Usage Analysis

The term analytics is usually associated with marketing campaigns. However, in marketing terms as well, analytics is most useful in optimizing the customer journey and get users to take intended actions. Analytics guide design insights that help in achieving the aforementioned objectives and are most frequently used in web products context. Some researchers believe that analytics aren’t as helpful in understanding the user’s motivations and intent – this is true when the sample size is small but for larger sample sizes, certain inferences can be drawn from Usage Analysis or Usage Analysis can be combined with other user probing based research methods.

User actions speak volumes in determining the relationship between users and digital as well as non-digital systems. Metrics such as the number of clicks by a user, the amount of time user spent on a page or an activity, the number of times the activity is performed or the user returns to the page and many others are indicators of user preferences, needs, and behavior. Similarly, analytics measures give an indication about those features that a user needs in the products they use, but are absent at the moment. Figuring out whether the user is performing clicks on a page out of interest or merely browsing, what brings the user back to the page, whether using the product is an easy or challenging task for the user, whether the users could achieve their goals when performing a certain action or abandon the activity mid-way from frustration, along with whether the digital product is intuitive and user friendly or clumsy are important insights to gather.

The role a researcher plays in Usage Analysis is to understand the underlying user motivations for using a product, in a way of how a certain product is used and how the product adds value to the user.  Additionally, an experienced researcher can gain insights about the intent behind people’s product usage by analyzing patterns from the analytics data collected.
Let us consider a few key steps and metrics that an analytics tool captures for online products –

  1. Before a researcher can start looking at the analytics data, it is important to define the key questions that they would want to be answered. This is especially important because there are so many data points that can be captured during usage that it becomes cumbersome to measure everything and then filter out the data that is actually valuable.
  2. What percentage of visitors clicked on specific links, the channels that drive traffic to the page, which pages do users visit next, how much time do users spend on the pages, the percentage of people who abandoned the page or bounced off the first page they visited and other such metrics are all good measures of user behavior, user experience, and user journey.
  3. Based on the analytics data recorded in the second step, researchers can explore the possible explanations for the user behavior and suggest solutions to alter that behavior to one which is desired as well as the one that meets the user expectations.

Analytics allow researchers to understand users as well as aid in solving problems that act as roadblocks to the user accomplishing goals while using the product.
Analytics help to identify user experience challenges for further exploration, validate design assumptions and confirm or debunk research findings.

This method can be supported by qualitative data collected through other research methods such as interviews and guided tours, among others to give a clearer design direction than shooting in the dark.


Advantages of Usage Analysis

01 Large sample size

Usage Analytics helps collect and analyze large amounts of data using different online tools.

02 Time and Cost effective

Online tools help gather large chunks of data quickly and in a relatively inexpensive manner. Most online tools that aid in gathering Usage Analytics data provide the basic features free of charge.

03 Limited researcher bias

As the researcher doesn’t accompany the users at the time of using a prototype or product, the user follows the natural flow that seems to come most naturally to them.

04 Applications

Usage Analytics can be applied to determine user behavior, whether they could accomplish goals through a product and whether the user expectations and needs were met through the devised solution. Usage analytics can be applied at different stages of the design process to provide rich insights that can aid in tweaking user experience.


Disadvantages of Usage Analysis

01 User intent and motivations

The user intent and motivations require a specialist to analyze the usage data and deduce. However, if the right kind of data isn’t collected to answer the most critical of questions, the researcher may end up missing the user intent and motivations altogether.

02 Assumptions

The researcher can add some bias at the time of making assumptions based on the Usage Analytics data gathered.

Think Design recommendation

Usage Analysis is an essential step when we want to improve or transform an application, based on insights from actual usage. In digital design (app/ application/ portal/ website) especially when working on change management or digital transformation, expertise in data-driven design comes in handy in informing strategic moves. As explained above, it is however very important to have a sight of key metrics, questions or indicators so that we have a structure to measure up against.

Think Design has developed a set of about twenty indicators that can be generalized to several contexts. This list may serve as a ready reckoner to your teams when you are planning to analyze usage:
  • Exits: Volume of exits on a particular page, an event or an action
  • Conversions: Volume of pre-defined destinations/ goals clocked
  • Leads: Volume of contact details captured
  • Call volume: Volume of support calls made by users
  • Tickets: Volume of support tickets raised
  • Lifetime Value: Revenue/ User from his/ her registration to de-activation
  • CTR/ Click Through Rate: Volume of successful clicks on a link
  • Time to task: Amount of time taken by users from task initiation to task completion
  • Session duration: Amount of time users are engaged on the site, app or a webpage
  • Attrition: Volume of de-activations
  • Impressions: Volume of appearance in search results/ queries
  • Avg. Ticket price: Average amount transacted by the user
  • Comprehension: Users’ understanding of a message
  • Engagement volume: Volume of users who took action/s on the app
  • User satisfaction score: A number indicating users’ satisfaction using your product/ service
  • Errors: Volume of errors logged
  • Cart abandonment: Volume of carts that didn’t convert to purchase
  • Form abandonment: Volume of forms abandoned after initiation
  • Unique visits: Volume of visits by unique users
  • Referrals: Volume of visits initiated from referrals (social media, third party sites, email links, forums etc.)
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