It is hard to identify anyone in the sector who has not faced challenges during the turbulence since 2008. Whilst for many there is optimism that this is the year of a return to more stable times, for some, the choppy ride continues. Amongst the challenges and opportunities you face are:
- Rising customer expectations of the flexibility and personalisation being delivered by your competitors and other sectors.
- No let up in the continued attention by the regulators.
- Rapid shifts in customer behaviour, such as the 36% decline in branch usage seen by RBS in 5 years.
- Rising levels of fraud coupled with a major shift in its form.
- New challengers from both within the sector and outside of it including Apple Pay, ClearScore and others.
Presidion can enable you obtain and action insights from your data, to improve the balance sheet, achieve growth and avoid reputational risk.
Featured Solution
Marketing and Customer Experience
Embrace the power of Advanced Predictive Analytics to provide differentiated and personalized customer experience. Use a holistic analytical marketing approach and a comprehensive CRM strategy that will support decision making, optimization and automation across different marketing activities and CRM operations in financial institutions.
- Use Enterprise Data to leverage customer intelligence and personalize customers banking experience and satisfaction.
- Reveal customer insights to identify new marketing opportunities and effectively address customer needs in real-time.
- Develop financial products or services tailored to banking behaviors.
- Optimize offers by determining Next Best Action for individual customers, and drive profitability by presenting the right offers, in the right channel, at the right time.
- Understand the factors behind customer acquisition, loyalty and retention and reduce churn.
- Combine Enterprise Data, Big Data, Social Media, Text Analytics and Social Network Analysis to predict major CRM events and resolve issues using Advanced Predictive Analytics applications for Marketing and Banking.
Case Study
Addressing Customer Satisfaction Issues in Real Time.
- Production cycle reduced by two thirds thereby freeing analyst’s time to focus on strategic initiatives
- Ability to efficiently parse the thousands of verbatim responses collected each cycle
- Ad-hoc requests from internal customers can now be turned around in hours rather than days
Case Study
- Fiserv clients are experiencing anywhere from 50 to 60 or more percent savings on the costs side, on the processing side.
- For Fiserv, one of the main values of smarter computing is the ability to now start to build out solutions that weren’t possible a few years ago.
White Paper
Customer Analytics in Banking - Four Steps to Maximize Customer Profitability
In this white paper, you will learn how your financial services organization can use analytics to understand customer profitability and lifetime value across all products and business lines!
You will read about:
- Step 1: Consolidate customer information
- Step 2: Predict what customers want
- Step 3: Personalize customer interactions
- Step 4: Optimize your predictions
Use Cases
1. Gain Customer Insights
Consolidate all available information from different sources of data, inside or outside the organization, into a single structured and detailed set of marketing customer attributes and key performance indicators. This includes Financial Positioning, Product Ownership, Transactional Behaviour, Channel Preferences/Usage, Profitability and Customer Value, Credit Risk Information, Investment and Loyalty Profile.
Benefits
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Case Study
First Tennessee Bank - Banking on Knowledge See how First Tennessee Bank achieved a:
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2. Improve your Customer Base Segmentation
Develop segmentation schemes that divide customers into useful and actionable segments and reveal customer insights by exploring different aspects of customers banking behaviour (spending patterns, demographics, channels preferences, transactions activity, customer value index) during customer lifecycle.
Design marketing Analytical Strategies and match profitable products, based on customer segments to increase sales and generate profits by focusing on individuals’ customer banking needs.
Benefits
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Case Study
Union Investment GmbH
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Union Investment Benefits:
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3. Optimize Offers and Marketing Efforts
Maximize profitability by using Advanced Predictive Analytics and propensity models for Banking to identify best prospects for new product offerings, increase existing products/services usage or substitute existing products and services with new more profitable ones.
Predict the response to an offer and minimize marketing costs by determining customer Next Best Action or offer, weighted on banks’ profitability metrics and individual customers’ risks scores and assessments.
Combine data from Social Media for accurate targeting or Combine Social Network Analysis with propensity models to uncover customer’s relationships inside the organization and focus on targeting customers that can influence other customers as well.
Benefits
- Helps to understand the profile of customers who are best prospects for offers and provides recommendations on product and services
- Provides personalized and differentiated Offers based on Customer Profile, profitability index and risk adjustments
- Optimizes campaigns’ response rates and minimizes marketing costs
- Supports Sales processes, drive profitability and optimize customer lifecycle value
Case Study
Getin Nobel Bank Personalizing offers to meet customers’ specific banking needs raises savings deposits by 20 percent. Benefits:
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Rabobank Leading bank uses Predictive Analytics to lower costs and generate higher returns on marketing campaigns. Benefits:
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Case Study
DekaBank See how Predictive Analytics is helping DekaBank to:
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ANZ Bank ANZ Bank leverages IBM Big Data & Analytics to gain a comprehensive view of their customers & their needs |
4. Improve Customer Retention, Acquisition and Loyalty
Adopt a proactive analytical approach to identify the risk factors that influence customer acquisition and retention, and prevent churn effectively, early in time to enhance customer loyalty.
Determine early warning signals such as a reduce in transactions volume, in credit spending or in deposit balances and send customized offers to the people most at-risk of churn.
Combine Customer Insights from Segmentation models and offers optimization to develop targeted retention campaigns.
Combine Social Network Analysis in Churn Prediction to uncover customer’s relationships inside the organization and proactively improve retention rates.
Benefits
- Reveals changes or patterns in behaviours, indicating factors which may cause disloyalty or churn
- Predicts churn and analyse risk indicators early in time with confidence
- Develops effective and personalized customer retention strategies and improve products or services design to enhance customers’ loyalty
- Examines Customer Service and Satisfaction, by using Text Analytics and analyse customer interactions such as Branch visits or call centre calls
- Identifies the reasons behind customers’ disloyalty inside the organization
- Helps to Understand previous successes and challenges in trying to attract more and better customers
Case Study
First Tennessee Bank - Winning New Business Online
See how First Tennessee Bank is winning new business online with streamlined customer journeys across all devices.
Other Solutions
Complaint & Claim Handling
Regulators continue to review not just historical issues, but also agreements subsequently made in relation to them. Coupled with this, with access to Social Media, the Voice of the Customer has never been a more powerful force:
- Understand how to prioritise complaints and claims, to optimise resources and deliver outcomes which achieve higher customer satisfaction
- Prevent reputational damage by focusing resources on those issues and customers, where the risk is greatest.
Fraud Detection, Investigation and AML
Money launderers and fraudsters continue to work night and day, shifting to channels offering the greatest opportunities:
- Move beyond rigid, historical, rule based detection approaches to analytics approaches that learn from the data, to identify high risk transactions in real time.
- Utilise law enforcement and national security agency grade technology to investigate cases
Case Study
Statistical modeling used to identify hidden predictors in loan applications, reduce defaults and increase capital reserves.
Benefits:
- Significantly contributed to the increase in the bank’s capital reserve by reducing loan defaults
- Reduced loan defaults with increased accuracy of predictive models, pinpointing low-risk applicants and high-risk accounts
- Increased the accuracy of revenue forecasts by predicting the impact of changes in loan payments as well as fluctuations in the finance industry
Case Study
Bancolombia achieved a 40% improvement in the quality of it’s suspicious transaction reporting.
- Achieved a 40% improvement in the quality of its suspicious transaction reporting
- Productivity savings of nearly 80% while increasing reporting by 200%
- New flexibility in adapting its models to meet rapidly changing money laundering techniques
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