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Embedded analytics (EA) are data assessment and representation tools designed to be seamlessly implemented into third-party software. They are immensely useful to any organization or institution that relies on data, and are highly influential in the field of business intelligence (BI). EA technology helps businesses make informed decisions and achieve predictable outcomes. Its utility is virtually limitless, as embedded analytics increase user engagement, contribute to revenue growth and add significant value to a wide range of software applications. Here are some examples of how the technology can be implemented in ways that benefit both businesses and their customers.
Transparency as a Value-Added Service
Business intelligence is at the core of embedded analytics. But as the general public becomes more aware of data collection practices in the online space (over social media as well as elsewhere) and its potential perils, trust in BI solutions seems to be degrading among users. The internet is growing, and so is the complexity of security and best user practices.
Many businesses gather data in order to understand their customers as best they can, so that they remain competitive, profitable and relevant through meaningful customer interaction. And businesses aren’t the only entities that collect and analyze data – public institutions benefit from analytics as well. But whatever the institution and its objectives, breaches can potentially shift a great deal of power over data from well-meaning organizations to any number of bad actors with black hat motives.
The opacity of BI operations can serve as a major pain point for customers. Recent news about data collection by way of social media APIs for the benefit of specific political campaigns has resulted in widespread outrage and a new level of mistrust toward some of Silicon Valley’s largest tech companies. It sounds bad for big data business, but the good news is that embedded analytics can actually address this issue by giving users access to some, most or even all of the same insights that are visible on the business side.
EA technology can not only help businesses make choices, but can help users and customers as well. If users are allowed a clear view of which data is being collected, how and why, then trust in EA-enabled products could be established. Transparent, EA-enabled user experiences definitely have a place in the new BI paradigm. Analytics can be accessible – to anyone – and it should be.
Embedded Analytics and Clearly Defined Customer Pain Points
Pain points are essential data when it comes to user research. They are the problems that users experience, the ones technology sets out to solve. Software products generally aim to solve a defined range of user pain points, which either exist within or outside of user engagement. Although software is built around the idea of eliminating user pain points, it doesn't always succeed – and can even create more problems than it resolves.
One feature upon which embedded analytics technology continually improves is the development of accurate, dynamic and up-to-date user profiles. When people form a relationship with software, the relationship can improve over time if the user gives insight into their behavior, preferences and pain points. User problems are opportunities for software to create solutions.
There are a number of different ways that embedded analytics can help software companies measure user pain points. In order to best understand user experience, companies must model it with as wide a sample of business intelligence data as possible. This data gives them the insight needed to assess existing features and capabilities in an actionable way.
In the past, pain points had been measured by way of data collected through laborious research and user outreach. Now, thanks to embedded analytics, user profiles can be formed automatically through natural engagement, creating the conditions for streamlined problem-solving.
Read: The 5 Biggest Data Visualization Mistakes Everyone Can Easily Avoid
Embedded Analytics and Predictive Marketing
New technology, such as machine learning and artificial intelligence, is rapidly changing how marketing campaigns operate. As a result, software companies must not only analyze past and present trends but also look toward the future in order to compete. In addition to current patterns, embedded analytics can illuminate data that helps businesses anticipate what is yet to come.
Trend analysis allows businesses to prepare for whatever the future has in store for their market. But predictive analytics require such an advanced level of data mining that third-party solutions are often most efficient. In other words, embedded analytics provide a white label option for predictive marketing.
Using statistical algorithms, predictive analytics leverage data to model trends in order to measure their trajectories over specified time ranges. The more comprehensive the data, and the more accurate the analysis, the greater the advantage access to that information provides.
Embedded analytics technology not only makes predictive analytics possible for just about any business, but it also renders that data highly accessible to users of every experience and engagement level. With streamlined usability and data visualization, the comprehensive business intelligence of embedded analytics affords users insight into market trends that can extend well into the future.
Analytics and Business
The ways in which analytics benefit the modern business are immeasurable. But now and in years ahead, things like user trust and transparency will likely play increasingly significant roles in business intelligence. Users want to interact with products that improve their lives in the short and long term. That means that businesses should focus on what their users’ values really are and how they can accommodate them. Embedded analytics can help companies make these assessments as well as the adjustments needed to add true value to people’s lives.
