5 key data analytics

5 KEY DATAANALYTICSFEATURES YOU NEED IN YOURSAAS PRODUCT

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5 key data analytics features you need in your SaaS product 03

Embedded analytics explained 04

Feature 1: Automated business monitoring 05

Feature 2: Natural language generation 08

Feature 3: Data storytelling11

Feature 4: Contextual analytics 13

Feature 5: White label analytics 16

embedded-analytics partner 16

Table of Contents

5 KEY DATA ANALYTICS FEATURES YOU NEED IN YOUR SAAS PRODUCT

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Gartner forecasts that revenue from the Software as a Service (SaaS) market will reach an estimated $123 billion in 2021. This growth can mainly be attributed to companies rearranging their operations as they recover from the impact of the COVID-19 pandemic. With revenue expected to reach $436.9 billion in 2025 at a compound annual growth rate (CAGR) of 12.5%, getting a strong foothold in the SaaS product market is proving to be highly lucrative for companies worldwide.

The competition is high in this fast-growing industry. Those who successfully keep up with customer demand know that embedded analytics has become a standard part of what users now expect from their cloud-computing software applications.

But what, exactly, are “embedded analytics” and which features should you be looking to include in your SaaS product?

$123Billion

2021 2025

$436.9billion

In the context of this eBook, the term “embedded analytics” refers to third-party analytical capabilities that are fully integrated into SaaS products or, to put it differently, the seamless integration of third-party analytical capabilities and data visualisations, including real-time reports and dashboards, into a SaaS application. This approach differs from the traditional one in that business intelligence (BI) isn’t retrieved via a standalone platform; it’s obtained from within the SaaS product itself.

One of the many reasons why SaaS products that lack embedded analytics fall short of their competitors is that these products compel their

needs. As a result, users spend less time using the main SaaS product and are left with the

intelligence they need elsewhere.

Leading SaaS companies now know that embedded analytics enable their end-users to make faster, better business decisions – and that this is incredibly attractive.

While basic analytics features like dashboards, report building, and data visualisation still involve a learning curve for many line-of-business users, the latest features made possible in modern BI platforms provide a level of guidance and streamlining, helping users to make the most of the analytics that are available to them.

technologies, embedded analytics have become more accessible for the average user and a non-negotiable element to include in future-proof SaaS products.

If you’re convinced that embedded analytics should be added to your product, read on. We take

expecting users to rely solely on dashboards for their BI needs simply isn't enough anymore.

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EMBEDDED ANALYTICSEXPLAINED

The creation, exploration, and monitoring of data, when done manually, is time-consuming and clumsy. Businesses that rely on this outmoded method to spot trends and anomalies in their data

Fortunately, over the last few years, several BI solutions that use automation have made it much easier for end-users to make full use of the analytics available. One unique example provided by analytics vendor is (ABM), which automatically generates and provides end-users with detailed insights into the many different performance metrics that apply to them instantly.

ABM describes the ability of an embedded analytics platform to auto-detect trends, patterns, and anomalies in real-time. This feature goes far beyond the point of providing dashboards to end-users. Part of ABM’s allure is that it extends, rather than replaces, traditional BI tools such as dashboards and reports. It also provides a more thorough, personalised analytics experience for end-users.

ABM uses machine learning algorithms to continuously monitor and analyse datasets, hunt for any

Importantly, it helps the user to distinguish between a trigger, a trend, a pattern, and a deviation.

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FEATURE 1: AUTOMATEDBUSINESS MONITORING

becomes easy. For instance, users can quickly pinpoint affected areas or departments within their business, provide exact times and dates of when an anomaly occurred, and make a call on whether the occurrence is an opportunity or an emerging problem that needs immediate attention. Automated monitoring furthermore offers the user possible reasons why an anomaly has occurred and provides the data on which this conclusion has been based.

These results can also be highly targeted to individual users. For example, , shown in the graph below, can detect the most relevant deviations from a set baseline, rank these deviations for each end-user based on the individual’s previous usage habits, and send them a signal to alert them to the change:

The real clincher? The software gathers and delivers intelligence much faster than humans can ever do. As this feature saves so much time, it’s likely to make your SaaS product popular with both employees and managers.

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For more context, please read this blog from Yellowfin.

Companies use these automated capabilities to detect fraud, manage ongoing costs, monitor the performance of software intermediaries, deal with users’ technical concerns, and track changes in

platforms that incorporate ABM could make your SaaS product indispensable to your end-users.

companies in the following ways:

ABM currently employs three technologies: machine learning, natural language generation, and specialised statistics. Here’s a quick reminder of what these technologies do:

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Machine learningrefers to the use and development of computer systems that can learn and

statistical models to see patterns, such as trend directions, baseline behaviours,

Natural language generationrefers to the software’s ability to describe the complicated processes and

Specialised statisticsrelates to the automatic collection, organisation, analysis, and

It lowers costs, human bias, and analysis fatigue

It increases agility as it makes it easier to

respond to changes or deviations in data

It saves time, giving employees a chance to focus on other duties

Natural language generation (NLG), a subtype of AI, refers to the ability of the analytics software to translate the complicated patterns it observes into easy-to-understand

the most important concepts in structured data and translating it into a consumable, text-based narrative. For the end-user, the message becomes a helpful, auto-generated summary produced in response to a query.

increases the possible number of users of your SaaS product’s embedded analytics offering enormously.

But ease-of-use isn’t the only attractive aspect of NLG: it’s a technology that makes text analytics, machine translation, text summarisation, and detailed answering of queries possible without ongoing human intervention. A SaaS product that saves time, and reduces cost and effort for your clients, is likely to be successful.

Traditionally, it was easy to spot the stilted, auto-generated text produced by software with NLG

these and be able to do the following:

FEATURE 2: NATURAL LANGUAGE GENERATION

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analysed data in a conversational manner

Highlight nuances and hidden

patterns

Provide summaries as well as detailed

explanations when users request information

any human can do and explain what it’s found in terms that most people can grasp.

NLG is mainly used to enhance self-service analyses, as users can query data and receive an easy-to-understand, text-based explanation of visuals such as dashboards. NLG is also used to produce human-like conversations with customers via chatbots or voice assistants.

Take a look at the below example of how NLG can enhance a user’s understanding of graphs by giving accessible text-based explanations. This image from shows how the user’s knowledge and skill set is enriched with instantly generated summaries, detailed explanations, and comparisons of the data in the chart (see the “Explanations” on the right).

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0 100 cm

Relative Change -100% 1,387%

Variance

Explanations

This chart measures variance of invoice by

campaign highlighting the relative change in

contribution between Asia and Europe (camp

region).

Campaign is the variable that most explains

difference in invoiced between Eurpe and Asia

The lowest 3 campaign (Email to Alumni,Ski

Japan Magzine & google Search) combine to

represent aa (100.00%)to total negative Variance.

• The top 3 campaign (Ski Press Online, Nastar

Disscussion Board & Google Ads) combine to

represent almost all (93.00%)to total positive

Variance.

• Google Search Had the largest Negative relative

change (-100%) Indicating that it’s Variance

($-1,433,318) was far less than expected

• The minimum value is $ -77,339,279 (Email to

Alumni) and the maximum is $103,522,068 (Ski

Press Online), a difference of $180,861,347.

Ski Press online

Nastar Discussi...

Google Ads

China Ski Alp

Shangai Ski Do

Snow White Online

Word of Mouth

Alberta Aipi

Barking Bea

Bravo Ski Italia

Schnee S

Ski Raci

Google Search

Ski Japan Magazine

Email to Alumni

Cam

paign

The main advantages of using NLG in your SaaS product’s embedded analytics are threefold. NLG technology:

Quite simply, NLG is used in today’s state-of-the-art embedded analytics platforms – which includes – as it’s easier to understand presented data when there’s a text-based

explanation accompanying the visuals.

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Helps to supplement users’ existing knowledge, making the decision-making process easier

Helps users to gain a greater understanding of their data (this is especially true for users who prefer text-based explanations)

Reduces the time users take to analyse data which, in turn, reduces the demand for assistance from data analysts

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Telling and listening to stories is central to the human experience. Data storytelling makes use of narrative techniques to share business insights and help companies with decision-making, and is a highly recommended feature to have as part of your SaaS product’s analytics offering.

Today, there are two approaches to data storytelling:

FEATURE 3: DATA STORYTELLING

Traditional reporting

This relates to a formal packaging of data, such as a quarterly report that explains what has happened, and how and why it’s happened. This type of storytelling is mostly based on numbers.

This refers to a text-focused, context-based style of reporting, where the report can include anything from presentations, images, and videos aimed at persuading others to make certain decisions. This format adds narrative to the reporting, providing insight and interpretation, which can make the metrics more meaningful to users.

By employing the second approach, data storytelling makes it easy for your clients to communicate their BI to different audiences, increasing their understanding and engaging them on a deeper emotional level than mere numbers and graphs ever could.

The way data storytelling is represented in modern analytics platforms is still evolving, but some

tool that allows your analytics users to write and share their data insights in various formats such as news reports, blog-style articles with accompanying visuals, and presentations. Instead of merely providing a straightforward description of what the data shows, data storytelling helps to provide context on, for example, previous experience. This turns the data into a valuable decision-making tool, largely by providing a bigger picture as background to the metrics.

With dashboard vendors (e.g. Qlik, Tableau or Power BI) and use a simple interface to bring their data stories to life with supporting videos and graphics. They can also use to dynamically refresh data in management reporting and pull it straight into their presentations.

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SaaS products can now provide contextualised analytics from within an application, not merely as an

seamlessly at the point of consumption (i.e. as the user is working within your product).

By incorporating the latest embedded analytics technology, your users can now demand forecasts, view trends, replenish orders, and react to alerts and prompts in real-time without exiting their

them to gain deeper insight into the trends, patterns, and behaviours they’re seeing immediately.

Dashboards and operational reporting have long been features of analytics tools. Although these are valuable in that they provide users with the metrics they need, they don’t traditionally offer the deeper context needed to make business decisions.

When analytics are provided as a separate tool, users also have to switch contexts from what they’re doing and go into another platform or add-on feature. This has three disadvantages:

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The switching of contexts is disruptive and time-consuming

Stand-alone dashboards usually don’t provide enough context

Switching causes friction in the user experience, which could discourage users from making full use of analytics

FEATURE 4: CONTEXTUAL ANALYTICS

These embedded components can take three forms. They can provide users with:

To enable a good user experience, the components should be synchronised with the rest of the page and the analytics should be updated when the user clicks on a selected row or opens the next page.

-ponents are updated as the page’s context changes:


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Store Order Category Order

STORE NAME

Sam’s Club 6979/ Ankeny

Sam’s Club 6472/ Council Blu�s

ORDER VALUE UNITS VOLUME Search

ndsor Height

Daverport

Sioux City

Sum Sales Amount $ Spiked on 14-10-2018 for store Name Sam’s Club 6344/ Windsor Heights

+258% ExploreBetweem 01-04-2018 to 31-12-2018

Delete Create Order

Create Task

$ 239,498,19

$152,825,4

$282,303,72

$215,841,75

$187,356,58

11392

7318

13903

11539

9226

14835

9658.5

17036.25

13463.5

11481

Edit

Edit

Edit

Edit

Edit

Edit

Edit

Edit

Edit

Edit

The Avg % Exit spiked on the 23 February 2019 where the sales is sometheing and can go over a few more lines

The Avg % Exit Change Trend on the 23 February 2019 where the sales Category is sometheing and can go over a few more lines

223%

18%

Between 28- jun-18 to 29-jun-18 and 28-jun-18 to 29-jun-18

Between 28- jun-18 to 29-jun-18 and 28-jun-18 to 29-jun-18

View: View name goes here

View: View name goes here

Explore All

My Tasks Threshold Alerts

Create New Report

Create New Report

Create New Report

Create New Report

Order Summary

Task DESCRIPTION STATUS DUE DATE

We need a consolidaed report created that inncorporate




Orders for all stores require summary table to be added to the

In Progress

In Progress

In Progress

In Progress

Past Due

Date Alert

Alert

Value

02-02-20

01-02-20

29-01-20

28-01-20

28-01-20

28-01-20

Whiskey Sales Above $3000

Whiskey Sales Above 3k



Gin S

Whis

Sum of sales above $3000

This is waht you should dobased on value of report

$3127

$3127

$4,134

$4,134

$4,134

The most appropriate visual components (e.g.

graphs and charts) to

Dynamic analysis of the data plus alerts

Triggers and suggested actions to support

combined within a single analytics component.

Dashboards will always remain important. After all, they provide the most important critical data at a glance, allowing users to monitor performance quickly and control the analyses. But embedded BI

decision-making.

While contextual analytics doesn’t replace your app’s existing analytics, it certainly adds to its capabilities, supporting your users when making important operational decisions for the future.

for an in-depth view on the topic.

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This feature enables your clients to rebrand and restyle visual elements such as colours, fonts, logos,

The best white label analytics solutions allow for the following:

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White label analytics refers to an embedded analytics solution that can be customised to look like it’s part of your client’s business application. In other words, the analytics can mirror the design of other corporate websites, apps, documents and systems, as illustrated in the example below:

FEATURE 5: WHITE LABEL ANALYTICS

Customisation by the end-user(even those who can’t code)

Customisation by developers, using Cascading Style Sheets (CSS)

analytics.

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It will be impossible to tell that the embedded analytics stem from another

Supplying white label analytics means that that it frees up developers to work on other tasks – your SaaS product has done much of the legwork for them

It’s cost-effective, as your clients don’t have to pay for the development of their own BI module to bring customised, embedded

analytics into the software they’re using

It allows for sub-branding of dashboards and reports, among others, for specially tailored experiences and professional-looking documents

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YELLOWFIN YOUR

PARTNER

that we’ve made it our business to help other companies extract, analyse, and use the powerful intelligence contained in their data. Help your customers to discover, understand, and act faster on the opportunities hidden in their data by including some of the data analytics capabilities explored in this eBook in your SaaS product. Use our developer toolkits, APIs, and Code Mode to rapidly assemble, extend, and embed dashboards and white-label analytics into your software, deploy at scale, and deliver exceptional analytics experiences fast.

Are you ready to elevate your product? Visit

one of our technical experts.

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