Today’s pioneering organizations — not only companies with highly technical products — are taking the initiative to develop their own applications with embedded analytics. In the process, they’re improving the customer experience, reducing costs, and driving revenue growth.
“The newest, fastest-growing and most interesting embedding activity I’m seeing is among end-user organizations in dozens of industries that don’t necessarily identify as independent software vendors or software as a service (SaaS) providers,” says Doug Henschen, principal analyst at Constellation Research. “They’re venturing into providing software or services for the first time, and they’re embedding analytical capabilities to make insights more accessible applications more compelling and data driven.”
In some cases these new, analytically enriched offerings are simple, internal-facing applications aimed at ordinary business users, whether they are employees or partners, says Henschen. “I’m also seeing lots of breakthrough, customer-facing applications and services that are adding value, making the relationship stickier and driving new sources of revenue,” Henschen adds.
By embedding analytics, you add reporting and analytical capabilities to your own software and Software-as-a-Service (SaaS) offerings. In this day and age when agility and flexibility are essential to stay competitive, embedded analytics provides you with an opportunity for market leadership and can set your organization apart as an innovator.
And today’s embedded analytics is not just for data analysts: it is explicitly designed to help your business users leverage data-driven insights to make better decisions. Less time is spent on data preparation and more time goes into analysis which can be put to practical use.
In this blog, we’ll dig into the many use cases for embedding analytics, including:
The most valuable aspect and use case of embedded analytics is its ability to help you design new business models that monetize the data that you already have, creating new revenue streams. What data do you collect that could be valuable for your customers? For example, if you offer a website building application, it’s likely you have data that can help your customers identify peak traffic times — and therefore help them understand the best day and time to publish a new piece of content.
You can also enhance your analysis with third-party data to gain additional insights into customer behaviors and preferences. It may lead you to opportunities you hadn’t thought of. Credit card companies, for example, are aggregating and anonymizing consumer spending data and benchmarking it. A restaurant can use this data to see how credit card expenditure at their location compares to other restaurants in their region, or restaurants that serve the same type of cuisine, such as Mexican food. They can then make informed changes to their pricing, menu, or hours to attract a demographic they may have initially overlooked.
Another use case for embedded analytics is driving efficiency. With many embedded analytics tools available, there’s no need to build analytical applications from scratch. You can take advantage of the tools and data you already have and get more value out of them by automating wherever possible — from data ingestion to insight delivery. Eliminating unnecessary human intervention is one way to accelerate time to value.
Embedded analytics also reduces costs and places analytics where they drive better business outcomes. Conventional reports and dashboards leave gaps between insight and action. Additionally, constantly switching between dashboards and transactional/productivity applications slows down the workflow. Embedded analytics not only saves time, it also ensures that carefully curated analytics based on known, reliable data sources are presented where they are needed — within the applications where decisions are made and actions are taken.
Before you embark on your embedded analytics journey, it’s important to understand the five categories of this technology, based on the level of sophistication presented. All levels can bring enormous value — but it’s up to you to determine which is most appropriate for your organization.
As a baseline, it’s expected that analytics provide descriptive and diagnostic capabilities. In other words: what happened in the past, and why. What’s next is delivering predictive capabilities: what will happen based on real-time data. That includes trending, forecasting, and prediction.
This essentially provides the ability to predict desirable or undesirable outcomes within context. For example, you could determine:
Next-generation machine learning (ML) can create closed-loop environments that learn from the outcomes of business actions taken within an application and then prescribe recommendations, which are delivered at key decision points. If the confidence level is high for these predictions, automation can be applied so that the action triggers automatically — without a human required for the decision. When business conditions change radically, as they have during the pandemic, you can determine which decisions worked well and which ones did not. By bringing the data from the business outcome back into the BI and analytics environment or the data science environment, you create an intelligent system that adapts over time.
We see this level of sophistication used in Uber and Lyft ride-sharing apps, for example. Transportation has evolved into a digitally connected experience. Riders connect with drivers through a digital platform and app. Predictive analytics tell the rider when to expect the vehicle, how long the ride will take, and how much it will cost. And real-time data feeds models that keep the driver and rider up to date on estimated times of arrival. When traffic conditions change, artificial intelligence automatically prescribes a new route and updates everybody on the new time of arrival. Over the next three to five years, we expect to see more learning, recommendations, and analytics-triggered automation capabilities like these.
In order for embedded analytics to gain a foothold in your organization, it’s critical to rally support. It’s an important step toward creating a truly data-driven culture. Here are some helpful pointers:
If you’re thinking about getting into embedded analytics, here are key features to look for in a platform:
According to Henschen, “Transformational, data-driven applications are only being built by innovators and fast followers — the leading 20% to 25% of organizations that are trying to differentiate and transform their organizations. Embedded analytics are often the centerpiece of such applications and services.”
To give you an idea of the positive business outcomes that can result from embedded analytics, here are some recent successes.
Henschen summed it up this way: “Digital leaders and fast followers recognize that data-driven insights, delivered in the context of decisions and transactions, can vastly improve the user experience. The approach can also be used to speed decision-making, monetize potentially valuable data, and unleash new business models.”
For a more personal touch, we can help you explore what an embedded analytics deployment would look like for your specific organization. You can get started here.