Since moving back into academia a couple of years ago, I've been thinking about digital marketing research and in particular, digital research methods. My belief is that the digital age requires a rethink on how we approach and treat data, and that there's a need to develop a new and innovative research methodology.
I advise my dissertation students (of social sciences, digital media, and marketing) to expand their literature search to include grey literature (particularly, reputable industry surveys, research reports, and blog posts). These sources are often more current than academic literature, and complement theories with for example latest social media usage statistics, mobile adoption, changing digital consumer behaviour (such as conversational search) or the effect of Google's and Facebook's algorithm updates on small businesses' ability to compete effectively online.
When it comes to Methodology, I favour an experimental approach largely driven by the nature of the business or research problem. In my PhD (Webfilm Theory), I used a mixture of Actor-Network theory and Discourse Analysis, and the beauty of ANT was that it allows you to pretty much follow any nodes. It's a meta-methodology!
For researching social media communications and online communities, I recommend to my students Kozinet's Netnography as a good starting point. And in terms of methods of data collection, I teach them how to use real-time data (e.g. from Facebook / YouTube analytics, search data, or Google Analytics).
Let's demonstrate this novel approach to research with an example involving the analysis and evaluation of search data!
Digital Research Methods - Search Data
Non-traditional data can be collected from search engines, for example, the terms people use to find information using Google.
This could also involve analysing and evaluating the html markup, content and link profile of other sites, to understand how they perform in the search engines (essential for competitor analysis, if you're a business using digital to sell online).
Below are two methods from search engine marketing that demonstrate why search data can be highly relevant to academic or business research.
1. Customer search analysis
Knowledge of consumer behaviour in relation to online search is essential to developing content and strategy that meets customer needs.
This isn't just relevant for market research - I've recently conducted an analysis into consumer search behaviour around suicide (in a post titled How to Kill Yourself). Try using this type of data instead of a focus group to uncover consumer behaviour around your research topic.
Primary, e.g. search volumes / related search / auto-complete (Google suggest), etc.
- Google Suggest Has Wildcards?! KW Planner Hides Data?! Crazy
- A Beginner’s Guide to Google Suggest For Marketers and SEO
- How to do Keyword Research in 90 Minutes
2. Competitor SEO Analysis
Knowledge of a competitor’s SEO strategy and profile is essential to discovering competitive advantage.
This applies primarily to business and marketing research, not so much social sciences. A while ago, I conducted a (light-hearted) competitor analysis of London's first Cat Cafe vs. the UK's first Cat Cafe (in Totnes) and discovered that the former's success had largely been driven by digital PR / use of social and the resulting earned links.
Of course you could extend the competitor analysis to social and online community research - just supplement the search analysis with Netnography on their Facebook / Twitter / any other social network pages.
Primary, i.e. html / meta data; Domain authority / backlink profile
Digital Research Methodology - Conclusion
The digital age has resulted in an explosion of 'big data'. Large-scale real-time customer data is now readily available for free, and search and social media data are excellent primary sources to conduct research, both in the social sciences and business and marketing (e.g. consumer behaviour).
For academic researchers at all levels (including student dissertations, PhDs and post-docs), a comprehensive search of sources must increasingly include grey literature and real-time sources such as industry-specific blog posts and other content available in the public domain. The risk of ignoring grey literature is that the resulting research will be at best out of date, and at worst, simply wrong.
Last but not least, a modern digital researcher must have a digitally native mindset. S/he must be able to master digital research and survey tools (such as Google forms / Google apps), collect and evaluate digital and social analytics data (such as Facebook Insights, Google Analytics), and overall be very comfortable with technology.
Above all, s/he must be a citizen of the Internet!