026)
  • CMOs naming completeness their biggest data-quality problem: 31%. (Adverity, Mar 2026)
  • CMOs naming consistency their biggest data-quality problem: 26%. (Adverity, Mar 2026)
  • CMOs naming uniqueness their biggest data-quality problem: 16%. (Adverity, Mar 2026)

The survey distinguishes respondents' confidence from their description of the data they use. It also asks for the biggest quality problem, which forces a priority choice. A selected leading concern does not mean the respondent had no other quality difficulties, and the findings are not CRM-specific error rates. These findings can help identify questions for a data review, but they are not a substitute for inspecting the underlying workflow. Determine where records enter the process, who validates them and which checks detect missing or inconsistent values.

What did teams with less automation report?

  • Low-automation teams naming completeness their biggest data-quality problem: 33%. (Adverity, Mar 2026)
  • Low-automation teams naming timeliness their biggest data-quality problem: 27%. (Adverity, Mar 2026)

Teams with less automation ranked first foundational problems such as complete and timely information. The source categorised automation across data activities, instead of measuring the number of AI tools deployed. Its low-automation group therefore should not be relabelled as companies that do not use AI. Workflow coverage and AI adoption remain different concepts. These findings can help identify questions for a data review, but they are not a substitute for inspecting the underlying workflow. Determine where records enter the process, who validates them and which checks detect missing or inconsistent values. Then evaluate improvements against the organisation's own baseline.

What changed across the more automated groups?

  • Medium-automation teams naming accuracy their biggest data-quality problem: 31%. (Adverity, Mar 2026)
  • Medium-automation teams naming consistency their biggest data-quality problem: 29%. (Adverity, Mar 2026)
  • High-automation teams naming uniqueness their biggest data-quality problem: 28%. (Adverity, Mar 2026)
  • High-automation teams naming consistency their biggest data-quality problem: 27%. (Adverity, Mar 2026)

The more automated groups reported different leading concerns. These are cross-sectional subgroup results, so they do not establish that automating a workflow causes a company to move from one quality problem to another. Treat the pattern as a prompt for an internal audit, then investigate the organisation's actual records and processes. These findings can help identify questions for a data review, but they are not a substitute for inspecting the underlying workflow. Determine where records enter the process, who validates them and which checks detect missing or inconsistent values.

How do marketing-data concerns differ across segments?

A single global headline can conceal different operational priorities. These industry and regional results are exploratory subgroups of the same marketing survey, not representative estimates for every business in each market.

Which concerns led among agencies, technology and commerce respondents?

  • Agency CMOs naming consistency their biggest data-quality problem: 39%. (Adverity, Mar 2026)
  • Technology CMOs naming consistency their biggest data-quality problem: 36%. (Adverity, Mar 2026)
  • Ecommerce CMOs naming completeness their biggest data-quality problem: 39%. (Adverity, Mar 2026)
  • Financial-services CMOs naming completeness their biggest data-quality problem: 37%. (Adverity, Mar 2026)

The selected respondents reported different leading quality problems depending on their sector. Consistency means that information can be reconciled across sources, while completeness concerns missing inputs. Neither response directly counts duplicate contacts or estimates the financial value of correcting an individual CRM record. The subgroup evidence is best used to develop hypotheses for further investigation. Survey sample composition and the size of individual segments matter when comparing percentages. Keep the source population attached to the claim and avoid turning a leading concern into an estimate of actual erroneous records.

Which concerns appeared in consumer-facing sectors?

  • Consumer-goods CMOs naming completeness their biggest data-quality problem: 41%. (Adverity, Mar 2026)
  • Retail CMOs naming completeness their biggest data-quality problem: 43%. (Adverity, Mar 2026)
  • Media-and-entertainment CMOs naming uniqueness their biggest data-quality problem: 34%. (Adverity, Mar 2026)

Consumer-goods, retail and media respondents supplied different sector-specific priorities. These figures describe the leading issue selected within each subgroup. They should not be combined into a weighted industry average without the source's underlying respondent counts, and they do not measure the entire customer-data estate. The subgroup evidence is best used to develop hypotheses for further investigation. Survey sample composition and the size of individual segments matter when comparing percentages. Keep the source population attached to the claim and avoid turning a leading concern into an estimate of actual erroneous records.

What did the regional comparison show?

  • US CMOs naming completeness their biggest data-quality problem: 35%. (Adverity, Mar 2026)
  • UK CMOs naming completeness their biggest data-quality problem: 38%. (Adverity, Mar 2026)
  • DACH CMOs naming consistency their biggest data-quality problem: 36%. (Adverity, Mar 2026)

The regional results concern the leading issue selected by surveyed CMOs. DACH combines Germany, Austria and Switzerland; it is not a result for Germany alone. The separate governance priority in the takeaways refers to the overall survey, instead of a specific regional subgroup. The subgroup evidence is best used to develop hypotheses for further investigation. Survey sample composition and the size of individual segments matter when comparing percentages. Keep the source population attached to the claim and avoid turning a leading concern into an estimate of actual erroneous records.

Glossary

Methodology and sources

Every figure on this page comes from the organisation that produced it: regulators, statistics offices, public bodies, research institutes, and companies reporting their own data or surveys. Each one links to the original report or dataset and carries its release date. Survey results keep their population and fieldwork period, and financial figures keep the measure the producer used. Figures from different producers are not added together or ranked against each other. The page is reviewed monthly and updated when a producer releases newer data.