cim digital summit 2016 - aquila insight: chris dobson's presentation

Big Data? Right Data!Chris Dobson: Director of Consultancy, Aquila Insight

Email: [email protected]

Web: www.aquilainsight.com

Twitter: @aquilainsight @Mr_Dobbo

Data’s always been big

2www.aquilainsight.com

But big tech is the real enabler


But first think….


What am I trying to achieve?

Is it right for the customer?


If HR did (bad) Big Data……


• Clear job spec

• Identified skill set needed

• Initial recruitment

• First interviews

Learning from HR

• Final interviews

• Job offer

• New join

• Development


• Understand the question

• Identify the what, when &

how often

• Do data discovery

• Remove the noise

Learning from HR

• Refine

• Ensure you have the right

permissions

• Use for purpose

• Measure, Adapt & Develop

Data pillars


•All data

storage and

transfer to

be done in a

secure way,

with set

procedures

and access

levels

•Clear

security

guidelines

and disaster

recovery

Secure

•Full legal

compliance

needs to be

adhered to

at all times,

from a

customers

T’s & C’s,

through to

data usage

Compliant

•Levels of

data

accuracy

should be

known, with

set levels of

tolerance for

missing data

Accurate

•Data,

analysis and

decisioning

need to be

available at

the right

time to

impact

business and

customer

decisions

Timely

•Analysis:

Enable

statistically

robust

analytics

without

impacting

performance

•Campaign:

Support

legally

compliant,

multi-variant

campaigns

with optimal

structure

Actionable

•The data

structure

should allow

for the

integration

of new data

types, and

the

derivation of

existing

variables

Scalable

•All data held

should impact

the business in

some way:

Measurement,

Analysis or

Decision

Making

Efficient

Real time multi-channel

consumer interaction

led

Optimising the digital

conversationDrive loyalty & retention via NBA

led activity

Increasing usage to drive

incremental revenue

Product UsageCustomer Development Digital Optimisation Customer Care

ConfidentialPA120/02/201222 22 Xperia Lounge PackagesOctober’14

Creating Stickiness and Value through One SonyIntegrated One Sony propositions for satisfaction, loyalty and acquisition

Logo design and packages names conceptual and TBC

PREMIUM ELITE

Enabling multi-device

experience evolution

Strategy, ATL targeting

optimisation, & competitor

tracking

Product and services design and

innovation, & support B2B goal

Finance, Product and

Application performance

monitoring and growth hacking

Performance TrackingProduct & Service Development Media Effectiveness IOT Enabler

DATA DRIVEN DECISIONING

What data can really do

www.aquilainsight.com

Commercialised consumer satisfaction modelling driving business investment, and

customer experience development

Business Problem

Sales based CRM

approach with no clear

buy in to wider

customer development

Consumer satisfaction

and NPS understood to

be important but could

not be measured,

commercialised or

drivers understood

Solution

Initial research aligned

with product & business

offers and services

CSI built and linked to

NPS & drivers modelled

Set business change

process, linking business

metrics/ROI

Move towards

behavioural CSI to

impact customer

journey development

14

Making a better customer experience


Data: Structured, Research, Financial, Operational,

Behavioural

Creating business change


Enabling the business to develop the customer journey through the proactive use of

complaint data

Business Problem

Reduce the volume of

complaints received by

40% per annum across

more than 200 distinct

complaint types

Customers can complain

through different

channels including

direct, branch, social

and web

Solution

Attribution: Identify

the interaction that

caused a complaint

Visualisation: ‘Set the

scene’ to understand

complaints landscape

Pre-emptive: Identify

customers that are

likely to complain

Predictive: Model the

propensity to complain,

allowing for specific

recovery journeys at a

customer level

Data: Structured & Unstructured, Complaint,

Customer Interaction, Value, Channel, Staff

Identifying issues


Utilising imagery to create a predictive model for eye disease

Problem

The goal is to identify

the likelihood of the

patient suffering from

neovascularisation using

fundus colour pictures

Solution

Observe the pictures

and in particular the

vessels around the optic

nerve to look for

abnormal thin vessels

Identify the area of

interest within the

image, and create a set

of characteristics that

will explain the disease

Build model using

derived metadata. Test,

train and prove model

Data: Structured, Unstructured, Created, Imagery,

Medical

The truth is out there


• Be clear on the business question…but don’t

limit your imagination

• Ensure you have the right legal permissions,

giving customers clarity and understanding

• Invest in the right tech to meet your current

and future need, not because it’s shiny

• Look for the pattern in the stars,

you never know what you might

find!

Edinburgh Office:Argyle House, 3 Lady Lawson Street, Edinburgh EH3 9DR

London Office:18 King William Street, Monument, London, EC4N 7BP

Derby Office:Office 4, Sadler Bridge Studios, Bold Lane, Derby, DE1 3NT

T 0131 290 2300E [email protected]

/aquilainsight

@aquilainsight


cim digital summit 2016 - aquila insight: chris dobson's presentation

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