Recommendations can be set based on individual attributes of the browsing visitor.
Recommend products to the browsing visitor based on a particular user segment or for a particular score-range.
Recommend products based on user profile data.
Recommend products based on past behavior or interactions of a particular user group.
Recommend product based on a particular product category or a particular product strategy.
Recommendations can be automated using overall data to apply association rules to arrive at recommendations.
Assign scores to data parameters and generate customer scorecards. Create customer segments across all data parameters.
You can also enable the workflow to run the recommendation widget based on associated purchase views. Data used for this analysis includes all sessions where the selected product is purchased and the recommendation can be either of products purchased or products browsed.
For cross-selling campaigns, You can also run a workflow to recommend products, by looking at other customers within a particular segment. This allows the marketer to fine tune recommendations based on lot of similar parameters, which can be optimize recommendation click-throughs.
By default, Plumb5 recommendation is set to “People who viewed the product also viewed” as this can be arrived with web analytics data. To enable the recommendations based on purchases, we need to integrate the ecommerce data and to recommend based on particular customer type, you will need to tag it to the customer segment created by you.
Based on the recommendation type selected, Plumb5 adds an identifier for every session to update the model of new incoming data. The new data is appended to get the new top 5 associated products. This product feed will allow the marketer to show the recommended products on the page.
Recommendations can be enabled, depending on the page pattern and click-through events. The recommendation widget can be used to control inventory based on demand or with products that go as a bundle.