A Method of Automobile Brand Clue Assignment Based on Analytic Hierarchy Process

Abstract

The invention discloses a method for allocating automobile brand clues based on analytic hierarchy process, which comprises the following steps: cleaning and storing original data; setting a strategy to preliminarily screen out data conforming to automobile brand dealers; Sorting; the data cleaning service provider uses the goal planning method to assign professional outbound personnel according to the customer's demand for sales leads, and provides the completed sales lead data to the automobile brand dealers. The present invention applies the analytic hierarchy process to the distribution of automobile sales leads with labeling characteristics, improves the utilization efficiency of the big data of automobile sales leads, achieves the expected effect of making the most of the number, and can significantly improve the automobile sales leads assigned by the data cleaning service provider The matching rate with the demand of automobile dealers can realize the precise distribution of sales lead data in the automobile industry, realize the precise marketing of the automobile sales market, meet the needs of customers for sales leads, and realize cost control.

Description

technical field [0001] The invention relates to a method for assigning automobile brand clues in the field of decision analysis, in particular to a method for assigning automobile brand clues based on analytic hierarchy process. Background technique [0002] With the enhancement of computer information processing capabilities and the leapfrog development of Internet technology, big data has entered a period of rapid development. The information hidden in big data has become an important part of corporate assets. For example, the automobile sales market uses big data to analyze and pursue information on potential car buyers, so that big data assets can be applied and realized. [0003] In the automotive industry, various web pages, mobile apps and other automotive portals store customer visit records and contact information left by customers, and through continuous accumulation, a large number of automotive sales leads have been generated. These clues reflect the attention ...

AI Technical Summary

Problems solved by technology

[0006] The above-mentioned traditional allocation method is mainly aimed at sales leads whose data volume is not too large. It cannot play an efficient role in the production capacity allocation of sales leads under big data. The defects are as follows:

[0007] The rotation method is easy to implement but cannot guarantee the conversion rate, ignoring the individual needs of customers; the shortcomings of the contribution ranking method are better, and the worse ones are worse. It cannot take into account different dealers, and it is easy to lose customers; The conversion rate of potential customer groups faced by regional distributors cannot be guaranteed; the disadvantage of the optimization method is that high-quality leads are quickly pulled away, and less attractive leads may be temporarily retained or even ignored; the blind selection method The disadvantage is that the randomness is too large, it is completely out of control, and the conversion rate is completely dependent on luck; the first strike is not suitable for the distribution of sales leads under big data; the disadvantage of the round-robin method is that the randomness is too large

Images