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.

1. Innovation and Transformation in
Financial Services
Create a 360-degree view of your customers

2. 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.

3. 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?

4. 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?

5. 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?

6. 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%

7. 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.

8. The Customer Life Cycle

9. 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.

10. 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

11. 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%.

12. 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.

13. 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”