Presented by James McHale
Regional community banks and credit unions understand they must outperform their national brand competitors when it comes to fully engaging their customers -- but sometimes overlook their direct and/or online-only banking counterparts. This session showed how advanced analytics tools provide unique actionable insights which, when coupled with a sustained marketing strategy, help them compete more effectively to maximize customer acquisition, engagement and retention -- and most importantly profit.
Baker Hill Prosper 2017 - Grow, Optimize, Protect: Using Business Intelligence and Marketing to Get a Bigger Slice of the Pie
1. GROW, OPTIMIZE, PROTECT: USING
BUSINESS INTELLIGENCE AND
MARKETING TO GET A BIGGER SLICE
OF THE PIE
James McHale, SVP/GM Analytics at Baker Hill
Bryan Thomas, VP of Lending at On Tap CU
3. Confidential & Privileged Document
According to Forbes, 87% of companies think big
data will make big changes to their industries
before the end of the decade. Even more think
that not having a big data strategy will cause
their companies to fall behind.
“Only the most informed and profitable will
survive and prosper …”
4. Confidential & Privileged Document
Profit risk management
Profit Risk: concentration of income statement
vs. distribution and diversification of income
streams
When profit risk is minimized, income volatility
is mitigated and income and capital are
sustained and grow, and the financial institution
remains viable.
5. Confidential & Privileged Document
Five most common uses of big data
1. Fraud Detection
2. Compliance and
Regulatory Requirements
3. Customer Segmentation
4. Personalized Marketing
5. Risk Management
6. Confidential & Privileged Document
Your call to action
1. Get your integrated database system up and
running
2. Start to understand profitability dynamics in
detail
3. Evaluate your income statement based upon
achieving sustainability
7. Confidential & Privileged Document
Your call to action
4. Define acceptable “Profit Risk” concentration
levels
5. Set “Profit Risk” objectives and goals to
spread profitability over a greater spread of
customer/member relationships, products,
markets, branches and officers
8. Confidential & Privileged Document
Your call to action
6. Establish “Profit Risk” discipline
7. Develop “Profit Risk” strategies and tactical
plans
8. Define institutional success based upon
increased profitability, earnings and capital
growth
9. Confidential & Privileged Document
What your institution will gain
1. Improved identification of market
opportunities
2. Higher levels of acquisition, retention
and protection of market share
3. Higher, irrefutable return on
investment with respect to revenue
capture
10. Building our future – Strategy shift to On-Tap CU
10
• Perceived Exclusive Field of
Membership
• Aging Membership
• Strategic Directional Change from
Billion Dollar Brand
11. 11
Organizational ScoreCard Elements:
• Millennial Wallet Share – Products
& Services
• Millennial Membership Growth
(Overall)
Building our future – Strategy shift to On-Tap CU
12. Marketing and growth initiative
12
• Opportunity: Target
Millennials
– Certificate of Deposit
• Cross sell to Millennial group without CD (3,873
Relationships)
– When a Millennial has a CD in relationship:
Average Profit per Relationship increases
1,290%
Cross-Sell Service Ratio increases 158%
Cross-Sell Account Ratio increases 278%
13. Confidential & Privileged Document
Marketing and growth initiative
• Deepen Indirect
Relationships
– New Interest
Rewards
Checking
– New Online
Banking
Solution (Q2)
13
Notas del editor
Customer Segmentation
Banks have been under pressure to change from product-centric to customer-centric businesses. One way to achieve that transformation is to better understand their customers through segmentation. Big data enables them to group customers into distinct segments, which are defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their homes. Promotions and marketing campaigns are then targeted to customers according to their segments.
Personalized Marketing
One step beyond segment-based marketing is personalized marketing, which targets customers based on understanding of their individual buying habits. While it’s supported by big data analysis of merchant records, financial services firms can also incorporate unstructured data from their customers' social media profiles in order to create a fuller picture of the customers' needs through customer sentiment analysis. Once those needs are understood, big data analysis can create a credit risk assessment in order to decide whether or not to go ahead with a transaction.
Customer Segmentation
Banks have been under pressure to change from product-centric to customer-centric businesses. One way to achieve that transformation is to better understand their customers through segmentation. Big data enables them to group customers into distinct segments, which are defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their homes. Promotions and marketing campaigns are then targeted to customers according to their segments.
Personalized Marketing
One step beyond segment-based marketing is personalized marketing, which targets customers based on understanding of their individual buying habits. While it’s supported by big data analysis of merchant records, financial services firms can also incorporate unstructured data from their customers' social media profiles in order to create a fuller picture of the customers' needs through customer sentiment analysis. Once those needs are understood, big data analysis can create a credit risk assessment in order to decide whether or not to go ahead with a transaction.
Customer Segmentation
Banks have been under pressure to change from product-centric to customer-centric businesses. One way to achieve that transformation is to better understand their customers through segmentation. Big data enables them to group customers into distinct segments, which are defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their homes. Promotions and marketing campaigns are then targeted to customers according to their segments.
Personalized Marketing
One step beyond segment-based marketing is personalized marketing, which targets customers based on understanding of their individual buying habits. While it’s supported by big data analysis of merchant records, financial services firms can also incorporate unstructured data from their customers' social media profiles in order to create a fuller picture of the customers' needs through customer sentiment analysis. Once those needs are understood, big data analysis can create a credit risk assessment in order to decide whether or not to go ahead with a transaction.
Customer Segmentation
Banks have been under pressure to change from product-centric to customer-centric businesses. One way to achieve that transformation is to better understand their customers through segmentation. Big data enables them to group customers into distinct segments, which are defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their homes. Promotions and marketing campaigns are then targeted to customers according to their segments.
Personalized Marketing
One step beyond segment-based marketing is personalized marketing, which targets customers based on understanding of their individual buying habits. While it’s supported by big data analysis of merchant records, financial services firms can also incorporate unstructured data from their customers' social media profiles in order to create a fuller picture of the customers' needs through customer sentiment analysis. Once those needs are understood, big data analysis can create a credit risk assessment in order to decide whether or not to go ahead with a transaction.
Customer Segmentation
Banks have been under pressure to change from product-centric to customer-centric businesses. One way to achieve that transformation is to better understand their customers through segmentation. Big data enables them to group customers into distinct segments, which are defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their homes. Promotions and marketing campaigns are then targeted to customers according to their segments.
Personalized Marketing
One step beyond segment-based marketing is personalized marketing, which targets customers based on understanding of their individual buying habits. While it’s supported by big data analysis of merchant records, financial services firms can also incorporate unstructured data from their customers' social media profiles in order to create a fuller picture of the customers' needs through customer sentiment analysis. Once those needs are understood, big data analysis can create a credit risk assessment in order to decide whether or not to go ahead with a transaction.
Customer Segmentation
Banks have been under pressure to change from product-centric to customer-centric businesses. One way to achieve that transformation is to better understand their customers through segmentation. Big data enables them to group customers into distinct segments, which are defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their homes. Promotions and marketing campaigns are then targeted to customers according to their segments.
Personalized Marketing
One step beyond segment-based marketing is personalized marketing, which targets customers based on understanding of their individual buying habits. While it’s supported by big data analysis of merchant records, financial services firms can also incorporate unstructured data from their customers' social media profiles in order to create a fuller picture of the customers' needs through customer sentiment analysis. Once those needs are understood, big data analysis can create a credit risk assessment in order to decide whether or not to go ahead with a transaction.