Embedded Analytics: a guide for B2B SaaS companies

If you’ve heard about “Embedded Analytics” but you are not sure exactly what it is, how it works, its challenges or its future… you are in the right place. In today's data-driven world and in the UK, your users need more than just a software tool; they require actionable insights to make informed decisions. Embedded analytics in B2B Software as a Service (SaaS) companies is one of the key elements to raise them to the next level and provide much more value to end users.

In this guide, we'll explore what embedded analytics is, its significance, and how it transforms SaaS platforms. By the end, you'll understand why embedded analytics is vital for B2B SaaS companies.

What is Embedded Analytics?

Generally speaking, Embedded Analytics is the integration of data analytical capabilities and content within business applications. For B2B SaaS companies, this means providing your customers with seamless access to data analysis and visualization tools directly within your software application.

Which are the main benefits? Embedded Analytics can help SaaS companies shorten their time-to-market, differentiate their products, improve customer satisfaction, and unlock new revenue pathways.

So… embedded analytics is like having a powerful data analysis tool right inside your software. Let’s explain it plain and simple with some examples.

Imagine a weather app that provides not only real time forecasts but also historical weather data, all without leaving the app. That's the essence of embedded analytics. It integrates data analysis tools directly into your SaaS application, eliminating the need to switch between different tools.

Think of it as the GPS system in your car. It not only shows you where you are but also suggests the fastest route to your destination. Similarly, embedded analytics takes your data and guides you to insights without leaving your SaaS application. It's all about convenience, but more importantly, about offering an integrated experience around data visualizations, KPIs in the form of dashboards.

Imagine you're using an e-commerce platform. Embedded analytics allows you to visualize real-time sales data, track customer behavior, and even predict which products are likely to sell out soon, all within the same software.

One more example. Imagine you have the power to build with your data and your own hands, the visualizations that we all can use in Google Analytics, embedded within a marketing dashboard that provides real-time insights on website traffic and user behavior.

How Embedded Analytics Drives Decisions

Embedded analytics isn't just about showing the numbers; it's also about turning data into actions. In other words, your users will regularly access the dashboards and data visualizations you prepare, if they solve real problems and enable them to continuously have “AHA moments” with their data, to make fast and valuable decisions.

Picture this: You're a marketing manager. With embedded analytics in your campaign management software, you can see which ad campaigns are performing best, understand customer demographics, and instantly adjust your marketing strategy based on these insights. This is how tools like HubSpot, Hootsuite and other marketing software, started using embedded analytics that enable marketers to track the success of email campaigns and social media outreach right within the platform.

How to go from being aware of what is happening to really taking action? Well… there are some ways, but you need the tools to arrange everything together. Interactivity inside the embedded dashboards is perhaps the most important aspect. Interactivity means:

• Being able to set up a powerful direct interaction between your application and the embedded dashboard, so that when the user clicks a button or any other visual element in the dashboard, you get the information (say in JSON format) and can perform the needed actions right inside your software.
• Facilitating your users with annotation tools. This is quite useful for them to write down their knowledge after analyzing the insights. Annotations can be used to state out why a spike occurred or why we didn’t accomplish the forecasted numbers.
• A direct communication between your application and the embedded dashboard the other way is also useful using a well defined parameter system, to filter data according to user identity inside your application.
• Other kinds of data interactions include navigation to other dashboards (inside the same scope), opening new insights in popups, filtering other visualizations, etc. The possibilities are quite endless.

Beyond these interactive capabilities, one crucial aspect for embedded analytics to be successful in terms of continuous usage and continuous value delivery for your users, is integrating an alerting system capable of sending to the users custom or white labeled notifications (with your own logo, your own colors, your own typographies, your own design, your own SMTP server). Smart data alerts can be understood as automatic triggers that happen when some defined rules are met inside data. Alerts enable you to automatically send an email, an SMS or any other message integrated with your application or even other third parties, when the conditions are met.

These alerts and notifications in almost real time are crucial in some types of business applications and enable fast response times to incidents, to undesired facts or any other cases that require an immediate action. Imagine you are a marketing manager and you are tracking the spend in ads in real time in some network that does not allow you to stop the campaign when other conditions, different than the specified budget, happen. This is the kind of alerts that a powerful data platform enables you to configure and integrate inside your application for your users to turn data into actions that can mean a lot of money or time saved, or a lot of money earned.

How Does Embedded Analytics Work?

You don't need to be a tech expert to grasp this. Think of embedded analytics as a recipe in a cooking app. It takes ingredients (your data), processes them (using query systems, formulas and algorithms), and serves up a delicious dish (your insights) in an easy-to-understand format. This magic happens behind the scenes through APIs, which are like the secret ingredients making it all work.

The integration process will be different depending on the data analytics platform and your application. A good data platform will do the heavy work for you, but there are still some technical steps that are necessary for the integration of both applications. Today’s modern business apps usually have in common these characteristics:

• Cloud based applications, so users connect from any place with any device connected to the Internet.
• Use of web technologies, so users consume applications using any web browser.
• Use of open standards such as HTML, CSS, Javascript, HTTP, web sockets.
• Usage of APIs (usually via HTTP Rest)

Usually the integration of data analytics with dashboards, KPIs, charts and other insights and visualizations, includes these topics, that can be done with the help of the embedded analytics provider:

• Establish and define embedded analytics inside your product or application strategy. The when, the how, the who and the what.
• Define the implementation parameters:
  • Which are your goals? For instance, you may want to start provisioning your first 25% customers by the end of next month and the 100% of them in two months. You can include here goals related to new features regarding data, for example, including advanced analytics inside your product with prescriptive analytics generating pricing recommendations or cross selling best opportunities (depending on your software of course!).
  • Which are your constraints? We are talking about factors that limit or restrict the implementation. For example… our engineers have limited availability (just 2 hours per day), the resulting dashboards and data visualizations must look and feel like our brand.
  • Define your “need-to-haves” and your “nice-to-haves”. Here you have to get the list of which functionalities are absolutely essential and which are beneficial but not required at least for the initial launch.
  • Define your MVP (Minimum Viable Product). As we usually say… working with data analytics is an incremental process, so trying to get perfection from the beginning is bad advice. Our knowledge working with many clients for fifteen years says the best approach is to work on the simplest, most basic product that satisfies your goals, constraints and “need-to-haves”, and then start iteration loops to keep improving the data analytics features, in parallel with your own application or product.
• Define your audience, that means:
  • Knowing your final users, who they are and which are their pain points.
  • Identifying the key questions the users need to answer regularly.
  • Mock up a list of functionalities the users might want to use.
• Define the requirements in detail:
  • Here we need to know what data is needed.
  • The visual elements to build.
  • How to secure the access to data (user tokens, groups, row access level, data policies, etc.).
• Work on the technical integration, that usually involves:
  • Working with Rest APIs (through HTTPs)
  • Working with standard HTML (IFrames), CSS (custom styles) and Javascript (web components, libraries, etc.).
• Test the resulting data analytics solution integrated within your application with internal users and/or a selection of users.
• Iterate and iterate.

Enhancing User Experience

One of the main concerns of product teams that face the possibility of custom development of data analytics solutions is the complexity around data and the final user experience.

Using specialized embedded analytics solutions integrated inside web applications is a powerful tool that can enhance the user experience in many ways. Surveys published confirm that using Embedded Analytics solutions enhances the overall application experience and increases end-user adoption.

Why? Because these solutions provide seamless access to analytics capabilities within existing applications, eliminating the need to switch between multiple tools or interfaces. This leads to reducing the learning curve and increasing user adoption, which also improves user satisfaction and retention, that means more money on the table at the end of the day.

Embedded Analytics enhances the user experience by providing real-time access to transactional data, which therefore needs no data replication. It works seamlessly on the data that is currently in the system, and at the same time it can connect with other data sources to blend and calculate as needed.

Overcoming the possible challenges

It’s well known that Embedded Analytics provides dashboards and reporting within the same software, and this means increasing operational efficiency, cost optimization, and innovation. However, adopting an analytics solution for a product comes with its own challenges. Here are the most common and how to overcome them:

• Legacy Infrastructure. Many organizations still use legacy systems to analyze and work with their data despite the need for real-time, up-to-date technologies and tools. Legacy systems are complex, outdated systems that use old technology or software that no longer meets users’ expectations. They don’t integrate well with new technologies, have stopped maintenance, and no longer allow for growth. To overcome this challenge, organizations can adopt modern embedded analytics platforms that support a full stack of integrated analytics functions on a unified, scalable architecture with common administrative and management functions.
• Unpredictable pricing. Pricing can be tricky when usage isn’t predictable. Per-object and per-query models introduce unpredictability into the pricing and might leave the finance department with a headache. To overcome this challenge, businesses need to be clear internally and with a potential partner about their requirements to make the best choice for their Business Intelligence software. To achieve a long term relationship, a custom pricing plan with technical support is also a good point to have in mind when selecting your platform.
• Lack of on-boarding efforts from the vendor. On-boarding is crucial for the successful adoption of an embedded analytics solution, and without it, can result in a slow and difficult adoption process. To overcome this challenge, businesses should choose a vendor that provides comprehensive on-boarding support.
• Lack of flexible data architecture. A flexible data architecture is essential for an embedded analytics solution to be able to adapt to changing business needs. So, the selected solution should have as many "flexible configurable points" as possible to configure things like security, connectivity, data models, versioning of data visualizations, multi language support, filtering data in different ways, and so on.
• Limited white-label customization. Each application is different and so has to be the data analytics visual elements embedded in it. White-label customization allows you to brand the final embedded analytics dashboard and visual elements as your own. Limited customization regarding custom styling, can result in a generic and unbranded solution that fails to reflect the unique identity of your business and application. To overcome this challenge, you should choose an embedded analytics solution that provides extensive white-label customization options.

The Future of Embedded Analytics

The future of Embedded Analytics is exciting and holds a lot of potential for businesses, specially B2B SaaS companies. Here are just five ideas about what awaits us in the future. It's important to realize that some of these points are being included in some way in current solutions but we are just starting. As an advice, it’s ok to try to sell based on the latest fashion, like ChatGPT related solutions, but more importantly, the real advantages of advanced analytics must be worked carefully to be perfectly integrated between platforms, data connections and the final perception for end users.

• AI-driven insights. AI is making it possible for analytics to automatically incorporate and process important context from a broad array of sources. This means that businesses can increasingly take advantage of contextual information in their applications to gain deeper insights into their data.

• Predictive analytics. Predictive analytics is becoming more powerful and easier to use. Analytical approaches that incorporate predictive models are beginning to displace merely descriptive approaches, allowing businesses to make more accurate predictions about future trends and events, especially for time series metrics.

• More user-friendly interfaces. Analytics are quickly becoming both easier to use and more powerful. Descriptive analytics, which continue to be valuable for many users, are evolving to make greater use of visual analytics and moving toward a self-service model in which nontechnical users can often develop their own analyses.

• Real-time insights. Embedded analytics solutions are providing real-time insights from business data, exposing data in plain business terms, and making information consumption simple, personalized, and dynamic.

• Seamless integration. Embedded analytics solutions are being seamlessly integrated into enterprise intelligence offerings that allows companies to optimally support their users with decision support and information at their fingertips.

These are just a few ideas about the future of embedded analytics. It’s an exciting time for businesses as they adopt new technologies and tools to gain deeper insights into their data.

Let’s recap

Embedded Analytics empowers your users to understand data and make better decisions right within your software. In this guide, we've explored what it is, how it basically works and its significance. As businesses embrace data, embedding analytics in SaaS products becomes essential. Just as your GPS guides you, embedded analytics guides businesses toward digital age success. It's not just a tool; it's a journey toward data-driven excellence for B2B SaaS companies.

So… what do you think? We’ve tried to summarize in this article the key points for B2B SaaS companies conforming to a starting guide for Embedded Analytics. Feel free to reach us if you want to learn more about the journey.

About Biuwer

BIUWER is an Embedded Analytics platform that allows software companies to build professional data visualizations within their applications, in a faster, safer and easier-to-use way.

The platform is focused on data-driven decision making, it has been designed to allow easy integration into your applications. So users can access real-time information and create interactive dashboards anywhere and from any device.

In addition, with the use of our software:

• You can deliver data through Embedded Analytics to hundreds or thousands of users with a contained monthly or annual investment, with high ROI.
• Ease of use, as it is a no-code tool, technical or programming knowledge is not necessary.
• It easily integrates with your own software, saving you a lot of time creating and maintaining data visualizations.
• Applies access control to the content and the data itself. We apply end-to-end encryption.

If you need more personalized information or in accordance with your company, you can contact us. Our team will be delighted to assist you.