Innovation and Transformation in

Financial ServicesCreate a 360-degree view of your customers

Challenges Facing Financial Services

Historically, financial services firms have struggled to target and tailor

their product offerings to the customer journey. Often only traditional

demographic information – gender, age, occupation – is collected with

no real insight as to what life stage a customer is in and how this could

influence their financial activity.

To compete in a consumer-empowered economy, it is increasingly clear

that financial services firms must leverage their information assets to

gain a comprehensive understanding of markets, customers, channels,

products, regulations, competitors, suppliers, employees and more.

Volume: Scale of Data

Technology and accessibility is rapidly

changing business processes.

How do you ensure Data Quality across

so many rapidly growing data sources?

Variety: Different Forms of Data

The variety of data is vast. From

transactional and social data, to enterprise

content, as well as contextual data derived

from sensors and mobile devices.

How do you achieve consistency across

all your data silos with so many

different frameworks in play?

Accuracy: Uncertainty of Data

Data inaccuracy is a major source of cost

for organisations. Duplications,

inconsistencies and incomplete information,

often result in wasted time spend reviewing,

cross-checking data and bad decisions.

Can you manage identification,

ownership and remediation of Data

Quality?

And track the cost to your enterprise?

Top challenges preventing organisations making better use of

customer analytics.Which are challenging you?

Managing and integrating data from a

variety of sources

Ensuring data quality from a variety of

sources

Getting staffing and management

commitment for analytics projects

Communicating and interpreting analytics

results

Finding the right kind of analytics talent

54%

50%

42%

38%

37%

The 360-Degree View

A 360-degree customer view gives

financial services firms the power to

truly understand what will be front of

mind for customers when it comes to

their financial decisions.

With this information it becomes

easier to predict behaviours and

recognise what products will be best

for a customer at a particular life

stage.

The Customer Life Cycle

Personal vs Customer Relationships

Diagram adapted from: http://www.slideshare.net/AnthonyBotibol/intelligence-versus-wisdom-the-single-customer-view

Human relationships need human memories.

This diagram shows how personal relationships

can be defined on a customer level within a

business.

Creating a 360-degree view of customers

requires getting to know them on a personal

level so you can cater your business

information to their specific requirements.

Why Information Management?

By enabling enterprises to organise, interpret and use the right data to glean the right

insights about a certain individual, Information Management (IM) helps create a truly one-on-

one encounter for a customer.

Things to think about when building a data management system:

• Data Sources

• Data Standardisation

• Data Validation

• Data Quality

• Matching segments

• Deduplication

Case Study: Insurance Company

A Fortune 500 insurance company with an annual revenue of $22.4B were facing some key

challenges in their underwriting process

• Data was being pulled from multiple sources

• There was an incomplete view of what their customers looked like

• The speed of the underwriting process was inefficient

The company concluded that they needed to create an enriched single customer view.

By condensing customer information from database, cloud and web sources, the company

created a Unified Data Layer that fed into a desktop application from which a network of

underwriting agents could access customer information.

The result was a decrease in time taken for underwriting decisions by 66%.

Case Study: Financial Institution

A prominent financial institution realised immediate benefits after an initial deployment of the Certus Data Quality Framework.

Starting with an implementation of the first five business rules against their customer data, they identified data quality errors with a potential business impact of over $1.3 million and a cost to remediate (to target) of less than $5,000.

Quantifying the financial impact of these data quality issues and making them visible to senior management gave the IT team business case justification for rollout across all the company’s data, plus the engagement of the business in the remediation of the data quality issues.

Case Study: Financial Services Company

A super fund that manages over $60 billion in retirement funds felt they could do more to increase the

efficiency and effectiveness of their customer conversations. They found that while structured data is great

for the initial customer segmentation process, these segments were still too large to personalise their

conversations with individual customers.

The organisation consolidated unstructured comments from previous interactions with the structured data in

these segments to created a unified view of each customer account.

With structured and unstructured data now consolidated in one place, the representatives can focus more

on having engaging conversations with their clients, as opposed to searching for client information while

they speak.

“Our business is growing exponentially and you can’t always just increase staff so

we have to use what we’ve got but just more efficiently and effectively and this

[Certus’ Data Quality Framework (DQF)] has allowed us to do that”