Product cross-selling is a very powerful policy that you might be familiar with. For instance Amazon.com was one of the early adopters of recommendation systems in a very sophisticated manner, and it boosted its online selling rates by hundreds of millions of dollars. If we move on to more algorithmic complexity, a more sophisticated example is Netflix, an online movie rental service, and the core of its business is its recommendation system and the hype that surrounds it. Finally, the most recent and simplest to implement is Last.fm with a very elegant and efficient recommendation algorithm. By adopting Drupal and Ubercart, things become pretty straightforward, as you have good modules that encapsulate the complexity of recommendation algorithms and require little configuration, and you may as well provide to your end customers a great consumer experience. In addition to that, do not forget the powerful and robust taxonomy mechanism that Drupal implements in its core and the site-wide content tagging it provides, so the relevant items and items that could be used in conjunction could be categorized. So now without further delay, we will go through all these interesting possibilities that our Drupal online shop could offer.
As we have already mentioned, taxonomies are the core of the Drupal system and grasping the high-level implementation can save us a lot of trouble most of the time. Taxonomies often help us create references for our Drupal system nodes, differentiate between them, and create easy-to-use, intuitive, and searchable views on our content. Therefore, in our example, the basic idea is to create a taxonomy not only for products that can be sold as groups (as we already have Ubercart product kit for that), but rather for the products administrator to be able to tag all these relevant products in a way that high-revenue electronic shops like ExpanSys and PixMania have adopted.
To achieve this we do not need any new module installation but rather the plain old Drupal taxonomy system. We will make two taxonomies: one for product mangers, which they can edit while they add new products, and another in which users can free tag your content. These free taxonomy vocabularies are also referred as folksonomies. Furthermore, everyday practice has shown that relevant taxonomy blocks can really boost your site traffic, page views, and eventually conversions that translate to purchases. The vocabularies that we will alter are the following:
To create the new vocabularies and change the existing one, we take the following steps:
This is an example of a user-defined term-tagging procedure on one of our products.
Use Taxonomies for Navigation and Menus
You can also use Drupal’s system pages using the taxonomy view module for category listings. The end of the URL should look like this: taxonomy/term/1 or taxonomy/term/2.
Note that taxonomy URLs always contain one or more Term IDs at the end of the URL. These numbers, 1 and 2 above, tell the Drupal engine which categories to display. Now combine the Term IDs above in one URL using a comma as a delimiter: taxonomy/term/1, 2. The resulting listing represents the boolean AND operation. It includes all nodes tagged with both terms. To get a listing of nodes using either taxonomy term 1 OR 2, use a plus sign as the operator: taxonomy/term/1+2
Recommendation systems have existed a long time and make a crucial contribution in some of the most successful online shops. In this section we will focus on examples of implicit data collection of the customer’s activities that include the following:
Having these data and customer behavior in our account, it is then easy to find the optimal item suggestions that fit people’s profiles. We can then provide sections like “customers who bought this book also bought” on Amazon.com suggestions.
Further to our discussion we will install recommendation API and two other modules that depend on it. The Ubercart-oriented module is the Ubercart Recommender module. This module collects data through the Drupal Core Statistics module about user purchases and provides suggestions about other products that could be relevant to the returning customer. All recommendation systems assign special weights in their recommendation algorithm to purchased products since this generates returned value and we have a fully converted customer. In order to handle suggestions to users that have not made any purchases yet from our online shop we will also use the Browsing History Recommender and Relevant Content modules. You can find more information about the algorithms and the recommendation procedure implemented in the Drupal Recommender API at http://mrzhou.cms.si.umich.edu/recommender.
Next we provide a synopsis of the added value and the functionality of each module:
To configure your online shop to provide content-related recommendations we need to perform the following administration steps:
You can find a very thorough discussion about all recommendation modules on Drupal at http://groups.drupal.org/node/12347.
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