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AutoQL for Product Analytics Software Users

~4 minute read

When SaaS marketing teams are looking to increase engagement and boost expansion monthly recurring revenue (MRR), they look to their product analytics software to find insights that help them make decisions that enhance interest from their market and drive user satisfaction.

As a software company scales its offering, it’s imperative that teams stay agile. Issues that cause confusion during onboarding and friction in the user experience need to be addressed as quickly as possible because even a single moment of frustration can lead a customer to churn, resulting in a loss of revenue.

With so many options available in the market, SaaS providers need to focus not only on attracting new customers and generating new revenue, but also on keeping their existing customers. It costs companies five times as much to attract a new customer than to retain an existing one.

The best way to make impactful product decisions that drive greater customer satisfaction and loyalty is to leverage user data to see what’s working and what needs improvement.

Read more: Powering Impactful Marketing Strategies with Insights on Demand

Though a robust product analytics system may support data exploration and KPI tracking, it’s critical that data-rich insights are accessible to everyone on a SaaS team so everyone can make informed and strategic decisions on an ongoing basis.

The product marketing team can’t afford to miss any details or insights as they are a key link between their external customers and their internal development team whose (expensive) time will be used to make changes in the software.

Streamlining Analytics for Agile SaaS Product Teams

With data on demand, agile teams can make data-driven decisions, faster, and iterate more rapidly than ever before.


With a data on demand solution like AutoQL embedded in the software they’re already using, the team doesn’t have to sift through or even set up different dashboards to track the results of their campaigns or A/B test success rates.

For example, if the developers deploy an A/B test into the software’s onboarding workflow at the beginning of a given month, anyone on the product marketing team can leverage a conversational user interface like Data Messenger to simply ask questions like “Average time new users spend in onboarding test A versus onboarding test B”, “Average time to conversion from onboarding test A versus onboarding test B” or “Summary of all feature requests by type this week” and receive immediate answers they can analyze and act on right away.

As an agile team, it’s critical to streamline data-related processes. Perhaps more importantly, making data access easy helps establish a data-driven culture across the board and ensures that insight discovery becomes second nature for every employee.

The team can also take advantage of the power of AutoQL to quickly set up their own dashboard with the same questions they may already have about the A/B test––without taking up the technical team’s time. They can ask high-level questions like “Average new sign ups by week last three months” and “Compare number of users who completed onboarding flow test A to number of users who converted after free trial” to get a better picture of the overall impact of new software updates.

Read more: Enhancing Digital Marketing Analytics with Intuitive Data Access

For SaaS product marketers, self-serve workflows that provide real-time access to data enables faster data-driven iteration and product planning that serves the core needs of customers and helps teams understand the effects of product changes on user satisfaction and revenue over time.

Software developers who build SaaS product analytics solutions can enhance the functionality and UX of their own product with seamless, intuitive data access that yields the results that drive those very same results: enthusiastic usage and increased expansion MRR from loyal customers who continuously find new and irreplaceable value in their analytics software.

Keagan Perlette

Author Keagan Perlette

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