Bad Data: What It Is & How to Fix It to Boost Sales Performance

Sales leaders know that good data is the lifeblood of a high-performing sales organization. Yet according to a study by Experian, the average company estimates that 30% of their data is inaccurate. For sales teams, bad data can have devastating consequences – from missed quotas to lost revenue and damaged customer relationships.

In this post, we‘ll take a deep dive into the world of bad data – what it looks like for sales teams, why it happens, and most importantly, proven strategies for fixing it. Armed with these insights, you‘ll be well on your way to building a data-driven sales engine that delivers results. Let‘s get started.

The High Cost of Bad Data for Sales Teams

While bad data can wreak havoc in any department, it‘s especially problematic for sales. Consider these all-too-common scenarios:

• Inaccurate contact data means sales reps waste hours trying to connect with prospects, only to find out they‘ve left the company or changed roles.
• Incomplete buyer profiles lead to generic, irrelevant sales outreach that fails to resonate.
• Dirty pipeline data results in missed forecasts and unpredictable revenue.
• Siloed data makes it impossible to get a 360-degree view of the customer journey.

The list goes on. According to a report by DiscoverOrg, bad data costs sales and marketing teams 550 hours and as much as $32,000 per sales rep each year. Yikes.

But it‘s not just about wasted time and money. Bad data undermines trust and alignment between sales and other go-to-market teams like marketing and customer success. It makes coaching and performance management nearly impossible for sales leaders. Worst of all, it can ultimately damage the customer experience and brand reputation.

Common Data Quality Pitfalls for Sales Teams

So what causes bad data to infiltrate sales databases? Here are some of the most common culprits:

1. Human error in CRM data entry

When sales reps are juggling dozens of opportunities and under pressure to hit quota, it‘s easy for data entry mistakes to happen. One mistyped email address or missing phone number can render a record useless.

2. Lack of standardized data governance

Without clear rules and processes for how data should be entered and managed, inconsistencies and errors will abound. For example, one rep might use "Company Inc." while another enters "Company Incorporated" for the same account.

3. Incomplete or missing data

Key fields like industry, company size, or buyer persona are left blank, making it difficult to properly segment and target accounts. Critical opportunity details never get logged in the CRM.

4. Siloed data sources

Sales data lives in a variety of systems – from the CRM to marketing automation to customer support platforms. When these systems don‘t talk to each other, records become fragmented and insights get lost.

5. Dirty import data

Purchased lead lists or tradeshow contact dumps are integrated into the database without proper cleansing, resulting in a swamp of duplicate, invalid, or irrelevant records.

6. Stale data

Data decays at a rate of 2-3% per month as contacts change jobs, companies go out of business, and phone numbers are disconnected. Sales databases are full of dead ends.

Strategies for Improving Sales Data Quality

The good news is, there are proven ways to combat bad data and set your sales team up for success. Implementing these best practices will go a long way:

1. Define and enforce data standards

Create a "data dictionary" that clearly documents your standards for key fields, formats, and processes. Make adherence to these standards part of your sales team‘s job description and performance evaluation.

2. Simplify and automate data entry

Use data validation rules, required fields, and pick lists in your CRM to minimize human error. Leverage automation to populate data from email and calendar activities. Integrate web forms that feed directly into your database.

3. Enrich and append data

Invest in third-party data services to fill in missing fields like company size, industry, and buyer intent signals. Use data append services to regularly update and cleanse your database.

4. Conduct regular data audits

Make data quality reviews a regular part of your sales operations cadence. Identify and remove duplicate records, standardize inconsistent entries, and purge stale data.

5. Break down data silos

Integrate your CRM with other go-to-market systems to create a single source of truth. Use a customer data platform (CDP) to unify records across touchpoints.

6. Educate and incentivize the sales team

Make sure reps understand the downstream impact of bad data. Provide training and resources to help them follow best practices. Consider tying a portion of variable comp to data quality KPIs.

7. Use sales-specific data quality tools

Invest in technology built for sales ops, like data cleansing, sales intelligence, and CRM automation solutions. Look for AI capabilities that can predict and resolve issues.

Measuring Sales Data Quality

Of course, improving data quality requires being able to measure it. Here are some of the most important data quality metrics for sales teams to track:

• Database health indicators like bounce rates, deliverability rates, duplication rates, and data completeness.
• CRM adoption metrics such as number of sales activities logged, percentage of fields populated, and record update frequency.
• Data-driven selling KPIs like lead-to-opportunity conversion rates, average sales cycle length, and sales productivity metrics.

To keep a pulse on these metrics, build regular data quality reporting into your sales management dashboards and review cadence. Set goals and celebrate progress to keep the team motivated.

Better Sales Data, Better Sales Performance

The impact of high-quality data on sales performance cannot be overstated. Organizations that prioritize data quality and management enjoy:

• More accurate sales forecasting and pipeline management. Complete and up-to-date opportunity data means sales leaders can predict revenue with greater confidence.

• Improved sales and marketing alignment. When everyone is working from the same clean, consistent data, it‘s much easier to execute coordinated plays across the buyer‘s journey.

• Higher conversion rates. Enriched data allows sales teams to craft hyper-relevant messaging and tailor outreach to the buyer‘s unique needs and interests. Engagement soars.

• Faster sales cycles. Reps can quickly identify and prioritize the accounts most likely to convert based on behavioral and firmographic data signals. No more wild goose chases.

• More productive sales teams. With less time wasted chasing bad data and more contextual insights at their fingertips, reps can spend more time actually selling.

Becoming a Data-Driven Sales Organization

At the end of the day, fixing bad data is more than just a one-time project. It requires an ongoing commitment to data quality and a culture of data-driven decision making from the top down.

Sales leaders must champion the importance of data and provide the tools, training, and incentives for reps to maintain healthy data hygiene. Sales operations must be empowered to establish and enforce data governance standards. And the entire sales organization must embrace a mindset of continuous improvement when it comes to data management.

The payoff is worth it. According to a McKinsey study, companies that use data-driven sales techniques increase revenue by up to 15% and see a 10-20% reduction in sales cycle time.

"In today‘s dynamic and data-driven business landscape, maintaining high quality sales data is not just a nice-to-have, it‘s a must-have for any organization that wants to remain competitive and drive predictable revenue growth."
– John Smith, VP of Sales Operations at Acme Corporation

The Future of Data-Driven Sales

As we look ahead, one thing is clear – data will only become more important to sales success. With the rise of AI and machine learning, organizations will be able to leverage vast amounts of data to drive ever-more precise and personalized selling strategies. Predictive analytics will help sales teams anticipate customer needs and proactively reach out at just the right moment.

But none of this will be possible without a foundation of clean, consistent, and comprehensive data. Sales leaders who prioritize data quality today will be well-positioned to capitalize on the data-driven selling opportunities of tomorrow.

Ready to take your sales data quality to the next level? Check out our roundup of the top sales data quality tools and our step-by-step guide to conducting a sales data audit. Your future revenue growth depends on it.