Visualizations are commoditized. In 2016, Google and Amazon released practically free tools – Google Data Studio and Amazon Redshift – allowing users to visualize the data created or contained in their platforms. While they are not market leaders in the analytics space, their decision portends a shift in the market. Visualizations alone have become table stakes – something expected to be included with little to no cost – in software that offers in-app analytics.
While Amazon and Google can afford to give away visualizations, as other revenue streams prop up their businesses, many software vendors rightfully see in-app analytics capabilities as a means to drive new revenue, reduce churn and grow their total addressable market.
By going beyond embedding visualizations and dashboards, product managers can create a uniquely compelling value proposition based on new analytics capabilities that allow users to do their jobs better, smarter and faster.
So, as 2017 dawns, Logi has compiled a list of the top 3 sophisticated analytics capabilities to make your product stand out from the competition.
1. Custom Styling
Logi’s 2017 State of Analytics Adoption Report has shown a continued trend away from data discovery applications and towards embedded analytics. And as software vendors begin to invest more heavily in this experience (vs. a traditional bolt-on experience), there is a need to create unique user experiences. In order to leverage in-app analytics capabilities as a selling point for your app, they must look and feel as if they were developed and designed by you alongside the rest of your application. Today there are opportunities to create completely white-labeled inline applications that not only look and feel like the rest of your application, but can provide unique interactions for each user based on their roles and rights.
2. Integrated Workflow
Many software vendors co-present themed analytics capabilities alongside their application. And while this presents a cohesive experience for users, it fails to make an application analytically driven. For example, let’s say a vendor offers a chart showing buying segments. If an analyst discovers an underserved group, and wants to alert their colleagues to kick off a marketing campaign, they would likely need exit the app and use three additional tools. However, by integrating analytics in the host app, users could simply e-mail colleagues directly within the app and click a button to send the segment to their marketing automation platform to launch a campaign. By integrating analytics in the host app workflow, vendors can create compelling and thoughtful use cases that allow their applications to offer real differentiation.
3. In-App Self-Service Analytics
If data discovery usage is declining, why invest in self-service analytics at all? Because one reason data discovery tools have peaked is that they add friction to a user’s workflow. By keeping everything in the apps people use every day, users’ reliance on them grows. We have seen first-hand that soon after software companies introduce in-app analytics capabilities, users begin to ask more questions that the visualizations don’t answer. Product teams often think they have two choices – either answer every ad-hoc request and create a bloated product, or ignore requests and focus on the core product leaving customers frustrated. However, by adding self-service, users are empowered to investigate the data that is important to them and share their findings with colleagues. By incorporating appropriately themed, styled and secured self-service analytics, product managers unburden their teams while turning a single application into one fit for infinite use cases.