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Which Is Harder: Customer Centricity or Solving a Rubik’s Cube?

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There’s a fun 40-minute documentary on Netflix called “The Speed Cubers,” about people for whom competitive Rubik’s Cube solving is somewhat of an obsession.  And it holds some useful parallels for those who may find themselves responsible for solving their companies’ digital transformation and customer centricity challenges.

First, here’s some interesting Rubik’s Cube statistics:

  • There are more than 43 quintillion possible configurations of the Rubik's Cube… that’s 43 billion-billion, or 43 followed by 18 zeroes.
     
  • It takes the average person – those willing to stick with it – about three hours to solve the Rubik’s Cube the first time they try.
     
  • Speed Cubers are two orders of magnitude faster: the world’s record is just 3.47 seconds, solving The Cube in as few as 20 moves. (In the time it took you to read that sentence, there are people who have solved a fully-scrambled Rubik’s Cube.)
     
  • But as fast as humans are, a machine developed by engineers at MIT managed to solve a Rubik’s cube in just 0.38 seconds – almost 10 times faster than the fastest humans – using machine-learning, robotics, and two Sony® PlayStation Eye® cameras.

Logic and Process Prevail When Solving Complex Problems

Given that the number of possible “wrong” configurations outnumber the single “right” configuration 43 quintillion-to-one, how is it even possible unscramble a Rubik’s Cube so quickly? 

As with any multivariate mathematical problem, the solution lies in solving the most important variables first and using them to help extrapolate values for the smaller ones. 

Think about a jigsaw puzzle: you start by putting the corner and edge pieces into place, right? It is the same thing with a Rubik’s Cube, only the eight “corners” are those with three colored sides; the 12 “edges” only have two, and the six center pieces have only one (and it is handy to know that those are in a fixed position relative to one another.)

With those constraints in mind, Speed Cubers – human and machine – solve Rubik’s Cube configurations section-by-section, using one of hundreds of memorized sequences of moves they call “algorithms,” each corresponding to a different arrangement of colored squares on the cube. Relying on simple pattern recognition, when a Speed Cuber or a robot sees an arrangement they recognize, they instinctively respond with the corresponding algorithm, each bringing the cube one step closer to being solved. The more experience you have, the faster you can arrive at the solution.

43 Quadrillion Possibilities… But Only One Right Answer

What does mean to companies trying to solve complex decisioning problems? Though automated systems are able to perform calculations and solve problems far faster humans, there is one very important difference between unscrambling a Rubik’s Cube and making good business decisions: when unscrambling a Rubik’s Cube, there is only one right solution, namely, getting all six sides the same color.  But in business decisioning, there could a myriad of possible “solutions,” most of which are good, but only one is optimal.

In data-intensive industries like insurance and financial services, there are even more moving parts to a decision than just the 54 squares on a Rubik’s Cube.  For large corporations, it’s not uncommon to have tens of thousands of employees trying to make real-time decisions involving millions of customers and billions of dollars. Or supply chains that incorporate hundreds of SKUs, involving thousands of suppliers and shipping destinations.

Companies struggle with balancing costs, personnel, responsiveness, inventory, competition, and other business pressures, especially since most of them come at the expense of the others.

Because many of these functions are automated, at least at a departmental level, you would initially think that striking the right balance is a problem for the IT department; but you would only half-right: the rest of the solution lies with business users, people with working, front-line (or “heuristic”) knowledge of customers, suppliers, distributors, etc., to infuse the street-smarts needed for book-smart technology.

Just like using algorithms to solve a Rubik’s Cube, in the corporate world, business rules, analytics, and validated data must all work together – not just from one or two departments, but from across the entire enterprise – can help companies quickly find the “corner pieces” of the best possible solutions.

Keep the Customer at the Center

Of course, the end goal of all of this to enable companies to make smarter, faster, more profitable decisions surrounding customer relationships, for the duration of the lifecycle. This is achieved via what industry experts call “Customer Centricity,” synergizing your people, processes, and technology in a personalized manner around every customer, made possible by the new apex of decisioning, “Centralized Decisioning.”

It sounds costly and complicated but it’s really quite methodical, involving:

  • Unifying and mobilizing the enterprise: Employing a unified, scalable decision platform that weaponizes all information from across the enterprise, so holistic decisions occur at every customer touchpoint across the lifecycle.
     
  • Ensuring a personalized user experience: Hyper-personalized user experiences – like we all expect today, thanks to Amazon, Netflix, and Google – lead to higher customer satisfaction, which leads to improved customer retention… the key to extending the customer lifecycle, and maximizing share-of-wallet throughout.
     
  • Broadening and optimizing the use of knowledge assets that enables enterprises to re-use and leverage connected decision assets across the customer lifecycle. This gives companies the ability to create customized, target decisioning strategies that are consistent, transparent, and expandable as needed over time.

Simulation and Optimization: Know Before You Go

Once these assets are synergized, simulation and optimization come into the picture: they allow companies to quickly solve complex multivariate problems under a wide range of scenarios, adjust variables and parameters, and iteratively fine-tune a simulation to engineer the optimal outcome… not just “good” ones, but the “optimal” one.

Simulation and optimization are not new – they have been in the realm of data scientists and operations researchers for some time – but two important aspects are very different from tools of the past:

  1. The scope and speed of problem-solving is dramatically improved over previous generation offerings.  Today’s systems can solve far more complex problems, involving a much high number of variables, and virtually any sort of structured or unstructured data – in real-time – than was imaginable just a few years ago.
     
  2. Simulation and optimization are no longer the exclusive purview of data scientists and operations researchers:. The fastest growth in analytics is now occurring among business users, working both independently and in parallel with their in-house technical colleagues. 

All of these come together create a synergistic, upward spiral for the business: by empowering business users to manage their own analytics strategies, more scenarios can be explored, faster than ever before… which means higher odds of finding winning strategies.  This democratization of analytics also frees data scientists and operations researchers to focus their talents on bigger, higher-ROI strategies.  This is a win-win for companies and their customers, because moving significant portions of analytics strategy to the business side of the house results in the most customer-friendly possible plans and programs.

So, while solving your most challenging business and operations problems may feel like a Rubik’s Cube, rest assured that – armed with the right data, in the hands of the right people, processes, and technology – the perfect answer is a lot closer than your think.

FICO is a recognized leader in enterprise decisioning with a proven track record in helping clients across all industries achieve their digital transformation goals.  For more information on please visit our new Digital Transformation Hub. For industry-specific information, visit our our Centralized Decisioning solutions for financial services and insurance

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