Is Your Data Clean? 10 Ways to Stop Database Errors That Cause Lead Leakage! 

Being a sales-driven company, manufacturers’ representatives are often busy acquiring leads, processing orders, managing jobs and quotes, and myriad other sales operations. 

Managing the database of all your sales operations, which includes customer databases, quotations, and bids, comes with its own challenges. The sheer volume and complexity of information that needs to be input into the database make it an arduous task. 

Organizing the vast array of information, such as client data, quote details, opportunities, etc., seems to be often neglected due to a focus on more important tasks like talking to customers. Moreover, the database is not handled by one but by several team members, each managing it with their own idiosyncrasies.

If left unmanaged or neglected due to time constraints, it can cause data discrepancies and increase the risk of errors.

The database should be such that reps are equipped to retrieve data swiftly at a moment’s notice, whether it is to address clients’ inquiries, track lead order statuses, or make high-level data decisions.

Behind these logistical hurdles, there is a more insidious threat: LEAD LEAKAGE! This happens when potential sales opportunities slip through the cracks due to inadequate data management practices and can threaten the rep’s firm’s bottom line.

A Gartner study found that most organizations surveyed estimate they lose $14.2 million annually. The financial cost of bad data is the most obvious cost, but the consequences run much deeper.

Chances are high that you’re also among the firms losing money and opportunities due to bad data! 

Now, you must have these two questions in mind:

1. What are you doing wrong? 
2. And how do we solve it? 

To answer your questions, this blog lists a breakdown of some common database issues that we face almost every day but never notice.  

We’ll also provide some easy-to-implement best practices suggested by experts that will 

help you keep your data clean on an everyday basis. 

Understanding Common CRM Database Issues

Dirty data can cost you more than sales; it can permanently damage your relationship with your customers. According to IBM, bad data costs U.S. companies three trillion dollars per year. Let’s take a look at the most common issue in this regard. 

1. Data Duplication

If you do data entry and maintenance manually, chances are high that, without proper controls, you might end up with duplicate entries for the same client or order.

For instance, consider that John Smith, a client, is listed twice in your CRM database with slightly different spellings (“John Smith” and “Jon Smith”) Or a large order from one of your customers is recorded twice in your system, perhaps due to manual entry errors or system glitches.

Now, this duplication leads to misentries. Possibly partial entries in both records lead to confusion about which contact details and orders are accurate. 

People might not know which information is correct. So, they might end up using the wrong details to contact or deal with the customer. This leads to confusion and mistakes in how you handle their orders or requests.

Think it through, If your reports and data analysis are based on duplicated information, they might not give you the right picture. 

Fixing duplicate data takes time and effort. You have to find the duplicates, figure out which one is correct, and then merge them together. It’s like cleaning up a messy room before you can use it properly.

---

💡 Pro Tip: If you use Excel, use conditional formatting to highlight potential duplicates without having to scan through the data manually.

---

Apply a data duplication plug-in to your CRM database. Use unique identifiers, such as matching names and addresses.

2. Data deletion 

Data deletion is the removal of obsolete or superfluous information from a database. It happens for various reasons, including clients suspending operations or canceling orders abruptly. 

Inactive client data occupies space within the database, potentially leading to unnecessary bloat. This can increase storage costs and slow down database performance, especially during queries and maintenance tasks. Over time, inactive client data may become outdated or irrelevant. This can degrade the overall quality of the database, making it more difficult to extract meaningful insights or perform accurate analysis.

Similarly, when a canceled order’s data remains in the system, it affects sales data and inventory estimates, which are critical to decision-making. 

Inaccurate sales data can cause incorrect estimates, resulting in inventory imbalances such as overstocking or stockouts. Such differences have a direct impact on business operations.

On the other hand, regular data deletion helps to maintain a simplified database, allowing teams to focus on active possibilities and make sound decisions based on correct information. 

3. Incomplete or Inaccurate Data

Incomplete or inaccurate data in a database can lead to significant challenges and negative outcomes for businesses.

When client records lack essential details like contact information or purchase history, it becomes difficult to personalize interactions and anticipate client needs. 

Without a complete picture of the client, reps struggle to provide tailored solutions and effectively engage with their customer base. 

This leads to lead leakage, missed sales opportunities, and reduced customer satisfaction.

Similarly, sometimes a database shows ample inventory of a particular product, but the information is outdated or incorrect. 

Relying on such inaccurate data can result in situations like stockouts or overstocking. Stockouts lead to lost sales opportunities, as customers are unable to purchase the product they need. On the other hand, overstocking ties up capital in excess inventory, leading to increased carrying costs.  

It can also damage relationships with clients and suppliers, as delays or discrepancies in fulfilling orders may impact trust and satisfaction levels.

4. Poor Data Quality

Poor quality data, like inconsistent customer records and incomplete sales data, can reduce efficiencies and impair decision-making.

If your customer database has misspelled names and outdated contact details, it can lead to, miscommunications, customer frustration, and bad branding. 

You need accurate sales data to know how well you’re doing, spot trends, strategize, and plan for the future. Without it, you’re basically flying blind, which can really hurt your business in the long run.

So, accurate data is not just important, it’s essential. It helps you make smart decisions, keep your customers happy, and stay ahead of the competition.

Data Management Practices: Do’s and Don’ts 

10 Mistakes You Must Avoid to Prevent Data Pollution

1. Avoid neglecting data quality; prioritize accuracy and consistency in your database.

2. Don’t overlook the importance of training; ensure comprehensive training to the team on CRM database management best practices.

3. Don’t ignore data governance; establish clear policies and procedures for managing data quality and compliance.

4. Avoid delaying data maintenance tasks at any cost; schedule regular maintenance activities to keep your database up-to-date.

5. Don’t rely solely on manual processes; leverage automation and technology to streamline database management tasks.

6. Don’t overlook data decay; regularly refresh and validate data to prevent decay and maintain accuracy.

7. Don’t underestimate metadata management. Effectively catalog and organize your database to enhance searchability. 

8. Don’t forget to monitor data quality regularly; track key metrics and address any issues ASAP to maintain data integrity. 

9. Don’t underestimate the importance of backups; ensure that your database is regularly backed up to prevent data loss.

10. Don’t neglect data lifecycle management. Develop a strategy to govern the creation, storage, usage, and retirement of data, optimizing resource allocation and mitigating risks of data proliferation

10 Practical Methods to Keep Your CRM Database Clean and Up-to-Date

1. Regular Data Audits

One of the easiest yet most efficient ways to keep your data clean is through regular data audits.

You can conduct routine audits of your database to identify – 

• inconsistencies,
• errors, and
• outdated information.

For example, you can implement a system of regularly reviewing customer records to ensure accuracy and completeness. This will ensure that your database remains reliable and actionable.

---

💡 Schedule a “Data Cleanup Day” on the first Friday of every month.

---

2. Standardize Data Entry

Another simple and quick way is to implement standardized formats and guidelines for data entry to maintain uniformity and consistency.

For instance, to establish clear formats for addresses, phone numbers, product names, and other data fields, use data validation features in spreadsheet software or database management systems to set specific criteria for data entry. 

Conditional formatting can visually highlight entries that do not meet specified criteria, such as invalid phone numbers or long product names. 

Format cells in spreadsheet software like Microsoft Excel to display data in a consistent manner, such as converting phone numbers to text. 

Identify key data fields within the database or spreadsheet, such as designating specific columns for addresses, phone numbers, and product names. 

Use freeze panes to keep header rows or columns visible while working with large datasets. 

Specify document formatting guidelines and provide training to users on how to format addresses, phone numbers, and product names. 

These options ensure consistent data formation and structure, making it easier to manage and analyze effectively.

3. Deduplication Tools

Technology is truly man’s best friend.

To avoid data duplication, you can always rely on deduplication tools to identify and merge duplicate records.

For manufacturers’ reps, this could mean consolidating multiple entries for the same client or order, ensuring that your CRM database remains clutter-free and accurate.

How to deduplicate data

Identifying duplicate records can be done using deduplication tools or by running queries based on criteria like customer name, email address, or order number. 

Merging the records into a single record involves selecting one “master” record to retain and merge relevant information. 

Resolving conflicts involves determining which information is accurate or prioritizing certain fields over others. 

Updating related records: ensures data consistency across the database. 

Validating changes, such as conducting post-de-duplication checks or audits, ensures the database remains accurate and functional. 

Here’s a quick tutorial video on how to do that: https://youtu.be/aYLmyUY4Dd8?feature=shared 

4. Data Validation Rules:

You can set up validation rules to enforce data accuracy at the point of entry.

Let me make it simpler for you. 

Setting up data validation rules implies creating a standard for your customers while filling in the required information. 

---

💡 Validation rules are like those little helpers behind the scenes checking what your customers type. If they miss out on it, a message might pop up saying, “Please enter a valid email address or phone number.” 

---

Data validation rules are quite crucial in data management systems to ensure data accuracy, reliability, and consistency. They enforce specific standards for data entry, preventing errors and inconsistencies. 

 In the domain of business, accurate data is essential for informed decisions and reliable analyses. Validation rules prevent the entry of invalid or incorrect data, ensuring the data remains trustworthy over time.

How to Set Up Validation Rules

To ensure data accuracy and validity;

• Identify specific requirements, such as data type, format, range, or character presence. 

• Define validation criteria based on these requirements, which can be simple or complex. 

• Implement these rules within the data entry interface or management system, using built-in features or customizing them. 

• Provide meaningful feedback to users when data fails to meet the criteria, explaining the error and how to correct it. 

• Test and refine the rules based on user feedback and real-world usage scenarios to ensure they effectively catch errors and enforce data standards.

Data validation rules also streamline the data entry process by providing real-time feedback and reducing errors. Consistent data is crucial for accurate reports, meaningful analyses, and data exchange between systems.

Some typical examples of data validation rules are: 

email validation (requires email addresses to have an “@” symbol and valid domain name) 

numerical validation (specifies acceptable numeric values for a field) 

date validation (date validation ensures valid dates are in a specified format) 

required fields (required fields mandate specific fields to be filled out) 

format validation (format validation enforces specific data entry formats.)

 5. Update Contact Information

Regularly keep updating the contact information of your customers to ensure that you can reach customers and prospects effectively.

Maintaining accurate contact information is crucial for effective communication with customers and prospects. 

It enhances customer satisfaction and improves business efficiency by ensuring timely delivery of important updates, marketing messages, and customer support, reducing wasted time and resources. 

How to Update Contact Information Regularly:

• Regular reviews of customer contact information can identify outdated or incorrect details, which can be done manually or automated through CRM software. 

• Customers can be prompted to update their information during interactions, such as email reminders or phone calls. 

• Offering multiple channels for updating information, such as online forms, email, phone, or in-person visits.

• Leveraging technology can streamline the process. For example, provide pre-filled forms with existing customer data for easy review and editing.

---

💡 Pro Tip: Some of the best ways to get your hands on updated contact information include:

Email verification: requires customers to verify their email addresses when updating their contact information online. 

Annual check-ins: conducted to review and update contact details through personalized outreach or automated email campaigns. 

Subscription Renewal: Customers must verify their contact information before renewing subscriptions or memberships to ensure up-to-date communication channels. 

Event registrations: require attendees to verify their contact details for timely updates and communications.

---

6. Segmentation and Tagging

Segmenting your database enables targeted marketing campaigns, personalized communication, and lead leakage prevention. 

By delivering relevant messages to each segment, tagging records based on criteria enhances the customer experience and develops stronger relationships. This also prevents potential opportunities from falling through the cracks.

To segment your database, define criteria like industry, location, purchase history, demographics, behavior, interests, or engagement level. 

Collect data from various sources and segment the database into distinct groups. Tag records based on additional criteria and use automation tools or CRM software to streamline the process and ensure consistency across segments.

For example, you can create segments for customers based on their 

• industries like healthcare, technology, finance, retail, etc. 

• based on their geographic location, such as country, region, or city. 

• purchase history, such as frequent buyers, first-time purchasers, or customers who have made large-ticket purchases.

• level of engagement with your brand, such as active users, dormant users, or high-value customers.

This allows you to deliver localized marketing campaigns and promotions.

7. Data Cleansing Automation

You can implement automated data cleansing processes to streamline the removal of outdated or redundant information.

For instance, you can schedule regular automated clean-up tasks to remove inactive or obsolete records, keeping your CRM database clean and efficient.

Here’s a quick tutorial on the steps of data cleaning: Data Cleansing Steps Explained 

With a CRM, you can set up rules and triggers to automatically clean and standardize data as it’s entered into the system, saving time and ensuring data consistency.

8. Remove Inactive Records

You must periodically remove inactive records or disengaged contacts from your database.

Regularly removing inactive records from your database improves database efficiency, reduces clutter, and enhances data quality. 

This ensures marketing efforts are targeted towards active contacts, generating value for your business and enhancing the effectiveness of marketing campaigns and customer interactions.

For reps, this might involve archiving clients who haven’t placed orders in a certain timeframe. This keeps your database focused on active and engaged contacts.

---

💡 Pro Tip: You can archive clients who haven’t placed orders within the past six months or archive contacts who haven’t attended any events or webinars in the past year. Remove those who haven’t opened marketing emails or clicked on links in the past year, and remove those who haven’t visited the website.

---

9. Train Your Team

Comprehensive training on CRM database management improves data quality, efficiency, and decision-making by ensuring team members understand the importance of maintaining clean, up-to-date data. 

This training streamlines processes, reduces errors, and empowers them to contribute to data-driven decision-making within the organization.

Offer guidance on data entry, validation, and maintenance to ensure that everyone contributes to keeping the database clear. 

In order to do that, assess team knowledge and skill levels, identify areas needing additional training, and develop comprehensive training materials. 

Conduct hands-on workshops, tailor training sessions to specific roles, and schedule regular updates to ensure everyone remains proficient in their tasks. 

Tailor training sessions to specific roles, such as sales representatives or marketing specialists, to ensure they understand the importance of database maintenance.

10. Backup Data Regularly

Regular backups are crucial for preventing data loss, ensuring business continuity, and preventing lead leakage. 

The good thing about automated backups is that they enable quick data recovery, restoring databases to their previous state with minimal downtime. 

This helps maintain the integrity and confidentiality of customer data, even in the event of data loss or corruption.

Key takeaways

Clean data is vital for manufacturers’ reps, providing accurate insights, efficient operations, effective targeting, and improved customer relationships. 

It aids in making informed decisions on sales strategies, inventory management, and customer relationships. 

Data duplication and data deletion issues can result in lead leakage and inaccurate reporting. 

To prevent these, it’s essential to implement best practices and regular data cleaning. 

Data management practices should prioritize accuracy, consistency, training, clear policies, maintenance, automation, and backups. 

Regular data audits, standardizing data entry, deduplication tools, and validation rules can help maintain a clean and accurate database.

FAQs?