Customer-centricity starts

with Data Quality

Customers and data

Introduction and what to expect:

Data quality 101

Data quality frameworks

Making the most of 1st, 2nd, and 3rd party data

Practical experiences

Introductionand what to expect…

Introduction

Big DataOmni-channel

Real-time

Seamless Integration

Low Hanging Fruit

Transparency

What not to expect!

Introduction

Data

Quality101

“Degree of excellence exhibited

by the data in relation to the

portrayal of the actual scenario”Wikipedia

Data Quality…

“The state or desired state at

which data assets realise their

maximum potential”Marketsoft

Data Quality…

Data Quality…

“Data is the new oil” Clive Humby

The good

Data Quality…

Best Practice

Data Quality Best Practices

boosts revenue by 66%

Goes a Long Way

If the median Fortune 1000 company were to

increase the usability of its data by 10%

company revenue would be expected to

increase by $2.01 Billion dollars.

Data Quality…

The bad

Dirty Data Barrier to Entry

$600B

The cost of bad or ‘dirty” data

exceeds $600 Billion dollars

for US businesses annually. of survey respondents

cite Data Quality as a

46%

for adopting BI/Analytics

products.

BARRIER

Data quality has impacts across this entire journey

Data Quality…

Strategic Journey

Segmentation

Lifecycle

Timing/frequency

Platform(s)

Device

Treatment

Message

Offer

Quality

Data

QualityFramework

Traditional approaches

1. Do nothing (despite the risk)

2. Enterprise data quality

3. Ad-hoc using common tools (Excel)

4. Outsource

Framework…

Data Quality Maturity

Framework…

Valu

e

Difficulty

Ad-hoc

Reactive

Defined

Proactive

Predictive

Awareness

1st Party Data

Marketing

• Affinities

• Behaviors

• Location

• Identity

Business

• Propensities

• Experiences

2nd Party Data

Partner

• Affinities

3rd Party Data

External

• Demographics

• Missing data

Data Quality implications…

Consideration

1st Party Data

Marketing

• Triggers

• Segmentation

• Value

Business

• Suggested

Treatments

2nd Party Data

Partner

• Personalisation

3rd Party Data

External

• Event triggers

Purchase

1st Party Data

Marketing

• Experience

• Cross-sell/upsell

Business

• ROI

2nd Party Data

Partner

• Optimisation

3rd Party Data

External

• Credit

• Risk

Advocacy

1st Party Data

Marketing

• LTV

• Next Best Offer

Business

• Customer

experience

• Support

2nd Party Data

Partner

• Influence

3rd Party Data

External

• Influence

• Nurturing

1st Party Data

Marketing

• Analytics

• Marketing

automation

• CRM

Business

• Sales

• Customer

service

2nd Party Data

Partner

• Partner CRM

• Partner

Analytics

3rd Party Data

External

• <various>

Customer JourneyData Points

Framework…

Discovery,

Benchmark and

Audit audit and benchmark

to assess current state

against desired state

Matchingfuzzy and analytical

matching

methodology

Monitoringreporting,

dashboards,

and KPIs

Batch & Real-time

deployment

Standardisation

data point and

field level

Map your data assets – data type, ownership, governance

• Owned marketing data

• Customer/CRM data – name and address, phone, email,

activities – affinity groups, conversion path

• Analytics – model scores, customer journey

• Marketing automation – behavior, engagement

• Owned business data

• Sales data – transactions - LTV, Risk class

• Customer service – sentiment, trends, leading indicators

• 3rd Party

• Data appends

• Census, ABS, macro data

Framework…

Discovery, Benchmark and Audit

Define a customer journey – arbitrarily or analytically

Map data assets to customer journey

Cluster by size and importance to customer experience

Audit and Benchmarking

1

2

3

4

5

Outcomes – Discovery, Benchmark and Audit for each data point…

1 Consistency

2 Completeness

3 Accuracy

4 Precision

5 Missing or unknown

Types of Data Quality Considerations…

Framework…

Discovery,

Benchmark and

Audit audit and benchmark

to assess current state

against desired state

Matchingfuzzy and analytical

matching

methodology

Monitoringreporting,

dashboards,

and KPIs

Batch & Real-time

deployment

Standardisation

data point and

field level

Reporting, Dashboard, and KPIs

In order to move beyond symptom treatment to a proactive approach,

a consistent monitoring layer is critical:

Types of Data Quality Considerations…

General data point changes

over time

Validation changes to benchmark

Data coverage of key fields

to benchmark

Key shifts in data consistency

Qualitative outcomes

PracticalExamples

Reactive

Case #1

Scenario: Digital marketing database used for automated email marketing

Data: Single database, digital focus, data quality never assessed

Practical examples

Data Quality Outcomes

1. Inconsistency: Identified and cleansed 10 fields with data variable

inconsistencies due to manual data capture

2. Incompleteness: Filled data gaps in key fields including title/gender,

address elements, and phone numbers

3. Accuracy: Identified 7% of hard-bounce email addresses; identified

15% of people who had changed companies based on public social

sources

4. Precision: 7% Duplication; also unrealised potential to segment at a

family, address, or true company level

5. Missing or unknown: 0.5% deceased customers; geo-demographic

profile information used to augment segmentation model

Practical examples

Ad-hoc

Case #2

Scenario: B2B sales focused organisation with new large, “thin”

prospect database, yet limited sales capacity to action

Data: Several new B2B prospect databases,

existing customers and pipeline

Practical examples

Data Quality Outcomes

1. Inconsistency: Identified key inconsistencies across the new

prospects, including level of company information provided

2. Incompleteness: Gaps in company details in known operating markets

3. Accuracy: Identified issues with key variables such as Employee Size

and applied model to improve accuracy by 43%

4. Precision: 80% Duplication; also unrealised potential to segment at a

true company level and to show parent to child company relationships

5. Missing or unknown: Appended and modeled key variables such

including employee size, annual revenue, operating markets, HQ

market, revenue velocity

Practical examples

KeyTakeaways

1 Define your objectives

2 Understand your data assets

3 Tie everything back to a customer experience

4 Establish a framework

5 Find a trusted data partner

Takeaways…

Thank you

“The state or desired state at which data assets

realise their maximum potential”

Marketsoft