Abstract: Lead to recognition time delay data, leads and sales data, and lead distance data for leads arriving at a system within a time range can be used to determine a time variable close rate and a distance adjustment thereof which, in turn, can be used to generate a predicted close rate for each lead. Using the predicted close rate for each lead, an address drive distance for each lead, and dealer-level discount information, a logistic regression model can be trained to generate a score for each combination of a dealer, make, and zip code. Each score indicates a probability of closing a sale between a lead in the zip code and the dealer in the combination. Scores thus generated can be used by a computing facility in recommending dealers to a consumer in the zip code who has expressed an interest in purchasing a vehicle of the same make.

Abstract: Lead to recognition time delay data, leads and sales data, and lead distance data for leads arriving at a system within a time range can be used to determine a time variable close rate and a distance adjustment thereof which, in turn, can be used to generate a predicted close rate for each lead. Using the predicted close rate for each lead, an address drive distance for each lead, and dealer-level discount information, a logistic regression model can be trained to generate a score for each combination of a dealer, make, and zip code. Each score indicates a probability of closing a sale between a lead in the zip code and the dealer in the combination. Scores thus generated can be used by a computing facility in recommending dealers to a consumer in the zip code who has expressed an interest in purchasing a vehicle of the same make.

Abstract: A vehicle data system receives a lead submission through a website supported by the vehicle data system and determines, utilizing a machine learning model, a user value for a lead associated with the lead submission. The user value represents a probability of the lead purchasing a vehicle from a dealer through the website. The vehicle data system determines a user lifetime value for the lead based at least on the user value for the lead. Subsequently, the vehicle data system obtains clickstream identifiers from a search engine and assigns a corresponding user lifetime value to each clickstream identifier. The vehicle data system aggregates the clickstream identifiers and corresponding user lifetime values in a single file and communicates the single file to a search server for consumption. The user lifetime values are utilized by the search engine in search engine marketing processes.

Abstract: Lead to recognition time delay data, leads and sales data, and lead distance data for leads arriving at a system within a time range can be used to determine a time variable close rate and a distance adjustment thereof which, in turn, can be used to generate a predicted close rate for each lead. Using the predicted close rate for each lead, an address drive distance for each lead, and dealer-level discount information, a logistic regression model can be trained to generate a score for each combination of a dealer, make, and zip code. Each score indicates a probability of closing a sale between a lead in the zip code and the dealer in the combination. Scores thus generated can be used by a computing facility in recommending dealers to a consumer in the zip code who has expressed an interest in purchasing a vehicle of the same make.