In 2009, the scientific community treated search and social media marketers to what seemed like a never-ending supply of meaty and game-changing research projects, covering everything from information retrieval, to personalized search, to integrated SERP analysis, to studying user behavior on social networks. I decided to compile and share a list of my favorite Search and Social Media Marketing Research Projects of 2009. Hopefully, these research papers will inform and inspire your marketing efforts going into 2010.
By the way, if after reading all the papers on this list you find that you're jonesing for more, here are two great resources for marketers to find research papers on social media marketing and search engine marketing:
So fire up your neurons and let's dive into the search and social marketing eggheaded goodness.
Top Social Media and Search Marketing Research Projects of 2009
- Sponsored Search Research Papers
- Search Query Analysis Research Papers
- Personalized Search Research Papers
- Social Media Research Papers
- The BuyerSphere Project
- Affiliate Marketing Research Papers
- Web Analytics Research Papers
Does the presence of organic search results positively impact the click-through rates of PPC ads, and vice-versa? According to NYU Stern Professors Anindya Ghose and Sha Yang they do. This study assesses the impact of simultaneous organic and paid listings on consumers, advertisers and search engines. Anindya Ghose theorizes that search engines can strategically modify organic search rankings to boost revenue from paid search.
NYU Stern Professors Anindya Ghose and Sha Yang are at it again, challenging the conventional wisdom that the first position is the most profitable in paid search. Based on a six-month panel dataset of several hundred keywords which were collected from a large nationwide retailer that advertises on Google, this study quantifies the effect of keyword type and length, ad position and the relevancy of the advertiser's landing page on consumer search and purchase behavior.
This paper by Penn State Professor Jim Jansen and Professor Amanda Spink or the Queensland University of Technology investigates the impact of integrating paid and organic search results into a single SERP listing. The premise underlying this research is that searchers are chiefly interested in relevant results, yet have a negative bias towards PPC ads. By separating paid vs organic listings, the engines may be doing a disservice to users as it might not direct them to relevant websites. For more on Jim Jansen, read our recent interview on the the current state and future of search marketing.
Using a proposed "robust query classification system that can identify thousands of query classes, while dealing in real time with the query volume of a commercial Web search engine," the team comprised of members Yahoo! Research, PUC-Rio, UCLA and Professor Tong Zhang from Rutgers University focus their research on rare search queries, and believes that their methodology will lead to better matching of online ads to rare queries and a better user experience.
Michael Bendersky and Bruce Croft, Department of Computer Science, UMass Amherst, analyze long tail search queries with the goal of identifying the characteristics of the most commonly occurring types of queries. Their research looks at the distribution of query lengths in large scale search logs, and addresses the issues involved with using them effectively in a search engine.
Jaime Teevan (MIT’s Computer Science and Artificial Intelligence Laboratory), Susan T. Dumais and Eric Horvitz (Microsoft) examine the "explicit relevance judgments and implicit indicators of user interest" to try and better understand the variation in what people using the same query are searching for. And given that personalized search was just integrated by Google, this paper is definitely worth reading.
Jaime Teevan and Susan T. Dumais team up again this time with Daniel J. Liebling from Microsoft Research to examine variability in user intent, using both explicit relevance judgments and large-scale log analysis of user behavior patterns. The team attempts to characterize user queries and with this data builds predictive models to identify queries that can benefit from personalization in search engine results.
David Harry, President of Reliable SEO, purveyor of search knowledge at the HuoMah SEO blog and one of the foremost authorities on personalized search, released this must-read paper on psearch. Harry performed rounds of testing to examine Google personalized search and rankings flux, looking at transactional and informational query spaces and isolating geo-related factors.
Using a case study approach, Penn State professor Jim Jansen and team, which included Abdur Chowdury (Twitter and Illinois Institute of Technology) and Penn State student Kate Sobel, analyzed more than 150,000 tweets (focusing on range, frequency, timing, and content) and found that 19% of tweets contain mention of a brand, while that roughly 20% of tweets are information-seeking and providing.
Social media use is exploding and services like Facebook and Twitter have enabled new forms of human collaboration. Researchers from the University of Maryland, Human Computer Interaction Lab and Microsoft (Derek L. Hansen, Dana Rotman, Elizabeth Bonsignore, Nataša Milić-Frayling, Eduarda Mendes Rodrigues, Marc Smith, Ben Shneiderman, Tony Capone) take a look at how Social Network Analysis tools can help practitioners make better sense of social media data.
The BuyerSphere Project is a major B2B research initiative analyzing buyer behavior, which was conducted by Enquiro Research with input from Google, Business.com, Covario, Marketo and DemandBase. The five BuyerSphere Project research papers are all fantastic reads.
- BuyerSphere Part 1: Mapping the BuyerSphere
- BuyersSphere Part 2: Integrated Persuasion: Online and Offline
- BuyersSphere Part 3: Maximizing Online: Leveraging Your Online
- BuyersSphere Part 4: Building Business Online: Your Digital Persuasion Portfolio
- BuyersSphere Part 5: The Rise of the Digital Native
Despite's its ubiquity, affiliate marketing has seen fewer studies than most other forms of online marketing channels. This research paper by members of the eBay Partner Network (Will Martin-Gill, Steve Hartman, Umesh Lalchand, Jarrod Schwarz and Chad Wehrmaker) features insights on performance data analysis which suggests that advertisers may be able to improve ROI and performance by structuring their affiliate programs to better calculate and compensate for quality delivered by affiliates rather than quantity.
This research paper proposes a concept of next generation Web Analytics, known as Web Analytics 2.0. Daniel Waisberg (Head of Analytics at Easynet Search Marketing and Avinash Kaushik(Analytics Evangelist for Google) advocate a holistic approach to website analysis, where they consider several sources of knowledge: website data, multichannel analysis, testing, competitive analysis, and customers’ voice.