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Using Machine Learning to Detect Customer Acquisition Opportunities and Evaluating the Required Organizational Prerequisites
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This paper aims to investigate whether or not it is possible to identify users who are about change provider of service with machine learning. It is believed that the Consumer Decision Journey is a better model than traditional funnel models when it comes to depicting the processes which consumers go through, leading up to a purchase. Analytical and operational Customer Relationship Management are presented as possible fields where such implementations can be useful. Based on previous studies, Random Forest and XGBoost were chosen as algorithms to be further evaluated because of its general high performance. The final results were produced by an iterative process which began with data processing followed by feature selection, training of model and testing the model. Literature review and unstructured and semi-structured interviews with the employer Growth Hackers Sthlm were also used as methods in a complementary fashion, with the purpose of gaining a wider perspective of the state-of-the-art of ML-implementations. The final results showed that Random Forest could identify the sought-after users (positive) while XGBoost was inferior to Random Forest in terms of distinguishing between positive and negative classes. An implementation of such model could support and benefit an organization’s customer acquisition operations. However, organizational prerequisites regarding the data infrastructure and the level of AI and machine learning integration in the organization’s culture are the most important ones and need to be considered before such implementations.

Abstract [sv]

I det här arbetet undersöks huruvida det är möjligt att identifiera ett beteende bland användare som innebär att användaren snart ska byta tillhandahållare av tjänst med hjälp av maskininlärning. Målet är att kunna bidra till ett maskininlärningsverktyg i kundförvärvningssyfte, såsom analytical och operational Customer Relationship Management. Det sökta beteendet i rapporten utgår från modellen ”the Consumer Decision Journey”. I modellen beskrivs fyra faser där fas två innebär att konsumenten aktivt söker samt är mer mottaglig för information kring köpet. Genom tidigare studier och handledning av uppdragsgivare valdes algoritmerna RandomForest och XGBoost som huvudsakliga algoritmer som skulle testas. Resultaten producerades genom en iterativ process. Det första steget var att städa data. Därefter valdes parametrar och viktades. Sedan testades algoritmerna mot testdata och utvärderades. Detta gjordes i loopar tills förbättringar endast var marginella. De slutliga resultaten visade att framförallt Random Forest kunde identifiera ett beteende som innebär att en användare är i fas 2, medan XGBoost presterade sämre när det kom till att urskilja bland positiva och negativa användare. Dock fångade XGBoost fler positiva användare än vad Random Forest gjorde. I syfte att undersöka de organisatoriska förutsättningarna för att implementera maskininlärning och AI gjordes litteraturstudier och uppdragsgivaren intervjuades kontinuerligt. De viktigaste förutsättningarna fastställdes till två kategorier, datainfrastruktur och hur väl AI och maskininlärning är integrerat i organisationens kultur.

Place, publisher, year, edition, pages
2019. , p. 12
Series
TRITA-EECS-EX ; 2019:304
Keywords [en]
Consumer decision journey, Customer acquisition Customer relationship management, Machine Learning, Organizational prerequisites, User activity, Random forest, XGBoost
Keywords [sv]
Användaraktivitet, Consumer decision journey, Customer relationship management, Kundförvärv, Maskininlärning, Organisatoriska förutsättningar, Random forest, XGBoost
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-263056OAI: oai:DiVA.org:kth-263056DiVA, id: diva2:1366207
Examiners
Available from: 2019-11-12 Created: 2019-10-28 Last updated: 2019-11-12Bibliographically approved

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