Develop a Data-Driven User Experience (UX)

Related Topics: Data & Analytics, Mobile Apps

by Clare Villalva –

Thinking back 25 years to 1996, the idea of ordering a household product, clothing, or purchasing insurance from your phone was considered futuristic, even far fetched.  In those days, cell phone technology was analog and  was in its early stages so much so that two of the the most high tech things you can do apart from making calls is sending text messages or playing a game of snake.  Then just a year later a revolution would occur – Amazon would open its doors and start selling books on the new concept called a webpage accessible via a dial-up Internet connection.

What followed is advances to digital technology at speeds never seen before and now we live in a world where your company is considered a dinosaur if you don’t have a mobile app that is accessible everywhere, even while flying and connected to the plane’s wifi.  Amazon, that online book-selling start-up from the 90’s  is now a powerhouse selling almost anything and delivering it to your door in two days.

Thriving companies with powerful online presence are successful because they have harnessed the technology wave. Needless to say, there are multiple facets to technology but one of the most impactful and gets the rightly deserved attention is User Experience (UX).  UX is so much more than just the graphics on the screen, it is the total customer experience – the delightful UI presentation, functionality, response time, ease of use, data collection, ability to influence, and the list goes on and on.

Unraveling the UX is complicated and detailed, in this blog, we will review developing a data-driven UX.

What is a data driven UX?

Data driven UX is designing, testing, and refining the UX based on data captured through UX tracking, market trends, and consumer responses.  The data provides insight into which areas of the UX require refinement versus a redesign versus working as expected through objective lens.

Data driven UX has three different types of data usage designs:

1. Data Driven Design

Relying solely on quantitative data to make design decisions for improvement.  This approach is best used when the UX is focused on performance optimization

2. Data Informed Design

Combines the objective nature of quantitative data with qualitative details such as instinct, details, prior experience, or conversations with customers.  This approach is best used when creating a brand new UX.

3. Data Aware Design

Weighs data insights equally with other decision making factors such as marketing input, qualitative experiences, other industry trends.  This approach is best used when fine tuning a productive UX

These design patterns are not mutually exclusive but should be considered part of the whole when defining the UX.  There may be portions of the UX that work well such as the payment portal, where improvements can be found in the data.  Other areas such as the policy application process would use data aware approach as it functions but the consumers may take a long time to complete or report negative results on a survey.

Where to find the Data?

Developing a data driven design isn’t about gathering as much data as possible but rather leveraging data that provides thoughtful and insightful knowledge of the user base and their desires.  The data come from two key sources, market trends/insights and internally gathered measurements.

UX market trends/insights use research data, customer analytics, surveys, and other data types to determine how to provide the optimal user experience at an industry level. Some of the insights come from:

Competitive Analysis

Reviewing your key direct competitors in your line of business but also looking at other highly successful retail and service company’s offerings.  Innovative ideas easily transcend industry boundaries

Understanding your users

Purchasing an insurance policy or paying a premium has similar trends as retail purchasing and shopping cart abandonment rates.  Learning the industry trends for how to entice customers turning prospects into insureds

Creating effective designs

Applying proven and trending UI designs to meet customer expectations to keep the user experience engaging, intentional, and easy to navigate

Leveraging data to drive innovation

Understanding your customer journey through the UX and presenting personalized messaging, offers, and other suggestions to create a sense of welcoming and helping the customer secure the ideal product

Tracking the consumer behavior flows and traffic

Using tools such as Google Analytics will enable the measurement of current behavior to the ideal work flow giving insight to poor design areas and areas that are working well.  This data can give you insights such as:

UX Metrics

Tracking the customer journey and understanding where customers abandon the process, spend a significant amount of time, page heat maps where they focus their efforts, and other insights on the journey

Digital effectiveness

Examining the number of unique transactions, sign-ups, bound rates, on page timings, and other digitally performed activities.  This can include tracking results from marketing campaigns using tracking codes or unique digital links

A B testing

Randomly selecting one of two user experiences and tracking the customer behavior, such as purchasing a policy to gathering feedback from surveys

Surveys

The best insight will be from the users directly from surveys taken during or after the UX.  It is best to keep these surveys short and well thought out to prevent user abandonment.

Using a bit of art and science, UX designers can take marketing trends and internal data to create the ideal digital experience that aligns with the company’s branding. 

How to use the Data Insights?

Before applying the data to the UX, it must be analyzed for new insights and trends. Previously, the UX designers and analysts would pour over the data manually, looking for directions, run models, and test hypotheses.  Applying artificial intelligence, machine learning, and other modern data analytical tools to find previously hidden visions is now possible in the digital era.  The AI/ML processes can be set up to continually monitor the new data to confirm previous assumptions or discover new insights.

Four fundamental principles should be applied in developing the ideal data driven UX:

Great design is based on data

UX designers are very talented in developing unique and comforting experiences, but weighing talent against data insights is essential.  The data will inform the designers of the user’s desires and expectations, and if met, success will follow.

Continue experimenting

The UX does not standstill.  It is continually evolving as different industries and competitors continue to deliver new and innovative experiences.  The tastes of customers continue to evolve as they become more sophisticated in their needs.

Personal Experience

Personalization is everywhere, such as your name upon logging in, historical records, etc., but the UX needs to feel like a conversation with your good friend. The experience needs to be welcoming and comforting while focusing on solving the customer’s specific problems.

Focus on the ROI

It is tempting to build the latest flashy trend or invest too much into solving a perceived UX problem because it looks impressive.  Use the data gathered to evaluate the investment and ensure it is impacting the sales or reducing expenses.

Conclusion

Building an excellent UX can easily be achieved with proper investment and research.  Maintaining the unique UX requires investment into a data driven approach that requires analytics, machine learning, artificial intelligence with continual experimentation to keep the experience fresh.

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