Here are the factors to consider when adding analytics to a SaaS product, from product management and UI (and UX) to DevOps and revenue generation.
Engineering leaders and product managers at high-growth software as a service (SaaS) companies recognize that their customers’ end users need data-driven insights. Analytics enhance user engagement, improve productivity, and drive adoption—all of which impact whether or not a customer decides to stay with a product or switch to a competitor.
But the truth is adding analytics to a SaaS product entails more than most assume.
Adding analytics to a SaaS product always starts with the debate around building or buying, often leading to a cost analysis between the two approaches. It turns out that the traditional approach—building analytics functionality in-house—often falls short of expectations while pulling resources away from other features within the product.
Analytics in a SaaS Product: What You Need to Think About
From product managers seeking higher retention to DevOps teams requiring control over deployment, adding analytics to a SaaS product requires a solution that balances power and ease of use while addressing architectural and data engineering challenges. If done successfully, you can empower teams to deliver a seamless user experience, optimize performance, and drive revenue growth.
Here’s what you need to consider when adding analytics to your SaaS product.
#1. Product management
Product managers are engaged with the entire lifecycle of the product. Not only do they need to accurately capture and translate user needs into the product, but they’re also tasked with determining how effectively the technology they choose fits into those requirements and integrates with the existing tech stack.
Of course, there will always be trade-offs between everything on the end user’s wishlist and what can be practically delivered (and maintained), so building internally to add analytics to a SaaS product all but guarantees you’ll never keep up.
Embedded analytics through a white-label solution, such as Qrvey, provides an effective path to pursue. Product managers can optimize the time and effort required to add analytics to a SaaS product, and they’re even able to introduce self-service analytics, which allows end users to customize views, queries, and filters.
In adding analytics to a SaaS product, product managers ultimately need to deliver on users’ desires—as frequently as they may shift. Embedded analytics should be customizable—especially in a low-code environment. You’ll ensure timely delivery and maintenance, without borrowing resources from a core focus area or spending more on engineering resources.
#2. User interface and user experience
End users care deeply about the user interface. After all, they’re taking advantage of several SaaS products in their workflows daily or at least a few times per week. This means that analytics in a SaaS product should be intuitive enough for end users to become proficient without requiring large amounts of training.
As a result, there’s a balancing act between power and ease of use. In fact, since not all end users are created equally, some may be far more adept and able to use advanced functions, while others may simply want basic charts and reports.
Think about the arduous process of building analytics internally to serve both types of users. You’ll likely overcompensate on serving one, which adversely affects the other.
Perhaps more importantly, when it comes to the user experience, it’s critical that the look, feel, and branding remain consistent across the entire SaaS product. This is why white-labeling capabilities become critical when comparing embedded analytics vendors.
#3. Development
Development teams need the analytic solution they’ve chosen to add to their SaaS product to align with their design criteria and practices. Otherwise, it’ll represent an obstacle to achieving their roadmap milestones. The product management team must recognize this internal requirement.
Take an ‘APIs in, APIs out’ approach. Every element in your analytics should be an embeddable widget, featuring all Javascript and no iframes—and remember that iframes present serious security risks. You get exactly what you specify, with fine control over function and presentation.
As a result, there’s a connection between what product management specifies and what engineering delivers.
#4. Architecture
Within development teams, specialists are concerned with multi-tenant architecture’s unique challenges.
Ask yourself the following questions when adding analytics to a SaaS product:
- Can the solution handle large volumes of data?
- How does the solution maintain a separation of data between tenants?
- Beyond security, does the flexibility exist to allow each tenant client to have the freedom to use (or create) their data?
Embedded analytics should accommodate these challenges. Not only should it include a high-performance data engine ingesting and transforming structured and unstructured data, but there needs to be a semantic layer that makes implementing role-based security simple—without requiring you to build and maintain a separate permissions database.
#5. Data engineering
Engineering teams typically have individuals focused on the data itself, and they’re tasked with figuring out the type of data store being used and how performance will be ensured. Whatever implementation they choose, the data workflow must be efficient and scalable.
A high-performance data engine for embedded analytics allows data engineers to use relational databases, data lakes, data warehouses, or any combination that ensures security and performance.
#6. DevOps
Your DevOps team needs to control every aspect of deployment, updates, and maintenance, so embedded analytics should be fully self-hosted.
Never make your company beholden to a third party for scheduling, maintenance, or performance scaling. Instead, you want a deployment and update process that allows for specifying particular builds for defined groups of clients and automating delivery.
This means the team can handle different subscription levels and feature sets easily, ensuring every customer receives the right build at the right team—all without needing an army of resources.
#7. Support
Support is a ‘forever’ commitment, and your support team must answer questions and concerns while providing onboarding and training. It’s a wide range of responsibilities, but your end users need to feel comfortable with your product and get every bit of value out of it.
When building analytics functionality internally, you’re often flying blind. It’s unclear whether you’re on the right track, and a misstep can lead to hundreds or thousands of disappointed end users.
However, the right analytics partner should provide access to development professionals with advanced knowledge of the technology you’re implementing. Not all embedded business intelligence (BI) or embedded analytics vendors provide a high level of customer support. So just as you want to support your end users, your analytics partner should support you every step of the way.
#8. Revenue
SaaS companies never undertake an analytics project unless it contributes to the bottom line, whether generating more revenue, expanding markets, increasing competitiveness, or lowering costs. As mentioned earlier, the risk of building includes a near-endless number of costs associated with an unpredictable reward.
Adding analytics to a SaaS product through a turnkey, multi-tenant solution should allow for different pricing models and strategies that optimize adoption and revenues. The right analytics partner will help you build cost models around your enhanced analytics offering to ensure the investment continues to pay off year after year.
Navigate Embedded & Multi-Tenant Analytics Easily
Successful implementation of embedded analytics requires SaaS companies to anticipate and plan for several challenges, from designing, creating, and deploying the product to ensuring efficient—and profitable—implementation over time. This explains why the perspectives of individuals from across a range of responsibilities need to be considered from the beginning.
SaaS companies must remain acutely aware of their limitations, too. It’s unlikely to be an expert in analytics, and journeying down the path to build an in-house solution distracts teams who need to focus on the core business.
Knowing the hurdles of building analytics functionality in-house, it’s clear that SaaS companies adding analytics to a SaaS product should choose a purpose-built solution. It’ll deliver better analytics faster than ever while providing enterprise scale at a fraction of the cost.
When you add a complex feature like embedded analytics, building initially seems like the right approach—use Qrvey’s Build vs Buy calculator to uncover the hidden costs and find your return on investment (ROI).
Ken is the Chief Revenue Officer at Qrvey, the leading provider of embedded analytics software for B2B SaaS companies. With over two decades of experience in B2B technology marketing, Ken brings a wealth of knowledge and expertise to his role, where he leads the sales and marketing teams in developing and executing Qrvey’s corporate business strategy.
Throughout his career, Ken has been at the forefront of driving growth and success for innovative technology companies. His passion for analytics and data-driven decision-making has been a driving force behind his contributions to the industry, and he is a strong advocate for empowering product managers and software developers with the tools they need to deliver exceptional data-driven experiences.
Ken shares his insights and perspectives on the latest trends and best practices in embedded analytics, data visualization, and the role of analytics in product development for SaaS companies.
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