Analytics are present in almost every app and application on the market today. Consumers demand analytics as a standard when buying products. For instance, a fitness tracker that does not display trends in health data is unlikely to achieve much adoption.
From a development perspective, offering analytics to users is challenging. Not only do UX designers and developers have to transform backend data into a user-friendly format, but they must also anticipate user needs and adapt constantly.
Embedded analytics is a great way of transferring data control over to users and increasing app engagement. Here’s why embedding analytics is a game-changer for UX.
Offers Instant Insights
Embedding analytics into your application gives users the insights they want instantly. You can offer users low latency dashboards summarizing data in a user-friendly manner. Popular backend infrastructures such as Snowflake or Clickhouse (compare Clickhouse vs. Snowflake here) make this task an easy one.
Most developers underestimate how data-hungry their users are. Modern users are unwilling to contact technical team members or personnel to retrieve the insights they want. They want instant feedback on their actions to design a better experience for themselves.
For instance, gamers leverage in-game analytics to view their performance and improve their UX. A racing sim can offer insights into lines taken around courses, speed, and brake point information to help the user improve their performance and draw more out of the game’s engine.
Thus, embedded analytics gives users the ability to design their experience, leading to an app that offers something for everyone. These features carry over to the B2B world as well. Embedding BI data dashboards within enterprise apps gives every user in the organization the ability to offer insights.
Data access and analysis are thus democratized, something that was unheard of less than a decade ago. Companies can leverage their data better and draw conclusions from multiple sources. Embedding analytics into apps is critical to these goals.
Embedding analytics also hastens a company’s digital transformation initiatives. The primary goal of a digital transformation program is to install a data-driven culture. If data is siloed to a few teams or hidden behind walled applications accessible only to those with special knowledge, transformation efforts will fail.
Democratization demands embedded analytics. The result is a leaner, more flexible organization that leverages more from its resources and future-proofs its business. Better UX is a by-product of all these goals.
In the early days of analytics, users were forced to switch between applications. For instance, enterprise users had to leave their primary apps and navigate to a separate analytics dashboard. They had to import data (often manually via spreadsheets) into their analytics platform and wait for the app to crunch numbers.
The resulting UX was exactly as painful as you might think. Even if data imports were automated, switching between screens led to data lost in translation. Users often ran into formatting issues where the analytics platform would label data differently, compared to the enterprise’s app.
As a result, most users would resort to requesting data from their IT teams since the data dump would be formatted appropriately, even if it was on a spreadsheet.
Embedding analytics into apps removes this problem. Users can preserve context by remaining on the same screen and avoid wasting time switching between screens. The time lost switching screens might not sound like much, but it adds up when analyzing millions of rows of segmented data.
In this scenario, constantly switching screens adds an hour or more to data analysis. Throw in user fatigue and data formatting issues, and you have a recipe for a terrible user experience.
Preparing for the Future
Analytics is set to become more sophisticated. Currently, most dashboards present information to users and leave it up to them to turn it into insights. Users are increasingly expecting their apps to tell them what to do as well.
For instance, an FP&A professional might receive information from their platform regarding increasing expenses in a division in their company. In current circumstances, the professional has to use this information and create projections to evaluate the impact to different variables affecting company performance.
However, the rise of AI and ML algorithms is driving the need to automate such analyses. The FP&A professional will soon come to expect their analytics package to show them the impact on business variables and recommend actions.
As the use of AI increases, embedding analytics into applications is the only way to help users leverage its power. The thought of using a standalone app while transferring data between systems is unthinkable. Thus, AI use in analytics enhances UX and requires embedded analytics.
Embedding Analytics is Essential
App engagement depends heavily on UX, and embedding analytics is a surefire way of boosting UX. Given the current trends within consumer demands in B2B and B2C, developers must focus on embedding analytics within their apps and give users what they want. Not only does this boost UX, but it also future-proofs apps.