CRM Data

Why data quality is critical for CRM success

Bad data is costing U.S. companies trillions of dollars every year. Some estimates put the average cost of bad data for a company at 12% of annual revenue.[1] Data that should be used to direct business strategy, enhance marketing messages, and guide sales tactics is ineffective if it’s riddled with missing form fields, outdated addresses, inactive emails and wrong phone numbers.

How does data go bad?

No one intends to fill their CRM with bad data, it happens over time.

  1. Human error: Consumers sometimes make typos when inputting their own contact information. Sales professionals might make typos when entering a prospect’s contact information.
  2. Bots: the rise of artificial intelligence is a double-edged sword. Although AI can be a great tool for marketers, it has also led to an influx of bad data from bots entering ‘opt-in’ campaigns.
  • Decay: Every hour, about 521 business addresses will change, 872 telephone numbers will change and 1,504 URLs will be created or updated. The natural churn of modern business means that CRM data will decay over time.

This is how bad data eats away at revenue:

Sales team productivity

Inaccurate or incomplete data means that sales professionals need to spend more time researching and confirming contact information for their leads than actually selling. In fact, Forbes estimates that sales professionals spend less than 36% of their time selling.[2] No one wants their sales team wasting time calling disconnected numbers or guessing email addresses. When it comes to sales, wasted time is wasted money. A CRM that’s stocked with quality data allows reps to spend more time on the phone and sending emails that will be delivered, shorting sales cycles and improving revenue.

Brand example:

A secure payment processing company noticed that, despite the sales team hitting the phones hard, their year-over-year ROI was down. They reached out to Data Axle to develop a strategy that would refresh their data reserves and revive the sales pipeline for their telemarketing team. The plan included a goal of doubling current conversion rates and lowering abandonment rates (leads that have to be abandoned because no one picks up the phone) by providing pre-verified data and removing suspect data. Suspect data is a common issue with unverified data – generally manifesting as form fields with a string of meaningless numbers or letters, created to avoid having empty fields for an entry. Removing suspect data would allow their sales team to work more efficiently and drive better results, since sellers wouldn’t need to waste time sifting through inaccurate data.

Data Axle deployed a solution to match, verify, cleanse, and append the brand’s CRM data. Removing bad data and filling empty form fields allowed the sales team to work more quickly and efficiently, as they didn’t have to waste time sifting through dirty data or researching contact information. Data Axle also employed a comprehensive data verification process, which included human verification of all business phone numbers, as well as the history of the phone calls over the last 12 months. Instead of wasting time following up on businesses that had moved or closed, as the company’s sales team had in the past, the team focused on targeting companies that were confirmed and verified within the last 12 months. Having access to more accurate, human-verified business data allowed the company to double their sales conversion rates, lower lead abandonment rates, and increase ROI.

Employee engagement and satisfaction

Loss of revenue is just as frustrating for sales members as it is for the companies that employ them. Commission-based compensation structures mean that sales professionals also lose out if the company they work for doesn’t stay on top of data quality. The best sales professionals don’t want to waste time researching contact information online, filling in empty form fields in their CRM, or constantly dialing into wrong numbers. Dr. Sean McPheat, of MTD Sales Training, said, “Many sales people would rather make an additional ten calls per day or go out on another two prospect visits than update their [CRM] records, especially as a lot of their commission is riding on the results that they achieve.”[3]

Bad data will lead to an unhappy and disengaged salesforce. It can even drive the best closers to leave, if the salesperson thinks they will be able to make more sales at a company with hotter leads.

Brand Example:

A point-of-sale hardware company wanted to cut down on the amount of bad data in their CRM caused by human error. The sales team was unhappy with the number of duplicates in the system, and the lack of accurate phone numbers. The sales team was expected to have three hours a day of call time, but they complained that was impossible since the numbers they were dialing only worked a quarter of the time. The company invested in Data Axle for Salesforce that would allow for regular updates to their data while also preserving the history of the entry. This integration filled out the missing phone numbers with verified ones and removed the need for the sales team to research and manually add numbers into their CRM. The company reported that call times increased, and the entire sales team was able to meet their 3 hours of calling every day for the past three months. Employee satisfaction rose, as the team had the tools to succeed and morale rose knowing that they had the support of management to meet their goals.

Informed decision-making

CRM data provides companies with actionable insights to improve marketing campaigns, sales strategy and customer acquisition and retention. Research has shown that data-driven companies are 23 times more likely to acquire customers, 6 times more likely to retain those customers, and 19 times more likely to be profitable.[4] This is because data-driven companies are able to use their data to determine who their customers are, what motivates them to buy, which communication channels they are most responsive to, and what types of messaging they find compelling.

However, data-driven companies are completely reliant on the data they have at hand to define their growth strategies. If the data that powers these strategies is inaccurate, companies won’t be able to reach their business goals.

Brand Example: The Society for Diversity

The Society for Diversity helps companies see diversity and inclusion as a tool for success. However, the Society needed some help itself. The company was relying on cold calling their database to drive membership sales, but they were falling short of their goals. They brought in a data analytics company, who did a deep dive into their CRM data. The data analytics company found that only one sale in the past 12 months had come from a cold call, even though each member of the sales team was spending 15 hours a week making cold calls.

The sales team was under the assumption that their efforts were working because they were fielding inbound calls from prospects who went on to purchase products, however, because they were not properly tracking their sales and marketing efforts in their CRM, the brand missed the fact that all the purchases were coming from subscribers to the company’s email list not from cold calls. The Society for Diversity devoted more resources to a content-based strategy and saw conversions increase by 300% – providing their sales team with more qualified leads and saving wasted time on ineffective cold calls.[5]

bad data joke

While the need for quality data is obvious to some, many marketers still need to build a business case for it.

Marketing ROI

Inaccurate CRM data is a blow to the marketing department’s ROI and makes it nearly impossible for marketers to execute a successful campaign. A July 2019 Forrester report found that marketers reported a number of negative consequences from poor data quality, including inaccurate targeting, lost customers and wasted media spend.[6] Marketers sending direct mail campaigns can lose thousands of dollars if prospects addresses are wrong. Marketers also lose opportunities to build revenue when email campaigns are filled with inaccurate or undeliverable email addresses. Bad email addresses can also affect a company’s sender reputation and inboxing rates because they lead to high email bounce rates. Quality CRM data allows marketers to deliver the right message to the right person consistently.

Brand Example: DB Squared

Business finance company, DB Squared, had a problem – their emails were bouncing at a rate of 60%. To put that in perspective, the benchmark for bounces is 2%, and anything higher than 10% is a red flag. In order to get that bounce rate down, DB Squared’s marketing team decided to work on increasing inbound leads – with the rationale that the inbound leads would provide correct contact information and inject their CRM with accurate data for future campaigns. They devised an elaborate content marketing strategy to entice inbound leads, combined with an aggressive social media marketing campaign. Through this initiative, DB Squared increased their website pageviews by 415%, saw a 253% increase in the number of sessions, and drove a 310% increase in unique users. They also reduced their email bounce rate from 60% to 20%.[7]  As a smart way to further reduce their bounce rate, the brand could address inaccurate email addresses in their CRM by working with a data provider to verify and update the brand’s email address.

Artificial Intelligence and Machine Learning Adoption

Brands are eager to apply artificial intelligence (AI) and machine learning (ML) technologies to improve personalization, drive revenue growth, and reduce costs in marketing and operations. However, AI and ML algorithms call for accurate data in order to be effective. AI and ML technologies draw upon data to make predictions and recognize patterns; if data is missing or inaccurate, the technology will make incorrect predictions, the patterns will be skewed and the models they build will fail.

Even powerhouse electronics company, IBM, is worried about data quality issues as they ramp up AI deployments. Arvind Krishna, IBM’s senior VP-Cloud and Cognitive Software, has said that 80 percent of work involved in AI projects is data preparation. In an interview with The Wall Street Journal, Krishna mentioned it could take a business an entire year of “collecting and cleansing data” before it would be ready for AI deployment. He went on to say that IBM is pursuing around 20,000 AI projects worldwide and data quality is the main reason the projects are moving at a snail’s pace.[8]

The solution – clean up the dirty data in your CRM

Data Axle can help brands ditch the dirty data. The application integrates seamlessly with Salesforce, delivering clean, human-verified data right into your instance in real-time. Data Axle matches and verifies data to ensure a clean list, free of outdated information and fills empty form fields with verified data, giving your Salesforce data quality a boost.


Investing in data quality is key for cultivating a productive and happy salesforce, boosting marketing ROI and enabling big picture decision-making.

Does your CRM data need a boost? Learn how Data Axle can help.


Natasia Langfelder
Content Marketing Manager

As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.