Human Written & Fact Checked · Updated November 2026

100 Marketing Automation Data Quality Statistics for 2026

Your automation can deliver the right message according to the workflow and still contact the wrong person. The quality of the records, the speed of updates and the completeness of customer context determine what that workflow actually knows.

The evidence below separates current marketing research from an explicitly historical CRM operations baseline. Adverity examines marketing data problems and governance priorities; Salesforce examines data access and activation; Validity reports the consequences of poor CRM data. These are respondent assessments, not audits of every record or experiments proving that a software purchase caused an improvement.

Headline figures

CMOs estimated that 45% of decision-making data was incomplete, inaccurate or outdated. (Adverity, Mar 2026)
85% trusted the quality and completeness of their marketing data. (Adverity, Mar 2026)
Improving data quality was the strongest expected performance lever for 30%. (Adverity, Mar 2026)
Access and ownership was the governance improvement priority for 28%. (Adverity, Mar 2026)
Audience segmentation: 36% reported delayed data. (Salesforce, Feb 2026)
58% had full access to service data. (Salesforce, Feb 2026)
41% of marketers using AI segmented audiences by predicted behavior. (Salesforce, Feb 2026)
61% had not fully integrated AI into their marketing technology. (Salesforce, Feb 2026)
62% reported revenue loss from poor CRM data. (Validity, Aug 2026)
67% reported delayed or abandoned campaigns from poor CRM data. (Validity, Aug 2026)
Historical 2024 survey: admins saying less than half their CRM data was accurate and complete: 24%. (Validity, Apr 2024)
Historical 2024 survey: respondents reporting organisational data silos: 50%. (Validity, Apr 2024)

Which data defects undermine marketing automation?

A completeness problem is not the same as an accuracy problem. The distinction counts because collecting another field will not repair a wrong identity, and faster delivery will not remove a duplicate.

Which quality problems did CMOs put first?

Completeness was the biggest data-quality problem for 31%. (Adverity, Mar 2026)
Consistency was the biggest data-quality problem for 26%. (Adverity, Mar 2026)
Uniqueness was the biggest data-quality problem for 16%. (Adverity, Mar 2026)
Accuracy was the biggest data-quality problem for 14%. (Adverity, Mar 2026)
Timeliness was the biggest data-quality problem for 12%. (Adverity, Mar 2026)
Validation was the biggest data-quality problem for 2%. (Adverity, Mar 2026)

Adverity asked respondents to identify their biggest data-quality problem. These are priority selections, not the measured proportion of defective records in a customer database. The study collected responses in Q2 2025; its webpage was updated in March 2026. Treat the categories as starting points for an audit of the fields that drive a workflow. Check whether required values exist, whether formats align and whether records represent distinct contacts. A single quality score can hide these different failure modes. The publisher's rounded percentages should be retained instead of adjusted to manufacture an exact total.

How do problems differ by automation maturity?

Low-automation teams citing completeness as their biggest quality problem: 33%. (Adverity, Mar 2026)
Low-automation teams citing timeliness as their biggest quality problem: 27%. (Adverity, Mar 2026)
Medium-automation teams citing accuracy as their biggest quality problem: 31%. (Adverity, Mar 2026)
Medium-automation teams citing consistency as their biggest quality problem: 29%. (Adverity, Mar 2026)
High-automation teams citing uniqueness as their biggest quality problem: 28%. (Adverity, Mar 2026)
High-automation teams citing consistency as their biggest quality problem: 27%. (Adverity, Mar 2026)

The most prominent quality problem changes across Adverity's automation groups. This describes an association between maturity and reported problems, not a demonstration that adding automation causes a particular defect. The groups reflect the share of data operations automated, including access, gathering, cleaning, validation, integration, reporting and analysis. Their experiences should not be treated as mandatory stages every company will pass through. A team can automate extraction while still relying on manual reconciliation. Before adopting the comparator, document which parts of your pipeline are actually automated and which depend on someone noticing a problem.

Do the leading quality problems vary across regions?

US respondents citing completeness as the leading quality problem: 35%. (Adverity, Mar 2026)
UK respondents citing completeness as the leading quality problem: 38%. (Adverity, Mar 2026)
DACH respondents citing consistency as the leading quality problem: 36%. (Adverity, Mar 2026)

The study's regional summaries identify the leading quality concern within each respondent group. They do not establish the share of all marketing databases in those markets that contain the corresponding defect. DACH groups Germany, Austria and Switzerland. Differences between these small survey subgroups can reflect the organisations represented as well as their data practices. Avoid treating the rankings as a reason to skip checks that matter to your own audience. For a cross-border workflow, document identifiers, field availability, update schedules and consent dependencies before comparing performance. The useful operational question is whether the required data survives the journey from its source to the action that uses it, with the same meaning at each step.

Which governance improvements do marketing leaders put first?

Governance becomes useful when it identifies who fixes a failed input and how quickly the error is detected. A written policy alone cannot tell a live campaign that its customer data is stale.

Which changes are expected to improve marketing performance?

Automating data workflows was the strongest expected performance lever for 22%. (Adverity, Mar 2026)
Improving data access was the strongest expected performance lever for 21%. (Adverity, Mar 2026)
Improving transformation and formatting was the strongest expected performance lever for 17%. (Adverity, Mar 2026)
Improving internal data skills was the strongest expected performance lever for 10%. (Adverity, Mar 2026)
Cross-team alignment was the strongest expected performance lever for 1%. (Adverity, Mar 2026)
26% of UK respondents ranked first improving data access as their strongest performance lever. (Adverity, Mar 2026)

Adverity asked which change would have the biggest positive impact on performance. The responses express expectations, not measured lifts following a completed implementation. The headline quality priority is already listed in the takeaways. The remaining selections show why a data improvement programme often requires both technical and organisational work. The UK figure is a subgroup result from the same survey and should not be combined with the overall response distribution. Define the operational bottleneck before treating the most popular answer as your next investment.

How do improvement priorities change with automation level?

Low-automation teams naming workflow automation as their strongest performance lever: 30%. (Adverity, Mar 2026)
Low-automation teams naming data access as their strongest performance lever: 30%. (Adverity, Mar 2026)
Medium-automation teams naming internal data skills as their strongest performance lever: 40%. (Adverity, Mar 2026)
High-automation teams naming data quality as their strongest performance lever: 30%. (Adverity, Mar 2026)

Teams with different levels of automated data operations stressed different improvements. The findings are useful for framing an implementation discussion, provided the local workflow is assessed directly. A low-automation team may need reliable access before sophisticated validation rules become useful. An established pipeline may instead require stronger checks on what the connected systems produce. These are hands-on interpretations of the survey, not outcomes tested by the research. Record the source, owner and expected refresh interval of each field used to trigger a campaign. Then identify whether the constraint is missing access, inconsistent transformation, inadequate expertise or a defect that repeats after the data arrives. Maturity labels alone cannot make that decision.

Which governance capabilities need improvement?

Monitoring was the governance improvement priority for 24%. (Adverity, Mar 2026)
Security was the governance improvement priority for 16%. (Adverity, Mar 2026)
Classification was the governance improvement priority for 14%. (Adverity, Mar 2026)
Reconciliation was the governance improvement priority for 10%. (Adverity, Mar 2026)
Transformation was the governance improvement priority for 10%. (Adverity, Mar 2026)

Respondents selected the governance area they most wanted to improve over the following year. That forward-looking question measures priorities at the survey date, not whether the improvement was later implemented. Access and ownership appear in the takeaways. The remaining choices are complementary controls: monitoring can reveal a missing feed, classification can define handling rules, and reconciliation can reveal differences between systems. A workflow should have an explicit response when a check fails, such as holding an audience update until its inputs are corrected. This is an operational implication, not a promised benefit quantified by the survey. Keep responsibility for the data visible even when a vendor operates the connector or automation platform.

How available and connected is marketing data?

A connected application does not guarantee timely customer context. Check both latency and access before relying on a data feed to suppress an inappropriate message or qualify someone for the next step.

How quickly can teams segment and execute campaigns?

Audience segmentation: 56% reported real-time data. (Salesforce, Feb 2026)
Audience segmentation: 8% reported unavailable data. (Salesforce, Feb 2026)
Campaign execution: 55% reported real-time data. (Salesforce, Feb 2026)
Campaign execution: 37% reported delayed data. (Salesforce, Feb 2026)
Campaign execution: 8% reported unavailable data. (Salesforce, Feb 2026)

Salesforce distinguishes real-time, delayed and unavailable data for each marketing activity. The categories reveal different operational constraints, even when the applications are nominally connected. The delayed-segmentation result appears in the takeaways. A delay that is tolerable for a weekly report may be unsuitable for a customer-triggered workflow. These survey responses do not measure the duration of the delay, so they cannot establish a universal service-level target. Set the freshness requirement from the action being taken: a suppression decision, a renewal reminder and a broad audience analysis may need different windows. Validate the actual feed against that requirement instead of assuming the presence of an integration is sufficient.

How accessible are analytics and departmental records?

Performance analytics: 55% reported real-time data. (Salesforce, Feb 2026)
Performance analytics: 37% reported delayed data. (Salesforce, Feb 2026)
Performance analytics: 8% reported unavailable data. (Salesforce, Feb 2026)
56% had full access to sales data. (Salesforce, Feb 2026)
51% had full access to commerce data. (Salesforce, Feb 2026)

Salesforce also reports the timeliness of performance data and full access to records from adjacent departments. These are separate questions: timely analytics do not imply complete access to customer history. The service-data result is reserved for the takeaways. Full access is the specific category used here; partial access should not be silently counted as equivalent. When investigating a customer journey, identify which operational events are absent from the marketer's view and whether a permission, integration or refresh dependency explains the gap. A campaign can appear successful within its own reporting system while omitting relevant service or commerce context. The survey establishes reported access levels, not the accuracy of the records that are available.

How satisfied are marketers with connected touchpoints?

Completely satisfied with connecting marketing, sales and service touchpoints: 26%. (Salesforce, Feb 2026)
Highly satisfied with connecting marketing, sales and service touchpoints: 46%. (Salesforce, Feb 2026)
Moderately satisfied with connecting marketing, sales and service touchpoints: 22%. (Salesforce, Feb 2026)
Slightly satisfied with connecting marketing, sales and service touchpoints: 6%. (Salesforce, Feb 2026)
Not at all satisfied with connecting marketing, sales and service touchpoints: 1%. (Salesforce, Feb 2026)

The original Salesforce chart reports satisfaction with connecting marketing, sales and service touchpoints. These responses measure perceived capability instead of an objective integration test. The published categories sum slightly above a complete distribution because the displayed values are rounded; they are preserved as published. For implementation review, translate satisfaction into specific checks: confirm that a changed customer status reaches dependent workflows, that identifiers map consistently and that failed updates are visible to an owner. A confident survey response cannot replace those checks. Equally, dissatisfaction does not identify which connection failed, so it should prompt investigation instead of a blanket platform replacement.

Which customer inputs support audience activation?

Segmentation is where a data-quality problem becomes a customer-facing decision. Review the fields used to include, exclude or put first a contact before optimizing the message that follows.

Which observed customer attributes are used for segmentation?

Purchase or transaction history was used for audience segmentation by 46%. (Salesforce, Feb 2026)
Usage or consumption patterns was used for audience segmentation by 44%. (Salesforce, Feb 2026)
Demographic or firmographic data was used for audience segmentation by 42%. (Salesforce, Feb 2026)
Expressed preferences or interests was used for audience segmentation by 41%. (Salesforce, Feb 2026)
Professional or role attributes was used for audience segmentation by 39%. (Salesforce, Feb 2026)

Salesforce's original chart identifies the attributes marketers use to build audiences. These are reported uses, not a ranking of which attribute produces the strongest commercial outcome. Transaction history can be incomplete, consumption patterns can arrive late, and declared interests can become outdated. Those are different risks that should be checked against the workflow's purpose. Keep lineage visible so unexpected segment membership can be traced to its source. The survey does not specify the quality threshold or refresh cadence used by every respondent. Your own threshold should therefore follow the consequence of an incorrect decision, with greater scrutiny for actions that materially affect a customer relationship.

How are predictive and lifecycle inputs used?

Predicted behavior was used for audience segmentation by 38%. (Salesforce, Feb 2026)
Engagement was used for audience segmentation by 38%. (Salesforce, Feb 2026)
Relationship or lifecycle stage was used for audience segmentation by 37%. (Salesforce, Feb 2026)
30% of marketers without AI segmented audiences by predicted behavior. (Salesforce, Feb 2026)

The report includes predicted behavior, engagement and lifecycle information alongside observed customer attributes. It also distinguishes predicted-behavior segmentation among marketers with and without AI. The AI-user comparison and incomplete-integration finding appear in the takeaways. These groups may differ in budget, skills, data readiness and business model, so the comparison does not isolate an AI effect. A prediction also needs a defined update process and an explanation of what action it may trigger. Review how changes in lifecycle status override a model score, how engagement signals expire, and how the system handles missing evidence. A plausible prediction should not silently take precedence over a reliable operational event that changes the customer's eligibility.

What consequences do marketers report from poor CRM data?

Bad data can move from a reporting error into an automated instruction. Validity's current marketing survey makes that risk concrete, while remaining self-reported evidence instead of an independent financial or compliance audit.

Do leaders act 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)

Validity's August 2026 release reports that respondents acted on recommendations they later suspected were wrong because of underlying data. Role-specific results describe different respondent groups. The pressure findings concern adoption even with unready data; they do not establish that every pressured team deployed an unsafe system. Treat them as a reason to define review responsibilities before delegating decisions. Record what evidence a reviewer needs to difficulty an output and what happens when that evidence is missing. The survey does not test a particular vendor's model or show that seniority itself causes the behavior. It reports perceptions and experiences within the sampled marketing organisations.

What campaign and governance failures are reported?

67% of C-suite respondents acknowledged occasional campaign-data manipulation. (Validity, Aug 2026)
38% reported campaign-data manipulation across organizations. (Validity, Aug 2026)
21% rated CRM data very well prepared for AI. (Validity, Aug 2026)
63% reported compliance exposure from poor CRM data. (Validity, Aug 2026)

The original release describes both manipulation of campaign data and operational consequences linked to poor CRM data. The campaign-delay and revenue-loss headlines are kept in the takeaways. Data manipulation and accidental defects are distinct problems. A validation rule may catch an invalid format without detecting a deliberately misleading interpretation of a campaign result. Preserve the distinction when assigning ownership and reviewing reports. Compliance exposure is also a reported consequence, not a count of proven legal violations. The survey can guide questions for governance review, but it cannot establish that a specific organisation breached a rule or that a particular control would have prevented every reported problem. Keep evidence and respondent opinion separate.

Which capabilities could improve confidence in CRM data?

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)

Validity asked which capability would most improve confidence and separately reported dedicated governance ownership. Preferences for future capabilities do not demonstrate that the selected intervention has already worked. The operational implication is to combine a named owner with a reliable detection and correction process. Platform consolidation may simplify part of that process, but a shared location does not guarantee consistent definitions or accurate source records. Monitoring is useful only if someone can investigate an alert and change the faulty input. Before expanding automation, document the response to missing, duplicated or contradictory customer data. The release provides a survey-based rationale for that conversation, not a quantified promise that a particular architecture will recover lost revenue.

What does the historical CRM operations baseline reveal?

Earlier CRM research helps identify persistent operational failure modes. It should not be presented as the current prevalence of those problems or as a directly comparable trend against a different marketing survey.

Which defects and daily data tasks were difficult?

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)
duplicate records among respondents with CRM quality difficulties: 53%. (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)

Validity's historical study separates respondents reporting quality difficulties from the wider survey population. Defect-type percentages below use the affected group; the task-difficulty figures describe the broader respondents. The fieldwork ran from March to April 2024. These observations describe CRM operations generally, both marketing automation users. Use findings to identify operational checks, not to estimate current prevalence.

What costs and ownership gaps were reported historically?

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 reporting poor data cost at least one-fifth of annual revenue: 31%. (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 no full-time employee dedicated to CRM data quality: 55%. (Validity, Apr 2024)

The historical survey also asked about unnecessary costs, delayed initiatives, data decay and responsibility. Its reported revenue effect is a respondent estimate, not an audited loss calculation. The average number of delayed initiatives applies only to affected companies. Ownership uncertainty and the absence of a dedicated employee are separate questions and should not be added together. For a marketing implementation, these findings suggest checking who maintains inputs after launch and whether there is capacity to correct defects.

What caused the historical data silos?

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)

Among respondents describing data silos, the report identifies technology and organisational causes. The overall silo prevalence appears in the takeaways; these causes should not be applied to every surveyed organisation. A compatible interface can help move data while leaving incentives, definitions and ownership unresolved. Conversely, a shared policy cannot make a legacy integration transmit an event it does not support. Use this historical baseline to frame an implementation review that includes both the connector and the organisation operating it. The results are not a current ranking of vendor interoperability. Selections may overlap; these are not mutually exclusive causes.

Glossary

CRM: Customer relationship management system or the records it holds.
DACH: Germany, Austria and Switzerland.
Governance: Responsibilities, definitions and controls governing data use.

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.

Adverity · Mar 2026
Salesforce · Feb 2026
Validity · Aug 2026
Validity · Apr 2024