{"id":4942,"date":"2024-04-29T07:29:31","date_gmt":"2024-04-29T11:29:31","guid":{"rendered":"https:\/\/orgzit.com\/blog\/?p=4942"},"modified":"2024-04-29T07:29:32","modified_gmt":"2024-04-29T11:29:32","slug":"is-your-data-clean-10-ways-to-stop-database-errors-that-cause-lead-leakage","status":"publish","type":"post","link":"https:\/\/orgzit.com\/blog\/is-your-data-clean-10-ways-to-stop-database-errors-that-cause-lead-leakage\/","title":{"rendered":"Is Your Data Clean? 10 Ways to Stop Database Errors That Cause Lead Leakage!\u00a0"},"content":{"rendered":"\n

Being a sales-driven company, manufacturers\u2019 representatives are often busy acquiring leads, processing orders, managing jobs and quotes, and myriad other sales operations. <\/p>\n\n\n\n

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. <\/p>\n\n\n\n

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.<\/p>\n\n\n\n

If left unmanaged or neglected due to time constraints, it can cause data discrepancies and increase the risk of errors.<\/p>\n\n\n\n

The database should be such that reps are equipped to retrieve data swiftly at a moment\u2019s notice, whether it is to address clients\u2019 inquiries, track lead order statuses, or make high-level data decisions.<\/p>\n\n\n\n

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\u2019s firm’s bottom line.<\/p>\n\n\n\n

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.<\/em><\/p>\n\n\n\n

Chances are high that you’re also among the firms losing money and opportunities due to bad data! <\/p>\n\n\n\n

Now, you must have these two questions in mind:<\/p>\n\n\n\n

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  1. What are you doing wrong? <\/li>\n\n\n\n
  2. And how do we solve it? <\/li>\n<\/ol>\n\n\n\n

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

    We’ll also provide some easy-to-implement best practices suggested by experts that will <\/p>\n\n\n\n

    help you keep your data clean on an everyday basis. <\/p>\n\n\n\n

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    Table of Contents<\/p>\n