Affiliate advertising is changing the way that people do business online. Retailers are now offering incentives to third-party publishers for advertising goods and services on their behalf in order to capture more of the market. Online advertising spending has already over taken that of traditional advertising in all other channels in the UK and is slated to do so worldwide as well [1]. In this highly competitive industry, the livelihood of a publisher is intrinsically linked to their web site performance. Understanding the strengths and weaknesses of a web site is fundamental to improving its quality and performance. However, the definition of performance may vary between different business sectors or even different sites in the same sector. In the affiliate advertising industry, the measure of performance is generally linked to the fulfilment of advertising campaign goals, which often equates to the ability to generate revenue or brand awareness for the retailer. This thesis aims to explore the correlation of web site evaluation metrics to the business performance of a company within an affiliate advertising programme. In order to explore this correlation, an automated evaluation framework was built to examine a set of web sites from an active online advertising campaign. A purpose-built web crawler examined over 4,000 sites from the advertising campaign in approximately 260 hours gathering data to be used in the examination of URL similarity, URL relevance, search engine visibility, broken links, broken images and presence on a blacklist. The gathered data was used to calculate a score for each of the features which were then combined to create an overall HealthScore for each publishers. The evaluated metrics focus on the categories of domain and content analysis. From the performance data available, it was possible to calculate the business performance for the 234 active publishers using the number of sales and click-throughs they achieved. When the HealthScores and performance data were compared, the HealthScore was able to predict the publisher's performance with 59% accuracy.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:630459 |
| Date | January 2014 |
| Creators | Miehling, Mathew J. |
| Contributors | Buchanan, William J. |
| Publisher | Edinburgh Napier University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://researchrepository.napier.ac.uk/Output/7250 |
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