Front-Office Empowerment: Analytical Workplace

Traditional technology with focus on repetitive automation cannot do the trick – but analytics to understand and gain insight into vast client segments and portfolios, paired with self-learning algorithms and machine-learning based recommender engines, will make a difference!

Ask us about…
…detecting and preventing churn
…identifying cross- and upselling potential
…automate marketing efforts with self-learning selections
…track distributed campaign activities

Are you asking yourself

  • Are you facing an inability to target customers with the highest chance to buy, due to large amounts of customers per RM (Retail / Affluent often over 500 clients per RM)?
  • Do you suffer from disconnected data pools and lack of front-optimized analytics tools?
  • Do you miss real-time insight forindicators of customer needs on digital channels?
  • Are you informed about key client events after the fact?

What you will get from us:

  • Over factor 3 lift of conversion rate with ML based, real-time marketing campaigns
  • 60% RM time saving for meeting preparation (around 0.5-1 million year/10 FTE)
  • No customer meetings without known cross or upselling potential upfront
  • Private bank customer service experience, but at internal cost below current retail banking relationship management
  • Prevent churn as you stay knowledgeable about your customer, despite less physical touch points
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Our Approach

  • Customizable, web-based GUI for flexible arrangement of key insights for the relationship manager

    Customizable, web-based GUI for flexible arrangement of key insights for the relationship manager, on all types of devices (incl. mobile and tablet), with the ability to include external sources of information (API-based)

  • Data & Analytics ETL (Extract-Transform-Load) stack

    Connection of broad range of data sources and leverage of different storage types, incl. real-time streaming especially of customer communication across channels, external market insights and social media Cognitive computing to build context between data and extract content or sentiment Machine learning libraries that allow classification of cases into event types, and corresponding recommender engines to use collective intelligence for next best actions.

  • Real-time alerting to RMs

    Real-time alerting to RMs, weighted by deal size and scored by chance to win - with process automation to track opportunities.

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