AI-Assisted Lead Scoring
Nygård, Robert (2019)
Nygård, Robert
Åbo Akademi
2019
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2019051015178
https://urn.fi/URN:NBN:fi-fe2019051015178
Tiivistelmä
Companies often gather a tremendous amount of data, such as browsing behavior, email activities and other contact data. This data can be used to estimate a contact's purchase probability using predictive analytics. The purchase probability can then be used by companies to solve different business problems, such as optimizing their sales department's call list. The purpose of this thesis is to study how machine learning can be used to perform lead scoring. Historical behavioral data is used as training data for the classification algorithm, and purchase moments are used to limit the behavioral data for the contacts that have purchased a product in the past. Different ways of aggregating time-series data are tested to ensure that limiting the activities for buyers does not result in model bias. Model performance was estimated using cross-validation. The results suggest that it is possible to estimate the purchase probability of leads using supervised learning algorithms, such as random forest, and that it is possible to obtain business insights from the results using visual analytics.
Kokoelmat
- 512 Liiketaloustiede [433]