Evolve Your Analytics Platform to Impact Your Business
Transcript of Evolve Your Analytics Platform to Impact Your Business
Evolve Your Analytics Platform
to Impact Your Business
Chris Engstrom
Director, Advanced Analytics & Machine Learning
10/31/2018 © 2018 eCapital Advisors, LLC.
Objectives
▪ Show how integrated Machine Learning (ML) solutions
can be applied to common business problems
▪ Showcase various algorithms/methods
▪ Classification
▪ Time series forecasting
▪ Clustering / segmentation
▪ Decision optimization
▪ Connect!
© 2018 eCapital Advisors, LLC. 210/31/2018
Focused on Applied Data Science
Chris Engstrom
Director, Advanced Analytics & Machine Learning
▪ Microsoft (8 years)
▪ Medtronic (8 years)
▪ LLC consulting (4 years)
▪ Med Tech startup
Domain Experience: Tech, Med Tech, Med Device, Sales
Finance, Healthcare, Marketing, Direct Marketing, Higher
Ed, Insurance, Sales Planning & Management
Data Math
Business
Every Client is in a Different Place
Quantitative
Applied Data Science Skills
TechnicalSkeptical
CuriousCollaborative
Motivations
▪ Excitement / Fear
▪ Leadership pressure
▪ Competitive pressure
▪ Acute needs
▪ Fast growth/decline
Barriers
▪ Confusion
▪ Where to start?
▪ Budget
▪ Leadership support
▪ Data environment
Capabilities
▪ Advanced
▪ Capable but siloed
▪ Getting there
▪ Just starting out
▪ Nowhere
Data Are Now Strategic Assets
5Source: IBM Analytics Life Cycle:
The Machine Learning market is expected
to expand from $1.4B to $8.8B by 2022
The Prescriptive Analytics market size is
expected to grow over $4.5B by 202131% CAGR
44% CAGR
▪ Data Science is happening NOW/FAST
▪ Transformational applications
▪ Not about IT. About Competitive Advantage
Data Science platform market size is
expected to grow to over $100B by 202139% CAGR
© 2018 eCapital Advisors, LLC. 610/31/2018
Descriptive
Analytics
AI Decision Optimization (DO)
Predictive
Analytics
Machine Learning
Cognitive Data
Science
Data
Mining
NLP
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Company X
A view from Finance and
Business Analytics
Finance and Business Analytics
Strategic▪ Centerpiece of the organization
▪ Oversee entire business
▪ Strategic thinkers / relied upon
▪ Enable execution of strategy
Tactical▪ Deep understanding of data sources
▪ Responsibility to maximize assets/capital
▪ Planning and forecasting across all functions
▪ Sees things functional areas can’t (mobile example)
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IT does not
have this
perspective
Positioned
to Drive New
Approaches
Company X Challenges
Top Problems for Finance and Analysts1. Flat revenue and irrational pricing
2. Forecast accuracy
3. High employee attrition
Related Analytical Challenges▪ Data environment largely impenetrable
▪ Result has been random self-service one-off analyses
▪ Analysis limited to Descriptive Analytics (rearview mirror)
▪ Limited understanding of customer purchase behavior
▪ Do not know true “Cost of Attrition” (COA)
▪ What correlates with employee attrition
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Flat Revenue and Irrational Pricing
© 2018 eCapital Advisors, LLC. 1010/31/2018
(Needs Attitudes Behaviors)
cluster algorithm
Customer Base
LTV = $40K
Cluster 1
Cluster 2
Cluster 3
C=1,500
LTV=$33K
C=900
LTV=$51K
C=600
LTV=$67K
“Needy Infrequents”
“Engaged Actives”
“Big Ticket Erratics”
Identify useful variance
C=3,000
Flat Revenue and Irrational Pricing
© 2018 eCapital Advisors, LLC. 1110/31/2018
Segment Name Size
%
Order
Vol
Order
Freq
Prod
Mix
X-Sell
Rate
Upsell
Rate
Margin
%
LTV
Big Ticket
Erratics
20% Low Erratic Low Low Med High $67K
Engaged
Actives
30% Med-
High
Regular High Med Low Low-
Med
$51K
Needy
Infrequents
50% Med Erratic Med Low Low Low $33K
Segment Profile and Priorities
Results
• Improved customer
understanding
• Uncovered margin
variance and
irrational pricing
• Customized Sales
and Marketing
• Better time/resource
allocation
• Improved customer
interactions lead to
increased revenueNeedy
Actives
Big Ticket
Forecast Accuracy
© 2018 eCapital Advisors, LLC. 1210/31/2018
Baseline ForecastsSegment
Needy
Actives
Big Ticket
Solutions
▪ Auto-reforecasts
▪ Auto-reforecasts
▪ Change detection
▪ Custom forecasts
▪ Custom reforecasts
▪ Change detection
Results
▪ Improved accuracy
▪ Improved time and resource allocation
▪ Seasonality identification
▪ Auto-forecast 10K customers or 100K SKUs
Employee Attrition
13© 2016 eCapital Advisors, LLC.
▪ Example: Voluntary employee attrition
▪ Problem: Limited HR resources
Direct Indirect
Cost of Attrition (COA) (1.5-2X Salary!)
Training costs
Onboarding costs
Recruiting costs
Relocation costs
Vacancy costs
Separation costs
Competitive risk
Customer impact
Team morale
Lost knowledge
Team productivity
Position productivity
Scheduling
Nightmare
Dynamic Dashboard
14
ID Months
Tenure
Employee
Title
Employee
Manager
Alignment
Risk
Engagement
Risk
Performance
Risk
Manager
Risk
Commute
Distance
Other
Risk
Attrition
Risk
Training
Investment
Business
Impact
65837493 4 Title 1 M. Shetland High NA Low High High High 0.90 $2,000 Low
72682738 45 Title 2 J. Barr High High High High High Low 0.81 $41,000 High
92840193 34 Title 2 J. Barr High Med Med High Low Med 0.78 $32,000 High
89283920 2 Title 3 S. Malevich Low NA Med High Med High 0.78 $1,000 Med
19201846 17 Title 1 P. Gupta High Med Low Med High High 0.77 $2,000 Low
85027292 53 Title 3 L. Stevens Low High High Low Med Med 0.75 $25,000 High
91802893 71 Title 4 K. Malik Low High Med Low Med High 0.74 $22,000 Med
.. . . . . . . . . . . . .
.. . . . . . . . . . . . .
.. . . . . . . . . . . . .
81034692 52 Title 4 F. Dolan Low Low Low Low Low Low 8% $43,000 High
23625272 34 Title 5 S. Adams Low Med Low Low Med Low 0.08 $28,000 Low
46736383 29 Title 2 K. Boyd Low Low Low Low Low Low 0.07 $23,000 Med
62835739 44 Title 2 Y. Zulan Low Med Low Med Low Low 0.06 $32,000 Low
72916829 62 Title 5 X. Zhang Low Low Low Low Low Med 0.06 $37,000 High
38399101 55 Title 4 S. Adams Med Low Low Low Low Low 0.06 $74,000 High
78378379 63 Title 4 R. Haloran Low Med Low Med Low Low 0.05 $38,000 Med
Predictive Category
(employee subgroup = 612)
Decision FactorsEmployee
Drill into each
employee record for
a detailed risk profile
Employees with High Alignment Risk are
in roles or on teams that do not maximize
their passion, skills, and experience.
Employees who have low tenure with a
manager or are working for a difficult manager
can be at very high risk of voluntary attrition.
Commute Distance can have a
profound impact on attrition.
© 2016 eCapital Advisors, LLC.
At-Risk Employee Interventions
15© 2016 eCapital Advisors, LLC.
Assumptions▪ 1,000 employees▪ $80,000 annul salary▪ Cost of Attrition = 2X salary
It All Begins and Ends with
Actionable Data
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Customer Transactions
Modern Data Platform
Customer Support
Decision Optimization
Engine
Marketing Analytics
Digital / Social Media
Predictive Attrition
Modeling
Time Series Forecasts
Actionable Insight
Etc. Advanced Analytics
DATA INSIGHT $VALUE
RESULTS
eCapital FastStart Approach
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Focus▪ ROI (true need)▪ ROI measurability▪ Deployability▪ (13 factors)▪ (readiness factors)
Project Candidates
Critical Success Factors
Smart Project Selection FastStart Development1 2
▪ Phased Roadmaps
▪ Rope Bridge approach
▪ Get data and get going
▪ Parallel IT/DS Roadmaps
IT Development
Predictive Analytics Roadmap
Deployment
Next Steps
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✓ Connect on LinkedIN
✓ Continue the discussion
Chris Engstrom
Director, Advanced Analytics & Machine Learning
https://www.linkedin.com/in/chrisengstrom
© 2018 eCapital Advisors, LLC. 1910/31/2018
APPENDIX
Employee Attrition is A Serious Issue
20Source: Chief Human Resources Officer insights from the IBM C-suite Study. This report draws upon input from the 4,183 CxOs we interviewed as part of IBM’s first study of the entire C-suite. It
is the 17th in the ongoing series of C-suite studies developed by the IBM Institute for Business Value. We now have data from more than 23,000 interviews stretching back to 2003.
Only 13% of Organizations Predict Attrition
21Source: Chief Human Resources Officer insights from the IBM C-suite Study. This report draws upon input from the 4,183 CxOs we interviewed as part of IBM’s first study of the entire C-suite. It
is the 17th in the ongoing series of C-suite studies developed by the IBM Institute for Business Value. We now have data from more than 23,000 interviews stretching back to 2003.
Cost of Attrition of a Single Employee is
1.5-2X Salary!
22© 2016 eCapital Advisors, LLC.
Source: https://www.linkedin.com/pulse/20130816200159-131079-employee-retention-now-a-big-issue-why-the-tide-has-turned/?irgwc=1
Model Improvement Results
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31%
61%
93%
10
20% of the employee file
contains 61% of employees
with highest risk scores.
Top 10% of employee file
contains the 31% of employees
with highest risk scores.
%%
© 2016 eCapital Advisors, LLC.