Data Analysis
A Fortune 500 insurance company hired an analyst team to assess how well their 2,000 independent agents used online marketing tools. The company had provided a one-page web template to all the analysts the previous year to, at a minimum, have a single result in a search for the name of their agency so people could find their contact information and perhaps a basic idea of the products they offered. A year later, the company hoped to discover that most of their agents had moved on to develop a more robust web presence. To conduct this analysis, the team worked with the company's strategic marketing team to identify what the company wanted its agents to use. For instance, was there a simple description of each of their products, was there a one-click method to get an insurance quote, were their links to the agents' Facebook, Instagram, Twitter, or other online social media account, had there been something posted on these accounts in the past week, etc.? Once these "robust web presence" parameters had been established, the analyst team painstakingly reviewed all 2,000 websites and used a spreadsheet to mark an "X" where each agent had each desired item on their website. These were not weighted but only tabulated to give each agent a "web use score". These scores were analyzed to determine how close each agent was to a "perfect" score, meaning they had all the desired items. A one-page snapshot was developed for each agency when the analysis was completed. It told them their overall score was out of the total possible, showing what they were doing well and where they needed to improve.
This was a classic case of using internal data for a business intelligence process to improve internal processes to increase market share. It is also a case of developing new indicators and a new analytic method to determine an outcome that me the client's requirements. The project could have continued and become a competitive intelligence project by assessing what share of their geographic market each agent had captured the previous year. This would likely have yielded more evidence for the company to persuade agents that more effective use of web-based tools could result in a competitive advantage...but ONLY if that is what the data indicated. One firm, for example, is one of the oldest in this company's agent "family". It is located within one block of the corporate headquarters. This agency only used a single template page and had a massive geographic market share. Its market was older, wealthier, traditional people with generational histories of using this firm. Their marketing was nil. It was based on families bringing new drivers who needed auto insurance, newlyweds who wanted to explore life insurance, etc., for their market. This model worked for them, but it is unclear whether it would remain sustainable as their customers became more web savvy. Thus, it is important to look at outliers, who may tell more of the story but not assume they tell the whole story.
Data Analysis
Whether your data is qualitative or quantitative, you need to analyze it before you can write about it. When you analyze your data, pay particular attention to the following items.
Patterns
What types of patterns can you find in your data? Where are there places where people seem to agree or disagree with each other?
If you have quantitative data, this is a good time to do at least some basic mathematical analysis: what is the mean
(average value), median (middle value), and/or mode (most often represented value)? If you can, running statistical analyses makes good sense here, too.
If you have qualitative data, patterns can still be observed/noted by examining where similar
answers were given or where similar behavior patterns were exhibited.
If you conducted a survey or completed observations, it is probably a good idea to do some deeper analysis here, too, in order to compare how certain subsets of your sample
group responded or behaved. For example:
- Did the first-year college students report feeling differently about a variable than the seniors did?
- Did the men and women behave in any discernibly different manners in your observations?
These types of comparisons can often lead to moments of insightful analysis in the discussion of primary research results.
Oddities/Outliers
As you find patterns in your data, you will also find data points that don't follow those patterns. This is normal; not all respondents or interactions will answer or function in the exact same way. Your task as a researcher is to analyze these data points.
- Are they random outliers?
- Are there patterns to the outliers as well?
- Are there other possible explanations for their existence in your data set?
You especially want to explain any variations when they can lead to further insights into your results or possible future research projects.
(Dis)Connections to Prior Research
One final step you want to take with your data is to see how well it matches up with prior research. This requires that you go back to the articles that informed your literature review and compare and contrast your results with those that prior studies
collected. Take note of where and how your results converge and diverge with prior data sets, and identify reasons for those similarities and differences.
Please note that differences are not necessarily negative: you likely introduced one
or more new variables or gathered a different sample set in one or more new ways. If your results vary from prior studies' results, then, yours may indicate the possibility of having gained some new knowledge about the topic.
Likewise, confirmation
of prior results can also be helpful: replicating past studies' results is sometimes, in itself, a worthwhile endeavor, as various scientific branches have begun confronting what has been termed the Replication Crisis. In short, many published studies
have not had their results replicated by other researchers, causing their findings to become doubted.
Lastly, if your results' connections to prior research are not clear, that may indicate any number of issues, including that your secondary
research may not have been focused well enough or that your primary study may have design and/or methodological flaws. The limitations applicable to your study - remember that all studies have limitations - should therefore be included in your discussion
of your results.
Source: Sarah Wilson and Trey Bagwell, https://courses.lumenlearning.com/olemiss-writ250/chapter/data-analysis/
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