Maximizing SaaS application analytics value with AI

Software
as
a
service
(SaaS)
applications
have
become
a
boon
for
enterprises
looking
to
maximize
network
agility
while
minimizing
costs.
They
offer
app
developers
on-demand
scalability
and
faster
time-to-benefit
for
new
features
and
software
updates. 

SaaS
takes
advantage
of

cloud
computing
infrastructure
and
economies
of
scale
to
provide
clients
a
more
streamlined
approach
to
adopting,
using
and
paying
for
software.

However,
SaaS
architectures
can
easily
overwhelm
DevOps
teams
with
data
aggregation,
sorting
and
analysis
tasks.
Given
the

volume
of
SaaS
apps
on
the
market
(more
than
30,000
SaaS
developers
were
operating
in
2023)
and
the

volume
of
data
a
single
app
can
generate
(with
each
enterprise
businesses
using
roughly
470
SaaS
apps),
SaaS
leaves
businesses
with
loads
of
structured
and
unstructured
data
to
parse.

That’s
why
today’s
application
analytics
platforms
rely
on

artificial
intelligence
(AI)
and

machine
learning
(ML)
technology
to
sift
through
big
data,
provide
valuable
business
insights
and
deliver
superior
data
observability.

What
are
application
analytics?

Broadly
speaking,
application
analytics
refers
to
the
process
of
collecting
application
data
and
performing
real-time
analysis
of
SaaS,
mobile,
desktop
and
web
application
performance
and
usage
data.

App
analytics
include:

• 
  App
  usage
  analytics,
  which
  show
  app
  usage
  patterns
  (such
  as
  daily
  and
  monthly
  active
  users,
  most-
  and
  least-used
  features
  and
  geographical
  distribution
  of
  downloads).

• 
  App
  performance
  analytics,
  which
  show
  how
  apps
  are
  performing
  across
  the
  network
  (with
  metrics
  such
  as
  response
  times
  and
  failure
  rates)
  and
  identify
  the
  cause
  and
  location
  of
  app,
  server
  or
  network
  problems.

• 
  App
  cost
  and
  revenue
  analytics,
  which
  track
  app
  revenue—such
  as
  annual
  recurring
  revenue
  and
  customer
  lifetime
  value
  (the
  total
  profit
  a
  business
  can
  expect
  to
  make
  from
  a
  single
  customer
  for
  the
  duration
  the
  business
  relationship)—and
  expenditures
  such
  as
  customer
  acquisition
  cost
  (the
  costs
  associated
  with
  acquiring
  a
  new
  customer).

Using
sophisticated
data
visualization
tools,
many
of
which
are
powered
by
AI,
app
analytics
services
empower
businesses
to
better
understand

IT
operations,
helping
teams
make
smarter
decisions,
faster.

AI
in
SaaS
analytics

Most
industries
have
had
to
reckon
with
AI
proliferation
and
AI-driven
business
practices
to
some
extent.

Roughly
42%
of
enterprise-scale
organizations
(more
than
1,000
employees)
have
used
AI
for
business
purposes,
with
nearly
60%
of
enterprises
already
using
AI
to

accelerate
tech
investment.
And
by
2026,

more
than
80%
of
companies
will
have
deployed
AI)

)AI-enabled
apps
in
their
IT
environments
(up
from
only
5%
in
2023).

SaaS
app
development
and
management
is
no
different.

SaaS
offers
businesses
cloud-native
app
capabilities,
but
AI
and
ML
turn
the
data
generated
by
SaaS
apps
into
actionable
insights.
Modern
SaaS
analytics
solutions
can
seamlessly
integrate
with
AI
models
to
predict
user
behavior
and
automate
data
sorting
and
analysis;
and
ML
algorithms
enable
SaaS
apps
to
learn
and
improve
over
time.

Using
comprehensive,
AI-driven
SaaS
analytics,
businesses
can
make
data-driven
decisions
about
feature
enhancements,
UI/UX
improvements
and
marketing
strategies
to
maximize
user
engagement
and
meet—or
exceed—business
goals. 

SaaS
app
analytics
use
cases

While
effective
for
some
organizations,
traditional
SaaS
data
analysis
methods
(such
as
relying
solely
on
human
data
analysts
to
aggregate
data
points)
sometimes
fall
short
in
handling
the
massive
quantities
of
data
SaaS
apps
produce.
They
may
also
struggle
to
fully
leverage
the
predictive
capabilities
of
app
analytics.

The
introduction
of
AI
and
ML
technologies,
however,
can
provide
more
nuanced
observability
and
more
effective
decision
automation.
AI-
and
ML-generated
SaaS
analytics
enhance:

1.
Data
insights
and
reporting

Application
analytics
help
businesses
monitor
key
performance
indicators
(KPIs)—such
as
error
rates,
response
time,
resource
utilization,

user
retention
and
dependency
rates,
among
other
key
metrics—to
identify
performance
issues
and
bottlenecks
and
create
a
smoother
user
experience.
AI
and
ML
algorithms
enhance
these
features
by
processing
unique
app
data
more
efficiently.

AI
technologies
can
also
reveal
and
visualize
data
patterns
to
help
with
feature
development.

If,
for
instance,
a
development
team
wants
to
understand
which
app
features
most
significantly
impact
retention,
it
might
use
AI-driven

natural
language
processing
(NLP)
to
analyze
unstructured
data.
NLP
protocols
will
auto-categorize
user-generated
content
(such
as
customer
reviews
and
support
tickets),
summarize
the
data
and
offer
insights
into
the
features
that
keep
customers
returning
to
the
app.
AI
can
even
use
NLP
to
suggest
new
tests,
algorithms,
lines
of
code
or
entirely
new
app
functions
to
increase
retention.

With
AI
and
ML
algorithms,
SaaS
developers
also
get
granular

observability
into
app
analytics.
AI-powered
analytics
programs
can
create
real-time,
fully
customizable
dashboards
that
provide
up-to-the-minute
insights
into
KPIs.
And
most
machine
learning
tools
will
automatically
generate
summaries
of
complex
data,
making
it
easier
for
executives
and
other
decision-makers
to
understand
reports
without
needing
to
review
the
raw
data
themselves.

2.
Predictive
analytics.

Predictive
analytics
forecast
future
events
based
on
historical
data;
AI
and
ML
models—such
as

regression
analysis,

neural
networks
and

decision
trees—enhance
the
accuracy
of
these
predictions.
An
e-commerce
app,
for
example,
can
predict
which
products
will
be
popular
during
the
holidays
by
analyzing
historical
purchase
data
from
previous
holiday
seasons.

Most
SaaS
analytics
tools—including
Google
Analytics,
Microsoft
Azure
and
IBM®
Instana®—offer
predictive
analytics
features
that
enable
developers
to
anticipate
both
market
and
user
behavior
trends
 and
shift
their
business
strategy
accordingly. 

Predictive
analytics
are
equally
valuable
for
user
insights.

AI
and
ML
features
enable
SaaS
analytics
software
to
run
complex
analyses
of
user
interactions
within
the
app
(click
patterns,
navigation
paths,
feature
usage
and
session
duration,
among
other
metrics),
which
ultimately
helps
teams
anticipate
user
behavior.

For
instance,
if
a
company
wants
to
implement
churn
prediction
protocols
to
identify
at-risk
users,
they
can
use
AI
functions
to
analyze
activity
reduction
and
negative
feedback
patterns,
two
user
engagement
metrics
that
often
precede
churn.
After
the
program
identifies
at-risk
users,
machine
learning
algorithms
can
suggest
personalized
interventions
to
re-engage
them
(a
subscription
service
might
offer
discounted
or
exclusive
content
to
users
showing
signs
of
disengagement).

Diving
deeper
into
user
behavior
data
also
helps
businesses
proactively
identify
app
usability
issues.
And
during
unexpected
disruptions
(such
as
those
caused
by
a
natural
disaster),
AI
and
SaaS
analytics
provide
real-time
data
visibility
that
keeps
businesses
running—or
even
improving—in
challenging
times. 

3.
Personalization
and
user
experience
optimization.

Machine
learning
technologies
are
often
integral
to
providing
a
personalized
customer
experience
in
SaaS
applications.

Using
customer
preferences
(preferred
themes,
layouts
and
functions),
historical
trends
and
user
interaction
data,
ML
models
in
SaaS
can
dynamically
tailor
the
content
that
users
see
based
on
real-time
data.
In
other
words,
AI-powered
SaaS
apps
can
automatically
implement
adaptive
interface
design
to
keep
users
engaged
with
personalized
recommendations
and
content
experiences.

News
apps,
for
instance,
can
highlight
articles
similar
to
the
ones
a
user
has
previously
read
and
liked.
An
online
learning
platform
can
recommend
courses
or
onboarding
steps
based
on
a
user’s
learning
history
and
preferences.
And
notification
systems
can
send
targeted
messages
to
each
user
at
the
time
they’re
likeliest
to
engage,
making
the
overall
experience
more
relevant
and
enjoyable.

At
the
application
level,
AI
can
analyze
user
journey
data
to
understand
the
typical
navigation
paths
users
take
through
the
app
and
streamline
navigation
for
the
entire
user
base.

4.
Conversion
rate
optimization
and
marketing.

AI
analytics
tools
offer
businesses
the
opportunity
to
optimize
conversion
rates,
whether
through
form
submissions,
purchases,
sign-ups
or
subscriptions.

AI-based
analytics
programs
can
automate
funnel
analyses
(which
identify
where
in
the
conversion
funnel
users
drop
off),
A/B
tests
(where
developers
test
multiple
design
elements,
features
or
conversion
paths
to
see
which
performs
better)
and
call-to-action
button
optimization
to
increase
conversions.

Data
insights
from
AI
and
ML
also
help
improve
product
marketing
and
increase
overall
app
profitability,
both
vital
components
to
maintaining
SaaS
applications.

Companies
can
use
AI
to
automate
tedious
marketing
tasks
(such
as
lead
generation
and
ad
targeting),
maximizing
both
advertising
ROI
and
conversation
rates.
And
with
ML
features,
developers
can
track
user
activity
to
more
accurately
segment
and
sell
products
to
the
user
base
(with
conversion
incentives,
for
instance). 

5.
Pricing
optimization.

Managing
IT
infrastructure
can
be
an
expensive
undertaking,
especially
for
an
enterprise
running
a
large
network
of
cloud-native
applications.
AI
and
ML
features
help

minimize
cloud
expenditures
(and
cloud
waste)
by
automating
SaaS
process
responsibilities
and
streamlining
workflows.

Using
AI-generated
predictive
analytics
and

real-time
financial
observability
tools,
teams
can
anticipate
resource
usage
fluctuations
and
allocate
network
resources
accordingly.
SaaS
analytics
also
enable
decision-makers
to
identify
underutilized
or
problematic
assets,
preventing
over-
and
under-spending
and
freeing
up
capital
for
app
innovations
and
improvements.

Maximize
the
value
of
SaaS
analytics
data
with
IBM
Instana
Observability

AI-powered
application
analytics
give
developers
an
advantage
in
today’s
fast-paced,
hyper-dynamic
SaaS
landscape,
and
with
IBM
Instana,
businesses
can
get
an
industry-leading,
real-time,
full-stack
observability
solution.

Instana
is
more
than
a
traditional

app
performance
management
(APM)
solution.
It
provides
automated,
democratized
observability
with
AI,
making
it
accessible
to
anyone
across

DevOps,
SRE,
platform
engineering,
ITOps
and
development.
Instana
gives
companies
the
data
that
they
want—with
the
context
that
they
need—to
take
intelligent
action
and
maximize
the
potential
of
SaaS
app
analytics.

Explore
IBM
Instana
Observability

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a
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