Lets say Mary visited a famous Shampoo website and the following sequence data is the digital trail left behind
31-Mar-2012 11:00 am ; Reads reviews on Shampoo Brand X
31-Mar-2012 11:02 am : Parametric Search for Shampoo Brand X and orders the results by price
31-Mar-2012 11:03 am : Clicks on 3rd product displayed on search
31-Mar-2012 11:07 am : Goes back to search results page
31-Mar-2012 11:08 am : Clicks on 7th product displayed on search
31-Mar-2012 11:14 am : Advocacy event : Mary sends this Shampoo brand link as an email to 5 of her friends
This activity which Mary exhibited - Advocacy is a very engaged action. Primarily because a mail from a friend has greater chance of being clicked by recipients of the mail clicking and engaging with the brand as opposed to the organisation directly sending a mail to the consumer.
In this context it is extremely important to find out if there are any upstream behaviors a consumer exhibits which are co-related across time to a downstream event of interest ( say an advocacy event ) ?
Apriori algorithms can be used to mine the sequence patterns which are statistically significant and use that to predict with a certain confidence level the probability of an advocacy event happening. Once we run the algorithm there can be multiple sequences which are statistically significant. Statistical significance can be measured both by 'Support' and 'Confidence' of a particular sequential pattern. Once we get sequential patterns which have crossed the threshold for support and confidence we can leverage these patterns to make an intervention in the digital shoppers behavior
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