The Era of Intimate Customer Decisioning #ficoworld

May 2, 2013

in Analytics, Business Rules, Data Mining, Decision Management, Strategy

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Next session for me at FICO World is one focused on where customer decisioning is going. John Rymer of Forrester began by presenting some of the major trends found in a recent Forrester survey conducted for FICO:

  • Customer modeling is getting much more sophisticated as companies try and understand large numbers of customers more precisely – segments to micro segments to individual perspectives
  • Focus on understanding customer behavior more quickly and responding more quickly is driving a focus both on real-time and on very rapid change, flexible technologies for treating customers
  • Investments in analytics and decision management technologies were #1 and #2 – these platforms are the next technology investment

This survey reinforces what Forrester has called the move to the age of the customer (following from an age of information).  The survey also found that a focus on existing customer was widespread with a focus on the  online experience for customers or expanding an existing relationship  (this one got the most top votes).  In a related study those companies with better than average customer experience out perform their competitors while those who lagged in customer experience did much worse than the average. Customer experience matters.

John was joined by PNC Bank, Boefly and ICICI Bank as a panel. Key points from the panel:

  • Customer-centric decisioning means making things simpler and less anxiety-inducing for the customer.
  • Privacy balanced with a sense of dialogue, a sense of fairness, is critical to getting new data out of customer like location data
  • There are lots of powerful new analytic tools and data sources that have tremendous potential. These offer real and immediate value in terms of customer-centric
  • The regulatory framework, and the current climate, is making adopting new information sources and analytic techniques harder
  • Marketing is important and relies on being able to predict what people will buy but existing customers are important and there its more about building a relationship
  • Even when trying to build a relationship financial services has to know what the best next product is as they don’t have all that many products to sell and focus will help direct the relationship
  • While new Big Data sources are potentially very powerful, financial services companies have a tremendous amount of data about actual transaction behavior that can, should, must be used first.Just because it’s not “Big Data” does not mean it’s not interesting!
  • Getting some of these new data sources like location data can be hard but you can keep collecting data at an ever more granular level in your own channels
  • Need to wrap analytics in a “don’t do anything stupid” layer that prevents things like ATMs or newbie call center reps selling a HELOC to a student or using the wrong language.  Might need to federate this rather than centrally manage it
  • There are clearly going to be regulatory issues with new data sources so if you think something inside the organization might be predictive then start collecting and monitoring it now as historical internal data is easier to get regulatory approval
  • Not clear why we are STILL having to remind people to use transaction data because its really predictive! Explosion of new data sources does not change this
  • Speed to deployment of changes varies – not everything requires the same degree of flexibility – understand that before making investments
  • In-memory and its ability to let analytics teams get feedback instantly is going to be a game changer.
  • Don’t ask technologists to make a business case – make sure you know what the business case is (check out this post or this webinar recording for instance on using decision requirements models for this)
  • The move to real-time and multi-channel decisioning is pushing people to ubiquitous deployment of decision management technology and analytic skills, driving a demand for resources and skills so organizations must invest in developing these skills internally and re-learn how to run these kind of development and training programs.
  • Layering a software infrastructure onto these problems can abstract some of the technical difficulties away to improve scalability
  • Feels like business, IT and analytic teams speak 3 different languages and that has to be resolved for success
  • A fast moving space like Decision Management requires an ongoing and dynamic approach to technology and analytic adoption – can’t just plan at the beginning of the year, must respond to what you see happening

A great panel with lots of good tips.

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