Recent advances in business intelligence technology have enabled data analysis tools to be used by every- day business people. Unlike the past, today's data analysis tools install quickly, provide "speed of thought" discovery and analysis, and require no statistical or database training.
Very quickly, using cutting-edge data analysis tools, the business analyst can determine which appeals are working well, and with which groups of prospects. Those appeals can be repeated and extended to other similar prospect groups, and unsuccessful appeals can be restructured or possibly just terminated
Consider the marketing manager who needs to assess the effectiveness of his or her e-mail campaign. With new data analysis tools the e-mail appeal send table can quickly and easily be loaded into memory, and then linked to the prospect table and purchase transaction table. The analyst quickly sets up a three page dashboard project – – the first page titled "appeals", the second page titled "prospects", and the third page "transactions". The analyst then pops up a couple of interactive data visualization charts on each page.
The charts on the first page would aggregate the appeals into different groups, themes, and potentially targets. This can easily be parsed out of appeal codes, appeal descriptions, or URL names. These charts would allow the analyst to select subgroups of the appeals, or to look at results on a comparative basis across the different appeal groups and types. Results come in three forms: (1) e-mail opens, (2) click through's, (3) results. The first two categories are captured in the appeal send table, the third is linked back from the “transaction” table to the “appeal sends” table based on some combination of prospect ID, e-mail, or name.
Now, 15 min. into his or her analysis, the analyst is ready to start understanding relative performance. A number of interactive data visualization charts make this kind of data analysis very easy. For example, a heat map would be a great way to show a portfolio of appeals grouped into different categories or types, sized by the number of sends, and colored by one of the relative performance metrics. This view would quickly highlight what is working well (large greener colored areas), and what is not working (any red areas, in particular large red areas). Perhaps the analyst additionally adds a couple of bar chart with goal lines which highlight in, below, and above band performance. Click on the above band performance goal line, and the heat map quickly show that selection in comparison to the entire portfolio of appeals. Insights jump quickly, understanding happens. Additionally, any of the charts on the prospect page or the transactions page will now show the characteristics and performance of the customers who had a positive response to the selected higher performance appeals.
In summary, analysis tasks that used to require complex analytical tools and training can now be undertaken by every-day business analysts and managers. And, the analysis takes an hour or two, not several days as in the past.
