Full life cycle tracking method and system based on big data

Through the full life cycle tracking method based on big data, the clue conversion rate and operation quality are monitored in real time, and the problem of difficult to track clue life cycle and evaluate conversion rate in the existing technology is solved, and the clue utilization efficiency and operation quality are improved.

CN119692868AInactive Publication Date: 2025-03-25CHENGDU SHENDI LINGHANG ENERGY TECH CO LTD
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Patent Information

Application Number
CN202510191678.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-21
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

It is difficult for existing technology to fully track the lead life cycle, accurately evaluate conversion rates and optimize, resulting in low clue utilization efficiency and reduced operational effectiveness.

Method used

The full life cycle tracking method based on big data is adopted. By obtaining the full life cycle data of the target product clues, tracking the clue database in real time, calculating the clue conversion rate, connecting the product operation database for operation quality evaluation, predicting the probability of abnormalities, and optimizing the cycle nodes.

Benefits of technology

It has achieved comprehensive real-time monitoring of the lead life cycle and comprehensive assessment of operation quality, early detection of operational problems, and improved clue conversion rate and overall operational quality.

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Abstract

The invention discloses a full life cycle tracking method and system based on big data, and belongs to the field of operation management, and the method comprises the steps: obtaining a full life cycle based on a target product clue; obtaining a clue database, obtaining P-1 clue conversion rates corresponding to the first P period nodes, performing integration, and outputting an initial clue set; performing target product search, establishing a clue search space, outputting an operation quality sample index, and performing operation quality evaluation index prediction on the initial clue set based on the clue search space to obtain a first abnormal probability; and obtaining a first optimization period node according to the first abnormal probability, and performing tracking feedback on the first optimization period node. The technical problem that the clue life cycle is difficult to comprehensively track and the conversion rate is difficult to accurately evaluate and optimize in the prior art is solved, and the technical effects of comprehensively knowing the clue conversion condition, finding the operation problem as soon as possible and improving the operation quality of a product line are achieved.
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