Case

Amazon

A paper by three Amazon engineers describes item-to-item collaborative filtering for shopping recommendations. Used to explain recommendation systems.

Industry: E-commerce (global)
Frameworks applied: Data-driven marketing loop Personalisation versus customisation: two five-stage processes

Frameworks applied

This case is read through the frameworks below.

Data-driven marketing loopWhen a marketing team holds plenty of customer data yet still decides on gut feel, how can it link collection, analytics, automation and testing into one continuous loop?
Personalisation versus customisation: two five-stage processesWhen a marketing team designs one-to-one offers, should the firm personalise them from customer data or let customers customise the offer themselves?

Sources

References

Listed A–Z by author surname, Harvard style. A source with a DOI links to https://doi.org/…; one without links to its page or says plainly that it has no DOI.

Linden, G., Smith, B. and York, J. (2003) ‘Amazon.com recommendations: item-to-item collaborative filtering’, IEEE Internet Computing, 7(1), pp. 76–80.doi:10.1109/mic.2003.1167344 Source page