The Ultimate Guide to Embedded Analytics [2023]

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Embedded analytics, at its core, is a technology that makes data analysis and business intelligence more accessible to any application or end-user. This guide dives into all things embedded so that you'll walk away empowered with the insights you need to make the most of your data. 

Embedded Analytics General

What is Embedded Analytics?

Let's start simple: embedded analytics is the integration of analytic content and capabilities within applications, such as business process applications (e.g. CRM, ERP, EHR/EMR) or portals (e.g. intranets or extranets).

Incorporating relevant data and analytics inside applications helps users work smarter and more efficiently by solving high-value business problems. As we mentioned, and perhaps more importantly, embedded analytics is a tool that makes data accessible to non-technical users. 

Embedded analytics - your Product your Branding

But how, you may ask, is embedded analytics different from traditional BI?

Embedded Analytics vs. Traditional BI

 

Business intelligence (BI) is a set of independent systems (technologies, processes, people, etc.) that aggregate data from multiple sources, prepare the data for analysis, and then provide reporting and analysis on that data from a central viewpoint.

BI is best suited for situations that aim to support management-level decision-making (which tend to require an aggregated view of information from across a department, function, or entire organization). These systems are specifically designed for people whose primary, or even sole, responsibility is often to perform data analyses.

Embedded analytics, on the other hand, is a set of capabilities that are tightly integrated into existing applications (like your CRM, ERP, financial systems, and/or information portals) that bring additional awareness, context, or analytic capabilities to support decision-making related to a variety of tasks. These tasks may require data from multiple systems or aggregated views, but the output is more than just a centralized overview of information. Rather, it's targeted information that supports decisions or actions in the context in which they take place.

Comparatively, traditional business intelligence can be thought of as needing to extract insights from data within the silo of data analysis.

For decades, BI and analytics tools have failed to be adopted by more than 25% of an organization. And even within that 25%, most people only use the tools once or twice a week (and don't tend to get much value from them). By inserting charts, dashboards, and entire authoring and administrative environments inside other applications, embedded analytics dramatically increases BI adoption.

The catch? Most business users don’t know they’re “using BI”— it’s just part of the application they already use. 

Let's use an analogy: 

Business Intelligence - Map Analogy

The business intelligence tool is like a map that we use to plan the itinerary before a long road trip. Embedded analytics is the GPS navigation inside your car that guides your way in real-time! So, turn up the volume, and enjoy that view.

More specifically, BI software isn't integrated into your software or platform's experience; these tools require navigation between separate interfaces to view reports, which means users have to go back and forth between multiple windows. 

BI solutions give you an overview of data coming from disparate systems, but fail to offer an easily digestible context. Insert embedded analytics, which makes it possible, and easy, to process data from multiple sources and integrate contextualized reports right where you need them. 

With the evolution of in-app analytics and business’ responses to it, there has been a fundamental shift in the way we consume data. Visualizations used to be comprised of static pictures and charts that presented a snapshot of a single point in time, making insights quickly irrelevant. Now,  analytics, interactive visualizations, and real-time reports are increasingly necessary to turn the massive volume of data into compelling stories that are actually valuable, which is why Forrester accurately predicted that embedded analytics would become the new normal (due to these increases in operational complexity).

Types of embedded analytics

Embedded analytics strives to join insight and action by integrating analytics deeper and deeper within business applications and workflows. The embedded analytics come in several architectural formats: 

Embedded Analytics - Types

Standalone analytics. Generally speaking, standalone BI and/or data visualization tools are built to create custom views of multiple data sources and to help you look at other relevant data sources together (or at least within a wider context). Standalone analytics also allow IT and software developers to embed analytics into their applications, enabling them to mash and merge data in order to answer complex business questions. The resulting visualizations can then be embedded into applications to make it easier for users to actually get the insights they need.

Detached analytics. This is a “diet” version of embedding where the host and analytics run separately but are tightly linked via URLs. The detached analytics approach works well when multiple applications use the same analytics environment, portal, or service. There is no commonality between the host application and analytics tool, except for a shared URL and shared data. The two applications might also share a common authentication mechanism to facilitate single sign-on (SSO).

Inline analytics. With inline analytics, the analytics tool becomes part of the host application. It looks, feels, and acts as the host. but runs as a separate element (or module) within it. There might also be a separate tab where customers can view analytics about their activity within the application. In most cases, the embedded components sit within an iFrame, which is an HTML container that runs inside a webpage.

Fused analytics. Fused analytics deliver the tightest level of integration with a host application. Here, the analytics (e.g., a chart, table, or graphical component) sit side by side with the host application components and communicate bi-directionally with them. Fused analytics are facilitated by JavaScript libraries that control front-end displays and REST API calls that activate server functions. 

Why are companies embracing embedded analytics?

Today, Over 90% of software companies are embedding analytics tools in their applications, according to the State of Embedded Analytics Report. So why is embedded analytics getting so much attention in the software industry and business intelligence space? And, perhaps more importantly, how are businesses using embedded analytics? Let’s take a look: 

Benefits of Embedded Analytics

Faster time to market
Dedicated analytics development requires lots of expertise, resources, and time. That's why it can be difficult to achieve speed, reliability, and scale with a homegrown solution.

By investing in a Software-as-a-Service (SaaS) embedded analytics solution, you no longer have the burden of maintaining infrastructure, storage, and computing power, so you can be confident your analytics solution will work at 5×, 10×, or even 100× users.

The right embedded analytics solution also positions you to go to market faster by drastically cutting down on your analytics development cycle. 

Embedded Analytics - Faster Time to Market

"Empowered non-technical users to build robust, customizable dashboards that were fully integrated and live in <1 month "  

Check out Storyslab’s journey

 

Stand out in the marketplace 
Businesses today want to differentiate their offerings and increase their product adoption rates, but the sad truth is that only a small fraction of users return to most applications after their first experiences with them.

One of the keys to more product sticky-ness is delivering insights to customers anywhere, anytime, in less than 3 steps. While traditional analytics solutions often require significant data wrangling, embedded analytics delivers on-demand insights within applications in one easy context. The ability to quickly and intuitively drill down into a report and cross-highlight it with other relevant data delivers significant value.

In fact, the difference in usage between embedded analytics and non-embedded solutions is significant: 43% of users leverage embedded analytics on a regular basis – double the adoption rate of traditional analytics. The trend is clear: the more accessible analytics are in your app, the more customers will adopt them.

Expanded revenue streams
An embedded analytics solution can impact your own business as much as it impacts your application. Embedded analytics often opens the door to new revenue streams and helps grow opportunity size. You can develop entirely new lines of business around embedded analytics services – such as white-labeled dashboards – as ways to expand customer loyalty and scale your business. More opportunities for upselling and cross-selling? Yes please! 

Embedded Analytics - Expand Revenue

"Generated new revenues by addressing new personas and empowered their 150+ users with data"

Learn more about Dataneo’s journey

 

Generate value, not just reports 
Your application developers would, in an ideal world, be focused on creating new features and improving core functionality, not custom building an extensive analytics engine or coding reporting features that constantly need to be updated. With the right embedded analytics, you don’t have to start from scratch to meet customer demands. This puts time back into the hands of your developers to improve core features and functionalities, all while delivering a state-of-the-art analytics experience. 

The more an application can guide users through decisions by weaving massive sets of data into something meaningful, the more likely adoption and usage will increase. As a software vendor, you need a combination of powerful analytics and visualization capabilities in order to deliver on the promise of turning data into actionable insight. 

 

Reap the rewards of others’ dedicated BI investments 
As soon as a feature becomes “industry standard” and is noticeably lacking within an application, it creates a challenge for businesses trying to grow their user base. And what if you want to integrate world-class analytics into your application? By leveraging the dedicated R&D of an analytics vendor, you lift a significant development burden from the shoulders of your developers and product managers. Critical updates and new features are automatically integrated into your application, and you get to reap the rewards.

 

Leverage a massive support community
The additional value provided by a community of experts, support staff, and customer care shouldn’t be underestimated. Dedicated onboarding, with step-by-step explanations and refined e-learning, gets your teams working faster and helps build expertise in your business. Global user communities also often lead to fruitful partnerships, inspire new offerings, or just help you troubleshoot a tricky issue. 

 

Self-Service Analytics for Everyone
Data analysis is no longer accessible only to the select few, highly trained, and technical people at a given organization. Ten years ago, this certainly wasn't the case, where IT department and analysts were responsible for most analytics and information delivery. Today, everyone needs to be a “data expert” in their own role to make intelligent decisions that drive a business forward. This influx of non-technical users has forced embedded analytics solutions to re-work the definition of self-service analytics. Users rightfully expect that the software they use will simplify traditionally difficult tasks of data preparation, data querying, and visual analysis.

Embedded Analytics- Self Service

"Kenzai uses data storytelling best practices to bridge the gap between complex data and everyday business users"

Learn more about Kenzai’s journey

Empower customers with confidence 

From business executives and data scientists to casual users, providing actionable insights to anyone who uses your app keeps them coming back for more. This can be easily accomplished with the use of embedded analytics. At the end of the day, it’s all about driving success for your customers and that means giving them not only the tools they need but also the confidence that they are making the right decisions for their business. Today, every role is a data role and, with embedded analytics, your customers have actionable insights where and when they need them most. 

Empower Users embedded analytics

 

The key to insight is embedded analytics 
With ever-growing volumes of data and the increasing complexity of business management, having timely access to data insights is more important than ever. But customers can’t be expected to make informed decisions based on raw data alone. They must be equipped with the means to understand it. Business applications with easy-to-use analytics are the critical link between customers and the data they need. Simple charts don’t do justice to your application, or to the challenges your customers face. That's why application data must be accessible anywhere, and in a way that tells a story. By investing in a complete embedded analytics solution, you can make this sort of impactful data exploration easy. Free your customers from exporting, give them fewer steps to insight and create more powerful self-service capabilities.

From cost centric to profit-centric with embedded analytics 
Today, data volumes, sources, and reporting complexity have grown, and the potential overhead has skyrocketed. This leaves many businesses wondering how to best expand their analytics and visualization capabilities. Does a custom-built platform make the most sense for your business's unique needs, or will an off-the-shelf solution free you up to focus on what’s most important to you? Real-time, self-service, and freely explorable visualizations provide massive competitive differentiation, but developing a homegrown set of visuals to represent your data strains the time and resources of software vendors and developers. Technical and business decision-makers alike know that building analytics from the ground up comes with serious challenges, and you’ve probably said something like this yourself:

Embedded Analytics - Profit Centric

Mature, embedded analytics and BI solutions are available off the shelf, enabling you to leverage the dedicated R&D, infrastructure, and development effort of a vendor that has BI and data expertise. If you, like many, don’t have this core competency in-house, choosing to buy becomes the obvious option. By eliminating the burden of development, your business will be free to invest in capturing additional value for you, your developers, and your customers. 

So, how will you provide in-app analytics to your customers? Will you invest in building a custom solution in-house, or purchase an embedded analytics solution off the shelf?


 

Why build or buy

When faced with the need to embed analytics into an application, most software providers arrive fall head first into the infamous “build versus buy” conundrum.

Why Build (or Really, Code)?

The first instinct many application developers have when it comes to analytics is to build the necessary reporting functionality with the help of code libraries or charting components. Amongst the many things that happen over time is that users ask for more functionality, more flexibility in their analysis, and more methods to gain insight without needing your help. Very few customers want to simply extract data into an excel file. Most want to be able to build custom dashboards and visualizations as they learn the product. With constantly increasing demand, it becomes difficult to build analytics in a scalable way.  

Application providers who stay on the “build” track are committing to allocating significant resources toward developing, supporting, and keeping up with advances in data visualizations and business intelligence over the long term.

Building Analytics Cost Employees

 

Why Buy?

Many software organizations are under pressure from customers or competitors to improve analytics capabilities, but they simply don't have the time or resources to build a reasonably strong solution on their own. In fact, in virtually every survey conducted with software providers, the top reasons for embedding with a third-party product are:

  • Cost to build and maintain capabilities on their own – it can be expensive to initially develop, provide ongoing support, and continually enhance analytics capabilities.

  • Need to get to market faster – there is usually only a small window of time available to satisfy customers without having them churn, differentiate a product offering, and stand out in the marketplace.

  • Desire to have internal resources focused on core application functionality – delivering functionality with a third party makes development teams more efficient and frees up resources for your core product.

Evaluating the build and the buy options requires an understanding of the targeted functionality to be implemented, the level of integration required, and a cost/benefit analysis.

Defining the Time Frame
As a general rule of thumb, we use 3-5 years as the time frame in which we compare technology implements. So how will building your own analytics solution compare to embedding an analytics solution in this timeframe?

 

Build

Buy

  • To deliver all the necessary functionalities “Build” generally takes 3x to 4x the time as "Buy" 
  • High dependency on internal teams, distracting them from focusing on core competencies
  • The need for constant coding updates and maintenance puts added pressure on developers
  • Longer go to market and longer time to value, 
  • Slowdowns for customer acquisition and adoption. 
  • In turn, reduction in the ability to retain customers and a lower selling price for your product. 
  • Dependence on internal development resources to implement new functionality limits the predictability of delivering over the long term
  • The “Buy” option delivers a great product with seamless interaction in <1 month. 
  • Very low dependency on internal teams, letting them focus on the core business.
  • Updates are performed by the embedded analytics vendor and automatically integrated into your product, reducing pressure on developers. 
  • Fast time to market (in as little as 1/4th the time as the Build option) 
  • Faster time to value 
  • More opportunities for customer acquisition, satisfaction, and adoption
  • Generally results in higher customer retention and higher average selling prices for your product.
  • Relying on embedded analytics with a wide range of functionality improves the clarity of your roadmap and your ability to meet evolving customer demand.

 

Compared to coding on your own, utilizing a third-party product gives you more capabilities in less time. The faster path to value usually drives the “buy” decision. If you are building quantitative ROI models, that difference in time shows up as achieving a breakeven point much earlier in the lifecycle of a project.

 

Now we compare the ROI on embedded analytics.

We certainly recommend coming up with a cost-benefit analysis over time so you can come up with an expected ROI for both the buy and build options.

We've done some of the work for you. To start with, let's take an ROI formula:

ROI [%] = Benefit / Costs  -1 

  • Benefits, in this case, are a combination of strategic benefits (e.g., revenue increase) and operational benefits (e.g., cost reduction).

  • Costs, in this formula, would include any investments required to develop and maintain an embedded solution.

  • “-1” assures that a positive ROI is achieved only when benefits exceed costs.

If it's tough to understand the exact benefits and costs that an embed solution would offer, here is an ROI calculator that can help you with the decision. 

If “build” seems like a good choice for embedding analytics functionality into an application, it's worth taking a closer look. Even if it appears, at first, to be a more cost-effective investment, the TCO (total cost of ownership) can be sneaky. Check out this chart: 

Embedded Analytics - 3 year Build vs Buy

Suppose the desired functionality requires you to have one full-time developer so that you can make it to market in 8 months (equivalent to $100,000). And it takes, let's say, one-third of their time to support and enhance those analytics capabilities in subsequent years ($40,000 annually). Adding up the technology, UX/UI, platform, and management cost, we end up with a total of around $259,000 for year 3. And that's being generous.  

Now, if you work with an embedded analytics vendor, those costs are wiped out right from the get-go, leaving you with technology and licensing costs that vary based on things like your company size, number of users, and so on. For illustration's sake, we can use $180,000 as a representative figure for that same 3-year window. The development time is cut to <1 month, and you break-even much sooner. Sounds pretty nice, huh? 

While we understand this is probably an oversimplified example, the point here is to assess both the benefits and costs when building a business case based on a comparison of ROI.

So let's say, for arguments sake, You’ve decided to invest in embedded analytics. Now what?

 

 

Finding the right solution

Evaluation Criteria

Picking the right solution involves thoroughly evaluating the technology, understanding the expertise offered by the vendor, and implementing a process to ensure success.

Let’s examine the evaluation criteria that are critical to strong embedded analytics implementations. 

  • Self-Service Capabilities: These are the core capabilities you will make available to your non-technical users which may include dashboards and reports as well as the interactive and analytical functions they can perform.

  • Scalability: While the solution you choose will connect to your current data environment and meet your data security needs, it should also be flexible enough to meet future demands as your data evolves.

  • Integration: One of the major ways embedded analytics initiatives differ from standalone analytics projects is the need to integrate with the application environment. This means providing a white-labeled solution to meet evolving business requirements.

  • Deployment: Since time-to-value is so critical to the success of an analytics project, having a development environment where you can create, style, embed, deploy, and iterate on embedded analytics will enable your team to deliver the functionalities that users want.

  • Customer Support: Choosing the right partner is not simply about the technology; it’s also about finding the level of expertise you require for training, support, and services, as well as aligned business terms that ensure shared success.

Evaluating an Embedded Analytics Solution

Now that we’ve established some of the criteria for evaluating embedded analytics vendors, let’s take a look at a process that can help you make the best decision for your business.

1. Determine your goals
Know exactly what you want the embedded analytics solution for. Who will use it? How will it help you? And why do you need a solution right now? Use a balance of quantifiable metrics like revenue, adoption rate, customer retention, etc., as well as soft metrics like user experience, competitive differentiation, customer satisfaction, etc. to reach clarity. 

2. Establish your timeline
Identify the steps you’ll take to reach your goals. Ask yourself, “When do I want to…”

  • Begin the selection process?
  • Have detailed vendor presentations and demos?
  • Finish a proof of concept?
  • Make my final decision?
  • Start development?
  • Release the product?

 

3. Assemble your team
Determine the stakeholders who need to be involved. Who cares most about embedded analytics internally (your IT team, product management, and the executive team) and externally (your key customers)? It's important to build your business case collectively so that everyone is on board before moving forward.  

4. Identify your requirements
Review your technical and non-technical requirements, and rank and weigh them. Researching your competitors and speaking with your customers will also help you develop a firm understanding of the analytics capabilities that are most important for you to add to your application. Understanding how end users will utilize your products can inform how you determine your requirements. Make sure you also consider who will use the third-party products internally. Understand their skill sets and identify any potential resource gaps as you move into the evaluation phase.

5. Research potential vendors
Utilize independent industry resources like G2 Crowd for Business Intelligence and Analytics Platforms report. There, you can find potential vendors, check peer reviews, and gain a deeper understanding of the ecosystem. Pay special attention to vendors who specialize in the OEM market for software providers.

You can schedule product demonstrations with an initial list of vendors to confirm a basic fit, during which time it makes sense to discuss your requirements and ask each vendor to demonstrate how they would deliver your specific processes and scenarios. Be ruthless! Ask tough questions and make sure the vendors show you the functionalities they promise. 

Don’t forget to enquire about pricing, or at the very least, get a ballpark estimate. Stick to your priorities and do not get dazzled by unnecessary features or shiny embellishments the vendors may try to distract you with. Evaluate each vendor’s ability to help you achieve success during the implementation process (through access to best practices, community, consulting, support, and training).

6. Refocus on your goals
Embedded analytics is far more than just pretty pictures. During your evaluation process, it can be easy to get lost among a dizzying array of charts and graphs. Don’t forget what we've discussed in this guide. Ultimately, you want to bring value to your application and your users through:

  • Embeddability, which is based on how tightly you plan to integrate analytics into the overall user experience, the existing application, and the workflow.

  • Customization, which speaks to your ability to white-label and control the look and feel of the application so that you can make it your own. You should be able to tailor functionality so every user has access to the capabilities that they alone may need.

  • Scalability, with the aim of having ultimate flexibility to create a unique application experience so your product stands out. Make sure you can also future-proof your solution so you can meet your users' evolving needs and desires. 

7. Complete technical evaluations with a select few
After you have your initial product demos, narrow down your list to the top two or three vendors that meet your needs so that you can begin a more structured evaluation process with each one. After understanding and evaluating all required features, it becomes about sticking to the timeline that you have in mind.

Now, you can dive deeper into the product(s) with assisted trials where support is generally available if you run into issues. In structured evaluation, you and the vendors are building a proof of concept together which should be implemented in a technical environment that is as close to the production environment as possible. 

In these technical evaluations, connect the embedded analytics solution to your data sources, integrate it with your security, and embed it into your application. If you host a SaaS application in the cloud, do not simply evaluate desktop tools or run analysis off a cleansed spreadsheet (unless that is what you expect your customers to do).

At the end of the evaluation, present the output back to your stakeholders to get feedback and validate your direction.

8. Check Peer Reviews 
Talk to other companies who have used the same vendor to get a better understanding of their experience. 

Ask the vendors you're evaluating for references. Solicit feedback from others in your personal and social networks. Look for references that are similar to your organization in industry, size, and use case. 

And don’t just ask whether or not they’re happy with the vendor! If you can, dive deep with them into the functionalities the vendor has delivered, the nature of support and training, the duration of deployment, and the roadblocks they have encountered. Understand how the vendor handled problems or issues.

9. Select a vendor and get started
Finally. Select the vendor you feel most confident in as a partner to reach your goals. Look beyond the software, for the vendor who gives you the best possible chance of success.

Make sure the vendor you've selected has the necessary resources to help you, both now and in the future as your company grows. Later on, you’ll appreciate being able to test ideas and leverage best practices as your needs evolve.

Get training for those who will be using the platform to create analytics. Create your first set of reports. Work with your vendor’s enablement and consulting teams to understand and employ embedded analytics best practices.

10. Monitor, adapt and optimize
There’s a lot that can be said here, given the endless possibilities that come from using embedded analytics. But here are a few general and useful tips:

  • Invest in the training you need to best utilize the embedded analytics solution.
  • After four to five months, touch base with your vendor and let them know how the solution is working for you. 
  • Suggest ideas for new features that you think will be helpful for you and others in your industry.

Using Embedded Analytics for success

Now that you've embedded analytics within your application, what comes next? 

Here are two of the many ways you can use embedded analytics to improve your product and revenue: 

  • Promotion: Generate excitement from your customer and user communities about embedded analytics and the value it brings.

  • Pricing and Packaging: For commercial application providers, create a shared value proposition for monetizing your embedded analytics offering.

This is all about bringing value to owners of internal IT applications who are looking to promote and build a user base for the embedded analytics rollout.

Embedded analytics is a journey. More often than not, once your customers see data in new and exciting ways, their thirst for more data and insights will only grow.

So be sure to monitor usage, actively acquire feedback from all stakeholders, and adapt your activities to optimize the success of your project over time. Listen to sales teams and prospects, as their feedback will inform future product development plans.

Promotion

Generate hype around the new features that were implemented to help your customers better visualize data and gain actionable insights from them. 

Show them visuals
Analytics are inherently visual, so the best way to showcase your new analytics capabilities is to show prospective users the visualizations they'll see in your product. So feature screenshots in your promotional materials, website, and presentations. Employ videos and webinars to guide users through new features and give them an understanding of how they could use them. Make them feel like it is a must-have for every growing brand...because it is. 

Leverage customer stories
When communicating the value of embedded analytics in your application, not much is more convincing than customer testimonials. Reach out to your customers regularly to get feedback, and ask if they would like to do a case study, webinar, or press release. Consider creating a Customer Success Gallery on your website to present all of your success stories in one place. Your customers are often the strongest advocates of your product, so let them be! 

Educate your customers
Today people like being educated, not sold to. Prospects should recognize your company as an industry leader with a compelling product that solves their challenges before they agree to a sales pitch. Educate your potential customers through engaging content aligned with each stage in the buying process, using white papers, solution briefs, and product demonstrations. Create content to generate increased interest in your embedded analytics offering.

Equip internal stakeholders with the tools they need to successfully communicate compelling value to external customers. Think beyond sales enablement. Ensure that teams involved in training, professional services, and marketing understand the embedded analytics value proposition and know how to best utilize the assets available to them.

Pricing Embedded Analytics

Determining the value embedded analytics has for your customers is key to monetizing your offering.

Typically, you want to relate the value that your customer receives to the price you offer for the product. Get down to the numbers. Software providers revealed these key insights when it comes to embedded analytics:

The value of embedded analytics relative to the value of the application has increased by 20% in two years. 93% of commercial SaaS providers say embedded analytics has helped them increase revenue. They charge an additional 25% on top of their core product offering, up from 15% last year.

The value is increasing over time, so the minimum value and functionality customers expect from your application is increasing. For anyone still on the fence about committing to or investing in analytics, there is a real danger of being left behind.

When it comes to pricing structure, charging a percentage of the core product is not the only approach you should be considering. Analytics pricing can also be based on the number of users, overall system usage, or simply a fixed dollar amount. There are multiple factors that go into determining the pricing metrics, including how the pricing fits into your existing pricing structure.

Start your pricing exploration with the question, “what is the value of embedded analytics to our customers relative to the overall value of our application?”

There are three common pricing models for embedded analytics: all-inclusive, module, and tiered. Let's look at each one: 

All-inclusive
Here, all embedded analytics functionalities are a standard part of the product, rather than charged separately.

Pros: 

  • You are clearly communicating the value of analytics in your product.
  • Customers will feel like you are giving them more functionalities than they are paying for, increasing their lifetime value. 

 

          Cons:

  • Customers who do not use analytics may not want to be “paying” for it and may rather have your analytics be an opt-in service.
  • Analyzing revenue that can be attributed to your analytics offering becomes more difficult. 
  • There is no upsell opportunity.
  • It doesn’t take into account the level of usage or number of users.

 

Module
Here, analytics capabilities are packaged into an offering presented as a separate module. This is much like an add-on with advanced capabilities that you charge for versus a basic module that all customers have access to.

Pros: 

  • Simple packaging model that customers can easily understand.
  • You have a clear path to upsell customers. 
  • Makes it easy to account for embedded analytics revenue.

 

          Cons:

  • Customers may feel nickel-and-dimed, especially your existing customers. 
  • It becomes harder to manage customer expectations.

 

Tiered Model
If you already have a tiered offering for your product, you can package analytics functionality into each level. If you don’t have a tiered pricing model, using it just for embedded analytics is a harder sell. 

Pros: 

  • You map capabilities to value and justify the pricing.
  • There is a clear path to upsell.

 

           Cons:

  • Understanding the revenue impact is harder than module pricing but easier than all-inclusive.
  • If the customer doesn't understand the value, they will feel like they are overpaying. One of the main things it comes down to is communication.

 

Keep in mind that how you package your analytics offering can be used as a competitive tool, as well. So be sure to take into account your own positioning in the marketplace. For example, if your competitors have some level of embedded analytics, customers will likely expect you to deliver that minimum capability at a similar (or lower) price. Another scenario could be that everyone in your industry charges for analytics separately and, to differentiate, you can decide to bundle your capabilities in an all-inclusive offering.

The future of embedded analytics: Data storytelling

Embedded analytics is constantly evolving, but one of the key trends driving this evolution is Data Storytelling, which is the ability to tell a story with data and to personalize what's being presented to fit the audience.  

Data visualization, which is understood as the art of illustrating numbers in a clear and pedagogical way, is not sufficient in today's market. Though it allows you to communicate complex figures and information by transforming them into visual objects, it is more suited for Data and Business Intelligence departments in large companies. Today all employees want (and need) to have greater insight into business activity and operational data. This is where the value of data storytelling really shines. 

To illustrate, a Marketing Director doesn’t have the same reporting needs as an operational manager responsible for digital campaigns. With data visualization alone, the Marketing Director will not be able to get a separate view of the data. But with Data storytelling, the Director can customize their data reports depending on their data needs to get relevant, actionable insights. 

Data Storytelling has many advantages, including:

  • Turning your data into action. With clear and usable data at your disposal, you are able to quickly identify trends and possible strategies for your business.
  • Improving team productivity. Data is automatically presented in a simple and interactive way. Your teams' time is thus spent more on high-priority, high-value tasks.
  • Finding more agility in your decision-making. Thanks to a simple and easy-to-use tool in a context where decisions must be made more and more quickly.

 

More Sophisticated Analytics Capabilities

Over time, we will undoubtedly see analytic capabilities themselves become more and more sophisticated. Gartner describes the journey that organizations take in their analytics maturity model.

 Embedded Analytics - Future

  • Descriptive Analytics: Describe what’s happening (e.g., sales are going up, and here’s a chart depicting that trend)

  • Diagnostic Analytics: No longer just describing but rather explaining why things happen (e.g., West Coast sales have plummeted because of bad weather)

  • Predictive Analytics: Here’s what the next quarter is going to look like

  • Prescriptive Analytics: Here’s what the future looks like, and here’s what you should do about it

The future for XaaS is embedded analytics. Application providers continue to mature the way they embed analytics into their products as well as the self-service functionality and analytics capabilities they offer. All these innovations will make your application more valuable to your users. And in the end, isn’t embedded analytics a means to make your product the best it can be?

 

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