Zeno Analytics provides services to help companies translate advanced analytics into effective business actions. They offer "Analytics Translator" and "Data Translator" services to close the gap between expectations of analytics investments and real outcomes. Their services include reviewing companies' current analytics processes, conducting executive workshops on analytics strategy, assessing analytics acumen, and helping to identify, define, design, implement, monitor and guide analytics solutions to improve business outcomes. For more information, contact Jaap Vink.

1. Zeno Analytics
Connecting decisions to data by translating advanced analytics to
effective actions
“Analytics Translator” and “Data Translator”
Close the gap between reality and expectations of your
advanced analytics investments & projects.
Contact: Jaap Vink
jgavink@xs4all.nl
+31 615 86 0115
https://www.linkedin.com/in/jaapvink/
© Zeno Analytics, 2018

2. Why business people
should ALWAYS be
involved!
Business & Analytics: Simple
Classification example

3. Simple Classification as example
• Yes/No outcome
• Responds?
• Conversion?
• Cancelation?
• Payment?
• Fraud?
• Cyber Security Breach?
• Analytical techniques commonly used
• Machine Learning
• Predictive Models

4. What are we classifying?
• Response or conversion?
• And conversion at what cost?
• Do we take (future) expected value into account?
• Payment or Payment within x days?
• How much collection effort?
• Cancelations or Voluntary Cancelations
• Do we want to keep the customer?
• Can we ‘save’ them?
• What do we consider cancelation?
• Fraudulent transactions or fraudsters?
• Do we want to stop a transaction or do we want to catch a criminal?
• Do we take value into account?
• …..

5. Some choices to make
• Where is the cut off between classification into Yes/No?
• Risk appetite/Aggressiveness/Budget/Targets/Resources/..?
• Should we actually use classification or should we use
the scores or an index as ranking?
• Is a Yes/No classification clear cut?
• When is a model ‘good enough’?
• How/when/where do we deploy the results?
• What data are available for deployment?
• How do we monitor the performance?
• ….

6. How to decide?
Examples

7. Classification Matrix
Prediction
Actual
Yes No
Yes True+ False+
No False- True-

8. Accuracy is overrated!
• Sum(true+, true-)/All
• Only useful in very specific applications
• Problem example: low number of occurrences
Prediction
Actual
Yes No
Yes 0 0
No 1,000 99,000 Accuracy=99%

9. Prospecting with ‘look-a-likes’
Prediction
Actual Customer
Yes No
Yes True+ False+
No False- True-
• Find others similar to your current
customer base
• True+ & False- are the only
important ones
• Only “Yes” cases to learn from
• False- need additional work
• False+ is the target category
• Most similar to you current customers
• True- is undecided and could be a
target for some pilot marketing
campaigns
• CAUTION: Make sure you’re not
recreating you previous targeting
tactics. Do not interpret any scores
as probabilities

10. Campaign Response
Prediction
Actual Response
Yes No
Yes True+ False+
No False- True-
• Find others that are likely to
respond to the campaign
• True+ & True- are the focus
• False+ & False- could be due
to other factors
• CAUTION: Model is only valid
for current campaign
characteristics
• Selection
• Timing
• Message
• Channel
• …

11. Credit Scoring
Prediction
Actual Default
Yes No
Yes True+ False+
No False- True-
• Determine if customers are likely
to default on their invoices
• True+ & True- should contain all
cases in an ideal world:
Accuracy
• False- increases your credit risk
• False+ decreases your
opportunity for business
• CAUTION: Credit Scoring is not
the same as failure scores,
delinquency scores or other
external bureau scores
• Although they may be used as valuable
input

12. Fraud Detection
Prediction
Actual Fraud
Yes No
Yes True+ False+
No False- True-
• Identifying potentially
fraudulent transactions or
fraudsters
• True+ & True- are the focus
• False- are cases you cannot
yet identify
• Need additional work
• False+ are possible fraud
cases you’ve missed
• CAUTION: With classification
you target “dumb” frauds.
You’ll also need other
approaches to identify
other/new fraud schemes

13. “Remember that all models are
wrong; the practical question is how
wrong do they have to be to not be
useful.”
Box, G. E. P.; Draper, N. R. (1987), Empirical
Model-Building and Response Surfaces, John
Wiley & Sons.
© Zeno Analytics, 2018

14. Be Agile
Continuously Learn & Adapt
Source: https://commons.wikimedia.org/wiki/File:OODA.Boyd.svg
© Zeno Analytics, 2018

15. Zeno Analytics
• Analytics Process Review
• Review your current analytics process based on business goals, data used,
analytics employed & deployment in business process
• Executive Workshops Analytics Strategy
• Analytics Acumen Assessment
• Identify & Define Analytics use-cases in your organization to improve business
outcomes and a roadmap for implementation
• (Re-)Design Analytics Solutions
• Organize, Monitor & Guide Implementation process
Contact: Jaap Vink
jgavink@xs4all.nl
+31 615 86 0115
https://www.linkedin.com/in/jaapvink/
© Zeno Analytics, 2018