A major part of business in the CRO industry lies in understanding and processing large amounts of data. A massive sector which is heavily impacting this business sphere is web personalization. Web personalization, per definition, is everything done in order to present the right content at the right time to the right user. In order to better understand this technology, we should analyze its algorithmic exploitation features. And identify how web personalization is ruling big data-focused marketing campaigns in this second half of 2019. Let’ s dive into its architectural setup.
Algorithmic projections via Big Data Gathering
As many may know, web personalization-related tools can simply be installed on an online portal’s backend. Once correctly set up, they process data gathered via cookies, email surveys, users’ behavior and everything in between. This is called Big Data Gathering and is used to build the actual personalization model. The better this is done, the more “personalized” the final results of the website will be. The gathering process is mostly associated with an R-coded algorithm which actively projects the results of each personalization scenario, hypothetically.
How does the personalization process happen?
An algorithmic point of view: why it’s important
In a world where technology evolves as fast as lighting, thoroughly understanding how a particular tool works is a must in order to progressively develop the matter in discussion. In this particular case, a complete evaluation of the algorithmic process will better tell us how the overall technology is basically “exploiting” big data. Especially given the fact that user data privacy isn’t properly regulated yet by the new GDPR. In fact, big data acquisition should be regulated by an agreement between the user who confirms or reject an online portal’s data storage. In this case, this doesn’t happen, given the fact that these tools are automatically gathering users’ behavior flow and clicks via a native analytics feature. However, we can expect a change in the way user data privacy is handled next couple of months, since triple-A companies are willing to understand and regulate big data gathering.
The re-targeting algorithm
In order to increase the conversion rate of personalized pages, there is a complex variety of retargeting functions applied to the algorithms that must be taken into consideration. Retargeting is usually associated with an external, promotional banner and ads displaying strategy. But, it’s really just an algorithmic technology. In fact, retargeting done within personalization is basically related to the transposition of certain content from a page to another one with higher chances of conversion. Retargeting functions have been an architectural matter since the beginning of digital personalization.
Algorithmic penalties within CMS
The majority of commercial websites, as stated by many digital agencies with a big CRO division, have been relying on CMS. However, this limits the usage of personalization tools, since they are heavily customized on every sites’ architecture. In 2018, automation features were applied to CMS-based businesses ranging from mortgage providers to residential conveyancing solicitors in the UK. Why? Because those financial sectors are the ones which are heavily relying on data acquisition. In fact, the need for such a personalized content architecture or setup has been requested by users who mentioned in multiple studies how “robotic to read and browse” the content on these pages is. With this being said, it’s also easy to understand how such algorithmic processes could impact your site’s performance, given the fact that every small or big code adjustment within CMS (WordPress, Shopify, Magento) naturally leads to a significant loss in terms of speed performance.
From a coding perspective
It’s important to understand the fact that these retargeting algorithms should be focused on specific purposes within the site’s code. For example, a variable which is set in Python for what concerns a specific value in object R, which is normally the one used for dynamic resources (such as tracking scripts, in this case) could be connected to another specific one which is elaborating and storing the data in a separate container, which could have been coded in JS, for example.
Numerically speaking, the tracking part of the code is quite easy to set up, especially if you’re working with simple architectures such as WordPress or Shopify, but the processing part which follows isn’t the most simple to set up: the usage of R-based algorithms, for example, limits the usage of certain variables, which are, sometimes, the ones linked to certain features when it comes to retargeting. The separation of gathering and processing in these algorithmic processes leads to a variety of complications which are then leading to a subsequent variety of errors in R, therefore these two should be linked into a dynamic container, whether if JS or PHP coded.
Dissecting a new algorithmic trend within a sector such as personalization is incredibly important in 2019, given the fact that these years will be pivotal for what concerns future updates and regulations within the sector. For now, saying that retargeting algorithms are regulating the entire personalization and CRO industry is definitely the right statement, but, in the future, things are most likely going to change once a proper big data regulation model will be set into place.