Progressive customer engagement
Many companies have ambitions to reflect their customers evolving relationships with their brands in intelligent and persuasive ways.
These companies are often let down by the way they design the data required to present those experiences, ultimately leaving it to technology to write the narrative.
For one retail client, the difficulty came in trying to understand how to move from a antiquated Web Commerce profiling, i.e. Anonymous, recognised and signed in customer states; and for another client in Wealth Management, it was about exposing unknown-knowns and where to begin in trying to understand the way customers think about investing.
Many large enterprises are endeavouring to make their Customer Experience capacities more intelligent applying a scalable architecture to them. Customer profiles are becoming data driven rather than the isolated pursuit of marketing.
The onset of big data and machine learning means that the intelligence required to construct an appropriate experience is no longer the isolated pursuit user experience capabilities.
The tri-polar responsibility for this problem is yet to be triaged in a cost effective way. Until then, it is the responsibility of UX to:
- Bridge gaps between customer experience and big data capabilities
- Ensure that solution architectures are conducive to scaling the problem
- Provide a framework within which big data can be responded to in an intelligent manner.
Some shorter term examples
Design processes for a wealth management client
The process started with audits of the following:
- Online login and registration processes
- IVR login and registration support processes
- Paper-Based registration.
It soon became evident that engaging customers to register their interest late in the investment research process or at the point of actual investment process was ill-considered. This conventional way of addressing the relationship missed opportunities in cementing the ground in the customers mind set and allowed the competition to have an influence. So as part of further exploration we started to examine:
- Market research
- Studies regarding investment behaviours
- Widened experience mapping.
For the wealth management client there were two major outcomes:
- A framework that allowed for the graceful escalation of issues such as sign in errors based on the customers relationship with the business
- And as an initial step, we worked with the solution architects to introduce a 'prospect' customer classification.
Design process for a major retail client
The process started with:
- Discovering what an idealised profile of a loyal customer and advocate of the brand would look like
- Interpreting that profile into a vision schema and future proofing a service around it
- Ensuring that developers don't create any new data points or schemes that contradict that vision schema.
Once that vision had been established and the risk of anything contradicting it mitigated, we:
- Looked the various customer planning my purchase and post order missions and tasks
- Mapped useful data points from the vision schema along those missions.
For the retail client, because of their adoption of SAFe for development the longer term thinking was as follows:
- Mapping of the optimal trajectory of a customer of the course of 18 months from a state of not ever having interacted with the brand to one of being a loyal brand advocate
- Determining what features allow that trajectory to be fulfilled and identifying how the customer can be nudged into providing some details in the context of that feature.
In the longer term
"If this problem sound familiar and your organistion could benefit from a systematic and scalable approach to tackling the relationship between UX and Big Data, get in touch".
- Leading and facilitating workshops and focus groups
- Stakeholder and client relationship management
- Feature prioritisation and product roadmap development
- Cross channel analysis
- Contact centre eavesdropping
- Task modelling
- Online analytics
- Content strategy
- Scamping and conceptual modelling
- Behavioural design
- User story mapping
- Data design
- Scenarios, User journeys and storyboarding