Introducing Data Science in big organizationsAdrian Badi

Senior data analyst

Demant

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Introducing data science can sometimes feel like getting

lost in the wilderness

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The best way is to imagine theprocess as a food recipe

Ingredients:

• One business case;

• Data supporting the case;

• The “master mind” pot stirring method

• Some basic understanding of ML;

• In a separate pan, make a success criteria;

• Plating the dish (preliminary results);

• Give taste samples (Marketing your findings and

involve management);

• Serve while it’s hot.

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The case

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Turn this … into this

The objective was therefore to

In other words: make the selection process easier for the salesmen – with data behind it!

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The data

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The “Master Mind”

pot stirring method

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Successful individuals such as Benjamin Franklin, J.R.R. Tolkien, Henry Ford, Andrew Carnegie and Thomas Edison, all met with groups of like-minded people on a regular basis, to help one another achieve common goals and grow. Today, this is called a “mastermind”, first coined by Napoleon Hill in 1925.

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DATA WRANGLING AND MACHINE LEARNING TEAMDIGITAL & IT CONSULTING TEAM

BUSINESS CASE TEAM

International Sales

Kasper L. Krogager

Business Development

Manager

International Sales

Mikkel Jarner Brevadt

Senior Business

Analyst

International Sales

Alejandra Garcia

Gonzalez, Student

Assistant

Sales & Marketing

Kasper Juul Jensen

Senior Manager

Sales & Marketing

Alessandro Pasta

Analyst

BI

Adrian Badi

Senior Data Analyst, BI

R&D

Anders Vinther Olsen

Audiology and DSP

Developer

Information

Technology

Julie Ingstrup

Digital Consultant

Information

Technology

Troels Christensen

IT Consultant

IT Strategy & Cloud

Bernadeta Jakubczak,

Data Scientist

KAPACITY

Bernafon & Sonic, DE

Danilo Krautz

Controller

Kapacity

Milan Mirkovic

Data Scientist

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A success criterion

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Success criterion:

A visit is successful if there is a sale no

more than 5 days after said visit.

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Success criterion:

A visit is successful if there is a sale no

more than 5 days after said visit.

An average salesman had,

historically, a 12% success rate,

based on this definition

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Success criterion:

A visit is successful if there is a sale no

more than 5 days after said visit.

An average salesman had,

historically, a 12% success rate,

based on this definitionSpoiler alert: our model reached 40%

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Tasting your food

while cooking

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Some ML knowledge

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Data sets

• Historical sales

visits

• Historical sales

• Customer targets

Data model

• (Potential Units /

Revenue)

• Zip code

• Total revenue YTD

• Revenue between

visits

• Days since last

purchase

• Days since first visit

• Days since last visit

• Is first visit

• Consignment Stock

• Current target ratio

• Sales targets units

• Sales Visits with

sales within 5 days*

Decision tree/Random forest

Second time: after feedback from sales people

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Plating

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0

500

1000

1500

2000

2500

3000

3500

4000

-45 -40 -35 -25 -20 -15 -10 -5 510

1520

2530

3540

Units b

ought

Days

Visit ”pulse”

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12,5% of sales visits create

sales within 5 days

About 3% of all transaction

match success criteria of

sales within 5 days

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Over the course of the experiment (approx. 1 month, 2 salesmen, with the final product), we

reached an increase of approx. 60%* on sales. (A/B testing)

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Serve while hot

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Tips

• Always base a business case on data;

• Find a Master Mind that will cover almost all aspects of that business

process;

• Test hypothesis on the fly and involve the users;

• Aim your data science project towards a success criteria from the get-

go;

• Make sure you market your results as much as possible to activate

stakeholders’ attention;

• Keep up the momentum – such projects are “eye openers”, but the

initiative must be kept alive by the master mind with more and more

projects.

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Hard pills you need to swallow to help with digestion of your meal

Backup

Time

Feedback

DeployBig organizations

have big agendas:

your time allocation

will be limited and

you need to accept

that in the beginning

Pill 1 Allocate

concentrated time

with the master mind

in order to make

sure you make

progress

Pill 2

When doing your

POC, make sure you

have your thick skin

on – you’re going to

need it!

Pill 3

Deployment will be a

sensitive subject,

since you don’t want

to “hand carry” your

solution forever. Talk

with your IT

department to see

what automation

solutions you can

adopt

Pill 4

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Build your proof of concept

(or MVP) as a “hand

carried” solution, as fast as

possible to get initial

feedback – make sure you

are solving a problem

Build

Show your MVP to the

users. See their reaction,

their usage and ask about

the good and the bad.

Show

Give your users time to

actually “consume” your

solution and see if they

can get any value out of it

– don’t count your chicken

until they hatch.

Use

Find out the pain points

you haven’t solved yet and

start over with the next

release cycle (version x.0)

Feedback

Hypothesis

test

Hypothesis

test

Hypothesis

test

The process

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The secret ingredient:

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Excitement

Thank You