Global Bank HNW and UHNW Customers

Insights to Outcome through real-time Innovation, Learning and Work Orchestration


This article is about how Work Orchestration was used to engage with HNW and UHNW customer interactions and sentiment by using AI and User Experience Research to define the Customer North Star for each market segment. The transparency created had not existed before and provided a key insight into customer intent and expectations in a clarity that had not been possible before.

Problem Statement

Although the bank was highly effective in onboarding new HNW and UHNW customers, existing customers would move within 2-3 years. Various reasons were provided as the the impetus for such behaviour but there was no clear data to inform decisions or monitor the impact of change.

The purpose of this implementation was to reduce the customer attrition rate.

Work Orchestration Ecosystem

One of the essential aspects of this Work Orchestration was to ensure communication between the Business and Technical Groups through effective Customer Requirements Mediation. Meaning and priority is often lost in conversations and email whereas in a chain of custody from cradle to grave orchestration everything remains accessible.

Work Orchestration is the coordination of complex processes (people and assets) and workflows to ensure they are executed in the correct order and with all their dependencies understood, mapped and risks mitigated (or accepted).

Customer Agility Framework ™ Work Orchestration

Customer Agility (simplest adoption) mid level fidelity


There were also massive issues around the team locations and time zones from the Business in APAC to Technical Leadership and Solutions in EMEA and Technical Delivery SA so coordination was critical.

AI and User Experience Research

Artificial Intelligence (AI) was used to seek patterns in customer (interaction and sentiment) data as opportunities, issues and risks. Then this WHAT data is refined through User Experience Research techniques to understand WHY (context, human perceptions and human value) they are important.

WHAT and WHY two methods have existed as long as there have been data research, they are Quantitative and Qualitative research, they are not only complementary they are essential to avoid making the wrong decisions. If data leads without human context, well I expect you can imagine, one leg chairs may become the standard manufactured item based on cost, but would be unusable based on the human lense. Further bias is a real problem in both data and humans thankfully User Experience Research has built in bias control methodologies to eliminate personal agendas and cultural bias.

While several AI’s were considered the POC route was to utilise Natural Language Processing to review customer sentiment across various social and website data, Expert Systems to define facts and rules and Fuzzy Logic to refine the key concepts, opportunities and risks in a singular data set. This data was then tested with target audience test groups to confirm resonance with the themes and be able to garner customer solutions to retest the AI data.

Customer Agility Framework ™ AI and User Experience Research

Customer Agility (simplest adoption) mid level fidelity

The resulting combined data was used to define the top 60 Customer Requirements to define the Customer North Star. This was then added into the Work Orchestration software as the Golden Source for all work. Business North Star and Technical North Star were based upon Business Targets and Strategy Documents. Each North Star was used to create a Persona with clear requirements and priorities. It was noted that each of these Personas had a very specific perspective which defines their action, reactions and values.

Customer Agility Framework ™ North Stars

Enterprise Agility mid level fidelity

The essential point of doing this was to define and prioritise the work to be undertaken and the Customers timeline and capability expectations of the work. It did not limit or eliminate other work but it did require all work to be linked to a Persona and to be confirmed with exactly what Customer Requirement, Business Requirement or Technical Requirement outcome it was delivering or underwriting.

It was also required to define how success would be measured using Brand Equity, Financial Impact, ROI, VFM, CSAT, NPS, ESG, PRI, Market Sentiment, Analyst Community, Shareholders or the Customer North Star in line with the Insights source data.

Overall Impact

The value in this approach was several fold

  • Clarity over work intent enabled clear conversations around priority
  • Clarity over intent enabled clear conversations around what was required to meet that intent and what was possible given the existing systems and capabilities, it also supported up skilling, dealing with technical debt and new integrations and technical platforms as a direct relationship between costs and outcomes could be shown
  • Reduction in unplanned work eliminating wasted time and finances
  • Go to market change from 18 months to 3 months for work using work orchestration
  • Customer sentiment gradual change as part of the POC
  • 3% uplift in 1 month and 8% in three months as perceptions can be hard to change once customers have had poor experiences and told their friends and family

Discovery: It was found that the use of AI and User Experience Research clearly defined “What work should be done” and secondly “How can work be qualified as impactful” for a Customer North Star in real-time.

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