“A lot of times, people don't know what they want until you show it to them.” – Steve Jobs.
Data science is gaining global recognition in various fields. The reason behind its ability is to provide a holistic view towards rapidly moving users and business needs. Data can be used to rationalize creative designing skills by offering quantity and quality in design research. Designers have a responsibility to fulfill users' needs. What would be better than being a step ahead in terms of knowing users' requirements by using new digital services explored through data analysis?
The fast-evolving users need demand loops of iterations which is quite tricky. Successful designs stand out because they do not follow the ‘one-size-fits-all’ techniques. To be particular about changing user needs and be continuous with design research are traits of a good designer.
Data sciences can help UXers to have plenty of choices for what to bring in for the users. Together they have enormous potential to complement each other and keep all, i.e., users, designers, businesses happy and growing. But HOW is the question here. Designers tend to ignore statistics and valuable data while getting hypnotized in their creative designing skills and busy assessing user demand. Data scientists do not consider the human factor while collecting facts and numbers. If both the skills are combined, they can offer the best to the users.
Get over the assumption game.
Data science and analysis refines the design research for UX designers. Designers must thrive for accuracy when it comes to following designers' needs. The development of hypothesis after research and testing is where data scientists become friends of designers. Processed valuable data and facts can ease the targeting of fundamental goals of UX designers of providing a good looking and smooth website.
When used by designers, data sciences can be boring and standardized; however, a good designer must bear in mind that this lack of innovation of data science does bring in him the user perspective and does not affect creativity.
The do’s and don’ts in data techniques.
As far as the technical stuff is concerned the data is about constant evaluation providing various new methods to improve your website and its functionality for users. Designers while building the design model have to be careful about the data errors. The A/A and A/B testing used for testing components of web pages is now a tedious one. Techniques like Heatmap analysis, Heuristics, and Wireframes are some modern analysis tools that can help designers manage the website better.
Change in design requires plentiful data too. Vigilance brought in by new data analyzation while modifying design is very much required as you never want the users to hop off your platform bringing in unfamiliar elements.
Hypothesis developed through raw data must not affect the creative thinking skills of designers. Designers must also know when and how to devise a technique at the right place, and for this, the aim of user usability should be clear. User tests, site analysis, surveys, etc must strive for creative UX solutions at the end of the day. However, do not overwhelm the users by providing them with new and quirky features for fun because that can fade away quite fast.
Predict users and track navigation.
This functionality of data analysis is the holy grail for UX designers. Google Analytics is boon for designers in this regard. Locating user's problems, expecting their needs, their time spent on usage of both the website and particular features in it, and navigating web pages are key designer concerns where a large volume of valuable data plays a prominent role.
For starters, let's take reference of what methods big corporations use to sell their product. After putting much effort into product research, they refine the content users will seek. That being done, next comes the implying of methods such as bounce rates, time spent by users on web pages, tracking traffic, demographics to identify the targeted audience. What we call screen time is device usage for designers and data analysts, monitoring of which is done through data metrics and is also helpful in design formation. A designer may use this beneficial information for his advantage to offer exactly what the users need without even the user knowing about his needs.
Hop on Machine learning.
From Artificial Intelligence (AI) to the Internet of Things (IoT), Machine learning is sweeping the way for an evolving digitalized world. This is where the maximization of the valuable data derived through data analysis is done. Make your designs smart with the research you’ve been doing by putting the outcome data in your UX design. Create models and categorize your website components to give the user a relatable vibe by showing them consideration for special behavioral aspects.
Personalize your platform.
As mentioned above one such technique to personalize your website is using the machine learning technique. Address the pain points of all your users and take notes of behavioral elements for personalization of your platform. Now this might be a little difficult, but data science can help you personalize each visitor's data. Reduce the element of generality in your website and embrace the uniqueness of your users. Avoid redundancy and provide your audience with visuals they connect with. You must have noticed how giant commercial websites display different products on your and your friends’ devices. This is how personalization is used based on user demand.
Thus, to get facts and metrics for better user behavior analysis, Data sciences can significantly contribute to UX designing. While it is of immense importance to keep track of data usability, keeping up with new trends for more user engagement is what a designer must stick to while cooperating with data analysis. In rising digitalization a designer must also have regard for ethical data collection and data usage practices. Don’t forget simplifying users’ life is the purpose data specialization is used on various platforms.