5 Reasons Why Business Are Implementing Embedded Analytics Right Now
Posted On 07/09/2021
What you'll learn:
In our previous article, we explained what embedded analytics are and why this market is growing. Companies who keep up with digitalization may now be left wondering why they should consider exposing data to their end-users. Are they at all interested in these insights?
In this article, we will cover the 5 reasons why it has become a pressing need for data facilitators such as SaaS companies, digital and IoT services providers to start opening up their data to their customers right now. Learn how client- or consumer-facing analytics help businesses increase user adoption and customer loyalty.
Exposing data to end users makes a business future-proof
Due to digitalization, businesses today are rapidly transforming. Those that keep up with digital transformation are quickly building their competitive edge. For those who lag behind, it will only get harder to catch up.
And within that process of digitalization, data is the core asset. However, the raw data in itself has little meaning. Only when it’s presented in a smart and interactive way, companies can easily derive insights from that massive amount of data.
As so often said “data is the new oil”, you could say that embedded analytics is the engine that gives it meaning. It translates raw numbers into usable business information for everyone, everywhere. Without it, decision-makers are left groping in the dark.
And that’s exactly why analytics adds a future-proof feature to any offering, whether you are a SaaS company, service organization, IoT provider, governmental institute, or non-profit organization. When used in the right way, it adds tremendous value for your end-customer.
As a result, companies that have added analytics to their offering are seeing 3 areas of growth:
their product has higher user adoption, and more loyal customers;
their analytics add-on grows to a new source of revenue, whichwe cover later in this series;
they immensely enhance the customer’s experience;
Now what about the final consumers of these analytics? They simply love it. For them, data has become a commodity, and not only in a business context. Just think of the spike in consumer apps with built-in analytics: from fitness apps like Strava or Fitbit to banking or productivity apps. End-users now evaluate any product or application on analytical capabilities above and beyond the core.
With that in mind, it won’t come as a surprise that several research studies expect a significant increase in overall analytics adoption to 50% by 2023.
Delaying analytics results in loss of business opportunities
That brings us to the next question: how much of a priority is analytics, really? Organizations can sometimes be reluctant to start a data project. We’ve seen it multiple times, for many understandable reasons:
it is not their core business, and they lack in-house expertise;
or they have tech-experts in-house but prefer not to distract them from the core product;
they are not sure if it would be valued by their customers;
they have no idea of business, functional or technical requirements;
All valid arguments. Most organizations, however, operate in a hypercompetitive, fast changing environment. We live in an “instant” world when it comes to information, taking choices or actions. Every day, acting agile, fast and flexible becomes more important. In order to achieve this, end-consumers rely heavily on data:
Users nowadays take access to crucial information and reporting for granted;
Data insights speed up the decision process, allowing for faster & smarter actions;
Businesses seek to understand the ROI of their digital platforms & products;
As a result, client-facing analytics will not only give SaaS companies – or any other data facilitator – a competitive advantage. It also generates additional opportunities and business growth for the organizations on the other end, consuming the data.
Given data is the new battlefield, late adopters will get run over by their competition. Not only do they lose out on new business and growth, they also slow down business opportunities for their end-users. No adoption poses a risk, but as will follow in the next paragraph, so does a slow adoption process.
Companies need a faster, off-the-shelf alternative to in-house development
There are 2 scenarios to consider building a client-facing analytics application in-house:
Data is the core business of the organization;
The organization is willing to hire adequate in-house expertise, dedicated on the long term;
But even then, the organization would have to accept a much longer development & deployment cycle, so it may still be well worth reading through the benefits of working with a third party tech partner.
In all other scenarios, we strongly recommend to source from an embedded analytics partner because it’s:
The advantages of an embedded analytics partner
First of all, although the first “back-of-the-envelope” calculations may tell you differently, embedded analytics is in fact cheaper. Hiring dedicated experts is not only very costly, but also very difficult. With the increasing shortage of developer profiles, the right experts are extremely hard to find. In addition to the initial deployment, it requires a long term staffing cost for maintenance, support and future feature requests from customers.
Secondly, working with a third party expert allows for faster deployment because:
most off-the-shelf solutions are low-code, plug-and-play building blocks, easy to deploy;
they have experience with the roll-out of different analytical solutions;
it minimizes delays that too often occur with development projects;
Companies have been able to speed up their time-to-market from months to just weeks, resulting in a much quicker ROI of their initial investment than with in-house solutions. This will be discussed in more detail in the next article.
Thirdly, the partner’s experience makes life easier on the in-house teams. It allows an organization to stay focused on their core offering, while having access to the best-in-class solution with minimal effort:
Embedded analytics partners keep their platform up-to-date with the latest industry standards and innovations;
They stimulate knowledge-sharing with others in the industry;
Finally, it offers much more future-proof and scalable solutions. It is extremely difficult to develop a personalized & customized dashboard experience for an end-user from scratch. Let alone to adapt dashboards to different languages or screen sizes. With embedded analytics partners, however, multi-tenant dashboards can be set up with just a few lines of code for thousands of users, instead of one by one. Likewise, localization or screen-adaptive dashboards can be either fully automated through API or managed in the UI at large by business users.
Factors to consider when selecting an embedded analytics vendor
When selecting an external partner, there are a few things to keep in mind:
For a faster deployment, tailored to the needs in a specific line of business, go for a solution that’s composed out of smaller analytical building blocks, versus an all-in-one suite. Modular packages allow more flexibility to deploy and use, and faster response to complex and changing environments. And it’s better for your wallet in case of budget constraints.
For accessibility, go for a low- or no-code solution that makes it easy for business users or citizen analysts to churn out good-looking data visualizations.
And finally, automated insights and guided recommendations will significantly increase user adoption of analytics in the future. A high degree of interactivity and actionability will become an increasingly important decision criterion when selecting vendors.
Analytics are a crucial step in moving up-market
Business or product owners naturally look to sell to larger enterprises in order to grow their average deal size. With a growing amount of enterprise companies going all-in on digital transformation, now is an excellent time for well-positioned SaaS companies to get a bigger piece of the pie.
But to stand out in a competitive market, they need a competitive advantage that will knock competitors out of the park. In fact, analytics proves to be a valuable asset to get a foot in the door.
With more advanced analytics offerings, a vendor will stand a much higher chance in the enterprise market.
For large enterprises, the analytics offering matters in their buying decision because:
Robust analytics signals product maturity;
Insights help larger businesses makes sense of their complexity & decision making;
Analytics are typically a straightforward business requirement, and fairly easy to prove ROI to their end-consumers;
For product owners, an off-the-shelf embedded analytics tool offers an easy way to meet the above demands:
Scaling an actionable reporting module that meets complex needs of a diverse client base now becomes easy, with a faster implementation time;
Embedded analytics allows to serve large businesses operating globally with localization features such as different languages;
Enterprise-level accounts expect to be offered premium add-ons such as advanced analytics to meet their evolving business needs;
What about the concern of adding third party technology to the mix? Enterprise end-users won’t even notice, thanks to native integration capabilities and complete customization through CSS. Embedded analytics completely blends in, feeling truly native to any interface.
On the contrary even, companies using third party embedded analytics will likely be perceived as innovative and future-proof with regards to analytical capabilities. It allows them to continuously offer state-of-the-art analytical features to their end-users, as part of their own product suite.
SaaS users have a latent need for data insights
Interest in analytics & data insights is organically rising within any type of businesses, and even among consumers. Very likely, the same is happening within your very own customer base.
Just think of the tons of data a user generates when working with their preferred toolstack. Unfortunately, that data is usually locked inside the SaaS platforms they are using, unless exposed by the platform in the form of interactive dashboards.
All that data could be highly valuable for customers to make better decisions and improve operational efficiency. And don’t just take our word for it. According to several industry surveys, end-users increasingly consider self-service analytics as critical to their business operations.
In addition to this visible trend, there are multiple ways a business can gauge the appetite for analytics among their customers
How to gauge interest in an analytics offering?
To gauge the interest for embedded analytics with the end-users of a SaaS product, there are multiple angles to consider. Following this idea, we’ve grouped a number of key questions to ask customers. This will give a rough idea of their business pains and needs, while sparking their interest in analytics.
a. Understand market competitiveness of your clients
The market a business operates in can have a direct impact on their need for analytics. The following questions could come in handy:
Do they operate in a highly competitive market? Could data be a competitive advantage? Are any of their competitors already offering analytics in-product?
Are they operating in a fast, volatile business market where speedy insights are key?
How important is it to take rapid actions and decisions based on actual, accurate operational business data to secure business revenue, keep customers satisfied, and respond to market changes?
How is ROI measured? What metrics are important to monitor and improve? Are these metrics currently visible to them?
b. Identify how smart reporting could help clients in their business
A SaaS platform or service provider should have a clear picture of how their users intend to use the data analytics made available through the platform. This will help understand which exact features clients are looking for, and they can offer:
Would a real-time view at a glance on key performance metrics better support their business operations?
How important is immediate action-taking? Do they currently require extra tools to make smart, tactical decisions based on the data available to them?
Would domain-specific insights at the right place, within their business workflows, enhance business operations and efficiency?
Could reporting dashboards directly in communication with their core business application maximize efficiency gain?
Does the end-customer want to analyze and leverage data insights, without requiring data expertise?
Does the end-user want to interactively consult reports? Or do they also want the ability to create customized reports on a self-service basis?
Is collaboration important?Do they need to share data insights and dashboards easily across departments, organization members, or even externally?
Should findings be consultative for their business on multiple devices? In multiple languages? At any time of day adapted to multiple time zones?
c. Understand the technical implications
To understand how the analytics solution should look like from a technical point of view for the end-users, the following questions are useful to get an idea:
How easily accessible are the data inside the SaaS platform at this moment? Does it require lots of manual manipulation or high-skilled effort from the end-users, and do they need this to be easier?
How do end-users typically capture and manage data in their organization? Have they already deployed a traditional BI platform, or built an in-house analytical tool?
How are data insights used and shared across the organization? What tools do they use to collaborate (e.g. email, Slack,…) and share this information?
At what level is data used: strategically, or on a tactical level where the action is taking place?
What is the level of expertise in data analysis of the end-users?
Different ways to assess clients’ readiness
There are multiple ways in which a SaaS vendor could assess the interest of their customer base, including but not limited to:
Sending out regular customer feedback surveys;
In-depth customer interviews (ideally with ambassadors or heavy users);
Monitoring online reviews of your product that specifically handle analytics;
Adding an in-app feedback survey;
Tracking usage of analytical features that already exist (e.g. data export functions or basic data visualizations);
In addition, further market intelligence can be gathered beyond the company’s own customer base. One example is to monitor online reviews of competitors, and see what their users say about their analytics offering. It’s a great way to stay ahead of the game, and gain intelligence on what competitors are doing.
Besides proactively asking customer feedback, another approach is to gather feedback through actual usage. Companies can consider offering a basic reporting dashboard using embedded analytics to their customers for free. Or they could make it available for a select group of heavy users as a pilot project. This will result in true customer feedback, based on actual usage, and will give a much better idea of the actual latent needs. In addition, this is an opportunity to prove the value of analytics to end-users by giving them a taste of it.
It has become clear that hardly anyone will question the importance of data within the ongoing process of digitalization. Businesses who offer real-time, actionable insights within a business application will improve the usability and context of the data for any end-consumer.
In addition, using an off-the-shelf analytical building block removes the need for data-savvy staff, long development cycles and steep learning curves. On the contrary, it will speed up time-to-market and save costs.
That’s what makes embedded client-facing analytics packages a strategically interesting feature for any data facilitator. Especially within the competitive landscape of SaaS and providers of digital and IoT services, it enhances customer experience, ultimately driving business growth.
In the next chapter, we will dive deeper into what that business growth might look like. We will discuss the different forms of return on investment, both for data facilitators and data consumers.
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