Data is a critical ingredient of any business, so it’s the factor that can either make or break it. The quality of data has a profound impact on a company’s success.
Inaccurate, incomplete, or outdated data can harm sales efforts, hindering customer acquisition, retention, and overall revenue growth. This blog post aims to shed light on the dangers of bad data and offer actionable tips to keep your data clean, thereby enhancing your sales performance.
What Is Bad Data and Why It’s So Harmful to Your Business
The term bad data refers to inaccurate, incomplete, or misleading information within a dataset or database. It can arise due to various factors such as human error, system glitches, data entry mistakes, technical issues, or issues with data collection processes.
Even if your data is of high quality at the point of capture, it can go bad over time. This decay happening within your CRM can hurt your database and render your sales team’s efforts ineffective.
Some of the consequences of having dirty and obsolete data include the following:
- Lost opportunities. According to a study from Gartner, the average financial impact of bad data on organizations is $12.9 million per year. Harvard Business Review (HBR) estimates this figure is nearly $3 trillion annually across all industries. Bad data can increase poor-quality leads, low conversion rates, poor customer satisfaction, high employee turnover, and lost revenue.
- Damaged reputation: Bad data can also harm the credibility and trustworthiness of your business. For example, sending out emails to the wrong or invalid addresses, misspelling names in emails, addressing prospects by the wrong names, offering irrelevant or outdated products or services, and failing to acknowledge past interactions due to corrupt data can annoy or offend potential or existing customers as well as erode trust and diminish customer loyalty.
- Reduced efficiency: Dirty data can also reduce the productivity and effectiveness of sales teams. For example, spending time cleaning and verifying data, correcting errors, de-duplicating entries, updating contact information, or searching for missing information can take away time from selling activities. According to a report from Forbes, up to 25% of companies’ client and prospect records contain critical data errors that directly affect sales. No wonder sales reps sell only 36% of their time when they have to spend hours trying to find the contact information.
- Misinformed decision-making. Bad data can lead to flawed decision-making processes, as decisions are based on inaccurate or incomplete information. This can result in misguided sales strategies, ineffective targeting, and missed opportunities to engage potential customers.
How to Keep Your Data Clean and Prevent It from Decaying
Data hygiene is a concept that will keep your data clean and prevent all the bad scenarios we discussed in the previous section. Make sure to give your database a good scrub regularly to minimize the odds that incorrect entries will creep up on you and sabotage your company’s growth.
Don’t forget that data decay happens quickly — even if your contact information is accurate and correct at the point of entry, it can go bad within months. People change jobs, phone numbers, email addresses, and companies fold, all these changes create dirty data.
Here are some steps to help you maintain a squeaky-clean database.
Define data quality standards
The first step is to establish clear and consistent criteria for what constitutes good data and bad data. For example, define what fields are mandatory, what formats are acceptable, what sources are reliable, and what rules are applicable for data entry and update.
Some of the required fields include:
- Full name of the contact
- Their title
- Company
- Industry
- Phone number
- Email address
- Location
- Company size
These fields provide valuable information for segmenting, targeting, and personalizing your sales outreach. They also help you avoid duplicate or incomplete records that can lead to wasted time and resources and allow everyone on the team to be on the same page regarding the types of data they should obtain from prospects.
Audit your data sources
After defining data quality standards, analyze and audit your data sources.
This means identifying where your data comes from, how it is collected, stored, and updated, and who is responsible for it. You should also evaluate the quality and relevance of your data sources and eliminate outdated, unreliable, or redundant information.
Making it easier for your prospects to share their data with you also matters. Don’t overwhelm them with too many requests and fields to fill in. They might get discouraged and leave your landing page. Instead, aim for less but better data.
How?
By asking only for the essentials and using smart software to fill in the gaps.
You can use cookies to track your prospects’ behavior and preferences over time without bothering them with endless forms. As a result, you can get more relevant, accurate, and up-to-date data without sacrificing conversions.
The simpler your data capture forms are, the easier it will be for your prospects to share their information, and the more likely they will do it and stick around.
Implement data quality tools
The next step is using software tools to help automate and streamline data quality processes.
For example, use tools to validate, standardize, enrich, de-duplicate, and clean data regularly. Check for spelling and syntax errors, format phone numbers and email addresses, append missing information, and flag or delete invalid or inactive contacts in bulk.
Also, use tools that can integrate and synchronize data across different systems and platforms.
VanillaSoft has a built-in contact database packed with millions of clean B2B leads verified in real time that you can include directly into your phone and email campaigns. This credit-based option allows you to search and select only the prospects that best fit your products or services, giving you control over your cost.
It’s also worth mentioning that it’s important to integrate various data sources and systems within your organization to maintain a unified view of your customers. By connecting CRM, marketing automation platforms, and other relevant systems, you can ensure data consistency and reduce data silos.
Segment your data
Having clean data is crucial, but segmenting your data will allow you to make the most of it. This procedure means grouping your data into meaningful categories based on criteria such as industry, location, company size, revenue, purchase history, or customer behavior.
Segmentation can help you target your prospects more effectively, personalize your messages, and measure your results more accurately.
Train and educate your sales team
Provide comprehensive training to your sales team, educate them on the importance of data quality, and ensure they understand their role in maintaining clean data. They also need to know how to enter, update, and use data correctly and consistently.
Apart from data entry best practices, such as using standardized formats, avoiding duplicates, or validating information, your sales reps should follow up on leads promptly, as speed to lead is a factor that can profoundly affect your bottom line. You should also encourage data-driven decision-making by training your sales team to leverage data insights for prospecting, qualifying, pitching, and closing deals.
In addition, emphasize the significance of data security. Consider using a Virtual Private Network to encrypt data with your CRM, along with multi-factor authentication and regular updates for enhanced data security. Foster a culture of data stewardship by rewarding good habits and holding everyone accountable for data quality.
Finally, you should foster a culture of data stewardship within your organization by rewarding good data habits, providing feedback, and holding everyone accountable for data quality.
In Conclusion
Quality data is not a luxury but a necessity for any sales team that wants to hit and exceed sales targets. Data quality can boost sales by improving lead generation, people management, cross-selling, and pricing.
But achieving data quality is not easy. It requires setting up data quality standards, organizing data in one place, enriching CRM systems with high-quality data, forming a data-friendly culture, and training your sales team. However, you can make the process less stressful with the right tools.
By following these steps, businesses can unlock the power of data analytics and drive better decision-making that leads to growth, efficiency, and effectiveness. Data quality is a competitive advantage that every business should strive for.
