This post was written by Ronald van Loon, Director at Adversitement. Ronald is a Logi Analytics partner.
Traditional business applications are changing, and embedded predictive analytics tools are leading that change. Embedding predictive analytics in your applications can not only help improve the end user experience, but it can also help with increasing revenue, improving decision-making time, and save costs. According to Logi Analytics’ 2018 State of Embedded Analytics Report, predictive analytics is the number one feature being added to product roadmaps.
Traditional Business Intelligence vs Embedded Analytics
Embedded analytics is a step above traditional business intelligence (BI). Traditional BI was limited only to extracting insights from within the scope set for analysis. It was good for aggregating and preparing data for analysis, but it was lacking when it came to offering solutions for management level reports. Moreover, users were also required to create their own reports.
Embedded analytics leaves traditional BI behind, because it has integrated capabilities within applications. All applications have the capabilities to run integrated analytics. These embedded applications help encourage smarter working habits that are achieved through combining insights and actions together. Moreover, the analytics performed by an embedded system are deeper in comparison to traditional BI and generate authentic insights on the go.
How Embedded Predictive Analytics Can Increase Value in Existing Applications
Embedded predictive analytics can work perfectly within existing applications, as it can increase the value associated with them.
To begin with, these applications can empower business users and increase user adoption. This will not only ensure that you have more users, but will also make sure that users are satisfied while using the application. The use of embedded predictive analytics will also deliver an improved UX to users.
Business applications that have embedded predictive analytics will stand out in the market because of faster processes and improved UX. By embedding predictive analytics in business applications, businesses can achieve faster revenue growth, and increase operational efficiency. The business application can also help reduce customer churn by focusing on the customer.
Once you include predictive analytics within your application, your team can work together to achieve advanced capabilities. Insights that require quick action can be handled immediately based on the gravity of the situation.
Industry Benefits of Embedded Predictive Analytics
Predictive analytics presents a few challenges, which when mastered, can provide benefits to multiple industries.
The first challenge is experience, which can impact action integration. Experienced workers are required to make sure that everything remains exciting. The presence of experienced workers will help your application stand out. Moreover, model development is required for all predictive models in play within the application. Finally, information distribution is also a must.
The industry-specific use cases for embedded predictive analytics include healthcare for proper documentation and analysis of patient records, retail for inventory check, and eCommerce for tracking. Other industries include hospitality, finance, and manufacturing.
In summary, the shift towards embedded predictive analytics can help provide solutions for numerous high-value business problems. It can also help businesses keep up with market innovations. The end result is that of increased business intelligence, which every organization is striving to achieve today.