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Мой личный склад идей

#119 · Published: 2025-12-25 11:47 UTC

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Original post

Are people submitting inquiries on your website but then not answering the phone? And if they do answer, they often say they left no message at all? Perhaps some of you or your acquaintances have encountered this problem. This issue has been gaining momentum for quite some time. Sometimes it's the work of competitors, other times it's the result of unscrupulous advertising platforms that "bribe" users to leave junk inquiries. Main problems with junk inquiries: 1. The time managers spend processing these inquiries 2. Good inquiries are left waiting while time is spent on bad ones 3. Wasting additional budgets on SMS/WhatsApp/email notifications 4. Burnout of managers from "empty" work And this is in addition to already paying for each inquiry. But I think I’ve found a way to minimize resource expenditure on such junk inquiries. While experimenting with a new technology, I came across the possibility of analyzing user behavior on the website, identifying patterns with AI and machine learning that correspond to bot behavior or those intentionally leaving false inquiries. Just as I can identify patterns of junk inquiries, I can also detect patterns of truly valuable inquiries. This allows for a better understanding of your audience—those who actually bring you money. As a result, this could become a very valuable tool for automatic lead scoring and direct integration with your CRM. I’m currently considering a pilot project idea and looking for interested participants to test it out. If you're interested, send a message — let’s discuss.
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Summary

The post addresses the common issue of junk inquiries received through websites, which often lead to wasted time, resources, and increased operational costs for businesses. These inquiries may be generated by competitors or unscrupulous advertising platforms that incentivize users to leave irrelevant messages. The main problems include managers spending excessive time processing these inquiries, delays in responding to genuine leads, unnecessary expenses on notifications via SMS, WhatsApp, or email, and staff burnout from handling non-productive work. To combat this, the author proposes a solution involving AI and machine learning technologies that analyze user behavior on websites to identify patterns indicative of bots or false inquiries. This approach aims to distinguish valuable inquiries from junk, enabling better lead scoring and integration with CRM systems. The author is exploring a pilot project to test this innovative method and invites interested parties to participate and discuss further, promising a more efficient way to manage and prioritize genuine customer inquiries.

Keywords

junk inquirieswebsite inquiry managementAI lead scoringmachine learning customer analysisbot detection onlinereducing inquiry spamCRM integration toolslead qualification technologywebsite user behavior analysisdigital marketing efficiencycustomer inquiry optimizationautomated lead filtering

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