PMs are the CEOs of their product. This challenging role requires people management, expectation management, and delivering business results. In a SaaS environment, the challenge is compounded as results are measured daily, monthly and quarterly highlighting the importance of SaaS product management KPIs.

However, new vendors have emerged to assist Product Managers in delivering cutting-edge experiences to their end-users that align to important KPIs. Below are five ways embedded analytics for SaaS companies help PMs exceed their key performance indicators.

Check our guide that goes into the details of what is embedded analytics.

KPI 1 – Growing revenue while improving customer retention rate

As discussed in the blog post Yes, Analytics are Just Another Feature analytics are a revenue driver for most SaaS companies. There are three potential models:

Tier 1 – Pre-canned reports.

These are typically basic, static reports and dashboards that offer information deemed most important by the software vendor. They do not necessarily drive new revenue streams but deliver enough value to engage and retain users.

Tier 2 – Self-service.

The application allows the end user to select from a library of chart types, connect to the data they deem necessary, establish parameters and add these visualizations to a custom or pre-defined dashboard. These capabilities often require an increased level of subscription – paid on an ongoing basis.

Tier 3 – Custom reports created by a Professional Services team.

Unlike one-off requests for assistance, this level of service is a paid offering and a gold standard for customers. Customers pay a fixed fee for analytics services delivered by a dedicated team of experts at a significant premium.

Embedded analytics also offers the additional benefit of customer retention which allows for ongoing revenue generation. Users who have developed a series of reports which deliver the information they seek, optimized to their particular workflow are less likely to leave for another vendor where they need to re-establish this reporting infrastructure again.

Applications with well-thought-out embedded analytics can be extremely sticky and help dramatically reduce churn. The right analytics deliver revenue upside in a variety of ways for SaaS software.

The right platforms that enable this will allow PMs to offer multiple tiers of analytics by applying rights and roles that expose certain data and capabilities to different users based on their permissions. Each tier can be priced differently, allowing for revenue optimization.

KPI 2 – Delivering rapid time to value

For many applications, dashboards and visualizations are the primary way in which customers interact with information as highlighted in a recent blog post. Banking applications offer charts and graphs of spending and balances, cybersecurity apps show network nodes, and franchise applications show per-store performance.

Nearly every business application – and most consumer applications – rely heavily on dashboards and visualizations to present important information. How quickly a customer can understand this information, assess what it means, and act on it is what delivers rapid time to value.

The combination of pre-canned reports – developed to accommodate the most typical use cases – combined with self-service reporting accomplish this end goal of delivering actionable insight to end users. The critical element is doing what PMs do best – understanding their user base to deliver the most valuable, easy-to-use visualizations out of the box.

This reduces the need for training and delays use of more advanced capabilities such as self-service to a time when users are more familiar with the app.

Ultimately, users become acquainted with the application quickly and see benefits immediately while growing into an advanced use case.

Moreover, there is an accelerated time to market to launch much sought-after analytics capabilities by leveraging the right embedded analytics platform vs. building in-house. This saves developers time and allowing them to focus on core differentiators, important SaaS product management KPIs.

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KPI 3 – Earning a high net-promoter score

Net Promoter Score®, or NPS®, measures customer experience and predicts business growth. This proven SaaS product management metric provides the core measurement for customer experience management programs. NPS is a simple metric asking customers how likely they are to recommend your service from 0 to 10.

Measuring NPS is simple – just survey customers with a single question. However, there are several reasons why PMs suffer from a low NPS according to Mattsen Kumar including product performance and usability. These issues typically manifest themselves as new user frustration and negatively impact NPS – reflecting poorly on a product manager.

The best way to combat these issues is to understand your user and deliver a user experience that is powerful yet easy to navigate. While visualizations play a critical role in this paradigm, so does application performance.

This is why good embedded analytics platforms will have invested significant development time into their data workflows.

Allowing for the rapid processing of large datasets, and supporting a variety of structured, unstructured and semi-structured data sources are essential to modern embedded analytics platforms.

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KPI 4 – Increasing Gross Margin / Profitability

Self-service analytics increase gross margin and profitability. Easy to use self-service allows end users to create reports, charts and visualizations on their own without the need to request and wait for someone else to deliver it.

Without self-service analytics, users turn to support or other technical teams for help in collecting data and creating the visuals they need.

These interactions are margin killers and require an individual employee to invest time in a 1:1 interaction with a customer. These individuals must invest time pulling data, visualizing data and often iterating several times to deliver on customer expectations.

Enough of these interactions require investment in additional employees with little revenue upside. Could companies absorb this cost with little impact on profitability? Yes. But only if usage is limited – an anathema to the SaaS business model.

In fact, as software usage scales the need for assistance skyrockets putting pressure on margins and many customers are unlikely or unwilling to pay for intermittent use of these support personnel. Embedded analytics help to solve this problem.

This feature – which goes beyond standard embedded reporting – allows customers to create their own reports, pull data as needed and reduce the number of interactions with support.

The result is an increase in gross margin and product profitability, SaaS product management KPIs that all product leaders should be following closely.

KPI 5 – Conversion rate from trial to paid

Most SaaS applications offer a limited trial during which software must deliver value for customers to upgrade to a paid license. Embedded analytics are key to a successful trial. Why?

  1. Get Users Invested. Investing time in setting up the application commits users. Once a user has identified useful reports, created useful custom visualizations, and begun relying on provided data as part of their workflow, reverting to what was used before simply doesn’t make sense. Further, going through the effort again with another application proves to be an unnecessary time investment.
  2. Deliver Insights. Delivering insights is critical. Understanding your users and delivering a highly valuable library of reports will provide insights right out of the box. As users want to explore data more deeply they can use self-service reporting to better understand their data.
  3. Achieve Better Outcomes. Ultimately, your application must deliver business impact. If the trial period can deliver insights that positively impact a business within a small window of time, users will be invested in paying for the application.

Delivering these outcomes requires a holistic user experience. This is why next-generation embedded analytics – known as embedded analytics – are essential. This evolution has moved from simply mimicking the look and feel of your application to mirroring the user experience as well.

Beyond UI/UX enhancements, newer platforms also deliver automation capabilities to not only provide insights but also make them immediately actionable.

Overall, analytics and action are important drivers that help turn a “nice to have” software into a “need to have” software. While UX presents challenges to having clean SaaS product management KPIs to measure it, regular use of surveys is a great tool to track satisfaction with user experience.

Not all Analytics are Created Equal

While we have focused on the value embedded analytics, dashboards and visualizations can offer to end-users, many solutions will deliver a sub-par experience. Here are some potential red flags:

  1. Your analytics do not exactly match the look and feel of your application
  2. Analytics require a separate interface divorced from the rest of your application, often served via iframe
  3. Performance suffers in your analytics making the whole application feel slow
  4. You need multiple tools just to get your data ready to be consumed by your analytics tool

For all the benefits the right vendor offers, if the vendor you choose causes any (or worst case all) of the issues above then you will not meet your KPIs. New users will grow frustrated, existing users will tire of performance issues, and big customers will demand more input into your roadmap to ensure course corrections are coming. So, choose the right vendor.

As we stated at the outset – product managers have an incredibly difficult job. But the essence of the role – delivering solutions to problems – is the goal. End-users – be they consumers, businesses or governments understand the value of data and insight and will demand these front and center in their applications.

The best embedded analytics platforms – such as Qrvey – are designed to help product managers deliver solutions to some of the biggest problems – organizing my data to make it actionable and understanding my data to make better business decisions.

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