Salesforce's 2024 State of Sales report finds reps spend only 28% of their week selling. This guide unpacks the ROI of fixing the bad data that wastes time.

According to the Salesforce State of Sales, 5th Edition, sales reps spend only 28% of their week on actual selling activities. [1] The remaining 72% is consumed by non-selling tasks, many driven by poor data quality, such as manual data entry and prospect research. [1] Gartner research quantifies the cost of this inefficiency, estimating that poor data quality costs organizations an average of $12.9 million annually. [10, 17, 18, 22, 25] The 2024 benchmarks show a clear link between high-quality data, increased selling time, and higher quota attainment.

TL;DR

Sales reps spend just 28% of their week selling, per the 2024 Salesforce State of Sales report. [1]
Gartner estimates the average annual cost of poor data quality is $12.9 million per organization. [10, 14, 17]
B2B contact data decays at a rate of 22.5% to 70.3% per year, making continuous verification essential. [6, 11, 13]
In 2024, average B2B sales quota attainment dropped to 43%, down from 53% in 2020, highlighting widespread system inefficiencies. [12]
High-performing sales organizations are 1.5x more likely to prioritize data hygiene to improve outcomes. [4]

The Productivity Drain: Reps Spend Only 28% of Their Week Selling

The latest Salesforce State of Sales, 5th Edition report, which surveyed over 7,700 sales professionals globally, confirms a persistent and costly inefficiency: sales representatives spend only 28% of their week on direct selling activities. This means a staggering 72% of their time is consumed by non-revenue-generating tasks, a figure that has remained stubbornly high despite massive investments in sales technology. This productivity drain is not a minor issue; it represents the single largest operational bottleneck for most sales organizations. According to a Forrester Activity Study that tracked 3,031 reps, the average salesperson loses the equivalent of 37 selling weeks per year to these ancillary activities. This lost time directly correlates with performance, as top-performing reps consistently spend more of their week selling (34%) compared to their lower-performing peers (23%). The vast majority of a representative's week is spent grappling with internal processes, administrative duties, and data-related chores that, while necessary, pull focus from the core functions of engaging prospects and closing deals.

A detailed breakdown of a representative's 40-hour workweek reveals exactly where this time goes, with prospect research and manual data entry emerging as significant time sinks. The Forrester study shows that account research and call preparation consume about 14% of a rep's week (5.6 hours), while CRM data entry and pipeline updates take up an even larger 17% (6.8 hours). These two activities alone account for more than a full day of non-selling work each week. This manual effort is often a direct consequence of poor data quality and disconnected systems. Research from ZoomInfo and Everstage indicates that reps can spend over 27% of their time working with inaccurate contact information, leading to wasted effort on bounced emails and calls to contacts who have already left the company. This constant data maintenance, which should be an automated background process, instead becomes a primary, time-consuming task that prevents reps from building relationships and advancing opportunities through the pipeline, as detailed in a Salesmotion analysis.

The administrative burden is further compounded by significant tool sprawl, with the average B2B sales representative now using approximately eight distinct tools to manage their workflow and close deals. While many of these tools are powerful in isolation, their lack of integration creates data silos and forces reps to spend valuable time toggling between applications and manually reconciling information. This constant context-switching is more than just an annoyance; it directly contributes to the 72% of time spent on non-selling tasks. According to a Gartner Sales Survey from 2024, 42% of sales reps report feeling overwhelmed by the sheer number of tools they are expected to use, and this feeling has a direct impact on results. The same research indicates that sellers who feel overwhelmed by their tech stack are 45% less likely to achieve their quota, making a compelling case for the aggressive consolidation and simplification of the sales technology ecosystem, a trend highlighted by a recent ZDNET article.

High-performing sales organizations are actively addressing this productivity crisis by prioritizing data hygiene as a foundational requirement for leveraging modern technologies like artificial intelligence. These leading teams understand that AI is only as effective as the data it is trained on, and they are more likely to invest in cleaning up their CRM and standardizing data formats to ensure their AI models deliver accurate recommendations. This focus on data quality is critical; without it, AI tools can amplify existing chaos, leading to flawed predictive lead scoring and personalized outreach that misses the mark entirely. The strategic advantage is clear: organizations that successfully implement AI, built upon a foundation of clean data, see tangible results. A 2024 survey of 5,500 sales professionals found that teams using AI were significantly more likely to have experienced revenue growth in the past year (83%) compared to teams without AI (66%), demonstrating that a commitment to data quality is a direct investment in revenue performance.

Task Category	Specific Task	Percentage of Week	Hours per 40-Hour Week	Source
Active Selling	Calls, demos, negotiations	28%	11.2	Salesforce / Forrester
Non-Selling	CRM data entry & pipeline updates	17%	6.8	Forrester
Non-Selling	Internal meetings & syncs	15%	6.0	Forrester
Non-Selling	Account research & call prep	14%	5.6	Forrester
Non-Selling	Email triage & general admin	14%	5.6	Forrester
Non-Selling	Scheduling & logistics	12%	4.8	Forrester

The Financial Impact: Quantifying the $12.9 Million Cost of Bad Data

Gartner's long-standing benchmark estimates that poor data quality costs organizations an average of $12.9 million per year, a figure that remains a critical business concern through 2026. [2, 4, 6] This substantial financial drain is not the result of a single, catastrophic failure but rather the slow, cumulative effect of widespread operational friction. [7] These costs manifest as wasted marketing spend on campaigns targeting outdated customer profiles, lost sales opportunities from incorrect contact information, and significant operational time consumed by employees manually correcting and reconciling data across disparate systems. [7] For example, a finance team might spend days manually reconciling ledgers because two source systems disagree, or a sales representative might waste hours pursuing leads based on faulty CRM data. [2] According to research from the MIT Sloan Management Review, companies lose between 15% and 25% of their total revenue due to these inefficiencies. [9, 12, 13] The problem is pervasive, with one analysis from MIT Sloan research showing that 47% of newly-created data records contain at least one critical error that will impact downstream processes, demonstrating how these costs become deeply embedded in day-to-day operations. [9]

The macroeconomic impact of bad data is even more staggering, with a widely cited estimate suggesting it drains as much as $3.1 trillion annually from the U.S. economy alone. [3, 13] This figure, originally attributed to IBM research from 2016, highlights how localized data errors aggregate into a massive drag on national productivity. [3] The cost is borne by decision-makers, managers, and data scientists who must constantly accommodate incorrect or incomplete information in their daily work. [17] This translates directly to lost revenue at the company level, with research from MIT Sloan consistently indicating that organizations lose 15-25% of their revenue directly because of poor data quality. [9, 12, 14] For a mid-sized B2B company, this percentage represents a quiet, six-figure loss distributed across dozens of small, incorrect decisions, such as killing marketing channels that are actually working or misallocating sales resources. [12] The issue is compounded by high rates of data duplication, which industry analyses place at 10-30% of all records in business systems, creating widespread confusion and operational inefficiency. [9] These figures from sources like Gartner and MIT Sloan underscore that the financial consequences are not abstract but are felt in flawed analytics, wasted engineering time, and poor downstream decisions. [14, 15]

Beyond the direct operational and financial costs, poor data quality fundamentally erodes trust in analytics and actively delays strategic initiatives like the adoption of artificial intelligence. According to the Salesforce "State of Sales" report, 51% of sales leaders with active AI initiatives cite that disconnected systems and the resulting bad data are a primary factor slowing down or limiting those projects. [5, 8, 11] This is a critical barrier, as an AI agent cannot provide a relevant point of view or a useful recommendation if it cannot access a complete and accurate customer history. [11] Recognizing this, 74% of sales teams with AI are now prioritizing data hygiene as a foundational requirement for success. [11] This erosion of trust has a broad impact; a 2025 report from Precisely found that leadership's distrust in data for decision support has risen to 67%. [13] When executives and managers stop trusting the dashboards and reports they are given, they revert to manual spreadsheets and fragmented data silos, quietly undermining the entire data investment and making it impossible to build the data-driven culture necessary for modern competition. [2, 7] This makes data quality not just an IT problem, but a central obstacle to future growth and innovation.

Data Decay: Why 1 in 4 of Your Contacts Is Wrong by Year-End

The widely cited industry benchmark for B2B data decay is 22.5% per year, a figure originating from early MarketingSherpa research and consistently validated by platforms like HubSpot. This metric, which translates to a monthly decay rate of approximately 2.1%, means that in a database of 10,000 contacts, at least 2,250 records will become materially inaccurate within just twelve months. Material inaccuracy is not trivial; it refers to data so outdated that it results in a bounced email, a failed call, or outreach that reaches the wrong person entirely. This continuous degradation stems from natural, uncontrollable changes in the business world, such as personnel moves and corporate restructuring. The core problem for revenue teams is that this decay happens silently. A CRM system does not flag a contact who changed roles three months ago, leading sales and marketing teams to execute campaigns based on a database that looks complete but is fundamentally misleading. Without a system for continuous verification, a static contact list becomes a significant liability, actively undermining the efficiency it was meant to create.

While the 22.5% figure provides a reliable baseline, the actual rate of data decay can be far more severe, with some studies finding it can reach as high as 70.3% annually. This accelerated decay is particularly prevalent in high-turnover industries like technology and professional services, where employee mobility is rampant. In these fast-moving sectors, the average B2B buyer may change jobs every 18 to 24 months, compared to a broader private sector median tenure of 3.5 years as of January 2024. This rapid churn means that a significant portion of a database can become obsolete in a very short time. For instance, recent analyses from late 2024 have shown email address decay alone accelerating to 3.6% in a single month. This extreme rate highlights the inadequacy of periodic, quarterly data clean-ups. By the time a team runs its scheduled refresh, a substantial percentage of its target accounts may have already experienced significant personnel changes, rendering entire outreach strategies ineffective. The 70% decay figure represents the upper limit for businesses operating in dynamic markets, where continuous data enrichment is not a luxury but a fundamental requirement for survival.

The primary drivers of this relentless data decay are specific, predictable events that render contact and company information obsolete. Job changes are the single largest contributor, as a single move simultaneously invalidates a contact's title, work email, and direct-dial phone number. According to the U.S. Bureau of Labor Statistics, the median tenure for private sector workers was 3.5 years in January 2024, a figure that drops to under two years in high-growth tech sectors. Email addresses, a critical channel for B2B communication, are particularly volatile, with research showing they become outdated at a rate of 23-30% annually. Some analyses show this can be even higher, with an annual decay rate of 37.3% for emails and 42.9% for phone numbers. Company-level events like mergers, acquisitions, and rebrands further compound the problem, with sources like Dun & Bradstreet estimating that 20-30% of firmographic data becomes obsolete each year. Even a simple domain change following a rebrand can invalidate every email address at a target account. These compounding factors ensure that without active, ongoing maintenance, a B2B database quickly becomes a repository of incorrect assumptions, leading to wasted resources and missed opportunities.

Data Point	Annual Decay Rate (%)	Primary Cause	Impact on Sales/Marketing
Job Title / Role	25-66%	Promotions, company changes, career moves	Incorrect personalization, wrong value proposition, reaching non-decision-makers.
Email Address	23-37%	Job changes, company domain changes, IT policies	High bounce rates, damaged sender reputation, failed campaign delivery.
Phone Number	18-43%	Job changes, office relocations, shift to remote work	Wasted SDR time, low connect rates, inability to follow up on leads.
Company Firmographics (e.g., name, size)	20-30%	Mergers, acquisitions, rebrands, bankruptcy	Inaccurate account scoring, poor segmentation, targeting non-existent companies.
Contact's Company	22.5% (as part of overall decay)	Employee turnover (median private tenure is 3.5 years)	Contacting individuals who have left the company, wasting outreach efforts.
Technology Stack	20-30%	Adoption of new tools, abandonment of old software	Pitching irrelevant integrations, incorrect competitive positioning.

How Data Quality Directly Impacts Quota Attainment

Persistently low quota attainment rates highlight a systemic issue in B2B sales, with data from the RepVue Cloud Sales Index Q4 2024 showing that average attainment fell to 43.14%. This marks a challenging environment where a significant majority of sales organizations are failing to meet their targets. According to a joint study by Sales So and Pavilion, only 30% of B2B representatives successfully hit their quota in 2024. This widespread underperformance is not merely a reflection of sales talent or strategy, but is deeply rooted in operational inefficiencies. A primary driver of this problem is poor data quality, which forces revenue teams to operate with outdated or incorrect information. This foundational weakness leads to wasted effort, misaligned targeting, and missed opportunities, directly contributing to the gap between sales goals and actual performance. The financial and operational drag is significant, with Gartner research estimating that poor data quality costs organizations an average of $12.9 million annually, a figure that has remained consistent in recent analyses.

The direct link between data quality and sales productivity is stark, with research from ZoomInfo and Everstage revealing that sales representatives spend 27.3% of their time grappling with inaccurate data. This lost time, which translates to approximately 546 hours per representative annually, is consumed by non-revenue-generating activities such as correcting flawed CRM records, pursuing contacts who have changed roles, and navigating bounced emails and disconnected phone numbers. This 'bad data tax' is one of the largest hidden costs within a sales organization, directly eroding the time available for active selling. The Salesforce State of Sales, 5th Edition, which surveyed over 7,700 sales professionals, found that reps spend only about 30% of their week on actual selling activities. The remainder is lost to administrative tasks and research, much of which is necessitated by unreliable data, creating a structural drag on revenue capacity and making it nearly impossible for teams to reach their full performance potential.

High-performing sales organizations differentiate themselves by prioritizing data integrity, which translates into superior conversion rates and revenue growth. Research shows that companies leveraging accurate B2B contact data achieve 66% higher conversion rates compared to those with compromised databases. This performance lift is a direct result of focusing sales efforts on qualified, in-market buyers with verified contact information, minimizing wasted outreach. Furthermore, findings from the Salesforce State of Sales, 5th Edition, indicate that high-performing organizations, defined as those with significant year-over-year revenue increases, are more likely to focus on improving data accuracy and fostering cross-functional alignment. By establishing a foundation of reliable data, these leading companies empower their sales teams to act as trusted advisors, personalize their outreach, and accurately identify cross-sell opportunities, ultimately driving predictable and sustainable growth.

The Local Business Gap: Why Incumbent Data Vendors Fail SMBs

Large B2B data providers like ZoomInfo and Apollo.io are architecturally optimized for corporate contacts, creating a significant data gap for companies targeting local, service-based small businesses. These platforms build their databases by scraping sources that favor corporate footprints, such as LinkedIn profiles, press releases, and SEC filings. [11] This methodology renders a five-person roofing company or an independent restaurant owner, who often lack a curated LinkedIn presence or a frequently updated corporate website, effectively invisible. [11, 31] Consequently, sales teams report that when prospecting for local businesses, 60-70% of their target list can come back empty or incorrect. [10] While ZoomInfo offers deep firmographic data for mid-market and enterprise accounts, its coverage thins out dramatically for companies with fewer than 50 employees and outside of North America. [31] Similarly, while some tests show Apollo winning for SMB coverage compared to ZoomInfo, its reliance on similar LinkedIn-centric sources means it still misses a substantial portion of the local business market, a structural weakness acknowledged in multiple 2026 vendor analyses. [2, 10, 34]

A directory-first data sourcing model directly addresses the visibility gaps left by incumbent vendors, providing verified owner contacts for local businesses with superior accuracy. Instead of relying on web-scraped corporate profiles, this approach prioritizes sources where small business owners actually appear, such as state license boards, local chamber of commerce directories, and Google Maps listings. [3] This methodology is crucial because a high percentage of local business decision-makers do not maintain active LinkedIn profiles, making them inaccessible to platforms like ZoomInfo and Apollo. [20] The result is a more reliable dataset of plain-facts leads containing the business name, owner's name, and verified contact details. For instance, a 2026 analysis of B2B cold calling benchmarks from Cognism's 200,000-call dataset shows that using verified mobile direct-dial data can increase phone connection rates from a baseline of 8-12% to between 18-22%, effectively doubling outreach efficiency. [14] This contrasts sharply with the performance of generic lists, where connect rates for unverified data can be as low as 4-6%. [33] By focusing on verified, directory-sourced contacts, companies can achieve significantly higher engagement and bypass the data decay that renders up to 30% of typical B2B email lists outdated annually. [23]

Positioning verified, plain-facts leads as a strength against 'AI-slop tools' provides a clear competitive advantage by prioritizing data quality over opaque, often misleading metrics. While many platforms promote AI-driven features like predictive fit scores, these tools are only as effective as the underlying data they analyze, a principle Forrester highlighted in its Q1 2026 Data Quality Solutions Wave report. [24] If the input data from a CRM is messy or incomplete, which is common in the SMB space, an AI model will simply amplify those flaws, a classic “garbage in, garbage out” scenario. [17, 29, 40] In fact, Forrester's research shows that data quality is the primary factor limiting GenAI adoption in B2B settings. [17] Many AI scoring models are a black box, making it difficult to understand why a lead received a certain score. [26] This lack of transparency can erode trust, especially when a model assigns a high score to a prospect who is clearly a poor fit. [29] In contrast, a lead defined by a verified owner, a confirmed business address, and a direct-dial phone number with a 99% connection rate offers undeniable, actionable value without the need for complex, and often unreliable, AI-generated justifications.

Offering backup contacts for every lead delivers a crucial advantage for sales teams targeting small businesses, where decision-making roles are often consolidated and key individuals are difficult to reach. In a typical SMB, the owner may also be the primary operator, service manager, and head of finance, making a single point of contact a significant bottleneck. If that primary contact is unavailable or unresponsive, the opportunity is often lost. Providing a verified secondary contact, such as a general manager or a senior family member involved in the business, effectively doubles the chances of initiating a conversation. This approach directly mitigates the challenge of data decay, where B2B contact information becomes outdated at a rate of 2.1% per month, or over 22% annually. [39] According to a 2026 analysis by The Bridge Group, a US B2B sales development representative can lose up to 35% of their day to manual dialing and non-productive time, much of it due to low connect rates. [14] By ensuring a fallback option for every lead, businesses not only improve their connect rates but also build a more resilient pipeline that is less susceptible to the frequent personnel changes and role-shifting common in the SMB ecosystem.

A Modern Framework for Evaluating B2B Data Vendors

A modern framework for evaluating B2B data vendors begins with a rigorous assessment of their billing and contractual models, demanding flexibility over rigid, long-term commitments. Per-credit pricing, a common model, can create perverse incentives for vendors to maintain lower accuracy, as customers must repeatedly purchase credits to replace outdated or incorrect contacts. [7] An analysis from January 2025 highlights that if a vendor's revenue is tied directly to data consumption, there is less financial motivation to achieve accuracy rates above a certain threshold, as higher quality would mean fewer replacement credit sales. [7] Therefore, it is critical to seek out vendors like UpLead, which offers a 95% data accuracy guarantee and refunds credits for any bounced contacts, a commitment written into their 2026 contracts. [12] This approach contrasts sharply with models that rely on credit expiry and forced annual renewals, which can inflate costs significantly. [19] The most effective evaluation prioritizes vendors offering transparent, self-serve, month-to-month contracts or subscription models that align vendor success with customer outcomes, rather than trapping customers in auto-renewing agreements with datasets of questionable quality.

Data sourcing transparency is a non-negotiable component of vendor evaluation, as the methodology directly impacts match rates and data integrity. Vendors who rely on a single, proprietary database, such as ZoomInfo or Lusha, often exhibit strong coverage in their core regions but can have significant gaps elsewhere. [6, 14] An analysis from June 2026 found that single-source providers typically achieve match rates of only 55% to 70% on a standard B2B contact list. [6] In stark contrast, a multi-source waterfall enrichment process queries multiple providers sequentially, dramatically improving results. This method, employed by platforms like Cognism, involves collecting data from diverse sources like press releases, public registries, and third-party providers, then layering verification and compliance checks. [2, 5] Research from August 2025 shows that waterfall enrichment can elevate match rates to 80% or even higher, with some systems reaching 93%. [3] This multi-layered approach not only increases the likelihood of finding a valid contact but also cross-checks information to filter out the stale data that plagues static, single-source databases. [3]

Evaluating the granular quality of the data itself requires moving beyond vague assurances and demanding quantifiable metrics, particularly for email deliverability. Many vendors display a simple checkmark or a generic "verified" tag, which provides little real insight into whether an email will actually reach its destination. A truly transparent vendor provides a numerical email deliverability percentage and is clear about its verification process, such as whether it uses live SMTP checks or merely infers addresses based on patterns. [25] The consequences of poor deliverability are severe; a high bounce rate, often defined as anything over 15%, can damage your sender reputation and lead to wasted sales efforts. [23] When evaluating a vendor, it is crucial to run a test campaign with a sample of at least 100-200 records to measure the hard bounce rate yourself, as this provides a more accurate picture than relying on third-party verification tools that cannot always assess catch-all domains. [9, 19] A vendor's policy on incorrect data is also revealing; ask if they provide credits or replacements for bounced emails, as their response indicates their true commitment to quality. [18]

Finally, a vendor's platform must be judged on its usability and how it integrates into a modern sales workflow, prioritizing targeted searching over inefficient bulk list processing. The most effective data platforms, such as ZoomInfo's SalesOS, frame the user's action as a 'search' for specific, high-intent accounts and contacts, not a bulk 'run' of an entire list. [8, 22] This paradigm shift enables sales development representatives to focus on quality over quantity, identifying ideal customer profile (ICP) matches with precision. [18] Integration friction is a silent killer of ROI; a platform that does not seamlessly sync with your existing CRM and sales engagement tools will force your team to spend valuable time on manual data hygiene instead of selling. [18] Therefore, a thorough technical review is essential. Ask about native integrations, API capabilities, and automated data refresh cycles to ensure the data flows into your systems without creating additional work. [18] A platform that empowers users to build highly segmented, accurate lists based on firmographics, technographics, and intent signals is fundamentally more valuable than one that simply provides a massive, undifferentiated contact dump.

Frequently Asked Questions

What is the ROI of high-quality B2B data?

High-quality B2B data delivers a strong return on investment by directly boosting revenue and cutting operational costs. Companies using high-quality data have seen up to a 66% increase in lead conversion and marketing ROI. Organizations that invest in clean, verified databases can increase sales productivity by around 25% and shorten sales cycles by nearly 30%. Conversely, some analysts estimate that companies lose between 15-25% of their revenue due to the inefficiencies caused by poor data quality. [1]

How much time do sales reps waste on bad data according to Salesforce?

Salesforce research indicates sales reps spend only 28% of their week on actual selling activities, with the remaining 72% lost to non-selling tasks. [12, 16] A significant portion of this non-selling time is spent wrestling with the effects of bad data, such as correcting records, chasing down wrong numbers, and researching incomplete contacts. [11] Other research quantifies this further, showing reps can lose 27.3% of their time, or 546 hours annually, just from working with inaccurate contact data. [16, 19]

What percentage of B2B data is inaccurate or decays each year?

B2B contact data decays at a rate of 22.5% annually, meaning nearly one in four of your contacts could be inaccurate by year-end. [8, 9, 10] This decay is driven by predictable events like employees changing jobs, companies being acquired, and phone numbers being updated. Some sources show this decay rate can be even higher, with certain studies suggesting it can reach up to 70% per year. This constant degradation makes regular data verification and enrichment essential to maintain a reliable CRM.

How does B2B data quality affect sales quota attainment?

Poor B2B data quality is a primary driver of missed sales quotas because it creates massive inefficiency for sales teams. When reps spend their time on non-selling tasks like fixing bad data, it directly reduces the hours they can spend actively selling, which is already as low as 28% of their week. [12] This inefficiency is critical, as recent data from 2024 shows average quota attainment has fallen to around 43%, with some reports indicating 91% of sales organizations missed their targets. [23] Clean data allows reps to focus on the right leads and activities, which directly correlates to a higher likelihood of hitting revenue targets. [17]

What is the average cost of poor data quality for a business?

The average cost of poor data quality for an organization is $12.9 million annually, according to research from Gartner. [1, 5, 10] This financial impact stems from several areas, including operational inefficiency, flawed analytics leading to bad business decisions, and wasted marketing and sales efforts. [5] Beyond the direct costs, poor data quality also leads to lost revenue, with some estimates suggesting businesses lose 15-25% of their revenue to data-related inefficiencies. [1] In the United States alone, the total economic cost of bad data is estimated to be as high as $3.1 trillion per year. [6]

Last updated: July 2026

B2B Data Quality ROI: 2024 Salesforce Benchmarks