The Complete Guide to Embedded Analytics

THE COMPLETE GUIDE TO

EMBEDDED ANALYTICS

Table of Contents
Introduction.....................................................................................................................2
Part One: What is Embedded Analytics?.................................................................3
Part Two: Why is Embedded Analytics So Hot Right Now?.................................8
Part Three: Ways to Approach Embedded Analytics..........................................14
Part Four: Common Features in Embedded Analytics.........................................21
Part Five: The Business Case for Embedded Analytics.....................................26
Part Six: How to Be Successful with Embedded Analytics...............................40
Part Seven: Buying an Embedded Analytics Solutio...........................................52
Part Eight: The Future of Embedded Analytics....................................................60
Part Nine: Logi Analytics’ Approach to Embedded Analytics...........................63
Conclusion......................................................................................................................65

1

Introduction

Why Should I Read This?
Every software company has one thing in common: the desire
to create great products. But many software providers fail to
leverage the full value of the data collected in their applications
to deliver a superior user experience.

The Complete Guide to Embedded Analytics is designed for software executives and product managers to answer any
and all questions you have on this topic. It will show you what embedded analytics is and how it can help you build product
value. It will also explain:
• Why embedded analytics is so hot right now
• How embedded analytics differs from business intelligence
• How to build a business case and convince internal stakeholders to act
• How to select the right solution
• The future of embedded analytics
We’ve also included worksheets that you can use to determine whether your company is ready to invest in embedded
analytics and how it will help you improve user experience, attract new customers, and increase sales.
We hope this guide will be your ticket to a new world of more effective, efficient, and lucrative application development.

Introduction

2

PART ONE

WHAT IS EMBEDDED
ANALYTICS?

3

Embedded Analytics Defined
Let’s begin with a definition.
Embedded analytics is the technology that integrates analytic
capabilities into software applications. When implemented
successfully, embedded analytics can bring to life the data collected
by your applications and provide a superior user experience.
Common Analytics Capabilities within Software Applications
• Dashboards and data visualizations: charts and graphs that
display performance metrics
• Static and interactive reports: tabular views of data with or
without parameters and scheduling capabilities
• Self-service analytics and ad hoc querying: enables users to
ask their own questions of the data by exploring a set of data
• Benchmarking: comparing performance metrics against best
practices from external data
• Mobile reporting: ensures interactive functionality on mobile
devices and takes advantage of capabilities specific to
mobile devices
• Visual workflows: incorporating transactional capabilities
directly within the analytic user interface, sometimes referred
to as write-back

Part One: What is Embedded Analytics?

4

How is Embedded Analytics Different
from Business Intelligence?
It’s all about context.
Business intelligence 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 view point. It is
most optimized for supporting management-level level decisions
that require highly aggregated views of information from across
a department, function, or entire organization. These systems are
specifically designed for people whose sole responsibility is to
perform data analysis.
Embedded analytics is a set of capabilities that are tightly integrated
into existing systems (like your CRM, ERP, marketing automation,
and/or financial systems) that bring additional awareness, context,
or analytic capability to support decision-making related to very
specific tasks. These tasks may require data from multiple systems
or aggregated views, but the output is not a centralized overview of
information. It is targeted information to support a decision or action
in the context in which that decision or action takes place.


Said another way, business intelligence is a map that you
utilize to plan your route before a long road trip. Embedded
analytics is the GPS navigation inside your car that guides
your path in real time.
While traditional BI has its place, the fact that BI applications
and business process applications have entirely separate
interfaces forces users to switch between multiple
applications to derive insights and take action. Instead,
embedded analytics puts intelligence inside the applications
people use every day to improve the analytics experience
and make users more productive by combining insight and
action in the same application.

5

Who Uses Embedded Analytics?
By Industry
Companies across all industries and functions (e.g. marketing,
sales, finance) choose embedded analytics to help users make
sense of their data so they can make better, more informed
decisions. While many business applications rely on embedded
analytics to differentiate their products, early adopters (as well as
more advanced implementations) have primarily been consumer
applications, like Amazon and Kayak. We will explore several
examples in detail later in this section.

Commercial vs. Internal Apps
Embedded analytics is about satisfying users’ analytics needs
at the exact moment they might question something within the
applications they use every day. Software companies have long
recognized the value of embedded analytics to their customers
(and the resulting benefits to themselves), but internal application
developers have been slow to integrate capabilities between
analytic and operational apps. With the rise of the business user,
there is mounting pressure on IT and increased expectations that
more people should have access to analytic information. Particularly
in large enterprises where most of the operational systems are
proprietary, developers are increasingly embedding analytics
into these systems to provide relevant information for knowledge
workers to make better decisions.


6

Are You Ready for Embedded Analytics?
Use this worksheet
to determine if your
company is ready for
embedded analytics.
For each category, select the appropriate
number that corresponds with your level
agreement (5=strongly agree, 1=strongly
disagree). When you’re finished, tally your score
to determine your results.
If you scored > 35, you’re ready for
embedded analytics.
If you scored between 20 - 35, you’re moving
in that direction. You should consider getting
started with embedded analytics soon.
If you scored under 20, you may not be ready for
embedded analytics quite yet. But that doesn’t
mean you can’t keep reading!

Flip over to Part 2, and let’s
get started!

Factor

Strongly Agree
5
4

Strongly Disagree
3
2
1

Our customers consider analytics to be an important part
of our application.
We need to find ways to monetize the data captured in
our application.
We’d like to offer more sophisticated reporting and analytics
capabilities within our application.
Our customers are dissatisfied with the level of reporting and
analytics we offer today.
We invest too much in development resources to support
ad hoc reporting requests.
We are losing deals to our competitors based on their
reporting and analytics capabilities.
We could improve our sales demos if we improved the
functionality and/or look & feel of our reporting.
Data drives the majority of our customers’ decisions, and
we want to capture that value in our application.
Our product generates (or has plans to generate) a significant
level of personalized reporting for each customer.
We plan to migrate to a SaaS environment and question
whether our current reporting will be compatible.


7

PART TWO

WHY IS EMBEDDED
ANALYTICS SO HOT
RIGHT NOW?

8

Trends Driving Embedded Analytics
While reporting inside software applications
isn’t anything new, it wasn’t until 2009 that
Google recorded any significant traffic for
the term “embedded analytics,” which has
increased steadily since then.
100
Interest Over Time

80

40
20

20
13

20
12

20
11

20
10

20
09

0

20
08

In many ways, the rise of embedded analytics is a
natural evolution of emerging analytic technologies
and the business intelligence market. From Crystal
Reports in the 90s, to OLAP multi-dimensional analysis
at the turn of the millennium, we’re now entering a
new wave where users expect to benefit from data
analysis in every application they use on a daily basis.
Their needs are more varied and require a shift in the
traditional BI thinking.
In Forrester’s 2013 Global Tech Market Outlook report,
Forrester explains that software companies looking
to deliver analytic capabilities should not rely on
general business intelligence tools, since these tools
often require users to create their own reports, which
doesn’t help them to work smarter. Enter embedded
analytics, where software companies take care of “the
last mile.”

60

20
07

Natural Evolution of the BI Market

Source: Google Trends

Part Two: Why is Embedded Analytics So Hot Right Now?

9

Trends Driving Embedded Analytics (Continued)

Increased Data Volume, Velocity,
and Variety
With so much hype around big data, this one shouldn’t be
a surprise. People now expect information to be available
at their fingertips – whenever they want it, wherever they
are. Yesterday’s fact-finding missions that started with an
encyclopedia or a visit to the library have become quick Google
searches on your phone to find an answer mid-conversation.
Users now rely on technology to comb through massive amounts
of ever-changing data to organize relevant information to solve
their problems. People need answers quickly, and they choose
to use technologies that optimize the analysis experience and
answer questions in a visually attractive way.

Data in the Hands of Novice Users

This influx of non-technical analysts has forced BI and other B2B
software companies to rethink their analytic capabilities and
how to present information in a simple, more user-friendly way.
Users expect the software to take care of the last mile instead of
pulling information and mashing it together.

Consumer Apps that Utilize Analytics
Companies like Amazon and Kayak have mastered the art of
simultaneously providing data-driven information and driving
frictionless transactions in a seamless manner. These companies
have embraced analytics to create a user-friendly experience
that attracts more users and creates a competitive advantage. As
a result, users now expect more from the business applications
they use regularly to do to their jobs, which puts pressure on
software providers to satisfy their needs.

Data analysis is no longer relegated to a few highly trained,
technical people. Ten years ago, most analytics and information
delivery was taken care of by IT. In the past several years we’ve
seen the rise of data discovery, which takes control from IT and
puts it in the hands of power analysts. But even that falls short
of today’s needs because ALL workers are now expected to use
technology to gain efficiency and increase productivity. Everyone
needs to be a “data expert” in their own domain so they can
make intelligent decisions that drive business forward.


10

Embedded Analytics Drives Smart
Process Applications
Companies that provide embedded
analytics in their applications achieve faster
revenue growth.
In Forrester’s 2013 Global Tech Market Outlook report, they define a
new class of applications called smart process applications. These
applications provide:
• Embedded awareness data relevant to the business activity
• Embedded analytical tools designed for the task at hand
In these applications, analytics is a core capability, such that the
lines are blurred between transactional capabilities and
analytics capabilities.

Process
Applications

Smart
Process
Applications

Analytics
Applications

According to Forrester’s research,

Smart process applications are
growing at double-digit rates (18%)
that are significantly faster than
the overall software market (7%).”
Forrester estimates double-digit growth for smart
process applications from $20 billion in 2012 to $28
billion in 2014.


11

B2C Applications Pave the Way for Embedded Analytics
Fundamentally a transactional
application (selling books)

Satisfies customers’
informational needs

Makes it easy to initiate
the right transaction

Fundamentally an analytics
application (flight prices)

Price Trend
To create a customer-friendly experience
at the point of transaction.

To build an entirely new business
model around this concept.

Amazon

Kayak

Amazon is the Gold Standard for providing relevant analytics
to encourage on-site conversion. Fundamentally, Amazon
exists to sell books. They have great processes to support the
ecommerce experience – including fast shipping, low prices,
and Buy Now with 1-Click. But Amazon also satisfies customers’
informational needs by providing product ratings, video reviews,
and suggested products. They’ve added tremendous value by
providing relevant analytic information at the point of transaction
to create a superior customer experience.

Another good example is Kayak. In this case, what started out
as a pure analytic application (enabling users to search for
flights and compare prices across multiple airlines and travel
sites) launched an entirely new business model by integrating
transactional capabilities at the point of analysis. So not only
do you have all of the information you need to choose an
itinerary, but you can also initiate the purchase within the Kayak
application, without having to reenter your search criteria when
you are redirected to the airline booking site. Now Kayak is able
to make money by providing leads to airlines and travel sites like
Expedia and Orbitz.

27.1% growth rate in 2012

(vs. 15.8% across Top 500 US ecommerce sites)

2012: Closed IPO Day with Shares Up 30%, 2013:

Acquired by Priceline for
$1.8 Billion

12

B2B Applications Lag Behind in
Embedded Analytics
In business applications, the analytics is
typically presented in a separate tab - like the
Reports and Dashboards tabs in Salesforce.com.

What If I’m Behind?
After seeing how Amazon and Kayak utilize embedded analytics in
their applications, however, this model somewhat pales in comparison.
As Amazon customers browse their site, they actually feel smarter and
more confident that they’re making the right choices. That’s because
analytics is a core capability, not an adjunct capability with no real
connection to common workflows and overall user experience.


You might be thinking, “But wait! I don’t even have a
reports tab. Can’t I take some baby steps first?” The
answer is yes. In fact, we recommend evolving your
analytics capabilities in phases over time. In the next
section, we’ll explore the embedded analytics maturity
model, which illustrates the four ways you may choose
to embed analytics within another application.
13

PART THREE

WAYS TO APPROACH
EMBEDDED ANALYTICS

14

What is a Maturity Model?
In this section, we’ll present the Embedded Analytics Maturity Model, which outlines the various approaches
from simple to complex. To set the stage, let’s use the example of GPS in cars to illustrate the innovation
model for embedded technologies. As you can see, what started out as a separate product evolved into a
core car component, which created a new product category of self-driving cars. The same evolution exists for
embedded analytics.

Independent
GPS Device

Embedded GPS
Navigation

Integrated
Functionality

GPS as a Core
Car Component

KEY FEATURES

KEY FEATURES

KEY FEATURES

KEY FEATURES

• Bolted on your

windshield - disjointed
user experience

• Within driving console improved user experience

• Highlight gas stations
when fuel is low

• Create new ways of
travel (e.g. self-driving car)

• Auto manufacturers
capture value

• Route based on traffic
and weather

• Reach new user groups
(blind, elderly)

• No value for the
auto manufacturer

Part Three: Ways to Approach Embedded Analytics

• Build new businesses

15

Embedded Analytics Maturity Model
Below are the four stages of the embedded analytics maturity model, which illustrates the progression of
increasing integration and product differentiation. Along the bottom are the common elements shared by the
core application and the analytics capabilities, including data source, security, UI, and workflows.

Standalone Analytics
Application

Analytics
App

Your App

Gateway to
Analytics

Analytics
App

Your App

Inline
Analytics

Infused
Analytics

Module
Your App

UI

Non-Embedded
8%

UI

Embedded
29%

47%

16%
Distribution of Current Implementations
(2013 State of Embedded Analytics Report)

Data Source


Security

UI User Interface

Workflows

16

Stage 0

Standalone Analytics Application
The Standalone Analytics Application is Stage 0, because
the analytics are not embedded into the core application
at all.
Much like the standard GPS device, the data lives in the core application, and the analytics
live in another application. The only integration concern is how to get the data into the
analytics application.
Data access is typically provisioned through an API or a data export. From a user standpoint,
it is a disjointed experience because users have to work with two separate applications,
which likely look and operate differently and have no security integration. A familiar example
is Microsoft Excel. Users often export data from one application and export it into Excel for
analysis, but all they’ve done is create a new version of the data. Once the data changes,
the Excel data becomes outdated.

Your App

Analytics
App

When to Pursue This Model
The most common use for a Standalone Analytics Application is when your product has no
business user interface, like Google Analytics. Programmers tag their website or application
with a piece of a code to track visitor activity. This visitor activity gets fed into the Google
data store, but business users only access that data by logging into the Google Analytics
website. Essentially it’s two separate applications, because the data store isn’t intended for
business users to see.


17

Stage 1

Gateway to Analytics
Gateway to Analytics is Stage 1, where the core application
serves as a gateway to the analytics application.
In this model, the analytics application is integrated with the core application at a security
level. Users only need one set of login credentials, which are passed from the core
application to the analytics application via single sign-on (SSO). Note that there are still
two applications, but the access to analytics is embedded in the core application. It’s still a
disjointed experience, however, because if users actually want to put insights to use they
have to switch back to the core application.

Your App

Analytics
App

There are three scenarios in which you might choose the Gateway model:
1. You’re working with hybrid architectures
2. Analytics is a distinct offering that customers purchase separately
3. This an intermediate step before moving on to the next stage of embedding
From a development standpoint, you also have separate work streams. Obviously analytics
needs access to the data, which requires security integration, but otherwise the functionality
is entirely separate from the core application.


18

Stage 2

Inline Analytics
Inline Analytics is Stage 2, and the most popular choice for
embedded analytics.
Just like the GPS example where functionality is integrated to adjust driving directions
based on traffic, analytics now appears within the core application.
Beyond data access and security, analytics is now integrated at the presentation tier and
shares the same look & feel as the core application UI. Note that this is the first model
where we have just one application instead of two. Inline Analytics is often implemented as
a Reports tab or module (as in the Salesforce.com example), but analytics are sometimes
presented in other key places as well, like the home screen.

Module
Your App

Analytics
Module

Software providers choose this model when users demand easy and frequent access to
analytics. From a work stream standpoint, there is more coordination involved, particularly
when it comes to UI, but they can still be separate when it comes to functionality. Most
third-party platforms and analytics applications can be embedded using this approach,
so there are a lot of options available. Users are also very comfortable with the reports
module approach, so it’s not surprising that Inline Analytics is the most common model for
embedded analytics.


UI

19

Stage 3

Infused Analytics
Infused Analytics is Stage 3, the holy grail of embedded
analytics.
Just like the GPS example where functionality is so integrated into the car that it creates
new forms of driving, analytics is now embedded at the application tier within user
workflows and becomes part of the overall user experience.
A good way to think about this model is that if you ripped out the analytics, you wouldn’t
have anything left. You can have a car without highlighting nearby gas stations or optimizing
driving directions for traffic, but you can’t have a self-driving car without a GPS. With Infused
Analytics, users are able to view/create analytics to derive insights, and immediately take
action. As users contemplate a choice, they can view relevant information to optimize
their decision. Note that this approach is the only one that qualifies as a smart process
application that we discussed in Part Two.

UI

Companies choose this model when their users have sophisticated analytics needs and/or
the competition’s analytics implementation is mature.
Keep in mind that because there is integration at the application tier, the work streams need
to be more tightly integrated. Developers working on the analytics will need to coordinate
with the developers responsible for the transactional capabilities. Particularly if the backend
is hitting backend APIs for data updates or interacts directly with the database, the work
streams must be entirely integrated.

20

PART FOUR

COMMON FEATURES IN
EMBEDDED ANALYTICS

21

Features Overview
Common embedded analytics features
look a lot like common “business
intelligence” capabilities, but with a twist:
the end user experience is integrated into
the overall application.
The capabilities embedded in each application vary, so we have
also indicated how often we see each feature implemented:
Common

Sometimes

Infrequent

Part Four: Common Features in Embedded Analytics

22

Information Delivery
Improving how data is presented to business users is often the top reason software providers look
to take on an embedded analytics project. Not only should these capabilities meet the needs of end
users, but the look and feel should adhere to the style requirements of the software provider.
Visualizations and Dashboards
A range of visualizations are utilized for users
to quickly draw conclusions and monitor key
performance indicators, such as bar charts,
line graphs, heat maps, and maps. They can be
presented in the context of a single chart, or in a
collection of visualizations in a dashboard.

Mobile
Data is made available to users on mobile devices,
ensuring not only the accurate visual display of
information, but also be compatible with mobile
device features such as touch input.

Reports
A tabular display of data, often with numerical
figures and/or listing of records within categories.
Reports can be scheduled for delivery, used in
conjunction with thresholds/alerts, or exported to
other formats for printing or offline access.


23

Analysis
Software providers look to enhance the value of their offering by allowing users to perform their
own analysis, creating benchmarks and applying proprietary analytics on their own data, and finding
innovative ways to incorporate external data sets.
Self-Service Analysis
Users are given a set of data for which they can
filter, create custom calculations, and create their
own reports or visualizations. The data set for
the end user is restricted to the particular form of
analysis and their user role.
Benchmarking and Data Modeling
This is centered on extracting valuable insights from
the existing data in hand and making these insights
available in the application. For example, SaaS
providers can create performance benchmarks
by analyzing data across their customer base and
make this data available for individual customers to
compare themselves to. Another example would be
applying predictive models to set expectations of
future performance.


External Data
Incorporating data from external sources and
delivering them into a single view or dashboard,
such that the application becomes a vital hub of
information. This could be in the form of third-party
industry benchmarks, data feeds (such as weather
and social media), and customer data from their
specific data stores.

24

Interactivity
Embedding analytic capabilities inside of applications presents interesting ways for users to interact
with those capabilities, as well as paves the way for a more informed and productive experience
inside the application.
Linking
This enables the user to click on a visualization or
report in order to navigate to a different analytic
screen or even another part of the main application,
and vice versa. In other instances, the interaction
simply changes part of the screen instead of the
entire screen.

Personalization
Users choose the visualizations or reports which
are most important to them, and place them at the
top of a dashboard or create bookmarks that can
be accessed quickly.

Workflow and Processes
Beyond linking, analytics can be more tightly
integrated with application functionality. Some
examples: charts embedded in-context on an
existing application page to guide user behavior; a
report with editable data cells, which enable user
to update the displayed data; visualization with
selectable regions (regions of a map or area of a
scatterplot) which enable the user to perform an
action on the selected records.future performance.


25

PART FIVE

THE BUSINESS CASE
FOR EMBEDDED
ANALYTICS

26

The Return on Embedded Analytics:
Core Business Case
To help you build the case for embedded analytics at your
company, let’s first review the major benefits.
Faster Revenue Growth
On average, we find that software companies that embed analytics using the Infused approach have
16% higher annual revenue growth. Revenue growth comes from three distinct areas:
1. New sales – gain new customers and increase average selling price
2. Customer retention – increase retention rate and average renewal price
3. Sales and marketing efficiency – increase opportunity and win rates, lower cost per lead

65%
63%
56%
54%

Create a competitive differentiator
Increase customer satisfaction
Give better sales demos
Improve user experience

41%
36%
33%

Attract new users
Increase overall revenue
Improve your win rate
0%

10%

20%

30%

Part Five: The Business Case for Embedded Analytics

40%

50%

Strong agreement

60%

70%

27

Benefits of Embedded Analytics
Create a Competitive Differentiator

Reduce Ad Hoc Support Requests

One of the major benefits of embedded analytics is to help you
differentiate your product. Many software providers only deliver
basic reporting capabilities through their applications, particularly in
the business application market. There is still a huge opportunity to
create a competitive differentiator by offering more advanced and/or
easier-to-use analytics in your application, which provides real value
to end users. In a recent survey, we found that the most cited benefit
of implementing embedded analytics was creating a competitive
differentiator, and 93% of respondents agreed they had realized
this result.

How many times have you been asked to create a custom
report for a customer? And once you deliver that report, the
customer comes back asking if you can change this or that,
and before you know it you’ve spent a significant number
of man hours supporting this request. Multiply that over
hundreds of customers and you’re no longer a software
company – you’re a development shop. Embedded analytics
helps you to bridge the gap between what exists in your
application today, and what your customers are asking for, so
you can focus on more important things.

Improve User Experience
When implemented properly, embedded analytics can have a
profound impact on your user experience. By enabling users to
solve problems quickly within your application, as opposed to
fumbling around in standalone reports trying to piece together
what’s happening, or forcing them to export data to slice and dice
in Excel, you make their lives easier. At the end of the day, it’s about
minimizing the time and effort that exists between insight and action.
The easier you make it for your users to do something about insights
they find in your application, the more value you can capture.


28

Competitive Differentiation from
Embedded Analytics
Deeper integration of analytics into your software application
enables greater ability to create a competitive differentiation.
Overall, 65% of software providers strongly agree that embedded analytics is a source of competitive
advantage, and that increased to 91% of software providers who choose the Infused Analytics Model.
This is great news if you’re thinking about adding new analytics capabilities, but also if you’re looking to
increase the value of your existing analytics by integrating them deeper into your application.
Competitive Differentiator by Maturity Level

91%
65%
29%

Standalone
Analytics
Application

46%

Gateway to
Analytics

Inline
Analytics

Infused
Analytics

Source: 2013 State of Embedded Analytics report

29

Building Product Value Through
Embedded Analytics
Not surprisingly, as you embed analytics more deeply within your application, the more valuable analytics
become as a percentage of the overall application.
Note that there is virtually no difference in the value of analytics between the Standalone and Gateway
models, but the value more than doubles as soon as you bring analytics into the core user interface.
Value of Analytics (Relative to Overall Application) by Maturity Model

51%
34%
15%

15%

Standalone
Analytics
Application

Gateway to
Analytics

Inline
Analytics

Infused
Analytics

Software providers with Infused Analytics also grow faster than other software companies. The 2013 State
of Embedded Analytics study found that companies with Infused Analytics report 16% higher annual revenue
growth than average.


30

Investment and Costs
Once you estimate the returns from
embedded analytics, you need to develop
the other side of the business case by
understanding the investment required.
There are various costs associated with developing/implementing
embedded analytics. If you decide to build, your costs will primarily
consist of initial development resources and ongoing maintenance
and enhancement costs. If you buy a business intelligence platform
or integrate with an embedded platform, then your investment will
consist of the following: software, development, consulting/training,
support, etc.
See page 49 for a further discussion around the build vs. buy
question for embedded analytics.


31

The Cost of Embedded Analytics
Choosing an analytics software provider to
extend the capabilities in your application
is a way to get to market faster and
reduce resources over the long run. These
providers may offer a variety of different
pricing models.
CUSTOMER-BASED PRICING assigns a cost to each of your
customers. This is the most common pricing model since it is easy
for companies to predict how many customers they will have in year
1, year 2, and year 3.
USER-BASED PRICING assigns a cost to each end user of your
application. This is a good model for companies with very stable
growth, but can be challenging for early stage software companies
and high growth software companies since it’s hard to predict how
many end users you’ll have in three years, particularly as you start
expanding your user base at each customer.


SERVER-BASED PRICING assigns a cost to each server. This
can be challenging because you don’t want to be limited in
your analytics capabilities, or face a steep price increase if
one of your customers grows.
Ideally, you want to find a vendor who is flexible enough
to match their pricing to your business model, so that
your analytics capabilities (and costs) are in line with your
customer base and revenue forecasts.

Other Investment Factors
Most embedded analytics vendors offer add-ons on top
of the software licensing costs—including new customer
enablement, premium support packages, consulting services,
and instructor-led education and training. The purpose is
to accelerate ROI and time to value, and costs tend not to
exceed 20% of the software licensing costs. You should also
take headcount and hardware costs into consideration, both
current state and anticipated costs in the future. The best
results come from looking beyond only the software. When
you OEM software and make it part of your application, you
should consider who offers the best package as a long-term
business partner.

32

Bring It All Together
Now it’s time to combine the improvement
in analytics and the anticipated costs to
calculate your return on investment.
The key steps are:
Gather baseline metrics such as number of current
customers, average selling price, customer retention, and
average renewal price
Estimate how embedded analytics will improve your
baseline metrics
Estimate the implementation costs, including software,
support, consulting, and development costs
Calculate how improved baseline metrics will translate into
revenue growth and lower development costs
Calculating ROI

Contact salesteam@logianalytics.com
to build a personalized business case
for your company using our Embedded
Analytics ROI Calculator.

33

Selling Embedded Analytics Internally
In some cases, not everyone will
immediately see the value of an investment
in embedded analytics.
To convince them, you’ll need to understand their priorities, and
what challenges stand in their way. Then you can connect the dots
for them and position embedded analytics to address their specific
concerns.
When you’re trying to gain internal support for an embedded
analytics purchase, position it as a potential solution to the current
problems your business faces, and how it will impact your customers
and the bottom line. That way your organization will see it as a
priority instead of putting it on the back burner indefinitely.
Your ability to deliver a compelling business case for embedded
analytics hinges on how well you structure your argument to resolve
the chief challenges and priorities of your audience. No matter which
executive you want to convince, make sure to manage expectations
about the time to value and effort required. Don’t overpromise and
under-deliver. Embedded analytics is not something you just turn on
and see instant results. Getting value takes a strong plan, time, and
effort. Start small, and evolve your analytics capabilities over time as
you and your end users become more sophisticated.


34

5 Tips to Sell Embedded Analytics Internally
Understand their goals so you can present a case that meets
their needs.
Create a financial case that proves how embedded analytics
is the key to accomplishing those goals. If revenue is the
main objective, make your case based on revenue.
Discuss, don’t present. Start by asking questions to
understand what they are looking to achieve. Then transition
by saying, “If I could show you how to meet those objectives
efficiently and effectively, would you be interested in learning
more?”
Support your case with real-life case studies, particularly from
companies that are similar to yours.

Enhancing our product
through new analytics has
assisted us in upselling into
the marketplace to win deals
with larger enterprises. During
the economic downturn, we’ve
been growing at 25 percent
and replacing competitors with
larger market share at Fortune
500 companies.”
– Mike Mercadante, CTO, VPI

Be ready if they say yes. Have your high-level plan
ready, an overview of the next steps, timeframes, and
required resources.


35

How to Position Embedded Analytics
for Each Executive
CEO

CTO

Chief Concerns:

Chief Concerns:

• Grow revenue and “hit the number”
• Manage costs, meet shareholder expectations
for profitability

• Ensure that the product(s) function appropriately from
an engineering standpoint over the long term
• Improve products continually to stay competitive

• Attract and retain talent

• Build a better user experience

• Innovate and out-execute the competition

• Manage and leverage third-party technologies to
deliver required capabilities

• Build and align the organization to enhance collaboration
• Manage risk
Connect the Dots:
Contextualize embedded analytics as key to business growth. Position it as a
solution to create a competitive differentiator so you can become a leader in
your space.
Discuss how embedded analytics can enhance sales effectiveness through
better demos, shorter sales cycles, etc.
Show how embedded analytics improves the user experience of your product
to increase customer satisfaction, and drive new revenue.

Connect the Dots:
Educate them on the available technologies and architectures to show
how embedded analytics platforms enable long-term growth.
Discuss how embedded analytics can ease the burden on development to
deliver analytics enhancements on time.
Discuss options of working with an embedded analytics partner who
is familiar with security, white labeling, and UI/UX requirements so the
implementation goes more smoothly and allow their team to maintain their
focus on core functionality.

Discuss how an embedded analytics platform enables you to go to market faster
with new analytics capabilities without sucking up development resources.


36

How to Position Embedded Analytics for Each Executive (Continued)

CFO

Head of Sales

Product Manager

Chief Concerns:

Chief Concerns:

Chief Concerns:

• Manage expenses and cash flow

• Make quota

• Contain risk

• Get an accurate forecast

• Enable profitable growth

• Beat the competition

• Plan for the future

• Expand market share

• Success of the product line in
the market
• Deliver features and functionality
customers are asking for

Connect the Dots:

• Make customers successful

• Deliver a superior user
experience

Do the math. Present your developed business
case. Don’t worry too much about the fact that
you’re making estimates, provided that they’re
clearly labeled. You’ll build credibility simply
by walking in the door with a spreadsheet of
numbers, showing you can speak the CFO’s
language.

• Develop the sales force

• Maximize customer satisfaction

Discuss the notion that embedded analytics
helps you save time and resources fielding ad
hoc reporting requests from current customers.

Connect the Dots:

• Meet launch deadlines

Remember that sales is on the front lines and
often drives product direction based on what
they hear from their prospects. Explain how
embedded analytics can deliver the analytics
capabilities your customers are asking for
quickly so their team can start selling it.

• Beat the competition

Discuss how embedded analytics helps their
team to deliver better sales demos, decrease
sales cycles, box out the competition, and
drive new revenue.
Discuss the value of embedded analytics to
end users, which drives increased renewal
rates, exposes new opportunities to sell
more product, and possibly drives new revenue
streams (depending on your packaging model).


Connect the Dots:
Build the vision of how the product will
be enhanced through embedded analytics
– what will their users be able to accomplish
and what is the value of solving those problems?
Discuss how embedded analytics is a “need
to have” and how it can create a competitive
differentiator as well as enhances sales
effectiveness through better sales demos,
short sales cycles, and increased revenue.
Educate them about the options in the
market to add analytics quickly while meeting
launch deadlines and maintaining control
over UI/UX.

37

The Cost of Delaying
Here are some common reasons why people may delay investing in embedded
analytics platform, and how you can overcome them.
OBJECTION

RESPONSE

I don’t need to use a third-party
tool because I can build this
all internally.

That’s true. With enough time and energy, you can build everything an embedded
analytics platform can offer, but do you really want to spend years building
analytics? A third-party application allows you to develop faster and save money
so you can maintain your focus on advancing your core functionality. Embedded
analytics has evolved to the point where hundreds of companies spend 100%
of their time trying to make analytics better, faster, and easier for end users. By
utilizing a third-party platform, you can do what you do best, while relying on
analytics experts to make your product better.

Let’s just give our customers
access to the data and let them
do what they want.

You’re leaving value on the table, and instead of enabling end users to do their
jobs more intelligently, you’ve settled for becoming a data collection tool. Besides,
data exports are no longer going to cut it – your customers expect more. By only
providing exports, you’re inviting your customers to come back frustrated with
custom reporting requests that eat into your time.

We don’t have the resources
necessary to do this successfully.

All the more reason to integrate with a third-party platform, which brings the
domain expertise necessary to implement embedded analytics successfully,
including how-to guides, best practices, and in-person consultations. You can start
small, or look for tools that conform to your architecture and your development
process. The days of Big BI are over - there are some great options that will
enable you to add real value to your applications without taking over everything.


38

OBJECTION

RESPONSE

We know our customers want
something but we don’t know
exactly what it is they want.

If your customers are communicating some sort of pain, you need to investigate
what’s driving that frustration. A good embedded platform will support you
through a structured evaluation process where you build a proof of concept
together, which you can use to validate your direction with your customers. In
this case, it’s important to choose a platform that is easy to use and supports
rapid prototyping so you can pivot appropriately. You can also rely on embedded
analytics platforms to guide you in how to package an analytics offering for your
customers based on their requirements.

Embedded analytics is a nice to
have, not a need to have for us.

Educate them on the evolution of embedded analytics within B2C and B2B
applications, and how it is most definitely a need to have. Show how embedded
analytics builds product value and enables your users to work more productively
with your application. Present findings from the 2013 State of Embedded Analytics
study, which outlines the clear benefits of embedded analytics, including creating
a competitive differentiator, improving sales demos and conversion rate, and
faster revenue growth, to support your case.

Bottom line: there’s never a perfect time to roll out new software or
start a new project. Don’t let the delay go on too long. You’ll always
be busy. Once you’ve determined you’ll benefit from the investment,
the longer you wait to implement embedded analytics, the longer
you’ll wait to see a positive impact.


39

PART SIX

HOW TO BE
SUCCESSFUL WITH
EMBEDDED ANALYTICS

40

The Embedded Analytics Go-to-Market Plan
Being successful with embedded analytics requires more than great technology.
While there are important differences between embedded analytics platforms,
success is primarily determined by your strategy and ability to execute. Here is your
go-to-market plan to implement embedded analytics successfully.

User Experience

Implementation

Market Delivery

Business Value

Packaging & Pricing

Promotion

User Profiles

Agile Development

Sales Enablement

Capabilities Map

Build vs. Buy

Education

Part Six: How to Be Successful with Embedded Analytics

41

Step 1 - Define the User Experience:

Creating User Profiles
Repeat this mantra: I create value in my
software by solving customer problems.
The power of data is your customers can leverage your application to:

• Derive insights faster
• Draw accurate conclusions
• Increase efficiencies and productivity
• Reduce costs


42

Creating User Profiles (Continued)

When planning to implement embedded
analytics within your application, you’ll
need to determine:
• What problems do your users need to solve?
• What information do they need access to?
• What data sources do they need to connect to?
• How often do they need access to analytics?

These questions can help you flesh
out the user roles for your product,
which will inform your technical and
functional requirements. Use the
template below to define your user
roles and their pain points, their
transactional/analytical needs, and how
your product addresses them.

• Will static output be sufficient, or will they need to be able to
explore data, create their own reports, etc?
• Are they interested in benchmarking performance
against their peers?
• Where and how will they access the analytics (e.g.
mobile devices while on the road)?
• Can you increase adoption or attract new user types
by adding specific analytics capabilities?

USER 1

USER 2

USER 3

USER 4

Role
Behaviors
Attitudes
Motivations
Barriers
How We Help


43

Step 1 – Define the User Experience:

Using a Capabilities Map
Once you’ve identified your distinct user types, use a capabilities map to match them
with relevant analytics capabilities required in your application.
You may find that there is a lot of overlap in terms of the analytics capabilities required by your user types. That makes your job easy –
simply focus on delivering those capabilities first. However, you may find that there’s no overlap at all, and that you have distinct user types
that all want different things. Usually you’ll land somewhere in the middle, where you have some users who need mostly static dashboards
and reports (like executives), and other users who need the ability to slice and dice data, create their own reports, etc. (like power users).
Sample Capabilities Map
Write backs

Visual Workflow

Creating Reports

Customizable
Reports

Customizable
Dashboards

Creating
Visualizations

Drilling/linking

Interactive
Dashboards

Scheduling/Exports
Interactive Reports

Static Dashboards
Individual
Charts / Graphs

Static Reports
Reports

USER TYPE 2

USER TYPE 1

Visualizations


Benchmarking
USER TYPE 3

User
Data

MultiDimensional

Third-Party
Data

Data Analysis
and Exploration

Cross-Module
Data Joins

Data Exports

Mobile

Self-Service

Other Capabilities

44

Step 2 – Implementation:

Packaging Options for Embedded Analytics
Now that you’ve identified your users and their analytics needs, you
need to determine how to package and price your offering.
There are three common packaging models employed for embedded analytics:
• Tiered model – multiple editions, each with increasing analytics capabilities (see example below)
• Single, separate module – all analytics capabilities in one offering, separate from the core application
• All-inclusive model – standard core offering includes infused analytics capabilities
Sample Capabilities Map with Tiered Packages
Write backs

Visual Workflow

Creating Reports

Benchmarking

Customizable
Reports

Customizable
Dashboards

Creating
Visualizations

User
Data

Drilling/linking

Interactive
Dashboards

MultiDimensional

Third-Party
Data

Static Dashboards

Data Analysis
and Exploration

Cross-Module
Data Joins

Data Exports

Mobile

Self-Service

Other Capabilities

Scheduling/Exports
Interactive Reports
Static Reports

Individual
Charts / Graphs

Reports

Visualizations

Gold

Platinum

Silver

45

Packaging Options for Embedded Analytics (Continued)

Tiered Model

All-Inclusive Model

• Base offering with limited functionality, sometimes lends
itself to a freemium model

• Analytics capabilities are positioned as core to the
overall application value

• More capabilities are available at higher tiers, analytics
can be distinct options by themselves or bundled with
other application functions in each tier

• Typically not charged for separately

• Price for each tier can be relative to the base price and
structure (usage model) or a fixed dollar amount
The tiered model is appropriate when you have distinct user
types with different analytics needs, or specific analytics
capabilities that are inherently more valuable than others. In
general, you can define the tiers by 1) business value – base
set of report/data access vs. expanded set, 2) user type –
report reader vs. report writer, or business/functional role, or 3)
capabilities – reports vs. dashboards vs. self service analytics.

• Value is built in
The all-inclusive model is becoming increasingly common as
embedded analytics trends towards the Infused model where
it is a core component of the overall application. In this case,
software providers typically CAN justify a price increase for
their application because the value is higher. If you are in a
particularly competitive and/or sophisticated market, however,
the value of embedded analytics often manifests itself in a
higher win rate and/or higher retention rate instead of a price
increase. Customers don’t want to pay more for it because
everyone has it, but they’ll choose your solution over others
because the value is still there.

Single, Separate Module
• No tiers, just one optional analytics module
• Typically charged as a separate line item
• Single addition option for a given price
The single, separate module is less common today than six
or seven years ago. Now, it’s really only appropriate when
you have a very homogeneous group of users with only basic
reporting needs.


46

Step 2 – Implementation:
Tips for a Successful Embedded Analytics Project
Once you’ve decided how to package and price your
analytics offering, here are three tips to completing a
successful implementation.
Have a Vision, but Build in Phases
When you start building specific analytic capabilities into your application, it can sometimes become
overwhelming as you see how far you have to go to complete your vision, particularly as new ideas
surface along the way. That’s okay. Before you try to bite off the whole ocean, remember to start small
and build from your successes. Start with one user type, one problem, one report. Get feedback and
move forward. Requirements shift and evolve over time as users start to see what’s possible, so it’s
important to stay agile and approach embedded analytics in many iterations and phases.

Where should you start?
• By business problem
• By role of customer
• By capabilities
Keep in mind that you may have to decide between implementing the most valuable items vs. the
most widely utilized features first.
Bottom line: Walk before you run.


47

Tips for a Successful Embedded Analtyics Project (Continued)

Involve Internal and External Stakeholders
There’s nothing more frustrating than building out a really cool
feature that no one uses. To avoid this, be sure to get regular
feedback from internal and external stakeholders as you build
specific analytics capabilities into your application. Utilize screen
mockups early in the process and review them with current
customers to validate your direction. Ask what they like, what they
don’t like, how they would use it, and what suggestions they have
to make the product better. This will help you to stay focused on
solving real user problems with embedded analytics, and also
enables participants to become advocates when the capabilities
become generally available.


Perform a Usability Study to
Identify Gaps
With select customers, you should consider conducting
on-site usability studies to see how they actually use the
application and identify remaining gaps. The point of
conducting a usability study is to find out in advance what
problems will bother your users, so be prepared for users to
complain when they are lost or frustrated – that’s exactly the
kind of feedback you’re looking for. Avoid helping users to
get to the right answer. Instead, ask them to complete specific
tasks and learn how they expect to navigate your application
to accomplish them. Ask them to rate certain aspects of the
application, and prioritize enhancement requests. Ask openended questions to get the most feedback, not questions that
can be answered with yes or no.

48

Step 2 – Implementation: Build

vs. Buy

The build vs. buy argument is increasingly irrelevant for embedded
analytics as user expectations become more sophisticated.
There are many companies in the analytics space, whose sole mission it is to make analytics better, faster, and
easier to use. For this reason, software companies are increasingly more likely to integrate with a third-party
embedded platform, but regardless, here are the three options available to you.
Option 1: Build
Often the first option companies choose
because their requirements seem simple.
• Code-intensive approach

Option 2: Integrate a BI Application
The best option if your requirements truly are
simple, you are okay with a disjointed UX, and
your deadline is very tight.

Option 3: Embedded Platform
The Goldilocks approach…most of the functionality
you need, without being too restrictive.
• Flexibility to create UI/UX

• Development with third-party charting
libraries, or open source code

• Integration-centric approach

Pros

Pros

Pros

• Flexibility to create the desired UX

• Faster to acquire out-of-the-box functionality

• Accelerated time to market

• Low cost for low-complexity projects

• Less coding of functionality

• Flexibility to create desired UI/UX

Cons

Cons

Cons

• Cost of development, support, and
maintenance over time

• Need for specialized BI skill sets for
development and integration

• Need strong partner as value is wrapped
up in a third-party tool

• Time to market poor

• Less ability to craft your own UI/UX

• Usually a code-intensive process

• Harder to support advanced workflows for your
end users

• Bolt-on third-party application


• Quickly acquire and configure functionality
• Utilize existing resources and skills sets
with minimal coding required

49

Step 3 – Delivering Embedded Analytics

to Your Market

You’ve embedded analytics within your
application…now what? Here are some tips
for getting the most out of your investment.
Promotion Best Practices
Be Visual
Analytics are inherently visual, so the best way to showcase
your new capabilities is to utilize compelling visuals. Use
screenshots liberally on your website and sales decks, and
use webinars and videos to guide users through your new
features. Consider creating a Visual Gallery with topical or
customer examples.
Leverage Customer Stories
To convince prospects of the value of your application,
nothing is more convincing than customer stories. Reach out
to your customers regularly to solicit feedback, and ask if
you can quote them in a case study, webinar, press release,
and/or testimonial on your website. Consider creating a
Testimonials section and/or Customer Success Gallery to
present all of your happy customers in one place.


50

Delivering Embedded Analytics to Your Market (Continued)

Sales Enablement
As you roll out new analytics capabilities, be sure that your sales
team knows what’s coming and how to sell it effectively. You’ll need
to craft new messaging, determine the new value propositions for
each of your user types, and train the sales team before launch.
Remember to share new use cases and teach the sales reps to
identify when and how to target new user types. Create compelling
demonstrations that highlight the most valuable capabilities, and
how your product is different from competitors. Prepare interesting
demo data to simulate a rich production environment. Once you get
rolling, remember that sales is on the front lines and is capturing a
ton of feedback from prospects, so be sure to close the loop so that
feedback informs future development plans.

Think Education
Today’s selling process has transformed into an education process.
Your prospects need to recognize your company as a thought leader
that understands their frustrations and challenges before they’ll
agree to a sales pitch. Educate your potential customers through
compelling content aligned with each stage in the buying process,
including white papers, solution briefs, topical webinars, buyer’s
guides, RFP templates, and demos.


Tip for capturing testimonials:
While you’re on the phone with
customers, ask if they would be
willing to provide a testimonial.
If they say yes, send them an
iPad in the mail and have them
record a short video on their
experience with your product
via FaceTime. Then let them
keep the iPad as thanks for
their effort! It’s a win-win.

51

PART SEVEN

BUYING AN EMBEDDED
ANALYTICS SOLUTION

52

Buying Process
Great! You’ve decided to buy an embedded
analytics platform. Now you need to select
the right solution.
Of course, in our (admittedly biased) opinion, Logi Analytics is almost
always the best solution. But here’s an unbiased process you can
follow to buy the embedded analytics solution that is right for you.

Determine Your Goals & Timeline
To get where you want to go, write it down. Statistically
speaking, you increase your likelihood for success simply by
putting your goals on paper.
Hard metrics may be:
• Decrease sales cycle
• Improve win rate
• Improve customer retention
Soft metrics may be:
• Create a competitive differentiator
• Give better sales demos
• Improve user experience
Part Seven: Buying an Embedded Analytics Solution

53

Buying Process (Continued)

Identify the steps you’ll take to get where you want to go. You aren’t
ever “done” with embedded analytics, so build time to evolve and
learn from your customers.
Ask yourself, “When do I want to…”
• Start the selection process?
• Have detailed vendor presentations and demos?
• Finish a proof of concept?
• Make my final decision?
• Start development?
• Go to market?

Identify Technical Requirements
Review your administrative, integration, and technical requirements.
Research your competitors and talk to your customers to develop
a firm understanding of the capabilities you want to add to your
application. What other technologies do you have that the analytics
will need to work with? What data sources will you need to access?
What level of integration are you looking for with your core
application? Reference the functionality checklist in Part Four to
certify you’ll get what you need today and in the future.

The Importance of Easy
It should be easy to get started quickly, so
you can bring your product to market sooner.
Get all of your major questions answered
during the structured evaluation so you are
confident in your choice.
It should be easy to develop analytics, so you
can meet all of your capability requirements
while maintaining focus on advancing your
core functionality.
It should be easy for anyone on your
development staff to use, not just a select
few. Go with a platform that utilizes open
standards and offers an intuitive development
experience.

Consider who will use the platform to create analytics and their current skill set. How important is
ease of use? What level of additional services, training, and support will you need?
Turn the requirements into functional scenarios. Describe how you want your users to interact with
your application once you’ve embedded analytics, and what they’ll be able to accomplish.


54


Assemble the Team
Determine the stakeholders that need to be involved.
Who is going to care about embedded analytics internally
(executive team, product management, lead developers)
and externally (key customers, customer advisory board)?
Be sure to review the technical requirements with them
upfront, and build the business case collectively to get
buy-in to move forward.

Research Potential Vendors
Assign a point person to research potential vendors
and evaluate whether their functionality matches your
requirements. Look at industry sources like the Gartner
Magic Quadrant for BI Platforms to create your initial list
and pay attention to platforms that specialize in the
OEM market.
Get demos from each one and confirm a basic fit. Discuss
your requirements and ask each one to demonstrate how
they would deliver your specific processes and scenarios.
Ask tough questions and make sure the vendor actually
shows you that they have what they say they have in
terms of functionality. Confirm ballpark pricing to
move forward.

Avoid a feature bake-off. Instead, focus on the
requirements you identified in step 2, and try not
to be dazzled by features that don’t deliver on your
criteria. Of course during your search process you
may update your goals as you learn what’s possible,
but stick to what you can envision yourself using that
will provide value to your customers.

Complete Structured Evaluations
with Selected Vendors
Narrow down your list to the top 2-3 vendors and
begin a structured evaluation process with each
one. This is where you’ll define a proof of concept
and establish clear cut guidelines for what you want
to accomplish within, say, 30 days. The amount of
interaction you have with each vendor is based on
your preference – ranging from an assisted trial
where support is generally available if you run into
issues, to a true structured evaluation where you and
the vendor are building a proof of concept together.
At the end of the evaluation, present the output back
to the stakeholders to get feedback and validate
your direction.

Evaluate each vendor’s ability to make you successful
during the implementation process through access to
best practices, community, consulting, support,
and training.


55


Talk to References
Now it’s time to find out if your vendor can actually make
customers like you successful.

The Importance of Flexibility

Ask your vendors for references. Solicit others from your
personal and social networks. Look for references that
are similar to your organization (size, industry, use
case, etc.).

You know best how your users prefer to work with
your application, so you should absolutely maintain
control over the UI/UX. Choose a platform that fits
into your overall product vision, not the other
way around.

Find out whether your situation is similar to theirs. Don’t
just ask whether they’re happy with the vendor, drill into
what functionality they’ve delivered, what is support
and training like, how long was implementation, what
roadblocks did they run into, how did the vendor handle
any problems or issues?

You don’t want to invest a lot of time and energy
with a platform, only to rip it out a year later. While
you don’t want unnecessary complexity, you don’t
want to outgrow the solution either. Going too
small or cheap—without aligning to your future
requirements—is a clear path to failure.
When you integrate third-party technology into your
application, the licensing needs to make sense. Look
for vendors who can tailor their licensing to match
your business model.


56

Questions to Ask During a Reference Call
Success Criteria & Selection
• What were the key business processes and goals you set for this embedded analytics project? How well has the
system delivered on those goals?
• Were you the decision maker responsible for purchasing this solution?
• What made you choose the solution you selected?
Implementation & Ramp-up
• Tell me about your implementation – what was better than expected and where did you run into challenges?
• How long did it take you to learn basic functions, like creating a dashboard or report?
• How complete is the integration between the analytics and your core application (including security, white labeling,
etc.)? How hard was it to set up and maintain?
• What has your experience with training and support been like?
• How proactive has the vendor been to make sure you are successful?
Results
• Have you deployed your analytics solution yet? If so, what was the reaction like from your customers and prospects?
• Have you seen any specific benefits – like time to market, competitive differentiator, better sales demos, increase in
customers, and/or increase in revenue?
• What do you love about this platform, and what do you hate?
• Beyond the licensing, what other costs did you incur during implementation?
• If you were to do things over again, would you make the same vendor decision? Would you do anything else differently?


57


Select a Vendor and Get Started
It’s go time! Choose the vendor that you feel most
confident in as a partner to reach the goals you identified
earlier in the process. Of course you’ll have to compare
and negotiate terms and conditions, but look beyond
software for the vendor who gives you the highest chance
of success. Make sure your vendor has the resources to
help you, even if you don’t need the help today. 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 for best practices.

Monitor, Adapt, and Optimize
There’s a lot that could be said here, given how endless
your possibilities are when using embedded analytics. But
for the purpose of time and space, here’s an overview of
how to approach this phase of your process.

Customer reactions have
been astounding. Customers
are particularly interested in
this portion during the sales
process. Additionally, we find
that they engage a lot more
with the reporting because they
can control it and investigate
what they’re seeing. The new
dashboards and reports have
enabled them to see much
quicker results.”
– Andy Madge, Head of Technical Services,
Creative Virtual

• Invest in the training you need to be successful.
• After three to six months, do a check-up, and consider
reengaging with your vendor’s services. Evaluate
additional services that could take you to the next level.
• Engage with your vendor’s community to learn and
share best practices. Suggest ideas for new features
while you’re at it.


58

Additional Factors to Consider
Beyond the features and functionality previously discussed,
here is a list of additional factors to consider when selecting an
embedded analytics vendor.
Implementation & Ramp-up
• How long does it take to get live at a basic level? A medium level? An advanced level?
• Do you provide training live or on-demand?
• What skill sets will I need on my team to be successful?
Service & Support
• What’s your service-level agreement for technical support?
• How will you help expose our users to new ideas and best practices?
• How active is your online community? How quickly will I get answers?
• Who are your key service partners? Who provides strategy and process design, change
management, and technical integration support?
Likelihood for Long-Term Success
• What kind of ongoing costs and resources should I expect?
• Is your company viable in the long run? What funding and capital do you have? What’s your
growth rate?
• How well does your product roadmap align with my future needs?

59

PART EIGHT

THE FUTURE OF
EMBEDDED ANALYTICS

60

The Future of Embedded Analytics
Embedded analytics is a new and dynamic
industry, and we’re seeing new capabilities
all the time. Here are the three key trends
that will drive this evolution over the next
few years.
Infused Analytics Becomes the Standard
As we discussed in Part Three, we are seeing a trend towards
the Infused Analytics model, where analytics becomes a core
component of the parent application within a seamless user
experience, such that it’s impossible to tell the difference between
the two. Embedded analytics enables workflow so users can
derive new insights, draw accurate conclusions, and identify
ways to increase productivity and decrease costs. Just as B2C
applications have evolved from bolt-approaches to more infused
implementations, B2B applications will follow suit so users are able
to access analytic information and initiate transactions without
leaving the core application.

Part Eight: The Future of Embedded Analytics

61

The Future of Embedded Analytics (Continued)

Analytics Everywhere, for Everybody
We’ve already started to see the increased need for self-service
capabilities. Related to this trend, we anticipate more non-technical
users to start demanding access to analytics within the software
applications they use on a daily basis. This has repercussions on
the user experience required for those applications, as analytics
will need to be extremely easy to use, with built-in controls so
even novice users can arrive at accurate conclusions. The days are
gone that a few power users do 100% of the analysis and share the
results with the rest of the team. Now all team members will want
direct access so they can do their jobs more efficiently. You’ll likely
still want to provide basic reports for the majority of your users,
but prepare for more users wanting control over the output. Your
product management and developers will be more challenged by
self-service than they ever were by managed reporting. It’s harder
to enable your users to ask their own questions than it is to provide
dashboards that answer a few key questions. Eventually you will
need to deliver both.

More Sophisticated Analytics Capabilities
Hopefully it’s obvious by now that embedded analytics is here to
stay and that it is a need to have for all software applications. Your
customers expect it, and you need to meet their expectations to stay
competitive. However, the sophistication of analytics is still pretty
basic across the board. If you think about the range of analytics from
least to most advanced, you might have:

Part Eight: The Future of Embedded Analytics

• Descriptive analytics – describe what’s
happening with the data (e.g. sales are going
up, and here’s a chartthat’s showing that)
•

Diagnostic analytics – no longer just
describing, now diagnosing what the issues are
(e.g. West Coast sales have plummeted; this is
something you need to address)

• Predictive analytics – here’s the data, here’s
what it means, and here’s what the next quarter
is going to look like
• Prescriptive analytics – here’s what’s
happening, here’s why, here’s what the future
looks like, here’s what you should do about it
While 93% of software companies provide some level
of analytics within their applications, few are delivering
prescriptive analytics, where they not only present what’s
happening, why, and what the future looks like, but also offer
direct guidance to solve the problem. If you think about it
from an end-user perspective, that provides a lot of value.
And in the end, isn’t that what it’s all about?

62

PART NINE

LOGI ANALYTICS’
APPROACH TO
EMBEDDED ANALYTICS

63

Logi Analytics’ Approach to Embedded Analytics
Why Logi Analytics?

Seamless. Fast. Complete.

Architecture Made for Embedding

Interactive Dashboards and Reports

Logi Analytics offers the best platform to quickly create, integrate, and
iterate upon embedded analytics with minimal impact on development
resources and infrastructure.

Deliver superior data visualizations, charts, and graphics

Fastest Time to Market
Our development approach enables you to quickly assemble pre-built
elements to create even complex data visualizations and analytics with
minimal coding.

Your Partner for Success
Logi Analytics has a fully dedicated OEM team and over 10 years of
experience helping hundreds of software companies to embed reporting,
dashboards, and analytics. From visual design to feature selection and
integration strategies, we’ve developed best practices for every situation
and will work with you to make them part of your application.

Self-Service Analytics
Enable power users to create their own reports and explore data

Mobile-Ready Development
Deliver content on mobile apps through HTML and HTML5

Seamless User Experience
Your users will never know Logi is running behind the scenes

Elemental Design Approach
Use pre-built elements to create analytics with minimal coding

Security that Matches Your Application
Flexible and highly granular security layer ensures proper access for all users

Cloud or On-Premise Support
Including single-instance, multi-tenant SaaS environments

Extensible Platform
Create a uniquely tailored user experience for your application

Licensing to Match Your Business
Customized licensing aligned to your business
Visit www.logianalytics.com/oem-solutions to learn more.

Part Nine: Logi Analytics’ Approach to Embedded Analytics

64

Conclusion
So there you have it: everything you ever wanted to know and more
about embedded analytics. Whether you’re building your first product
or revamping an existing one, embedded analytics can help you solve
real customer problems, which builds product value and creates a
competitive differentiator to propel your business forward.
HAPPY DEVELOPMENT!
Contact Logi Analytics
North America: +1 703-752-9700

Website: www.logianalytics.com

Europe: +44 118 935 7256

Blog: www.logianalytics.com/blog

Email: salesteam@logianalytics.com

Twitter: @logianalytics

#CG2EA

65

The Complete Guide to Embedded Analytics

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