We are in the 21st century, where data analytics is one of the most popular terminologies and is used nonchalantly across industries.
It is because a lot of decision-making has shifted from gut-based and impromptu to data-backed. It not only includes designing but has seeped down to user experience too.
User experience design (UXD) is a process that goes beyond the design of a product or service. It encompasses understanding how users interact with a product or service and designing it to evoke the desired response. Data analytics has a significant role to play in UX design, as it helps us understand user behaviour and how best to meet their needs.
In this article, we will explore what data analytics is, its types, the steps involved, how it can help UX design, and some common use cases where we can apply its varying techniques. So stay tuned!
What is data analytics?
90% of corporations value data analytics as an essential competency to ace. But what is data analytics?
Data analytics and optimisation is about using raw data to improve decision-making. It can help you identify patterns and trends in your data, which we can use for planning and optimisation purposes.
Data analytics can help understand customer behaviour and preferences so that better customer service can be provided. In the end, data analytics helps organisations make better decisions faster by identifying opportunities and risks early in the decision-making process.
What is the typical process that a data analyst will follow?
Here are the steps a data analyst follows while conducting data analytics –
Step 1: Define the question(s) you want to answer
Data analysts typically start by understanding the business goals, which can be gleaned from business stakeholders. It will help you select the data collection methods most relevant to your inquiry. Once you have a good understanding of what information is required, you can then plan out the most suitable approach to collect the data as well as the approach for analysis and interpretation of data.
Step 2: Data collection
Gathering data is an important step in any analysis or research. It is the data analyst’s job to unearth all the relevant information and compile it in a reliable and accurate format. It will allow business decisions to be based on sound analytics rather than assumptions or guesses and shape a more secure future. Data could already be available (for example, past traffic and usage data of an existing mobile app) or it would need to be collected over a period of time (for a newly launched website).
Step 3: Clean the data
Now that you have ample data at your disposal, it is imperative to understand that not everything will contribute to improving the user experience or UX design process. So, you need to clean the data to filter unwanted, disconnected, or incomplete entries before moving on to the next stage.
Step 4: Analyse the data
The most important use of analytics is the ability to execute exploratory data analysis on the data which has been collected and cleaned.
By using data visualisation tools and dashboards, you can keep track of everything from customer engagement rates to website clicks through conversion rates. These are some very essential data points when planning and executing effective UX-related strategies.
Now that you have analyzed the data, the next vital step is to interpret your findings and validate them against your expectations. It will enable you to discover unforeseen trends and patterns crucial for your decision-making.
Step 5: Visualise and share your findings
It is also helpful when you are able to share the visualised data to different people in the organisation so that they can harness the benefit to make their decision. Sometimes it is also possible to provide live data visualisation in an online dashboard so that the business stakeholders/managers can keep monitoring certain important events.
Data analytics techniques
Data analytics has become an essential tool for UX design. It helps improve the quality and design of an interface by revealing user insights (e.g. how quickly a user takes to go through a set of steps using your interface). This information can be used to make changes, such as improving navigation or adding new functionality.
But it is vital to understand that there are a lot of data analytics techniques, and not every methodology can be helpful for your needs.
Therefore, you need to understand them all and carefully choose the ones that you can use. Following are some of the most commonly used types:
It is a data analysis technique that helps you understand trends and changes in your data. This can include calculating medians, mean, standard deviations, etc., to understand tendencies and variations within a data set.
The results help UX designers to understand customer behaviour. For example, analysing how users interact with a trendy website helps the designers to analyse if it is a factor that could contribute to higher sales.
The results of the descriptive analysis are usually the starting point for in-depth statistical analysis.
Unlike most data analytics types, diagnostic analytics focuses more on the reason behind the occurrence of an incident. It uses a myriad of data inputs and hypotheses to reach an outcome. This involves checking various factors like patterns, trends, and data relationships to understand what caused a problem or outcome.
For example, websites relying on customer data use diagnostic analysis to understand why customers do what they do and how their behaviour can help improve the product and UX.
A customer may have various reasons to abandon a product in their cart. By gathering feedback from the users, an analyst can diagnose if product design or usability is one of the reason behind abandoning it mid-way.
Predictive analytics is a vital tool in business operations that helps them predict future possibilities by analysing historical data. It allows you to extract insights from data so that decisions can be made better and actions are taken accordingly.
For example, an ecommerce platform may use it to forecast sales based on past year sales data and other external factors like seasonal behaviours, market economics, etc. This information can then be used to create new marketing campaigns or plan for new products/services.
It is a process of extracting insights from data to make recommendations that can help business decisions. It involves using testing and other techniques like mathematical modelling, A/B testing, etc. in collaboration with predictive analytics to recommend solutions for achieving desired results.
For example, a website may want to utilise prescriptive analysis to determine the most efficient page design, CTAs, etc, that optimise user interaction and sales.
A regression analysis is a data analytics technique that helps you to understand how users interact with your website or product. By analyzing how different user segments behave, you can identify and estimate the relationship between variables. It helps correct errors in your data so that the user experience is improved.
It is done by checking the correlation between a dependent variable and independent variable.
For example, you budget for an email marketing campaign and want to check how much revenue is generated using the same. In this case, the email marketing campaign is your independent variable, and the sales revenue is your dependent variable. A regression analysis helps to find out a positive, negative or neutral correlation between the two variables, which in turn helps you make an informed decision on future budget spending.
Factor analysis involves identifying patterns in user behavior and using this information to improve the design of products or services. The idea behind it is to reduce a large number of variables to a smaller number of factors. It concentrates large datasets into smaller, more manageable samples, enabling users to identify hidden patterns.
The results can be used for further analysis, ensuring you get deeper insights regarding their UX and how people react to changes in your design ecosystem.
In web design, for example, it could be used to understand relationships between various consumer behaviours or traits. A UX designer can conduct a consumer survey with a set of questionnaires to identify the underlying factors that drive variations within the data.
Cohort analysis is a data analytics technique that helps you understand user behavior and is a subset of behavioral analytics. It takes the data from a given dataset rather than individual units. This means that it involves grouping similar customers based on shared behaviours. These cohorts share similar characteristics, which helps identify patterns throughout the customer lifecycle.
A website, for example, may group its visitors based on their sign-up date, gender, age or demography. They can then track their purchases and cart abandonment to reveal differences in purchase behaviours of different groups. This, in turn, can help the designers or marketers to make more informed decisions on product changes, marketing strategies, etc.
Cluster analysis is a research technique that sorts different data points into clusters for decision-making. These are internally homogeneous and externally heterogeneous, which enables the team to find the data distribution in a given dataset and make decisions accordingly.
For example, a website launching a new product may want to segment its customers based on their purchase behaviour. Cluster analysis can be used here to identify those with similar purchasing habits, which can help create a targeted marketing plan.
Time series analysis
One of the most important ways to collect data is through time series analysis – understanding how customers interact with your product over time. It will allow you to identify changes in behaviour, use that information to improve design decisions, and measure the impact on customer satisfaction.
Charts and graphs are great tools for visualising complex data in a way that’s easy for humans (and machines) to understand. By using them wisely, you can create informative and helpful designs for users – rather than pretty or flashy ones.
Since time series analysis is used with non-stationary data (those that fluctuate with time), it is an excellent tool for ecommerce platforms to make more informed decisions.
When it comes to user experience, nothing is more important than understanding the sentiment of your users. By doing so, you can design a better product that meets their needs and expectations.
Besides improving user satisfaction, sentiment analytics also plays a vital role in customer retention and suggestion-based improvement processes for products and services. Knowing what users like helps you create content that’s engaging and useful – ultimately leading to long-term success for all involved!
An application, for example, may analyze its customer reviews to determine the overall sentiment on its usability and design. This could then help to make changes/alterations in the design to improve customer satisfaction.
Social media analytics
This is the process of collecting and analysing data from social media platforms to get insight and make better business decisions. It can be done by tracking metrics like the number of social media followers, engagement rate, etc. A marketing team can analyze the performance of their social media campaigns on platforms like Instagram and Facebook and later analyse the results to optimise their campaign.
Cross platform analytics
It refers to the process of collecting and analyzing data from multiple platforms and devices to get an overall picture of customer behaviour and how they interact with a brand at various touchpoints.
An ecommerce company, for example, can track the customer’s browsing and purchase behaviour on their mobile app, website, or retail store. A combined analysis of all these data can help them make informed marketing decisions and create better customer experience strategies for the future.
Loading speed analytics
This process measures and analyses the time it takes for a website or application to load. It aims to identify the slow-loading elements which may negatively impact user experience. Once identified, these can be optimised to provide better UX to the users.
Website owners, for example, may use tools like PageSpeed Insights or GTmetric to check loading speed and analyse the results to make improvements.
Heatmap for user behaviour
Heatmaps are a graphical representation of user behaviour on a web app or website. Here, colours are used to represent various interactions. These are used in UX research to see how a potential customer interacts with a page, hovers, scrolls, clicks, etc. It can help websites understand which products receive more interest, where the user stops on the page, etc.
For designers, this information can be vital to create better website designs and making more effective layouts to improve UX.
Data analytics examples
Here is how data analytics can change UX designing –
- Measuring the performance of different user interfaces, so that designers can user test, rethink and improve accessibility and usability.
- Finding the potential areas or points which user is leaving without completing the purchase. Then further UX research can be carried out to uncover the reason for such abandonment.
- Optimising UX of the conversion funnel, which then contributes to high sales.
- Finding out instances of rage-clicking and figuring out ways to minimize it.
- Rearranging content to ensure it goes with the user’s search intent.
Data analytics can play a vital role in the design process of user experience (UX) projects.
By understanding user behaviour and trends, data analytics can help designers make informed decisions about the design of user interfaces.
In addition to helping to design better user experiences, data analytics can also help optimise the design processes and improve the quality of user experience design.
Establishing a seamless experience for your users is a priority in terms of sales achieved and contributes to better business outcomes without significantly increasing manual labour.
Talk to our user experience consulting team on how you can explore and choose the right data analysis technique for your project.