Name
Abstract | ||
|---|---|---|
With the increased digitization of businesses and consumers, it is now not unusual for consumer businesses to solely engage with their customers through digital channels. Many such app driven businesses adopt a freemium business model, whereby use of the entry level service is free, whilst provision of premium content and services is for a fee. With no person to person contact with their customers, these businesses must rely on consumer data analytics to guide decisions on consumer marketing to incentivize users to adopt the premium services. This business scenario provides the motivation for this research project. In our research, using machine learning classification techniques, the team identified the key customer app usage attributes which were the main predictors of future paying subscribers. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-91716-0_31 | HCI IN BUSINESS, GOVERNMENT, AND ORGANIZATIONS |
Keywords | Field | DocType |
Data, Analytics, Computing, Machine learning, Mobile apps, finance | Freemium,Computer software,Digitization,Data analysis,Knowledge management,Entry Level,Analytics,Statistical classification,Business | Conference |
Volume | ISSN | Citations |
10923 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Mulligan | 1 | 0 | 0.34 |
| Carlito Vera Cruz | 2 | 0 | 0.34 |
| Donagh Healy | 3 | 0 | 0.34 |
| David Murphy | 4 | 0 | 0.34 |
| Margeret Hall | 5 | 8 | 3.02 |
| Quinn Nelson | 6 | 0 | 0.34 |
| Simon Caton | 7 | 159 | 16.20 |