Case Studies from the“Impact from AI in 30 Days” Program
www.aible.com/casestudies

1

Benchmark Study andCase Studies Report

Impact from AI in 30 Days

Serverless-First Approach for Data Exploration and Automated Machine LearningIntel® and Aible Performance Benchmark and Case Studies Report
According to MIT-BCG “a mere 10% of
Organizations Achieve Significant Financial
Benefits With AI”.*
The Gartner report ‘A CTO’s Guide to
Top Artificial Intelligence Engineering
Practices’ published 29 October 2021
states, “AI projects are characterized byhigh failure rates and take a long time tomove from pilot to production. Slightly
more than 50% make it from pilot to
production, and those take an average ofnine months.”
Reducing the risk and time to value of AI projects isimperative for the success of the AI ecosystem. The Intel®
Disruptor Innovation Initiative identified that Aible delivers
business value from AI in 30 days, and collaborated
with the enterprise AI company to change the “art of thepossible” in AI.

Aible: The only enterprise AI solution thatguarantees impact in 30 days1
Aible is rated #1 for the Automated Machine Learning use
case in the 2021 Gartner® Critical Capabilities Report for
Cloud AI Developer Services. The easy-to-use solution
meets business teams where they are with optimized AI
predictions and recommendations for a wide variety ofuse cases. The result? Organizations are enabled to makequicker, better, strategic and tactical decisions that deliver
business value quickly.
Intel does not guarantee the performance of 3rd party products. Please consult Aible
for more information on their satisfaction guarantee program. More details available at:aible.com/blog/the-aible-payback-guarantee-ai-that-delivers-impact-in-one-month* www.bcg.com/en-us/press/20october2020-study-finds-significant-financial-benefits-with-ai
1

The team behind Aible has collectively implemented
thousands of successful AI projects over two decades
across a wide variety of customers and industry segments.
Because the team has a proven history of deliveringresults, Aible’s offering includes a satisfaction guarantee.
To validate the “30 days to AI value” approach, Aible
partnered with Intel® and offered 25 organizations accessto the AI solution via the Intel and Aible Immediate Impact
Program. Each of the participating organizations, which
include Fortune 100 firms, defined one business objective
they wished to analyze and improve, and published thevalue they identified from this project directly to Intel.
Case studies for these 30-day engagements are available
in this report. These include:•

A Fortune 500 technology company used Aible toidentify actionable insights for sales opportunities in
29 days•

Another Fortune 500 Healthcare Provider found new
insights in Social Determinants of Health (SDoH) datawith a 20X improvement in speed to insight in 15 days•

Nova Southeastern University used AI from Aible topotentially improve student retention by 17% in 15 days•

A multinational CPG company used Aible to identify
ways to drive $10M in additional sales in just 17 days•

A global food company identified ways to reduce foodwastage by more than 10% in 27 days using Aible•

A global manufacturer identified ways to reduce theimpact of late shipments by more than $4M annually injust 17 days using Aible•

Another leading University used AI from Aible tomitigate student attrition by 12% in 30 days•

A Leading Food & Beverage services company used
Aible to identify actionable patterns and ways toimprove sales efficiency in 13 days

22

Some thematic elements are already becoming clear in these case studies that explain why the Aible “30 days to AI value”
approach is effective:
No one has perfectly clean data:In almost every case the original
dataset was not perfect and had
to be updated a few times before
the project could be completed. In
minutes, Aible Sense automaticallyevaluates whether the data has
signal and this made it easier for the
project teams to “fail fast” and iterate
until they got to the right data. Aible
Sense also automatically adjusts formany common data quality problems
and even automatically recommends
derived variables (also called‘features’) to improve the impact ofpredictive models.

Business user involvement iskey: In several cases the business
users suggested changes to thedataset, requested more focused
analysis (country-by-country forexample), or even changed the usecase (from demand forecasting
to overstock prevention in onecase). Aible Explore enabled the
project teams to engage with thebusiness stakeholders much earlier
in the project by collaborating withthem around an augmented open
world exploration of their data.
Because they didn’t have to wait
for the predictive modeling to becompleted, and could get businessstakeholder feedback earlier in the
project, the teams avoided having tochange the project after investing
months of effort.

AI must be grounded in businessrealities and objectives: In every
project, understanding the businessobjective was key to delivering value.
In one case, the customer’s key focus
was helping their salespeople maketheir first sale as quickly as possible.
They also had significant constraints
on how much effort they could spend
on training their salespeople and thus
had to assign the best candidates tothe more expensive training. Aible
Optimize automatically ensures thatthe predictive models are optimized
to deliver business impact in lightof the business objective and thebusiness constraints. This enabled
the project team to deliver $10
Million in business value in 17 days
(see Customer Spotlight #4 on
page 9).

Components of Cost of AI Training
Overhead
Data Processing
Servers

Feature Creation
Modeling

Serverless

Cluster scale-out | VM Launch | Copying Data
Establishing Network Connections | Control Plane

Data Processing

Minimal overhead

Feature Creation<30 sec elapsed time

Modeling<1% overall cost

Overheads and latencies
related to managing theoperation and costs of server
infrastructure

Figure 1

To validate this approach, Aible and Intel benchmarked the performance and total cost of ownership characteristics ofserverless computing vs. server architectures for model training.

3

Benchmark Study initial observations
Organizations like Aible can take advantage of newer Inteltechnology to improve workload performance and further
optimize their applications on serverless. In Aible’s case,when using Intel’s newer compute platforms, specifically
3rd generation of Intel Xeon Scalable processors, Aible's
autoML platform can leverage the wide range of Intel’soneAPI frameworks, such as Tensorflow, Numpy, Scipy,and Scikit-learn optimizations.
With server-oriented architectures, upwards of 70% oftime and costs are tied up in infrastructure overhead
which isn't improved by the performance of the processor.
These include overheads for cluster scale-out, VM launch,establishing network connections, copying data andother latencies associated with managing the operationand costs of server infrastructure. On serverless these
unrelated activities and costs are mostly eliminated andprocessor performance improvements more directly
affect cost, TCO, and elapsed time performance.

The study also demonstrated a better experience on
serverless computing compared to traditional server
architectures with comparable Intel processors. When
deployed to serverless functions the application was:•

2-3X more cost effective,•

3-4X lower Total Cost of Ownership (TCO), and•

2-3X fasterthan on server architecture.

Serverless vs. Servers: Results from Intel-Aible Benchmark Study

28m 58s$15.56$3,010$12.92$2,61522m 47s

12m 4s$7.25$1,070$4.15

7m 20s

$620
250K
Rows

500K
Rows

250K
Rows

500K
Rows

250K
Rows

500K
Rows

250K
Rows

500K
Rows

250K
Rows

500K
Rows

250K
Rows

500K
Rows

ServerServerlessServerServerlessServerServerless(M4.16xlarge)(AWS Lambda)(M4.16xlarge)(AWS Lambda)(M4.16xlarge)(AWS Lambda)

Cost Per Job: 2-3X Better

Total Cost of Ownership: 3-4X Better

Elapsed Time for Training: 2 -3X Better

44

Serverless computing is 2-3X less expensive
than server architectures

Serverless computing is 2-3X faster in terms ofelapsed time

The cost of end-to-end model training on server
architecture vs. serverless computing using comparable
microarchitecture for both assessments was reviewed.
Serverless costs were 2-3X more cost effective thanserver costs: $4.15 vs. $12.90 for 250K rows dataset and$7.25 vs. $15.50 for 500K rows dataset.

After assessing infrastructure cost differences, the study
then observed the elapsed time (wallclock time) of themodel training.

Serverless computing has 3-4X lower TCO
than server architectures
These costs only reflect the compute capacity required toactively train models and does not include theinfrastructure needed to operate the server cluster. With
the assumption that infrastructure cost (Kubernetes
backplane, control nodes and networking) is $675/monthand the cluster is used to conduct 150 model training jobs
over 30 data sets in a month, then the Total Cost of
Ownership (TCO) of this benchmark actually shows thatserverless TCO is 3-4X better than server with comparable
processors: $620/month vs. $2,615/month for 250K rows
dataset and $1,070/month vs. $3,010/month for 500K
rows.

The wallclock time for exploratory data analysis and modeltraining on serverless vs. server computing, using
comparable processors, was 2-3X better: 7 mins 20 secs
vs. 22 mins 47 secs for 250K dataset rows and 12 mins 4
secs vs. 28 mins 58 secs for 500K rows. Note that theactual elapsed time from when the model training wasstarted and the first models started returning was less thana minute for serverless. As such, if a mistake had been
made, that would become evident within minutes ofstarting the training. The analyst or data scientist is notbeholden to lengthy model processing times to begin
reviewing the data.

Note that typically most data science organizations
process a much smaller number of datasets, conduct
fewer model runs and will likely run their training on more
expensive server instance configurations. As such, theactual TCO difference is likely to be far greater thanoutlined above.

So what next?
The next section of this report highlights case studies where organizations
saw impact in 30 days. Go to: www.aible.com/casestudies to get additional
end-customer case studies as they become available. Each case study will talkabout how quickly end-customers were able to achieve tangible benefits from
AI by using the serverless approach. The final benchmark report will be posted
when available, later this year.

5Case Studies from the“Impact from AI in 30 Days” Program
USE CASES

CPG /Manufacturing
Healthcare

Sales
Optimization

Logistics
Optimization

Customer
Retention

Marketing
Optimization

Collections
EffectivenessPg. 10, Pg. 23

Pg. 12

Pg. 23

Pg. 23, Pg. 25

Pg. 18Pg. 17Pg. 22
Pg. 9, Pg. 13

INDUSTRIES

Education

RetailPg. 29Software /Technology

Pg. 7, Pg. 19,
Pg. 26, Pg. 31

Food &
BeveragePg. 14Pg. 21

Pg. 8

Pg. 24Pg. 11

Pg. 15Distribution

Pg. 20Transportation

HealthAnalytics

Pg. 27

Construction

Legal
ServicesPg. 16

CustomerExperiencePg. 28Pg. 306

CUSTOMER SPOTLIGHT 1

Fortune 500 technology company
uses AI from Aible to identify actionableinsights for sales opportunities in days

Challenge

Use Case & Project Details•

Use case analyzed: Sales opportunity
prioritization•

Communications andIT

As the company migrates productofferings from on-prem and perpetual
licenses to the cloud with subscription
licensing, their global sales team aimed
to accelerate business growth by
optimizing their sales outreach efforts
and better identify the highest value
opportunities.

Potential Project Results:
Actionable insights on sales
opportunities•

RegionSolution

Time from data provision to projectcompletion: 20 days•

GlobalInitial data preparation, data cleansing,and analysis were completed within 5days. Model training was completed, andanalysis was shared with the tech anddata teams, with only minor adjustments
to the sales use case. In a matter ofminutes Aible provided key insights fromthe data that were validated with theexecutive team.

Elapsed time from start of modeltraining to completion of over 1,394
models on serverless infrastructure:
2 hours total across over 5 project
iterations

OutcomeCompany Profile
Fortune 500
technology company

Industry

DAY

DAY
DAY

1

DAY

9
5

8 daysKick oﬀ meeting
to discuss use
case

Data securely
Staged foranalysisDAY

13

4 daysData validation& model training
completed(1062 models trained)

Aible helped the global sales team identify
ways to increase revenue materially withindays, using serverless AI.
DAY

14

1 day

DAY

22

29

7 days8 daysTech and data team review& model iterations with Aible(Additional 332 models
trained in 10 minutes)

Aible Team Involved

Business andexecutive team review& use case validation
with Aible

Revalidated insights found with abroader group of experts. Read out
with executive team on projectﬁndings and ways to materially
increase revenue

Waiting for Customer"The fact that in a matter of minutes Aible revealed actionable insights within ourdata that we were not already aware of was surprising. The way it spanned strategic
scenario planning, resource planning, analytics, and opportunity scoring in one
single platform can be transformative for the way we manage sales.”- Sales Technology Leader

7

CUSTOMER SPOTLIGHT 2

Fortune 500 Healthcare Provider finds new
insights in Social Determinants of Health (SDoH)
data with a 20X improvement in speed to insight

Challenge

Use Case & Project Details•

Use case analyzed: Identify insightsfrom Social Determinants of Health(SDoH)•

Industry

The business team wanted to leverageSocial Determinants of Health (SDoH)
insights to improve health outcomesfor members including ways to lower
emergency room visits, inpatientadmissions and inpatient lengths of stay.

Potential Project Results: 20X
improvement in speed to insight

Healthcare Services

Solution•

RegionThe business team was trained to use Aible
and self-sufficiently improve their speed
to insights. Aible enabled them to analyze
datasets in minutes by just choosing
the health condition they wanted tounderstand. Within days, they were able toperform in-depth analysis of SDoH across
5 unique health conditions by region.

Time from data provision to projectcompletion: 15 days•

Elapsed time from start of data
evaluation to actionable insights onserverless infrastructure: 3 minutes
per dataset on average

Company Profile
Fortune 100
American healthinsurance provider

US

DAY

DAY

1

2 daysData securely
staged on AWS

DAY

3Business team was shown
top-level valuable insightsfrom their data. Additionaluse case context fromdataset was provided.

20X improvement in speed to insights.

DAY

8

5 daysOutcome2 daysFinal in-depth analysis
and insights reviewed
for social determinantsof health (SDoH) across
5 unique healthconditions
Aible Team Involved

DAY

10

15

5 daysHands on training
session conducted
for technical andbusiness teams on
Aible Sense andAible Explore

Readout led by thebusiness ownerthemselves showing
insights and value found to
the executive team

Waiting for Customer"I absolutely love using Aible. As a Technology Manager I see the best fit for Aible in ourorganization. Our business users were able to get into the tool and explore patterns andinteractions in our data which seemed impossible to do prior to using Aible.”– Social Drivers of Health Program Director

88

CUSTOMER SPOTLIGHT 3

Nova Southeastern University uses AI topotentially improve first-time in college
student retention by 17% in 15 days

Company Profile
Largest private,selective research
university in Florida

Industry
Higher Education

Challenge

Use Case & Project Details

Nova Southeastern University wanted
to leverage their data assets to improvestudent retention and optimize student
welfare, particularly aiming at students
within their undergraduate program.

•Use case analyzed: First-time incollege student retention•

Potential Project Results: 17%
reduction in student attrition

Solution•

Time from data provision to projectcompletion: 15 days•

Elapsed time from start of modeltraining to completion of 500+
models on serverless infrastructure:
6 projects trained in ~25 minutes
per project

Aible helped identify students who weremost likely to leave. This helped the center
for academic and student achievementtarget and prioritize their retention efforts
to the most at-risk students.

RegionSoutheast US

Outcome
Aible helped identify ways to potentiallylower student attrition by 17%.
DAY

DAY

1

DAY

2

5 days

1 day

Dataset madeavailable in
University’s
cloud accountDAY

7

Initial models
trained and data
exploration
completed

DAY

8

1 day

Initial project
readout andreview withfull team

5 days

Project blueprint andbusiness assumptions
adjusted andvalidated

Aible Team Involved

DAY

13

15

2 daysReadout with full team,users identiﬁed androadmap forward
established

Licenses provisioned to entire
team, product training completed
and validated end-to-end by
University team members

Waiting for Customer"During a one hour meeting we went from a raw dataset, to exploring insights in thedata automatically highlighted by Aible, to creating and even deploying a predictivemodel. The collaboration with academic and financial aid advisors helped us further
optimize the models and made them more useful - but we went end-to-end from raw
data to deployed model in such a short amount of time.”- Don Rudawsky
VP Institutional Effectiveness, Nova Southeastern University

in collaboration with

9

CUSTOMER SPOTLIGHT 4

Multinational CPG company of beauty andcosmetics uses AI to identify ways to drive $10M in
additional sales by optimizing first orders in 17 days

Company Profile
Multinational
manufacturer anddistributor of beautyand cosmetics products

Challenge

Use Case & Project Details

A global cosmetics and manufacturing
company aimed to improve the rate of
consultants closing their first sales orderand optimize resources spent for sales
consultant recruitment and training.

•

Use case analyzed: Onboarding
and first order optimization•

Project Results: $10M revenue•

Time from data provision to projectcompletion: 17 days•

Elapsed time from start of modeltraining to completion of over 7300
models on serverless infrastructure:
4 hours total across over 30 project
iterations

Solution

Industry

Aible identified potential sales consultants
who are most likely to close their firstorder. This helped in determining which
candidates to prioritize for in-person
training versus less resource-heavy, alldigital training.

Consumer Goods
and Manufacturing

RegionInternational

Outcome
Aible helped identify ways to increase
revenue by $10M with serverless AI in
17 days.

DAY

DAY

1

DAY
DAY

3

2 daysData Access
GrantedDAY

55

2 days

Initial Analysis Complete(384 Models Trained in
3 minutes)

DAY

11

4 days2 daysFirst review
with data
science teamDAY

7

New data
received for next
analysis iteration

Aible Team Involved

3 daysNew ﬁndings
read-out withdata science
team

DAY

14

Executive read-out
discussion on
progress; additional
data requested tobe added for
detailed country
level analysis

DAY

15

1 Day

New ﬁndings
reviewed with data
science andbusiness teams tohelp hone in on use
case and drivers of
ROI/Value

17

2 daysFinal analyses
Read out with execcompleted (over sponsor on ﬁndings
7300 models
and how to increase
trained in less than revenues by $10M
4 hours total)
with Aible’s
guidance

Waiting for Customer"The speed of the Aible product and the rapid iteration it enables is outstanding.
I challenged them to train the models live in front of me. The first set of models completed
training in less than a minute and several hundred models completed in less than 3 minutes. Thiswas for a significant sized business dataset that had taken us much longer to analyze manually.”– Chief Technology Officer (CTO) & Chief Data Officer (CDO)

1010

CUSTOMER SPOTLIGHT 5

Global food company identified ways topotentially improve point of purchase overstock
food wastage by over 10% in 27 days

Company Profile
Global innovative
food company withthousands of locations
across 11 countries

Challenge

Use Case & Project Details

A global innovative food company wanted
to predict the right quantity of stock tosend to stores to minimize food waste.

•

Use case analyzed: Perishable goods
overstock & wastage•

Project Results: Reduce overstock
point of purchase wastage by over 10%•

Time from data provision to projectcompletion: 27 days•

Elapsed time from start of modeltraining to completion of 747 models
on serverless infrastructure:
105 seconds

Solution
Initial data preparation, data cleansing,and data analysis was delivered within 5days. Based on business user feedback,the use case was changed from demand
forecasting to focus on overstock foodwastage. Within 2 days, Aible provided
updated analysis on the adjusted use casethat was accepted by the business team.

Industry
RetailRegion
EMEA

Outcome
Aible delivered a model that could
potentially reduce food waste by over 10%
in 27 days.

DAY

DAY

1

DAY

5

4 days

Initial data staged
for identiﬁed use
case without
giving read accessto Aible

7 days

Initial analysis
provided

DAY

12

DAY

14

2 days8 days27

5 daysUpdated analysis
on adjusted use
case acceptedby businessBusiness review
Updatedof analysis. Useanalysis on
Case adjusted adjusted use
based oncase based on
feedbackbusiness user
feedback andinput
Aible Team Involved

DAY

22

Conﬁrmed
potential impact ofover 10% of foodwaste reduction

Waiting for Customer"Aible helped us figure out how to potentially improve our food waste
by over 10%. Their iterative process helped us dial in our use cases,
and drive towards achieving tangible business impact within 3 weeks.”-Business Intelligence Manager

11

CUSTOMER SPOTLIGHT 6

Global manufacturer identified ways toreduce the impact of late shipments bymore than $4M yearly in 17 days

Company Profile
A global leader inmanufacturing anddistribution of
specialized equipment

Industry
Region
US

DAY

1

First
iteration ofdatasetprovided

Initial modeltrainings andanalysiscompleted•

Use case analyzed: Late shipment
prediction and expedite optimization•

Project Results: $4M+ cost reduction

Solution•

Time from data provision to projectcompletion: 17 days•

Elapsed time from start of modeltraining to completion of over 2,600
models on serverless infrastructure:
Over 10 iterations in less than 10
minutes each

DAY

2

1 Day

Use Case & Project Details

Surface patterns around why certain
shipments are late, and mitigate loss byprioritizing which shipments to expedite
or take remedial action on.

Initial data preparation, data cleansing,and data analysis was delivered
within 5 days. Based on business user
feedback, the use case was strategicallyadjusted to incorporate critical businesssegmentations. Within 7 days, Aible
provided updated analysis on the usecase that was reviewed and accepted by
the business team. Numerous next steps
were identified.

Manufacturing

DAY

ChallengeDAY

5

3 days3 daysPreliminary
ﬁndings review
with Data Team

DAY

8

Review with business
team, businessobjectives andassumptions adjusted

Aible Team Involved

Aible helped identify ways to reduce theimpact of late shipments by over $4M
yearly, with serverless AI in 17 days.

DAY

9

1 Day

Outcome

DAY

10

1 day

Internal client
conversations
in light of initial
Aible insights

4 daysDataset updated
based on feedback
from broader set ofstakeholders

DAY

14

DAY

15

1 Day

Final
corrected
version of thedataset madeavailable

17

2 daysFinal analysis
completed

Project
ﬁndings
reviewed with
Executive
team

Waiting for Customer"Aible showed us how to unlock actionable insights in our data. The speed to insightwas way faster than we expected, and the insights found would likely never havebeen uncovered using traditional analytics.”–VP of Marketing & Analytics

12
12

CUSTOMER SPOTLIGHT 7

Leading University uses AI from Aible tomitigate student attrition by 12% in 30 days

Company Profile
Leading Private
University

Industry

Challenge

Use Case & Project Details

A large private university aimed to
improve student retention and optimize
student welfare, particularly among
undergraduate first-year students.

•

Use case analyzed: First-year student
retention•

Potential Project Results:
12% reduction in student attrition•

Time from data provision to projectcompletion: 30 days•

Elapsed time from start of modeltraining to completion of over 1,400
models on serverless infrastructure:
17 projects were trained in ~6 minutes
per project with an average of 83
models per project

Solution

Higher Education

Aible identified students who weremost likely to attrit and the best ways
to intervene to improve retention.
This helped the center for academic
and student achievement target andprioritize their retention efforts to themost at-risk students.

RegionUS

Outcome
Aible helped identify ways to reduce
student attrition by 12%.

DAY
DAY

DAY

51

DAY

2

1 day

First iteration of thedataset securely
staged. Initialinsights generated
in 30 minutes.
Reviewed live withclient team for 2
hours

DAY

7

5 daysAdjusted thedataset withadditionalvariables

DAY

8

1 day

Initial 581
models trained
and project
blueprint
generated

DAY

14

DAY

15

1 day

6 days

Project analysis andinsights reviewed withbusiness and technicalteams. Live analysis
conducted on focused
student segments. New
key stakeholders identiﬁed
and engaged.

Additional
498 models
completedand granularanalysis andinsights
generated

Aible Team Involved

DAY

29

14 days

Third projectreview withtechnical team

30

1 day

Project review with
Finalized models
primary stakeholders. trained and adjusted
Precise ways to action based on businessthe insights agreed project feedback.
upon with key
Final projectstakeholders
reviewed with team

Waiting for Customer"AI doesn’t work in a vacuum. Aible’s focus on actionable insights made it easy
for us to engage with key stake-holders to improve our business outcomes. This
single project also sparked new discussions on process improvements in both ouradmissions and student retention offices.”– Associate CIO for Solutions Development and Data Architecture

in collaboration with

13

CUSTOMER SPOTLIGHT 8

Leading Food & Beverage services company
used Aible to identify actionable patterns andways to improve sales efficiency by 5%

Company Profile
Direct-to-consumer
frozen fooddelivery company

Industry

Challenge

Use Case & Project Details

The CIO’s team was looking to identify
business levers across all functions
including sales, marketing and operations,
to materially improve overall revenue andprofit.

•Use case analyzed: Sales optimization•

Potential Project Results:
Identified specific patterns to help
increase expected sales efficiency
by 5%

•

Time from data provision to projectcompletion: 13 days•

Elapsed time from start of modeltraining to completion of 166 models
on serverless infrastructure:
17 minutes

Solution

Food and Beverage
Services

Within 13 days, Aible identified patterns indata that revealed that when salespeople
went to a specific residence type atparticular times of day, they saw higher
conversion rates.

RegionUS

This helped the delivery teams identify
which stops to make and when, optimizing
their routes to increase sales efficiency.

DAY

DAY

DAY

1

3 daysFirst iteration of the sales dataset
securely staged. Initial analysis
showed in 15 minutes that thedata provided was not suitable
for machine learning training

2 daysCorrected
training data
wasprovided

Aible identified ways to improve
conversion rate with a 5% improvement insales efficiency.

DAY

6

4

Outcome

DAY

7

13

1 day

6 days

A second scoping callwith business andtechnical teams set thefocus on a sales use case.
Initial analysis showed that
demographic data would
provide better insights.

Insights were generated thathighlighted signiﬁcant
impact for three use cases
across sales, marketing, andlogistics

Aible Team Involved

Waiting for Customer

Findings were reviewed
and accepted by theentire team including
executives"Just 5 minutes into the Aible presentation, I could already see the immense value we
could add to our business. I immediately asked my colleagues to drop other meetings
to see the report Aible had created within minutes of receiving the data. I’ve spent a lot
more money and more time with other tools and have achieved far less.”- CIO

in collaboration with

14
14

CUSTOMER SPOTLIGHT 9

Portfolio company of WhitneyWilder Drives
10% increase in revenue in 16 days by using AI
from Aible to optimize field service delivery

Company Profile
WhitneyWilder is aninvestment firm with a
specific focus in building
products, construction,and real estate service
companies

Industry
Construction, real estateand home services

Region
US

DAY

DAY

1

Use Case & Project Details

A portfolio company of WhitneyWilder
aimed to leverage the operational andcustomer data to optimize service deliveryof field technicians and drive bottom line
growth. Their primary goal was to predict
overtime and unused service hours toincrease the overall number of completed
jobs and billable hours.

•

Use case analyzed: Predict andoptimize field technician service
delivery•Potential Project Results:
10% increase in service revenue•

Time from data provision to projectcompletion: 16 days•

Elapsed time from start of modeltraining to completion of 90 models
on serverless infrastructure:
1 min 36 secs

Solution
Aible identified specific patterns withservice calls that highlighted a need to shift
how jobs are scheduled in a given day. This
allowed the team to schedule shorter jobs
in the morning and defer longer jobs tolater in the day. This arrangement helped
increase the number of jobs completed
in a week and contributed towards higher
revenue.

DAY

2

1 Day

Challenge

10% increase in service revenue.

DAY

3

DAY

9

Initial insights
identiﬁed andanalysis completed
from dataset

6 days16

1 dayModel training
completed(90 models trainedin 1 min 36 secs)

Analysis and ﬁndings
shared with business
teams

Aible Team Involved

DAY

15

6 days

1 Day

Client used Aible
Data securely
Sense and Aible
staged for
Explore to selectanalysis in
one dataset
customer’s cloudfrom manyaccount

OutcomeFinal executive brieﬁng
with model training results
showcasing ways tooptimize ﬁeld service
delivery

Waiting for Customer"The Aible analysis will help us schedule smarter and drive more efficiency/revenue
in a key portfolio company. We’re also very excited to leverage Aible for better
informed due diligence in many subsequent acquisitions.”- Tom Birchard, President of Home Services

15

CUSTOMER SPOTLIGHT 10

Leading education services company drives
50% savings in 17 days by using AI from Aible
to improve prospect targeting

Company Profile
Leading global providerof specialist higher
education and careers
information and solutions.

Use Case & Project Details

The Chief Data and Analytics Officer’s
team aimed to optimize their digitaladvertising budget allocation and overallmarketing spend as they targeted
prospects at recruitment events.

•

Use case analyzed: Marketing spend
optimization by targeting prospects(who hadn’t made decision)•

Potential Project Results:
50% marketing cost savings•

Time from data provision to projectcompletion: 17 days•

Elapsed time from start of data
evaluation to actionable insights
on serverless infrastructure:
8 minutes

Solution

Industry

Customer and marketing spend data
was staged in a secure AWS data lake.
With cost benefit information, Aible
identified only those prospects who had
not yet made up their decisions, in orderto optimize targeting and market spend.
Within days, Aible identified patterns
including one where their marketing efforts
7 days prior to any event wasn’t delivering
any business impact.

Education Administration
Programs

RegionGlobal

DAY
DAY

DAY
DAY

51

DAY

5
5

3 daysData was staged
and use case wasdeﬁnedChallenge

Insights shown to the CDAO
team revealed that theirmarketing eﬀorts 7 days prior
to an event was having no
impact. Within an hour theteam began considering how
to change their operations.

DAY

7

3 daysThe data teamprovided cost andbeneﬁt details to Aible
to help themunderstand how they
can optimize theirmarketing spend

664 models
were createdacross 6
product types tooptimize thecandidatetargeting eﬀorts

Aible Team Involved

50% decrease in marketing spend withouta change in conversion rate.

DAY

DAY

10

2 daysOutcome

17

11

1 day

Obtained
validation of
the insights
with the datateam

7 days

Reviewed ﬁnal project
results with the entire
business and data teams.

Waiting for Customer"Within 3 weeks, Aible demonstrated significant value. We are already spotting
opportunities for further cost saving and are looking forward to the roll out.”- Chief Data and Analytics Officer

1616

CUSTOMER SPOTLIGHT 11

Large hospital system uses AI
from Aible to increase speed toinsight by at least 10x in 10 days

Company Profile
Large Healthcare andHospital System

Industry
Healthcare Services

RegionUS

Challenge

Use Case & Project Details

Explore patterns associated with why aclaim may result in an underpayment toenable more targeted collection efforts.
Manual efforts were focused only on a few
hundred disputed claims instead of many
more underpaid claims each month.

•Use case analyzed: Underpaid claims•

Potential Project Results: 10X
improvement in speed to insight•Time from data provision to projectcompletion: 10 days•

Elapsed time from start of data
evaluation to actionable insights
on serverless infrastructure:
6 minutes per dataset on average

Solution
Data engineer / citizen data scientist wasable to go from raw data to confirming thedata has signal to exploring key underlying
patterns and even build a predictive modelin 1.5 hours.

Aible Team Involved

Outcome
10X improvement in speed to insight.

Waiting for Customer"Data security is crucial to our business. The Aible security architecture
allowed us to analyze data in our own cloud instance without giving Aible
access to the data. The implementation just took minutes on AWS andessentially only required us to approve one request on our AWS console.”- Technical Director, AI & ML

17

CUSTOMER SPOTLIGHT 12

Global manufacturer uses AI from Aible toidentify $2M potential working capital reductions(semi-annualizedsmoothing machine) in 15 days

Challenge

Use Case & Project Details•

Use case analyzed: Collections
Effectiveness•

Manufacturing

The executive team at Client aimed
to create value from their data assets
across their CRM and analytics systems.
Using readily available customer invoice
data, they zeroed in on a use case toimprove their collections effectivenessfor customers who had outstanding
payments over 30 days.

Potential Project Results: Potential
to reduce Days Outstanding on
receivables by 5 to 10 days. Expectedvalue $2M over the next 2 quarters ascollection processes are adjusted

RegionSolution•

US

Aible identified patterns with clientsand invoices where the client is paying
late and receiving discounts to sustain
the value of business. Aible helped thecollections team decide that they neededto advance the payment process andrecommended reducing discounts for
specific clients.

Time from data provision to projectcompletion: 15 days•

Elapsed time from start of modeltraining to completion of 415 models
on serverless infrastructure:
9 min 12 sec

Company Profile
Global industry leader
in glass, ceramics andconcrete manufacturing.

Industry

Aible Team Involved

Outcome
Expected value $2M over the next
2 quarters.

Waiting for Customer"When we used Aible Sense to evaluate the data in our new Salesforce and Snowflake instances,
we quickly found we were not collecting the data we need to predict sales outcomes. By quickly
detecting the problem, we could adjust our underlying processes so that we start collecting theright data. Don’t wait to fully implement systems before analyzing data. The analysis will inform yoursystem’s design so you don’t lose useful data.
- Business Analyst and Aspiring Data Scientist
1818

CUSTOMER SPOTLIGHT 13

Global software company uses Aible to prioritize
marketing leads to improve win rates by focusing insidesales reps on the best sales opportunities in 19 days

Company Profile
Leading global provider ofsoftware and solutions for
project-based businesses

Industry
Software

RegionGlobalChallenge

Use Case & Project Details

The company was looking to identify
patterns related to sales leads turning
into sales opportunities. They wanted
to figure out how to optimally allocate
sales resources to profitably convert
sales leads in their customer base toopportunities. They wanted to bring themarketing and IT teams together to solvea problem that otherwise would havetaken months to coordinate.

•

Use case analyzed: Sales opportunity
prioritization•

Potential Project Results: Increase inSales Productivity•

Time from data provision to projectcompletion: 19 days•

Elapsed time from start of modeltraining to completion of 415 models
on serverless infrastructure:Less than 45 minutes

Solution
Aible delivered increased speed toinsight tremendously by identifying
patterns that otherwise would havebeen unknowable without the use of AI.
Aible identified which leads were worth
qualifying with the potential to convert toan opportunity.

Aible Team Involved

Outcome
Identified potential to drastically increase
the speed to engage quality inbound leadsby allocating more time for outboundactivities. Potential is a boost in sales tocustomer base.

Waiting for Customer"We were able to go from raw sales data straight from Salesforce to actionable insights to predictivemodels telling us which leads to prioritize - all within one hour. In these changing market conditions, speed
to insight equals relevance. If analysis and model building takes months, you are working on stale insights.
We were able to iterate in a matter of minutes to improve our insights from Aible. Aible’s speed and ease of
iteration is key to getting value out of your data.”– Senior Director of Marketing
19

CUSTOMER SPOTLIGHT 14

Global Electronics Distributor uses Aible
to achieve 15x improvement in speed toactionable insights in 24 days

Company Profile
Electronics Component
Distributor

Industry
Distribution

RegionChallenge

Use Case & Project Details

The team wanted to improve the data
cleansing process and identify valuable
insights in their data. Additionally, thedata science and operations teams
wanted the ability to quickly analyze data
to improve processes and reduce costs.

•

Use case analyzed: Inventory
management and Operational
excellence•

Potential Project Results: 15X
improvement in speed to insight.

Solution•

Time from data provision to projectcompletion: 24 days•

Elapsed time from start of data
evaluation to actionable insights onserverless infrastructure:
5 minutes for each dataset

The business team was trained to use
Aible, empowering them to discover
speed to insights, specifically as they
analyzed operational data. The Data
Science team used Aible to automaticallyvalidate the dataset quality. This
dramatically sped up the process andcreated the potential to take on additional
projects.

GlobalAible Team Involved

Outcome
15X improvement in speed to insights.

Waiting for Customer"The speed in which Aible performs the heavy lifting of analyzing our data isincredible. Aible replaced our current manual processes with spreadsheetsthat took weeks to find insights with automation that now only takes minutes.”- Operations Manager

in collaboration with

20
20

CUSTOMER SPOTLIGHT 15

Anytime Fitness uses AI from Aible to identify
patterns around achieving fitness goals andimproving member experience in 12 days

Company Profile
Large fitness andwellness franchise

Industry
Health + Fitness
Services

Challenge

Use Case & Project Details

The project purpose was to investigate
what factors led to certain members
being able to achieve their fitness goals– including the member’s physicalprofile as well as the member’s exercise
activities.

•

Use case analyzed: Fitness goal
achievement•

Potential Project Results: New
insights generated around member
behavior and the achievement offitness goals•

Time from data provision to projectcompletion: 12 days•

Elapsed time from raw data toevaluation of 572,850 variable
combinations on serverless
infrastructure: Less than 10 mins

Solution
Utilizing Aible Sense and Explore, theorganization was able to quickly identify
patterns related to members achieving
certain fitness goals. This allowed
the organization to potentially target
consultations in a more effective manner,and improve member experience andoutcomes. The analysis showed theactual benefit of exercise visits net of
all other factors. It also showed theimportance of proper hydration.

RegionGlobalAible Team Involved

Outcome
Aible was able to immediately identify
patterns related to member success, thus
potentially improving member experienceand outcomes

Waiting for Customer"The time to insight and the fact we can push a couple of buttons to get there was extremely impressive.
The platform showed us some patterns we knew and many we didn’t. The insights allow us to provide
more curated experiences for our members and more effective and targeted recommendations toimprove their chance of success – most notably of which was to make sure to stay hydrated.”– Chris Sullivan, VP Enterprise Data and Security, Anytime Fitness

in collaboration with21

CUSTOMER SPOTLIGHT 16

National healthcare intelligence company
uses AI from Aible to shorten sales cycles
and improve win rates

Company Profile
Technology company
that supports electronictransmission ofprescriptions and general
health information

Industry
Healthcare Technology

RegionUS

Challenge

Use Case & Project Details

The company wanted to identify
patterns and leading indicators
related to improving sales, and identify
which opportunities were likely to be
successful.

•Use case analyzed: Sales
Optimization•

Potential Project Results: Upper
6-figures increase in annual revenue
and profits•

Time from data provision to projectcompletion: 29 days•

Elapsed time from start of modeltraining to completion of 800+
models on serverless infrastructure:
More than 800 models were trainedin ~1.5 hours

Solution
Aible “cuts through the noise in ourdata” to find new, meaningful customer
insights. Aible identified which
opportunities should be pursued andwhich should be deprioritized, whileachieving the overall business strategy ofincreasing revenue while keeping sales
resourcing constant.

Outcome
Potential for upper 6-figures annual
increase in revenue and profits.

Aible Team Involved

Waiting for Customer"Aible quickly identified our close ratios by customer type which will help us with oursales strategy. Our sales teams live in Salesforce, so the ability to integrate Aible's
predictions in Salesforce and monitor the outcomes will help us prioritize our pipeline
opportunities and new business targets”- Director of Sales Operations
in collaboration with

22
22

CUSTOMER SPOTLIGHT 17

Vertiv delivered 4 analytics and AI projects in
28 days and proved out solution for a scalable
Center of Excellence for Data Science teams

Company Profile
Brings together hardware,
software, analytics andongoing services toenable its customers’ vital
applications

Industry
Appliances, Electrical, andElectronics Manufacturing

RegionGlobalChallenge

Use Case & Project Details

Vertiv aimed to leapfrog theircompetitor’s approach in establishing a
Center of Excellence for Data Science,encompassing large teams of data
scientists at low-cost offshore locations.
They wanted to use Aible to show
success rapidly on a wide variety of use
cases to prove a different approach toestablishing a scalable CoE.

•

Use cases analyzed:• Channel partner churn prevention• Channel partner sales optimization• Global sales optimization• Marketing spend optimization•

Project Results: Delivered results
across 4 different use cases•

Time from data provision to projectcompletion: 28 days•

Elapsed time for model training tocompletion of 987 models onserverless infrastructure: 22 min

Solution
Aible delivered results for multiple
use cases: partner sales optimization,
churn prevention, marketing spend
optimization and global sales
optimization.

Outcome
Delivered recommendations for
all 4 use cases in 28 days.

Aible Team Involved

Waiting for Customer“AI is key to delivering our corporate strategy. We need something fast that will help us
adjust our actions in line with our strategy based on latest market conditions. Throwing a lotof people at the problem was not the solution. Aible showed us a different scalable way ofachieving the same goals at a far lower overall cost than alternative approaches.”- Martin Coulthard, Senior Global Director, Digital Customer Experience, Vertiv
23

CUSTOMER SPOTLIGHT 18

Leading UK University uses
AI from Aible to gain 10X
increase in speed to insightCompany Profile
Leading UK University

Industry
Higher Education

RegionEMEA

Challenge

Use Case & Project Details

The Employability Services Team wished
to understand which of their activities
have the biggest impact on high skilledemployment outcomes across different
student groups. They wanted to improve
employment outcomes by identifying andacting on students, based on insights learnt
about their behavioral and demographic
characteristics.

•

Use case analyzed: student
employment outcomes•

Potential Project Results: 10x speed
to insight•Time from data provision to projectcompletion: 15 days•

Average elapsed time from start ofdata evaluation to actionableinsights on serverless infrastructure:
5 minsSolution
The data and analytics team was able
to conduct in-depth analysis of student
outcomes with Aible Sense & Explore.
Within minutes, they uncovered previously
unknown factors and behaviors that drive
employment outcomes for various student
groups. The analysis also identified areas of
information gaps, helping steer future datacollection efforts.

Aible Team Involved

Outcome
10x improvement in speed to insights.

Waiting for Customer"Aible is impressive. It has helped our teams achieve a new level of confidence when we look atdata by fueling deeper discussions around insights in an unbiased manner. Our business users
were wowed by the speed to insights with Aible and I have no doubt that this is going to betransformative in the way we operate.
- Data & Analytics Leader
24
24

CUSTOMER SPOTLIGHT 19

Global CPG Company used Aible to deliverrich AI-generated insights for category growthideas within four weeks.

Company Profile
Leading CPG firm that
produces and markets
Health, Hygiene andNutrition Products

Industry
Consumer Packaged
Goods

RegionGlobalChallenge

Use Case & Project Details

The company wanted to understandproduct line growth opportunities
by geography from their complex
datasets. They wanted to understand key
macroeconomic and social determinantsof success, as well as how marketinginvestments influence success in theirmarkets.

•

Use case analyzed: Understand key
drivers / determinants of market,
category, and product success•Results: Delivered analysis andresults across 4 large datasets in days•

Time from data provision to projectcompletion: 29 days•

Average elapsed time from start ofData readiness assessment in under
5 minutes and training of 83 models
in under 5 minutes

Solution
Across four large data sets, the Aible
Professional Services team was able torapidly and iteratively assess data readinessfor this AI initiative using Aible software.
The data was transformed, combined andaugmented to improve insights within one
day. The speed and agility of the platform
provided insights rapidly while reducing
drudgery and avoiding dead-ends. By
automatically ranking and surfacing key
determinants and patterns in the data, theteam was able to, in record time, spot newideas and detect blindspots.

Outcome
Delivered insights to support growthideas and identify important trends,
opportunities and threats.

.

Aible Team Involved

Waiting for Customer“Thank you so much for your team’s effort in helping us discover new insights. The speed to dataprep
and the speed to insight was surprising. I was genuinely surprised that the project effectively got donein 15 days despite the multiple dataset changes. In my opinion the platform is a breakthrough.”- Global Head of Category and Insights
25

CUSTOMER SPOTLIGHT 20

Global technology company uses AI fromAible to templatize the ability to identify
meaningful sales patterns in just 28 days

Company Profile
SaaS Data Protection
Platform

Challenge

Use Case & Project Details

The company needed to easily identify
sales patterns across a variety ofdifferent potential datasets in a rapid,
low-touch, and reproducible manner.

•

Use case analyzed: Sales
Optimization•

Potential Project Results: Vast
improvement in efficiency•

Time from data provision to projectcompletion: 28 days•

Elapsed time from start of modeltraining to completion of ~600
models on serverless infrastructure:Less than 1.5 hours

Industry
SoftwareSolution

RegionUtilizing Aible Sense & Explore, theorganization was able to improve thespeed to insight substantally with
automated data preparation, cleansing
and analysis. Only minor adjustments
were made to create replicable
blueprints for additional projects.

GlobalAible Team Involved

Outcome
The platform was able to identify theimportant variables related to a sale aswell as highlight variables that should be
excluded from future iterations.

Waiting for Customer"We needed a platform that could easily automate and templatize the identification of whichdata elements were important to consider in an analysis – and show us those patterns. Aible
demonstrated it can do just that, faster than we ever thought possible.”- Senior Vice President, Product Management

26
26

CUSTOMER SPOTLIGHT 21

Multinational retailer uses AI from Aible togain deeper understanding of customer LTV
drivers in 30 days

Challenge

Use Case & Project Details

Major multinational
retailer that specialises
in selling clothing,
beauty, home products
and food products.

The company wanted to better understand thedrivers and levers of customer lifetime value.
There are a vast number of metrics available tomeasure customer behaviour and activity, andthey wanted to understand which of these aremost important, why, and how.

•

Goal: Understand key drivers anddeterminants of customer lifetime
value (CLV)•Results: Increased speed toactionable insights

Industry

Solution•

Time: 30 days

RetailWithin a day of data provisioning, the team
was able to assess their data quality for AI
projects and also obtained preliminary
insights using augmented analytics.
Automated transformations were leveraged
to further improve the quality and actionabilityof the results. The team was significantly
hands-on in driving this analysis. Within 2
weeks severalmembers of the analytics team
were additionally enabled on the product•Elapsed time from start of data
evaluation to actionable insights onserverless infrastructure: 10 mins

Company Profile

RegionMultinational

Aible Team Involved

Outcome
Delivered detailed understanding of the
top drivers and actionable insights forcustomer lifetime value (LTV) in 30 days.

Waiting for Customer“We were keen to understand actionable drivers that can lead to customer value growth overtime. We were impressed by the speed with which Aible’s platform was able to generate
deeper insights using our data, quite a few of which were previously unknown. This also
generated a lot of discussion internally and helped us take the analysis to the next level, which
would have otherwise taken us a lot longer to accomplish. I believe Aible’s platform can be avery effective for discovery and exploratory work.
– Head of Growth Insights
27
27

CUSTOMER SPOTLIGHT 22

International Law Firm uses
AI from Aible to increase speed to insightby at least 10x in 26 days

Company Profile
International Law Firm

Industry
Legal Professional
Services

RegionGlobalChallenge

Use Case & Project Details

The Business Analytics team wanted
to leverage augmented and predictive
analytics to model and predict matter(a legal engagement) profitability. They
wanted to understand what factors canhelp identify and mitigate profit-target
risks at the start and midway through amatter.

•Use case analyzed: Predict Matter
Profitability•

Potential Project Results: 10x speed toinsight and multiple use cases identified•

Time from data provision to projectcompletion: 26 days•

Average elapsed time from start of data
evaluation to actionable insights onserverless infrastructure: Over 1M
variable combinations analyzed and 664
models trained in 15mins

Solution
The Business Analytics team used Aible
Sense to load, transform and confirm
their data had the right attributes tomodel matter profitability, Aible Explore
was leveraged to accelerate their speedof analysis and discovery of previously
unknown factors and interactions andAible Optimize to demonstrate predictivemodeling to assist matter selection.

Aible Team Involved

Outcome
10x improvement in speed to insights andmultiple new AI use cases identified to help
predict new matters that will meet profit
targets.

Waiting for Customer“Aible helped us innovate and mature our analytics capability without the delays, risk and cost of
establishing a dedicated Data Science function. Aible empowered us to rapidly explore datasets
and uncover layers of insight which enabled us to move almost instantly from thought to action.
The ability to jumpstart and optimize AI use cases and identify new projects by quickly assessing
the quality and suitability of data sets for analysis is incredibly powerful.”- Head of Business Analytics

28
28

CUSTOMER SPOTLIGHT 23

Overstock uses Aible Sense to improve
speed to data-quality evaluation from weeks
to minutes per dataset on Google Cloud
Platform

Company Profile
Leading Online
Furniture and Home
Furnishings Retailer

Industry
RetailChallenge

Use Case & Project Details

Overstock wanted a faster approach to analyze
multiple datasets on GCP including data fromtheir website, digital marketing, and e-commerce
solutions. They wanted a comprehensive view ofwhere they had valuable data in order to identify
buying behaviors, key variables and insights
affecting a sale, and to improve their overall Gross
Merchandise Sales.

• Goal: Improve data quality readinessfor analysis and machine learning

RegionSolution

North America

Aible Sense scanned dozens of datasets acrossmultiple sources on GCP to detect data that isuseful for analysis and for creating predictivemodels. This filtered out datasets with weakersignal for analytics or predictions, and significantly
optimized the cost-effectiveness of their payper-query cloud data warehouses. This wasachieved by completely automating manual dataengineering tasks at extremely low cost by using
Serverless techniques and Google BigQuery.

Aible Team Involved• Results: Improved speed to dataquality evaluation from weeks tominutes per dataset• Time: 5 days
• Elapsed time from start of data
evaluation to actionable insights onserverless infrastructure: : 10 mins
per dataset

Outcome
Delivered detailed understanding of
the improved speed to data-quality
evaluation from weeks to minutes per
dataset.

Waiting for Customer"We extensively use Google BigQuery. Aible's seamless integration with BigQuery allowed us
to analyze datasets with a single click, and in a matter of minutes automatically get to a dynamic
dashboard showing us the key insights we need to see. This would have taken weeks of work
using our current best practices. When we can analyze data in minutes, we can get fresh
insights instantly as market conditions and customer behavior changes."- Joel Weight, Chief Technology Officer, Overstock.com

in collaboration with29
29

CUSTOMER SPOTLIGHT 24

In just 8 days, major international Airline uses
AI from Aible to predict flight delays for futureschedules

Company Profile
A leading US based airlinethat operates across six
continents.

Industry
Airlines and Aviation

Challenge

Use Case & Project Details

During scheduling, it is difficult to identify
where flight delays are more likely tooccur. Being able to identify potential
issues more than 30+ days in advancewould provide time to adjust the schedule
to mitigate delays.

•

Use case analyzed: Flight Delay
Prediction•

Potential Project Results: Identified
potential at-risk flights more than 30+
days in the future•Time from data provision to projectcompletion: 8 days•

Elapsed time from start of modeltraining to completion of 166 models onserverless infrastructure: 10 mins

Solution

RegionBegan evaluating a dataset with over 28M
variable combinations on day 1. Used Aible
Sense & Explore to refine the dataset
down to the critical factors. Refreshed
the data based on the important variables
identified. Trained 750 predictive models
to identify future delays as soon as theschedule is published.

US

Aible Team Involved

Outcome
Proactively identified thousands of at-risk
flights, 30 days in the future.

Waiting for Customer“Aible Sense quickly identified the quality of our datasets and extracted thevariables that contribute to delays. Just after few iterations of fixing data
quality, we began training the model to deliver predictions.”- Senior Manager

in collaborationwithin collaboration
with

30
30

CUSTOMER SPOTLIGHT 25

A vertical-specific strategy team experimentswith AI from Aible to explore factors influencing
successful account transformations

Company Profile
Global Technology
Vendor

Challenge

Use Case & Project Details

The strategy team wished to improve theirinternal data collection and to understandhow Aible could provide insights into theirstrategic activities.

•

Use case analyzed: Strategy explorationand experimentation•

Potential Project Results: 10x speed toinsight•

Time from data provision to projectcompletion: 70 days•

Average elapsed time from start of data
evaluation to actionable insights onserverless infrastructure: 5 mins

Industry
State, Local Government
and Education

RegionUS, Canada

Solution
The team used Aible Sense to load,transform, and explore the signal fromtheir team’s data. After confirming theinitial data lacked a clear driving metric,
the team reconfigured the initial dataset
and layered it with account revenue
data. Using Aible Explore with the newdataset led to the discovery of factors
and interactions driving account revenue
improvement that merited further
exploration.

Aible Team Involved

Outcome
Identification of gaps in initial data andimprovement for speed to critical queries.
Continued experimentation required to gain
actionable insights.

Waiting for Customer“Aible helped us invert our thinking to quickly get to valuable questions.
This way of thinking helps our team drive more-effective data-driven
decision-making amongst ourselves and with our customers.”- Strategist3131Intel Notices & Disclaimers
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. No product orcomponent can be absolutely secure.
Your costs and results may vary.
Intel technologies may require enabled hardware, software or service activation.
Intel does not control or audit third-party data. You should consult other sources to evaluate accuracy.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.

Performance, Cost, TCO (Est.) Claims

Related Config

AWS Serverless based on Intel(R) Xeon(R) Processor @2.50GHz Model 63 (Haswell) provides up to 3.1x and2.4x speedup in model training time vs. AWS Server
m4.16xlarge instances based on Intel(R) Xeon(R) CPU
E5-2686 v4 @ 2.30GHz for 250k rows and 500k rows
datasets, respectively.

Comparing AWS Lambda us-east-1 region, with 6x vCPU, Intel(R) Xeon(R) Processor @ 2.50GHz Model 63(Haswell), 10 GB containers, Amazon Linux 2, 4.14.165-102.205.amzn2.x86_64, running Aible Model Training
with 70 TensorFlow 2.7 models over a range of network topologies and custom loss functions, numpy==1.21.4,
pandas==1.3.5, scikit-learn==1.0.2, scikit-learn-intelex==2021.5.3, scipy==1.7.3, tensorflow==2.7.0,
python:3.7.12 test by Intel on 02/22/2022 vs. autoscaling 0-8VMs, us-east-2, m4.16xlarge instance with 64x
vCPU, Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz, 256 GB total DDR4 memory, (launching containers with
6vcpu/10GB memory), Debian GNU/Linux 11 (bullseye), 5.4.156-83.273.amzn2.x86_64, running Aible Model
Training with 70 TensorFlow 2.7 models over a range of network topologies and custom loss functions,numpy==1.21.4, pandas==1.3.5, scikit-learn==1.0.2, scikit-learn-intelex==2021.5.3, scipy==1.7.3,
tensorflow==2.7.0, python:3.7.12 test by Intel on 02/22/2022 with https://www.kaggle.com/ankitkalauni/bank-loan-defaulter-prediction-hackathon dataset.

AWS Serverless based on Intel(R) Xeon(R) Processor @2.50GHz Model 63 (Haswell) is 3.11x and 2.14x more costeffective (training jobs) vs. AWS Server m4.16xlarge
instances based on Intel(R) Xeon(R) CPU E5-2686 v4 @2.30GHz for 250k rows and 500k rows datasets,
respectively.

Comparing AWS Lambda us-east-1 region ($0.00016670000 $/s/instance), with 6x vCPU, Intel(R) Xeon(R)Processor @ 2.50GHz Model 63 (Haswell), 10 GB containers, Amazon Linux 2, 4.14.165-102.205.amzn2.
x86_64, running Aible Model Training with 70 TensorFlow 2.7 models over a range of network topologies andcustom loss functions, numpy==1.21.4, pandas==1.3.5, scikit-learn==1.0.2, scikit-learn-intelex==2021.5.3,
scipy==1.7.3, tensorflow==2.7.0, python:3.7.12 test by Intel on 02/22/2022 vs. autoscaling 0-8VMs, us-east-2,
m4.16xlarge instance ($0.00088888889 $/s/instance) with 64x vCPU, Intel(R) Xeon(R) CPU E5-2686 v4 @2.30GHz, 256 GB total DDR4 memory, (launching containers with 6vcpu/10GB memory), Debian GNU/Linux 11(bullseye), 5.4.156-83.273.amzn2.x86_64, running Aible Model Training with 70 TensorFlow 2.7 models over arange of network topologies and custom loss functions, numpy==1.21.4, pandas==1.3.5, scikit-learn==1.0.2,
scikit-learn-intelex==2021.5.3, scipy==1.7.3, tensorflow==2.7.0, python:3.7.12 test by Intel on 02/22/2022 withhttps://www.kaggle.com/ankitkalauni/bank-loan-defaulter-prediction-hackathon dataset.

AWS Serverless based on Intel(R) Xeon(R) Processor @2.50GHz Model 63 (Haswell) is 4.21x and 2.8x more
TCO effective vs. AWS Server m4.16xlarge instances
based on Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
for 250k rows and 500k rows datasets, respectively.

Comparing AWS Lambda us-east-1 region ($620 for 250k dataset and $1070 for 500k dataset incl. total
processing time and $0 backplane cost), with 6x vCPU, Intel(R) Xeon(R) Processor @ 2.50GHz Model 63(Haswell), 10 GB containers, Amazon Linux 2, 4.14.165-102.205.amzn2.x86_64, running Aible Model Training
with 70 TensorFlow 2.7 models over a range of network topologies and custom loss functions, numpy==1.21.4,
pandas==1.3.5, scikit-learn==1.0.2, scikit-learn-intelex==2021.5.3, scipy==1.7.3, tensorflow==2.7.0,
python:3.7.12 test by Intel on 02/22/2022 vs. autoscaling 0-8VMs, us-east-2, m4.16xlarge instance ($2615 for250k dataset and $3010 for 500k dataset incl. total processing time and $675 backplane cost), with 64x vCPU,
Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz, 256 GB total DDR4 memory, (launching containers with
6vcpu/10GB memory), Debian GNU/Linux 11 (bullseye), 5.4.156-83.273.amzn2.x86_64, running Aible Model
Training with 70 TensorFlow 2.7 models over a range of network topologies and custom loss functions,numpy==1.21.4, pandas==1.3.5, scikit-learn==1.0.2, scikit-learn-intelex==2021.5.3, scipy==1.7.3,
tensorflow==2.7.0, python:3.7.12 test by Intel on 02/22/2022 with https://www.kaggle.com/ankitkalauni/bank-loan-defaulter-prediction-hackathon dataset.

AWS Server m6i.16xlarge instances-based Intel(R)Xeon(R) Platinum 8375C CPU @ 2.90GHz provides up
to 2.24x and 2.16x speedup in model training time vs.
AWS Server m4.16xlarge instances based on Intel(R)Xeon(R) CPU E5-2686 v4 @ 2.30GHz for 250k rows and500k rows datasets, respectively.

Comparing autoscaling 0-8VMs, us-east-2, m6i.16xlarge instance with 64x vCPU, Intel(R) Xeon(R) Platinum
8375C CPU @ 2.90GHz, 256 GB total DDR4 memory, (launching containers with 6vcpu/10GB memory), vs.
autoscaling 0-8VMs, us-east-2, m4.16xlarge instance with 64x vCPU, Intel(R) Xeon(R) CPU E5-2686 v4 @2.30GHz, 256 GB total DDR4 memory, (launching containers with 6vcpu/10GB memory), running Debian
GNU/Linux 11 (bullseye), 5.4.156-83.273.amzn2.x86_64, average Model training time is the geomean ofnotebook runtime from 3 runs over 11 representative TensorFlow models, numpy==1.21.4, pandas==1.3.5,
scikit-learn==1.0.2, scikit-learn-intelex==2021.5.3, scipy==1.7.3, tensorflow==2.7.0, python:3.7.12 test by Intelon 02/22/2022 with https://www.kaggle.com/ankitkalauni/bank-loan-defaulter-prediction-hackathon dataset.

Gartner Disclaimers
Gartner, Critical Capabilities for Cloud AI Developer Services, 19 October 2021, Van Baker et. Al.
Gartner, A CTO’s Guide to Top Artificial Intelligence Engineering Practices, 29 October 2021, Arun Chandrasekaran et. Al.
Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest
ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner
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