BI Trends

New Study: Top 3 Trends in Embedded Analytics

By Brian Brinkmann
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Analytics has become a foundational requirement for any application. In fact, our recently published 2018 State of Embedded Analytics Report reveals that more than 85 percent of respondents have embedded analytics within their applications. What’s more, teams estimate it contributes 51 percent of an application’s total value.

>> Get Your Copy of the State of Embedded Analytics Report <<

As so many application teams turn to embedded analytics to increase the value of their applications, the competition increases—and the pace of innovation accelerates. Companies are seeking the next great feature to differentiate their software and drive customer value, and these factors have led to some interesting shifts in analytics.

Logi recently surveyed over 500 members of application teams—including product managers, software engineers, and executives—about the state of embedded analytics today. Here are the top three trends from our latest research report:

1. Application teams see substantial business benefits from embedded analytics

Every application team today—whether they’re an Independent Software Vendor (ISV) working on a commercial application or an IT team working on an internal application—faces the challenge of delivering more valuable software to end users. One way to do this is with embedded analytics.

But does it work? According to our 2018 survey results, yes. Here are a few stats from the report:

  • 96 percent of companies say embedded analytics contributes to overall revenue growth
  • 94 percent say it boosts user satisfaction
  • 93 percent say they’ve improved user experiences
  • 67 percent say time spent in their applications increased after they embedded analytics

2. Leading companies are evolving analytics beyond basic features

While every application has a minimum requirement to “offer analytics,” forward-thinking companies recognize the opportunity to differentiate themselves with unique features. They’re going beyond basic capabilities, like interactive dashboards and data visualizations, and embedding sophisticated features such as predictive analytics.

When an application includes unique capabilities, it’s able to further differentiate itself from competitors and drive more value. This year’s survey shows companies delivering sophisticated analytics capabilities are better able to deliver a range of business benefits compared to those only offering basic features—and we expect the gap will only continue to widen.

These benefits include:

  • Increasing revenue: Seventy-one percent of application teams with sophisticated capabilities see an improvement in revenue, compared to 60 percent of applications with basic features.
  • Differentiating their products: Sixty-two percent of application teams offering sophisticated features are able to better differentiate their products, compared to 52 percent of those with basic capabilities.
  • Reducing customer churn: Fifty-three percent of application teams with sophisticated capabilities reduce their customer attrition rates after embedding analytics, compared to 42 percent of those with basic features only.

3. Building analytics without help is no longer an option

Application teams have three options when it comes to embedding analytics: build in house, buy a solution, or take a combined approach. Historically, if an application team chose an approach that failed to deliver results, they could refactor their application and start over.

Today, we’re seeing that companies no longer have time for do-overs. New capabilities are being integrated (and commoditized) faster than ever before, and the competition is creating enormous pressure—making it nearly impossible to monetize late-to-market capabilities.

Let’s look at the benefits and drawbacks of each approach:

  • Building a solution using open-source components and custom code empowers developers to create sophisticated applications. But the costs and resources of maintaining and updating the analytics over the long term are unsustainable for most.
  • Buying a solution and bolting it onto your product allows you to deliver basic analytics features quickly. But most of the out-of-the-box data discovery solutions fail to deliver the breadth of capabilities of build or combined approaches.
  • A Combined Approach involves purchasing an analytics development platform and customizing it. Compared to building, application teams are able to get to market faster and reduce the long-term resources needed for maintenance and updates. Compared to buying, teams are able to completely integrate the look and feel of their analytics with their existing application and deploy sophisticated capabilities.

According to our survey, a combined approach is the best bet for most companies. Here’s why:

  • Of this year’s respondents, companies with a combined approach currently offer more robust functionality than homegrown solutions across nearly every category.
  • Seventy-four percent of commercial applications taking a combined approach are able to charge more for their analytics, compared to 60 percent of those that bought a solution.
  • Application teams that took a combined approach when embedding analytics are 19 percentage points more likely to increase revenue than those that bought a bolt-on solution.

Companies with the most successful applications have one thing in common: They leverage an analytics development platform to quickly deliver the most robust capabilities to the market. And by embedding analytics, application teams are able to differentiate their software products, enhance the overall application experience, and increase end-user adoption.

For more trends and statistics, download the 2018 State of Embedded Analytics Report >

Originally published June 7, 2018

About the Author

Brian Brinkmann is the VP of Product Management at Logi Analytics. Brian has over 15 years of analytics and BI software experience. Prior to joining Logi Analytics, he held senior product strategy, management, and marketing positions with MicroStrategy, creating BI applications for marquee customers such as Nike and Franklin Templeton. Brian holds a MBA and a MEM from Northwestern University, as well as a Bachelor of Electrical Engineering from the University of Dayton.