What is Embedded Analytics? Seriously.
In a previous post “Why It’s Easier to Start Fresh”, I mentioned that we, at Qrvey, are so focused that we can describe what we do in just seven words — “embedded analytics for SaaS providers on AWS”. But, what does embedded analytics really mean? If you ask 10 people you might get 10 different answers. So, I want to talk about what embedded means in the context of software providers that incorporate analytics into their product.
SaaS providers come in many forms today. There are opportunities to create SaaS companies in every industry. Just think about the industries we interact with on an everyday basis such as healthcare, insurance, automotive, real estate, restaurants, banks, etc. Each of them rely on software to conduct their business and now their end users require their reporting and analytics to go beyond simple static reporting, they want to customize their data views for reporting on their terms aligned with their individual goals.
Product management leaders are choosing third-party reporting and analytics companies to provide OEM components that they can embed in their SaaS software for resale versus building their own capabilities. They run into several barriers to success with the path of building themselves, but most often it overwhelms their valuable development and roadmap resources that are better put to use enhancing their application with features core to their industry.
Whether you have 500 employees or 5000, SaaS companies should not build their own advanced analytics components and features. It will always be inferior to their end users’ expectations.
Embedded analytics goes far beyond the architectural benefits or boosting developer productivity. These benefits extend to nearly every aspect of a SaaS provider’s business such as:
· CEOs gain confidence that their product can continually evolve to match their vision.
· Sales managers know they can sell more and sell faster with analytics capabilities that customers keep requesting; this also improves retention.
· Product management leaders can extend their product functionality beyond what they can develop in house. They also decrease risk since it shortens the time to market and builds their own credibility in the process.
· Product and marketing leads can create differentiated offers that make them more competitive in a fraction of the time.
· Lastly, engineering managers get an increase in developer productivity while saving time and resources by not building a custom analytics feature in-house. Using embedded analytics that is made by experts reduces the complexity for their SaaS development team.
Bottom line: SaaS products should focus on what they do best and outsource the rest.
It’s the combination of all of these benefits that encompass embedded analytics. The last point I’ll leave you with is that legacy BI vendors trying to pivot to embedded analytics are learning a few things the hard way, but one really important item is that their license agreements aren’t designed for embedded analytics, they’re created for user based licensing for internal use. This won’t be an easy pivot.
