a case for the “crawl, walk, run” approach - dan … · • the case for segmentation...

800.283.3227 www.ontariosystems.com © 2017 Ontario Systems, LLC. All rights reserved.

A Case for the “Crawl, Walk, Run” Approach - Dan Womack, Director of Software Engineering

800.283.3227 www.ontariosystems.com © 2017 Ontario Systems, LLC. All rights reserved.

Disclaimer: Ontario Systems is a technology company and provides this presentation solely for

general informational and marketing purposes. You should not rely on the content of this

material for any other purpose or as specific guidance for your company. Ontario Systems’

advice, services, tools and products described herein do not guarantee compliance with any law

or industry standard. You are ultimately responsible for your own company’s actions and

compliance efforts and should consult with your own legal and business advisors to obtain

specific advice on your company’s compliance, legal and other business needs. Despite Ontario

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information contained herein may become outdated quickly and may contain errors and/or other

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The presenter and Ontario Systems assume no liability for typographical or other errors

contained in the presentation and materials or for changes in the law affecting anything

discussed herein.

800.283.3227 www.ontariosystems.com © Ontario Systems, LLC. All rights reserved.

• Artificial Intelligent and Machine Learning for collections (the tech of

tomorrow)

• Human/Business Intelligence and Incremental learning (what’s achievable

today)

• How to utilize reporting, dashboarding, and analytics today

• How to learn from and build change models to perform A:B testing

• How to simplify the analytics models to drive real results

• Using your big data as a trusted advisor

• The case for segmentation – agents, portfolios, and workflows


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• This is a learning cycle. We may each be in different portions of the cycle.

Crawl, Walk, Run.

• Effectiveness of letters

• Email

• Phone calls

• Leaving messages

• Scoring and segregating

• Texting

• Portals, chat, bots


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Schedule work through workflow, pool building, campaign building

Prioritizing effort and investmentbased on analytic or score data


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• How many reports to you have? How many do you use monthly?

– Account Inventories

– Agency Analysis

– Agency Performance

– Compliance and Auditing

– Client Reports

– Process Control

– Production Monitoring

• What are your reports telling you?

• When does a report need to be a dashboard?

• Do you schedule, batch, publish, share, automate distribution of reports?


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• Steps needed to run an effective A:B test

• 1. Know where you are at today (KPI)

• 2. Know what you are doing today (Workflow)

• 3. Design test case isolating change and

method to measure result (Hypothesis)

• 4. Create segment of valid sample size and type

• 5. Run test

• 6. Evaluate and make appropriate changes

• 7. Rinse and Repeat


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• The case for segmentation – agents, portfolios, and workflows

• How are you utilizing A:B testing today?

• How are you measuring results?

• A few ways to consider doing so


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• Analytics needs to be driven by the Business not by the Technology

• Analytics 101 needs to answer business questions

– What happened

• Analytics 201 needs to prompt business questions

– Why did it happen

• Analytics 301 needs to do both and adds foresight

– What will happen

• Analytics 401 needs to create a formula

– How can we make it happen


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• Data is the 21st Century gold. It is so sought after there have been nearly a

hundred data breaches this year so far. 20,836,531 records leaked in March

alone.

• Sears

• Kmart

• Delta

• Best Buy

• Saks Fifth Avenue

• Lord & Taylor

• Under Armour

• Panera Bread

• Forever 21

• Sonic

• Whole Foods

• Gamestop

• Arby’s

• Atlanta city government system shut down due to ransomware attack

• US Army Intelligence and Security Command (INSCOM) data breach


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• Everything was analog -

now everything is digital -

and digital is forever.

• Now that you have a

treasure chest full of this

gold. You can talk to it.

Ask it questions


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• Define the Objective

• Gather the Data

• Prepare the Data for Modeling (Scrub)

• Selecting and Transforming variables to be easily understood by ML model.

• Process and evaluation of the model

• Validate the Model

• Implement the Model


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• Machine Learning and AI are everywhere. You interact with it every day, and

may not know it.

• Machine Learning started in the 1950s with the Turing’s Learning Machine

(1950), and the first Neural Network (1951), and IBM’s Checkers Playing

Machine (1952).

• Examples of ML:

– Netflix recommending content

– Google directions and Waze

– Uber and Lyft – surge pricing – dynamic heatmap

– Autopilot in airlines

– Email spam filter

– Gmail suggested responses to email


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• There are 2 of you, but they are not equal.

• The physical you, and the digital you


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• How might we use this in collections

• Mobile Check Deposits (texting or emailing check image)

– Uses ML for identify management, fraud detection, and OCR

• Credit Decisions (for creditors)

• Improved Tactics, Strategies, Workflows

• Predicting contact times and methods

• Voice-to-Text for automated IVRs, natural language responses, call quality

and analytics


Thank You

Dan Womack

Director of Product Engineering

[email protected]

www.linkedin.com/in/dan-womack

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mailto:[email protected]

www.linkedin.com/in/dan-womack

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