Scaling Your Enterprise With Data Science

Speakers
SUPERFLUID LABS LTD | Copyright (c) | 2
Timothy Kotin
Co-Founder & CEO
Superfluid Labs
Gilbert Langat
Data Scientist
Superfluid Labs
Yvette Titriku
Data Scientist
Superfluid Labs

3
Speakers Timothy Kotin Co-Founder & CEO
BS Computer Science and Engineering Research Scientist
MPhil Engineering for Sustainable Development Specialized
Consultant
Yvette Titriku Data Scientist
BSc Actuarial Science Industry Experience
MS Applied Statistics
Gilbert Langat Data Scientist
MS Mathematical Sciences Industry
Experience
MS Mathematics

Session Outline
4
Introduction and Overview
•Superfluid Labs
•AI, Machine Learning and Data Science
•Selected Client Case Studies
Data Science Strategy
•Roadmap to Becoming a Full Data-Driven Enterprise
•Building Internal Capacity, Resources, and Tools
•Critical Success Factors and Pitfalls to Avoid
•Ensuring Sustainability of Data-Driven Transformation
Data Science Business Process
•Data Science Workflow
•Common Tools for Data Science
•Tips for Learning Data Science

Introduction and
Overview
SUPERFLUID LABS LTD | Copyright (c) 2019 5

We’re a data analytics firm that facilitates enterprises to develop digital platforms and
new customer solutions driven by data, machine-learning and AI
Germany | Ghana | Kenya
OUR MISSION: To expand opportunity for people and businesses through the power of
data.

Our Vision
To be the preferred data-driven solutions partner for the most impactful
organizations
Industries and SDG Impact
Financial Services| Retail & Commerce| Agribusiness | Clean Energy |Technology

Market Validation and Experience
CustomersExperience

Recognized as leader in Financial Services, AI and Big Data
SUPERFLUID LABS LTD | Copyright (c) 2019 | 9
RecognitionsPartners&Compliance

What is data science, artificial intelligence
and machine learning?
Data Analytics - evolutionary step in analytics combining computer science,
statistics, mathematics and machine learning to analyze large amounts of data
and extract useful knowledge
Artificial intelligence (AI) is a branch of computer science dealing with the
simulation of intelligent behavior in computers.
Machine learning (ML) is the scientific study of algorithms and statistical
models that computer systems use to effectively perform a specific task
without using explicit instructions, relying on patterns and inference instead.

11
Why is data science important?
●Promotions
●Upsell
●Cross sell
●Pricing
●Shelf-space
optimization
●Risk Modelling
●Fraud
prediction
●Customer
segmentation
●Portfolio
optimization
●Market basket
analysis
●A/B testing
●Sales
forecasting
●Clinical trials
of new drugs
●Campaign
and sales
program
optimization
●Epidemic
forecasting
and control
●Chain
management
●Customer
acquisition
strategies
●Upsell/cross
sell
●Product
bundling
●Mobile user
location
analysis
●Customer
churn analysis
e-commerce Health TelcoBankingRetail

12SUPERFLUID LABS LTD | Copyright (c) 2019
Selected Case Studies

Predicting Customer Future Payment Behaviour
13
A distributed solar energy company that sells a wide range of solar off-grid units on credit using a pay-as-you-go (PAYGO) model.
Client sought a platform to harness the existing rich customer datasets to develop machine learning systems that can predict:
A. Future utilization rates of a portfolio of customers based on historical data
B. Customer upgrade propensities and outcomes for new accessories offered by the client
Challenge
SFL mined the dataset to build a customized model
that could (through prediction) enable…
Tree
N
Tree
2
Tree
3
Tree
4
Tree
1
Targeted
interventions
Early repossession Upsell to good
customers
✓ ✓ ✓
30% increase in Monthly revenues
Driven by early default
predictions alone
Success story 1 Success story 2 Success story 3
SUPERFLUID LABS LTD | Copyright & Confidential

Product Recommendation for Cross-sell of Electronics Devices
14
▪ Current qualification criteria upgrades a lot
of bad customers (44%) who have not yet
established meaningful repayment history.
▪ Losses from bad upgrades outweigh the gains from
good upgrades, leading to an average loss in LTV of
-13 USD per upgrade, and lowers overall
profitability
Current
situation
With SFL
Model
38USD
New average Impact of
upgrade with SFL Model
+51
increase
▪ By accurately predicting upgrade
outcomes (good or bad), our models
improved lifetime value of upgrades by
+51 USD
-13USD
Average impact of
each upgrade on LTV
Account Age at Upgrade
Account Age (days) before upgrade

Online Lender focused on E-Commerce
Merchants
15
SITUATION
Our client disbursed 1445 pilot loans to
small merchants on popular e-commerce
platforms.
● Total disbursed: USD 350,000
● Financing fee: 6% per month
● Processing fee: USD 2-7
● Loan tenor: 30 days
PROBLEM OUR IMPACT
$ 140,000 $ 231,000
+$91,000
repayments
❏ Good rate: 27.68%
❏ Bad rate: 72.32%
❏ Good rate: 65.85%
❏ Bad rate: 34.15%
x 2.38
65.85%
34.15%
27.68%
72.32%

Data Science Strategy

1. Culture – driving the change to a data-driven company
2. Buy-in – creating a corporate ambition for data science
3. Prioritization – choosing the right applications to deliver value
4. Team – building a data science capability
5. Speed – agile deployment
Key areas to focus on to become a data driven
enterprise

Data Culture
● Principle established in the process of social
practice in both public and private sectors
● Requires all staffs and decision-makers
● Focus on the information conveyed by the
existing data
● Make decisions and changes according to
data results

Embracing the cultural shift to a data-driven
• Recruiting a team of diversified backgrounds
and experiences
• Embedding new skills and ways of thinking
within the business
• Role model new capabilities and approach
• Creation of Data Science and Analytics
community
• Involving everyone in new ideas and joining
projects
19

You need wholistic Buy-in

Create a corporate ambition/ identify business
objectives
Deliver $100m benefit
over the next 5 years
using data science and
analytics.

Prioritization - What matters most to your business?
Guiding principles
● Must generate real benefits
● Customer perspective
● Clear ability to execute necessary business change
● Data – sufficient volume, quality and understood
● Business sponsorship
● Reuse and scalability

Building data science capability - define
your needs
● Be clear what you want – data analyst, data architect, data engineer,
data scientist, data artist(!)
● Hire for talent, train for tech skills
○ Analytical thinking and communication skills are harder to teach than SQL,
Python and R.
● Broaden your pool of candidates
○ Diversity-increases your revenue by promoting innovation and creative
thinking

Data science components

Building the Team-when starting out, a simple
team might look like this:
● Project Manager/Owner (existing management)
● Data Scientist/Data Engineer (one new hire, and one existing staff
member)
● Software Engineer (existing IT staff member)
Tip: If you are just getting started in data
science, it may be some time before you need
a true data scientist for predictive modeling or
machine learning — focus on hiring a data
engineer first.

Speed - Agile development
MVP a product with just
enough features to satisfy
early customers, and to
provide feedback for future
product development.
(wikipedia)

Superfluid Labs Project Success Factors
● Executive Sponsorship
● Business Sponsor (makes it happen on the ground)
● Over-communicate to all stakeholders
● IT – senior IT support to circumvent usual cycle times
● Data – early analysis for quality
● Business Change – run parallel alongside data / modelling – is there an existing
process to change?
● Team – right people, available, aligned and accountable
● Project Management – Agile methodology

1. Include all parts of the organization and
stakeholders in the conversation
2. Create a corporate ambition/ business objectives
3. Build a data science capability
4. Prioritize all the things you could do to figure out
where to start
5. Define your roadmap with an end-point in mind
6. Adopt agile methodology
7. Embracing the cultural shift to a data-driven
company
Data-Driven
Strategy Checklist

Data Science Business Process

Data science workflow
● Business - understand business operations and business problem
● Data - acquire and understand data components
● Exploratory Analysis - visually and statistically explore data to generate hypothesis
● Data Preprocessing - clean and transform data
● Feature Engineering - generate additional features/ variables
● Model Development and Deployment - train, test, deploy and monitor predictive model
● Data Visualization - visualize and communicate key insights to inspire stakeholder action
● Business Analysis - analyze impact/value of model on business vs business-as-usual

Popular Data Science Tools

SuperFluid Labs Data Analytics Platform
The Superfluid’s platform mines data to deliver these capabilities using proprietary algorithms and
artificial intelligence
SuperScore™
Digital Credit Scoring Solution
powered by artificial intelligence
SuperML™
Automated Data Science and
Machine Learning Platform
SuperBI™
Business Intelligence, Customer
Insights and Analytics Platform
SUPER
ENTERPRISE
PLATFORM
SuperLife™
Intelligent Marketplace and
Business Ecosystem Platform

Tips for learning and doing data science
● Understand the fundamentals
● Acquire domain knowledge
● Keep the data science problem in focus
● Learn the functions and modules frequently used during each stage of the
data science process
● Learn and improve programming skills
● Learn by doing. Create a data science portfolio

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