Using Omniture SAINT Classifications to generate more usable insights

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In reports where there are many unique & distinct values, it can sometimes be quite hard to analyse and extract usable insights out of it due to clutter. In such instances, grouping & classifying the data into major and easily understood categories is all it needs to take the analysis to the next level. For instance, the raw data on natural search/pay-per-click keyword report in telecommunications vertical makes more sense when viewed through the lens of holistic categories such as plans, prepaid, broadband and many more.

In Omniture SiteCatalyst, SAINT Classifications is one means to accomplish this. SAINT Classifications enable you to classify any custom variables (ie. Traffic or Conversion variables) into any numbers of categories and sub-categories that you have pre-defined.

In this example, we will SAINT classify natural search keywords.

Step #1:

Extract the data from the custom variable report. In this case, the natural search keywords in one of the custom conversion variables (ie. eVar).



Step #2:

Here we want to break it down by 2 levels: Keyword Type (ie. Brand or Generic) and Keyword Category (ie. Prepaid, Plans, Broadband, Mobile, Handsets, Content, Service, etc).



Step #3:

Set up the classifications in SiteCatalyst by following these steps:

Select the report suites of interest.





Then add the classifications and sub-classifications that you have pre-determined.



Step #4:

Download the template for the SAINT Classifications.



Step #5:

Populate the template with the categorisation that we have come up with in Step #3. After completion, save the file as Tab Delimited file.



Step #6:

Upload the Tab delimited file back into SiteCatalyst .



Step #7:

Now the data can be broken down into the categories that we have created earlier.



The implication for this is significant. Simply by doing this you can see that handset-related queries generate the most conversions for the site. You can also see that Prepaid products generate more orders compared to Plans products. Knowing this, you can ask questions such as “Is the website optimized for Plans-related queries to boost up its performance?” to further the success of the website.



You could also further breakdown the Keyword Category report by Keyword Type. Doing this, you can see for each of the major product categories, what is the order contribution of the generic terms? (Obviously the brand terms converted much higher). Is the generic term contributions for each category increasing or decreasing over the long term? If decreasing, perhaps more optimisation effort should be undertaken for the site.

For more detailed reference, view .

If you are interested in web data analytics and its implication on actionable insights for immediate wins, shoot us a quick email at insights@datalicious.com. Alternatively, drop us a comment below!











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