Why Your Website’s Personalisation Engine Isn’t Working (And How to Fix It)
Website personalisation is the new age’s conversion engine. It offers personalised recommendations, dynamic content and tailored experiences to customers based on their behaviour on the app or website. If the personalisation engine breaks down, it’s most likely because of issues like poor data infrastructure, scalability issues, lack of continuous optimisation and missing content structure. In this blog, we will see the reasons behind a failed personalisation engine to know where you’re going wrong. So let’s dive right in!
#1: Your data is not centralised
Personalisation works best when you have a unified strategy. Imagine keeping all your blogs in one CMS (content management system), sales data on a spreadsheet, and customer details on a separate inventory. The scattered data of the website creates confusion for the personalisation engine. The engine starts treating the same customers as multiple users and gives recommendations accordingly. This is when we say that personalisation isn’t working well. The bottom line is you need a robust, scalable and centralised data inventory for the personalisation engine to work consistently and accurately.
#2: Underutilisation of Personalisation Engine
In mobile app development agencies, AI engineers and developers build the personalisation engine based on the brand’s data inventory. They feed algorithms with relevant data to train the models to fit well in multiple scenarios (offering recommendations based on purchase history, current patterns and interactions with the website). Testers and quality analysts implement an A/B test framework to determine whether personalisation is actually working or not. You cannot use the recommendation engine to its full potential if you haven’t tested it extensively.
#3: Issues in Data Structure and Scalability
Poorly structured content leads to a bad personalisation engine because the automation tools and AI systems are only as good as the data they are fed. Machine learning models cannot learn new patterns if the data structure is poor. The personalisation engine on the website can deliver contextual content after analysing structured data on the website. Hence, you need the building blocks properly in place, as without them, the personalised customer experience will fall short.
Bottom Line?
Mobile app development agencies know the ins and outs of building scalable and enterprise-level mobile applications. In such extensive apps and websites, brands must turn to a top app development company in Chicago, like VerveLogic, where developers work with a strategic approach to build versatile personalisation engines for websites and apps. By adopting a scalable infrastructure, we begin with proper data segmentation, structuring and analysis so that the machine learning models in the personalisation engine can evolve.
