Accordingly, many companies are turning to Digital Adoption Platforms (DAPs).
DAPs work like a ‘teaching layer’ of software that sits beside the native application and offers tooltips to users who might find the use of new software intimidating. Imagine that a man in his late 50s or early 60s was employed at a marketing agency for his expertise and enthusiasm for old Land Rover cars, as in the above example. Let’s call him George.
George knows exactly which images to choose for presenting via new zealand telemarketing a platform like Hyperise, but he struggles with all the technical specifications like setting image resolution, proportional resizing, color editing, creating meta tags for SEO and all that sort of stuff.
A DAP would adjust its output for every individual user, as opposed to just helping where it’s not required. The DAP achieves this by its AI ‘studying’ George’s workflow patterns and analyzes where he makes common mistakes. In the example of using, say, Photoshop to color-edit an image, the DAP would guide George through that process. But crucially, the DAP would ‘learn’ when George had mastered the technique and wouldn’t help again on that particular subject.
Not only can DAPs be used in commercial situations, but they are becoming more commonplace at end user level. For instance, if a person uses an iPhone or Android app to top up their household electricity meter, once again, a DAP can walk the user through every stage of the process until it learns that it isn’t required. DAPs also work on named user accounts, so if two people in the same household use the same app – the DAP would react differently depending upon the end-user’s learning style; in effect, how quickly a person picks up new techniques with technology.
So hyper-personalization tech doesn’t have to be intrusive, it’s also very beneficial. The more often people use it, the less they need it, like a perfect teacher, allowing the pupil to join the world when their work is done.
That’s got to make a better world for all of us.Personalized images on websites can greatly enhance a user’s experience by making the content more relevant and engaging for them. The process starts with collecting data about users’ visits. This can be gathered through information provided by those users during sign-up or profile completion to a website, forum, gamer account, social media group or whatever.
As well as someone’s profile, cookies can assist in tracking user interactions on a website, such as pages visited, how long the visitor stayed on each one, links and images clicked, and return visits to certain pages. As mentioned above, the user’s location, time of day, the season of the year and even the prevailing weather can be thrown into the mix.
All this data gold can be mined by algorithms to present just what the platform’s Artificial Intelligence (AI) ‘thinks’ that the user would like to see as a personalized image along with an embedded textual message.
Many Content Management Systems (CMS) offer plugins or features to display personalized content based on user data. Accordingly, platforms like Hyperise allow for on-the-fly image manipulation based on user data. Recommendation engines, based on collaborative filtering, content-based filtering, or hybrid methods can suggest images to appeal to each individual.
Machine learning models can be trained by using data to predict people’s preferences and tailor displayed images accordingly. This can all be done in real-time, updating images as the user interacts with a website.
The deep tech
For hyper personalization to work effectively, coding such as JavaScript and AJAX can dynamically update content without refreshing a webpage. For real-time data exchange between the server and the client, Web Sockets are used.
A/B testing can also help to determine the effectiveness of personalized images. It’s a very simple concept, just showing different images to different user segments and measuring the resulting engagement metrics.
Tools like Google Optimize can also be used to run tests and analyze results.
For example, this technique can be particularly effective on e-commerce sites. Users can be shown product recommendations based on past purchases or browsing history, without the need for them moving from the current page being visited. It’s also less irritating than a ‘Pop Up’ box, which often causes website visitors to go elsewhere if they are too intrusive.
Content websites can also show relevant articles or blog posts based on user interests. A good example is a travel website, where highlighting destinations or deals can be tailored to user preferences, historic purchases and past travel history. Additionally, personalization can be extremely useful when a website owner’s personal branding is key.
Social media platforms are also highly suitable for hyper-personalization, customizing people’s feed images based on user interactions and their social graph.
Privacy and Security
It’s important to remember that when implementing personalized images, you must protect user data by ensuring that it is securely stored and processed in compliance with privacy laws such as the General Data Protection Regulations in the EU (GDPR) and the California Privacy Rights Act (CPRA) in the US.
Crucially, it’s necessary to allow users to manage their data and opt-out of personalization if they wish.
