Learning Centre

Machine Learning for Marketing: User centred design of a decision support system

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Wiberg, Rikard
dc.contributor.advisor Nyman, Mattias
dc.contributor.author Deleuze, Laura
dc.date.accessioned 2018-09-03T12:43:18Z
dc.date.available 2018-09-03T12:43:18Z
dc.date.issued 2018-08-20
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33765
dc.description.abstract Owing to the Machine Learning spread, in particular the Econometric modelling progress, marketers are now able to daily monitor and control the marketing effect of their campaigns, thus optimising their advertising investments. Although several decision support tools and Data analysts consultants currently provide marketers with these data-driven insights, only few manage to understand the algorithms outcomes and act upon the extracted insights on their own. This master thesis thus focuses on understanding marketers behaviours and job to identify where Econometric modelling would be relevant for them to use on a daily basis. To do so, this thesis studied an existing algorithm. The user research consisted in a manifest and a latent content analysis of semi-structured interviews conducted as rigorous Contextual Design Inquiries with 11 marketers familiar with Econo- metric modelling. The extracted patterns were then validated and eventually produced four representative set of marketers work-models and personas. They also grounded corresponding validated design guidelines to help designers build a user-centred tool delivering data-driven insights that marketers can extract alone and autonomously act upon on a regular basis. To deliver understandable and actionable data-driven insights, this user research concludes that Econometric modelling outcomes must be provided through a portable, pertinent and task compliant user-centered tool. First, it should dis- play a centralised overview of how their marketing strategies are currently doing on the market. Second, the tool should contribute to optimise their marketing budget to reach the marketers company business goals. Third, it has to enhance the communication between the marketers, their media agency and their top- management team. By realising these three jobs, the data-driven tool would then constitute a major business asset for the marketers company. Not only would it dramatically increase the efficiency and profitability of marketing activ- ities along with the business managers trust in marketing benefits, but it would also empower marketers to accurately control their budget marketing effect and negotiate their costs down with media channel publishers for instance. en
dc.format.extent 150 + 78
dc.language.iso en en
dc.title Machine Learning for Marketing: User centred design of a decision support system en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword marketing strategy en
dc.subject.keyword econometric modelling en
dc.subject.keyword decision support systems en
dc.subject.keyword user centred design en
dc.subject.keyword Personas en
dc.subject.keyword work-models en
dc.identifier.urn URN:NBN:fi:aalto-201809034890
dc.programme.major Human Computer Interaction and Design fi
dc.programme.mcode SCI3020 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Nieminen, Marko
dc.programme Master's Programme in ICT Innovation fi
local.aalto.electroniconly yes
local.aalto.openaccess no

Files in this item

Files Size Format View

There are no open access files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive

Advanced Search

article-iconSubmit a publication