Data as a catalyst for a new approach to innovation

Giovanna Miritello @gmiritello

26 countries

50 partner networks

430+ M connections

107k employees

Data overview

Enterprise & External data

External data is usually wide and shallow. Enterprise data is narrow and deep.

data-driven home searchtransportation

socio economics -unemployment-

so data

so data

but data is not numbers, it’s people.

How to build a product suitable for every different customer?

Let’s the data speak.

How to build a product suitable for every different customer?

Data-driven customer centric products

Data-driven customer centric products

Data-driven customer centric products

Personalised customer experience

Personalised customer experience

Why personalisation is important?

VS

Personalisation at Vodafone

slower 4G faster 4G

Why do we need more data & advanced models? The world is often simple to predict.

mobility needs apps/type of content

device health network performance in the area

Or not?

Network experienceNetwork experience might be related to: • home/work location • mobility patterns • needs and interests • device type/quality

Price-plan

2. We are social animals

1. Black or whiteand

Gb

£

data plan (Gb)

# contacts

% c

usto

mer

s

Understanding reasons to leave and motivations to stay

customer experience

OOB spend

spend on roaming

4G at home

Homophily and influence effects can be measured and predicted!

Don’t forget the social factor!

Jun Ding et al. Alone in the Game: Dynamic Spread of Churn Behavior in a Large Social Network a Longitudinal Study in MMORPG

Data is not numbers, it’s people.

What you can’t predict, you must at least see!

This customer feedback can go unnoticed if only structured feedback and scores get looked at.

This does not happen in one day.

Within the data science community

No organisation is perfect, but some good practices help [1/2]

Invest in standardisationStandardisation enables sharing and collaboration: lowering barriers, increasing expectations)

data format

platformtools

frameworkmethodology

Sharing knowledge

Data stories and visualisations as a daily practice

Avoid “data vomit”

D.J. Patil, Data Jujitsu: The Art of Turning Data into Product

VS

Solid software development practices: know your code!

http://thecuriouscan.com/learn-from-the-costliest-mistakes-in-history/

June 4, 1996 Ariane 5 rocket launched by the European Space Agency exploded just 37 seconds after its lift-off

7 billion dollars development of the rocket

The cost

500 million dollars estimated value of the destroyed rocket and its cargo

Solid software development practices: know your code!

http://thecuriouscan.com/learn-from-the-costliest-mistakes-in-history/

A software programming error!

A 64 bit floating point number relating to the horizontal velocity of the rocket with respect to the platform was converted to a 16 bit signed integer.

The number was larger than 32.767, the largest integer storable in a 16 bit signed integer, and thus the conversion failed.

The reason for the blast?

Model accuracy is important, but it’s not the only thing

Prefer interpretability at the beginning, then upgrade models.

Start simple, then iterate

• think it (reduces the risk) • build it (as fast as possible) • ship it (gradually roll out to all users) • tweak it (continuously improve)

Prefer high precision of one product instead than many sophisticated products.

The real risk is building solutions that no one needs:

D.J. Patil, Data Jujitsu: The Art of Turning Data into Product

No organisation is perfect, but some good practices help [2/2]

With the rest of the organisation

• Engage with stakeholders from day 1

It is a bidirectional direction thing: - a data science team must know the business priorities - the whole organisation needs to understand and engage with data driven results, stories and their value

• Engage with stakeholders from day 1

It is a bidirectional direction thing: - a data science team must know the business priorities - the whole organisation needs to understand and engage with data driven results, stories and their value

• Remove barriers & enable data connectivity

• Engage with stakeholders from day 1

It is a bidirectional direction thing: - a data science team must know the business priorities - the whole organisation needs to understand and engage with data driven results, stories and their value

• Remove barriers & enable data connectivity

• Agree on implementation and performance metrics

Thank you!

Giovanna Miritello @gmiritello