Introduction

Obtaining data to make better decisions is a compelling value proposition in the B2B and service industries. 

By incorporating embedded analytics into SaaS products or customer portals, you not only drive up the perceived value of your offerings. You also equip users with the tools they need to sharpen their decision-making and achieve successful results. 

Embedded analytics can also be used internally — enabling your organization to reap the benefits provided to end users. 

In this guide, we'll help you grasp the fundamentals of embedded analytics and create a roadmap to successful implementation.

What is Embedded Analytics? An Overview

Embedded analytics — just like regular data analytics — translates datasets into more readable visualizations like charts, graphs, and tables. 

The key difference is, these visualizations aren't viewed through a standalone or dedicated dashboard. As the name suggests, the visualizations are embedded into a separate software environment. 

This can be a: 

• Client or customer portal
• Custom web or mobile app
• Company website
• Software as a Service (SaaS) product
• Internal Content Management System (CMS)

Today, modern data analytics and Business Intelligence (BI) platforms make it easy to generate the embed codes you need to incorporate data visualizations into these environments. In some cases, light coding is needed for processes like injecting dependencies, initializing authentication services, and adding custom configuration values. 

Key Benefits of Embedded Analytics for Your Organization

Here are the top reasons why your organization should use embedded analytics:

• Enhance user engagement and retention on your platform. An in-app analytics or insights tool helps users make better decisions, take full advantage of your product, and get an overall better experience. Done right, this will ramp up your user engagement metrics across the board, including retention rate, stickiness, and session interval. 
• Leverage drag-and-drop analytics solutions. Using embedded analytics lets you save a ton of time and money than developing and implementing a DIY analytics system yourself. Some embedded analytics platforms also feature drag-and-drop data visualization tools, which require little to no coding. 
• Adopt a data-driven company culture. Turnkey embedded analytics solutions accelerate adoption within your organization. This helps foster a data-driven culture across teams, improving the decision-making skills of your data stakeholders. 
• Keep relevant insights within reach. With embedded analytics, you can empower your team members or customers to make data-driven decisions without leaving your website or app. In addition, you'll be able to present your visualizations alongside other meaningful content or software features.
• Advanced flexibility and customization. Top-of-the-line BI and data analytics software come with in-depth customization and white label features right out of the box. Embedded analytics adds an extra layer of customization by giving you full control over the presentation of data visualizations, the content around them, and the entire page's layout.
• Cultivate collaboration on collaborative platforms. Integrating embedded analytics into your project management tool, CRM solution, and other virtual workspace apps allows your team to make sharper decisions and more productive brainstorming sessions. This ensures everyone in the conversation is updated with the latest facts without having to shuffle through multiple reports or data dashboards.

Features You Should Look For in an Embedded Analytics Platform

Ready to start your journey with embedded analytics? 

If you already have an analytics tool, chances are you can embed certain data visualizations into your website or app after jumping through a few hoops. But if you're starting from scratch or looking to upgrade your data analytics solution, below are the seven features you should look for: 

1. Block-Level Customization

When creating client portals, it's important to use an embedded analytics tool that lets you customize the appearance and functions of data visualization blocks. 

Otherwise, your portal could end up being an unattractive pile of elements with analytics blocks that look out of place — unless, of course, you're willing to modify your portal's entire theme to match your embedded analytics. 

Polymer, for example, allows you to change up the colors as well as a handful of configuration options. This includes custom explanations, trend lines, number formats, annotations, and more.

2. Embed Code Generator and API Access

Embed codes let you sidestep a lot of the heavy lifting involved in pulling data visualizations into another website or app. 

A widely accepted practice is the use of inline frames with attributes you can easily modify. Just generate the embed code, copy the inline frame code snippet ("<iframe>"), and paste it to your destination. 

Double-check to see if you're copying the embed code of an entire dashboard or just an individual block. 

On Polymer, you can generate the embed code for an entire dashboard by clicking the 'Share Board' button in the upper-right corner. For individual blocks, click the ellipsis settings icon and select 'Embed.'

From there, just highlight and copy your embed code or click 'Copy Embed Code.' You're now ready to plug it into your portal or app.

While an embed code generator is important, you should also look for analytics platforms that offer API access. This allows you to access specific endpoints from your analytics platform, enabling a more modular approach to embedded analytics tailored to your app. 

3. Interactive and Presentation Features

Interactivity allows users to obtain more accurate insights from your data visualizations. For example, rather than estimating what a bar graph's value really is via the Y or X axis, they can simply hover over the chart to view the exact values. 

Presentation elements like scorecards and image carousels also make it easier to focus on crucial metrics and Key Performance Indicators (KPIs). Useful features like Polymer's goal values for metric scorecards also help your users evaluate performance or results relative to their objectives.

4. AI Tools and Features

Modern data analytics platforms are scrambling to integrate AI features, and it's not hard to see why. 

AI tools like chatbot assistants, automatic recommendations, and explainers reduce the barrier to entry and streamline the dashboarding process. In turn, you can get your embedded analytics out there faster and much more reliably. 

One of the best implementations of AI in data analytics right now is PolyAI — Polymer's own in-app chat assistant. 

Ask questions, request recommendations, or prompt PolyAI to generate ready-to-use visuals with written instructions.

5. Viewer Access Management

Controlling user access is essential not just in embedded analytics, but also in dashboarding and BI as a whole. 

On the dashboarding side, you need to be able to specify who can change or edit your visualizations. For embedded analytics, you should be able to toggle certain features like filters, sharing options, and explanations. 

For example, on Polymer, you can set viewer permissions when generating your embed code.

6. Real-Time Data 

There are plenty of platforms that let you embed aesthetic and highly readable data visualizations into various destinations. But if you have to manually update your charges with the latest data, it would be a huge bottleneck to your productivity down the stretch. 

Make sure your embedded analytics tool automatically pulls data through built-in integrations or APIs. That means the platform should automatically refresh your data visualizations with the latest data in specific intervals.

‍

7. Learning Resources and Technical Documentation

While there are plenty of data science skillsets that carry over across different platforms, it helps to have a library of learning resources to maximize your investment. 

For example, with Polymer, you get a glossary, API documentation, full-length guides, a help center, and a blog that's constantly updated with informative articles.

Don't forget that you can also count on PolyAI to answer all your questions. Just click 'PolyAI' in the sidebar while using the dashboard editor.

What Industries can Embedded Analytics be Useful For? 

Embedded analytics is useful for a large number of industries, including but not limited to:

• Life sciences and pharmaceuticals — Use embedded analytics for compliance, product R&D, investor relations, and more.
• Healthcare — Embedded analytics can streamline the operational efficiency of healthcare providers and augment patient services. 
• Retail and ecommerce — Embedded analytics helps with almost every aspect of running retail and ecommerce businesses, including inventory management, marketing, and sales. 
• Customer Relationship Management (CRM) — Internal CRM and sales teams can take advantage of embedded analytics to access customer insights, improve efficiency, and monitor results from a single workspace. For SaaS vendors, adding embedded analytics to your CRM platform is a huge value add to your customers. 
• Finance — Financial services companies can use embedded analytics to create client portals for tracking vital metrics about their investments. 
• Education — Whether you're an educational institution or an independent e-learning platform, embedded analytics can help both you and your students track performance KPIs.
• Hospitality — Stay on top of guest experiences using customer data and sentiment from review platforms. 
• Insurance — Use analytics to improve your risk assessment, track fund performance, and create data-rich client portals.
• Marketing services — Empower your marketing strategies while providing clients with more digestible reports and portals. 

Challenges of Implementing an Embedded Analytics Solution

Embedded analytics addresses a lot of challenges involved in deploying data analytics instruments in your proprietary software. 

However, implementing embedded analytics comes with its own set of challenges, such as:

1. Limitations with Legacy Systems

In simple terms, a legacy system is a technology infrastructure that uses old or outdated software. This may cause you to run into compatibility or integration issues, preventing you from utilizing the full potential of embedded analytics. 

2. Ensuring Seamless Visual Integration

One of the biggest challenges of embedded analytics is ensuring your data visualizations don't look out of place within your web property or app. Without proper planning and customization, this could create a disjointed user experience that hurts the adoption process. 

3. Consolidating Data from Different Sources

Combining data from various siloed data sources is a problem if your embedded analytics platform doesn't offer turnkey integrations. While you can always find ways to connect your data through APIs or apps like Google Sheets, built-in integrations are significantly more efficient and reliable.

4. Scalability Over Time

The bigger your database gets, the harder your analytics platform has to work to crunch the numbers and render your data visualizations. You can launch your own investigation of an analytics provider's capabilities by reading reviews and technical documentation — or you could contact their team if you need direct answers.

5. Cost-Effectiveness

Some data analytics platforms offer packages that may include features you don't need, whereas enterprise solutions that include embedded analytics may cost thousands of dollars per month. To manage costs, look for platforms with more cost-effective tools like embed codes and API access. 

6. Technical Challenges

Finally, you need to look into the technical barriers associated with different embedded analytics solutions. Some may require specialized skill sets, while others are built with non-technical features like drag-and-drop dashboard editors and templates.  

Final Words: Experiencing Embedded Analytics with Polymer

Embedded analytics has never been easier with modern BI software like Polymer. 

Here are some of the top benefits you should expect:

• API access
• Ready-to-use integrations with data sources like Google Sheets, Google Analytics, Stripe, Linear, BigCommerce, and more
• Block-level customization and white labeling tools
• A wealth of learning resources for technical and non-technical users
• Embed code generator
• AI tools to accelerate the data analysis and visualization steps

Get a firsthand feel of embedded analytics by starting a free trial here.