White Label Analytics: Key Benefits, Platforms, and How It Works

The difference between a good software application and a great one often boils down to analytics.

But SaaS leaders face an unavoidable conundrum. The options are limited:

1) keep offering bad or no analytics,

2) build analytics from scratch– an expensive and time consuming endeavor– or

3) turn to a 3rd party vendor that helps embed analytics into their program.

That third option is cheaper, easier, and can be done without overwhelming your development team. But it also usually comes with downsides: it could be clunky, slow, cumbersome or just too unfamiliar to your users, while its presence and branding take over your platform.

White label analytics offers a compelling solution to this dilemma, allowing you to embed sophisticated analytics capabilities while maintaining complete control over branding, user experience, and visual consistency. 

In this article, we’ll define white label analytics, explore why it matters for application providers, and show how to evaluate embedded analytics solutions to ensure they truly enhance your product rather than detract from it. Then we’ll list a few questions to ask to assess vendors’ white-label capabilities as part of an evaluation of embedded analytics vendors.

Whether you’re considering embedded analytics for the first time or looking to upgrade your current solution, understanding these capabilities is essential for making the right technology decision for your product and team.

What is White Label Analytics?

White-label analytics refers to embedded analytics software that can be rebranded and customized to blend in seamlessly with the parent application. 

Your users demand powerful data insights, but they expect these capabilities to feel native to your application. They don’t want to feel like they’re being shuttled between different platforms or learning new interfaces. Meanwhile, your development resources are already stretched thin across your product roadmap. 

White-label analytics platforms offer both better usability and differentiation that will set you apart from the competition.

Why is White Label Analytics Important?

When you white-label embedded analytics software, you make your charts, reports, and dashboards look like a seamless part of your software, instead of a third-party plugin. 

In, “How to Select an Embedded Analytics Product,” author Wayne Eckerson writes about how BI tools have traditionally been used by only about one-quarter of the average organization. “Embedded analytics changes the equation. By inserting charts, dashboards, and entire authoring and administrative environments inside other applications, embedded analytics empowers business users with insights and dramatically increases BI adoption. The catch is that most business users don’t know they’re ‘using BI’—it’s just part of the application they already use. “

The best embedded analytics solutions are invisible to users. White labeling, also often referred to as “customization,” is an important attribute for many BI tools.The Dresner Wisdom of Crowds® Business Intelligence Market Study rates vendors using a 33-criteria evaluation model, including “customization and extensibility,” within the category of “quality and usefulness of product.”

The Benefits of White Label Analytics

A white-label dashboard platform increases user adoption and ease of use.

 With proper white labeling, you can deliver all the functionality of self-service analytics tools in a high-quality experience that fits your brand.

Here are the top benefits of white label analytics:

1. Maintain Brand 

According to Vendasta, “The key to white labeling products is anonymity… Your brand gets all of the credibility, loyalty, and trust.” To maintain the user experience your team built, you must be able to fully white-label analytics within your SaaS application.

White labeling enables you to customize the analytics with your colors, logos, and other elements so it blends in with the SaaS application. 

2. Consistent UX without Added Development

Product owners invest significant efforts into developing a user interface that’s easy to use, aesthetically pleasing, and visually consistent. 

To maintain these efforts, white-label embedded analytics software capabilities must be able to also maintain that same look and feel.

When interacting with your self-service analytics capabilities, users should have no doubt that they’re continuing to use your same application. They shouldn’t have to wonder if they’ve left your application to work within a separate tool.

3. Seamless Integration

You need responsive designs that automatically adapt to different screen sizes. 

Equally important is the ability for users to customize their experiences to display exactly the types of data they want; in the manner they want to see it. 

With white-label dashboard software, you can integrate seamlessly, increasing user engagement so your SaaS app becomes sticky as users rely on it.

The best analytics tools are invisible to users. White labeling, also often referred to as “customization,” is an important attribute for many BI tools for creating true white label reports.

The Dresner Wisdom of Crowds® Business Intelligence Market Study rates vendors using a 33-criteria evaluation model, including “customization and extensibility,” within the category of “quality and usefulness of product.”

4. Accelerated Time to Market

White labeling enables you to maintain your app’s look and feel, without the need for your developers to rebuild significant components of the third-party analytics app. 

Comprehensive white label functionality can allow you to quickly go to market while minimizing investment and continuing to deliver a consistent user experience.

5. Minimal Development Effort

True white-label analytics software opens up the entire user experience to customization, including end-user custom report and dashboard creation. You should also be able to customize without coding.

Look for the ability to white label not only the logo, header style, and chart color palettes, but also spacing inside certain charts.

Common Challenges with White Label Analytics

1. Tools Not Designed for Embedding

Mr. Eckerson writes, “Most BI tools were not designed for embedding; converting a stand-alone, commercial product into one that can be easily embedded in both single- and multi-tenant environments with full fidelity is challenging.”

2. iFrames

While many BI tools can embed dashboards and some can embed individual widgets (charts), the functionality fails to meet the needs of SaaS providers. For example, many traditional BI tools rely on iFrames for their embeds which offer little customization beyond logo swapping.

AWS Quicksight white label features are a good example as it only allows basic updates with iFrames embeds lacking the customization that a SaaS leader would expect. (See more about Qrvey vs Quicksight here) 

3. Lack of Customization Options

Others BI tools that do support JavaScript widgets, may lack customization options such as CSS overrides for a true white-label embedded analytics experience.

4. Security Challenges

Moreover, without strong multi-tenant analytics security, SaaS providers may face challenges ensuring data isolation and privacy across different client accounts.

What Part of an Analytics Tool Can You White Label?

This will always vary by software. But the features that set you up for success include:

• logo
• header style (plays a big role in whether it blends into your app)
• chart color palettes
• fonts – type, sizes, color, and weights
• spacing inside certain charts – helpful when dealing with axis labels with long text

Key Features of the Best White Label Analytics Solutions

When evaluating white labeling, Mr. Eckerson advises, “What parts of the user interface can you customize without coding? The best tools let you create a custom graphical interface that blends seamlessly with the host application without developer assistance. The less coding, the quicker the project deploys.”

1) Extensive Customizations

• The basics: The most common are logos and color palettes used by charts. This is perhaps the most basic and where all solutions start.
• The canvas: Can you change the color of the canvas or dashboard background? If you use a light gray as the background for your SaaS app, you will want the same control of your reports.
• Style elements: How about fonts: family, size, weights, colors? Does it have defaults or is it chart by chart? How about widget elements such as borders and box-shadows?
• Chart titles: Can you change the font styles? Can you hide it all together?
• Dark mode: A simple yet more complex customization. Does your solution have it?

Your application’s UX designers take careful consideration of every other element of your application. If you cannot get down to these levels, it’ll be obvious to users that you’re using third-party software.

2) Customization of Elements that can’t be Configured with an Interface

Ideally you should be able to create stylesheets or pass in overrides in the embed code. Everything should be programmatic in the embed code or as API parameters.

3) No iFrames

Iframes make most engineering teams bang their head against their desk. Iframe solutions like Amazon QuickSight and Tableau make it the hardest to white-label given their lack of customization. What’s worse is when a business like SiSense has multiple products with different capabilities that may force you into using their iframes. Many Sisense competitors don’t have that complexity.

And some like Yellowfin, will offer both, but you’ll have to spend additional time vetting each method.

This is one of the biggest reasons we only use javascript for our embedded components. iFrames are simply too limiting and a security risk to be useful in multi-tenant analytics use cases by SaaS companies. Did you know Qrvey offers 65+ CSS style classes that teams use to seamlessly white-label Qrvey’s embedded analytics product? Check out some dashboard examples.

Who Needs White Label Analytics?

• SaaS Companies

With white label analytics, SaaS companies can provide their customers with more value while engineering teams ship faster. 

• Healthcare 

Embedded analytics enable teams to safely analyze sensitive healthcare data, from individual patient records to entire healthcare practice performance all within your SaaS application.

• Financial

Secure analytics solutions can enable teams to transform financial data into actionable insights. With the ability to connect to any data source, including real-time financial transactions, you can analyze multiple financial data sources on a single dashboard.

• Supply Chain and Logistics

Supply chain software generates large amounts of data around the procurement, processing, distribution and transportation of goods. White label analytics can deliver critical insights to drive efficiency.

Choosing The Best White Label Analytics Platform

When selecting a platform, look for the following features.

1. Fully Customizable

True white-label analytics software opens up the entire user experience to customization, including support for colors, fonts, chart and visualization attributes, and much more. By providing granular control, developers can ensure your analytics look perfect in all environments and on all devices.

2. Compatible with Self-Service Analytic Capabilities

Effective white-label features must not interfere with self-service analytic capabilities. Users should be able to fully customize their analytics and dashboards to suit their needs within the parameters that you, as the software provider, have defined.

3. Deployed

Your data should never leave your cloud. With a deployed analytics solution like Qrvey, you are always in total control of your data and your infrastructure.

4. Scalable

For low infrastructure cost with maximum enterprise-grade scalability, look for a solution that leverages on-demand services and container technology. Container technology, not servers, is the best option available to SaaS platforms to scale for growth while keeping costs in check.

5. Multi-Tenant Analytics

Creating performant, secure, and scalable multi-tenant analytics requires overcoming steep data engineering and infrastructure challenges that stretch the limits of most software teams. Your analytics solution should provide the link between databases and users. Qrvey’s cloud native analytics layer includes the necessary components to turn your database into a multi-tenant analytics data lake.

6. Support

Exceptional support starts with deep expertise. An engineer-led support team should deliver precise, actionable guidance, whether it’s troubleshooting a complex issue or optimizing your setup.

Implementing White-Label Analytics

Implementing white-label analytics platform doesn’t have to be a cumbersome process that will slow down your software development or analytics implementation. If you’ve selected a customizable option like Qrvey, your developers can simply use the native styling tools to create white label dashboards that match your brand and the rest of your application. Once set up initially, most white-label embedded platform settings will not need to be adjusted later on as additional analytics are deployed throughout your software. Check out our data visualization examples to see how white-label analytics software could function for you.

Want To Learn More?

Qrvey is the leading embedded analytics for SaaS solution, offering an analytics solution built for embedded and white label use cases exclusively for SaaS companies that want to offer self-service analytics using their own data.

At Qrvey, we certainly advocate to SaaS providers using our embedded analytics, but we also understand that your customers are using your tools. In most cases, they have no idea that the interactive dashboards and reports were built by Qrvey – and that’s exactly as it should be.

Learn about why SaaS companies rely on Qrvey to power better analytics experiences and the transformative power of embedded analytics.Sign up for a demo today to learn more. We’ll get you setup with a free trial and you can try our white label analytics features yourself.