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Drowning in Data, But Thirsting for Actionable Customer Insights

Sometimes we get so focused on short-term analyticssimulationoptimizationdecision modeling, and decision management. we lose sight of the long-term, sometimes unseen goal: the life-time value (LTV) of today's smart customer decisions.  We are drowning in data, but thirsting for insights that will fuel customer satisfaction and retention.

It reminds me of one of the more intriguing stories from the 1620 voyage of The Mayflower, the ship that brought the original 102 pilgrims from England to the New World, and a little-known incident that occurred when a young passenger named John Howland was washed overboard during a violent gale. He was presumed dead, but later a crewmember spotted Howland bobbing in the sea, holding onto a rope, and being dragged behind the ship. Despite the danger to himself, the crew member was able to reel Howland back aboard The Mayflower.

Drowning in data, data modeling, analytics, simulation, optimization, and decision management

Howland made the most of his new lease on life: once in America he was incredibly prodigious, with more than 10 million U.S. descendants… including  three U.S. Presidents (Franklin Delano Roosevelt, George Bush and George W. Bush), Governor Sarah Palin, religious leader Joseph Smith, poets Ralph Waldo Emerson and Henry Wadsworth Longfellow, pediatrician Benjamin Spock, PhD, and actors Humphrey Bogart, Chevy Chase, Christopher Lloyd, William H. Macy and the Baldwin brothers (Alec, Stephen, William, and Daniel.)

Ship logs didn’t record the crew member’s name, but his decision to spring into action and save Howland’s life – with no knowledge of the future implications – changed world history for forever. Had Howland been able to anticipate his near-death experience, he could have taken appropriate safety precautions; but in this instance, he owes his good fortune to sheer “dumb luck.”

Today’s Actions are Tomorrow’s Results

Businesses can experience dumb luck too, both good and bad.  In the business world, we aren’t always aware of the long-term consequences of our actions, and too often, we recognize future opportunities only until after we’ve missed them.  Retrospection and hindsight being 20-20, we would all love to go back in time and invest in prospects that pay off handsomely – like buying Amazon stock at $18/share – if only we could have known then what we know now

Companies don’t generally make life-or-death decisions, but they do have to decide which of dozens of possible business strategies live and die based on their future ROI.  The problem is, it’s hard to accurately predict what that ROI will be... and impossible to travel forward in time, analyze outcomes, and bring back the right decision with you.  Though we are drowning in data, we are thrashing about looking for solid customer insights to grab on to.

In the digital decisioning age, every company needs to make smarter, faster, and more profitable customer decisions than its competitors. Not just for the short-term ROI, but for the lifecycle-long revenue each customer potentially represents… as well as the many degrees of new business they might bringing in with referrals to friends, family members, and business associates over the life of the customer relationship.

But getting there isn’t easy.  According to recent research from American Banker and Digital Insurance, currently only about 5% of financial institutions and 20% of insurance companies have mastered forward-looking decisioning… though 85% are rapidly developing those capabilities, according to Accenture.

No Crystal Ball Yet, But…

Projecting the long-term future value of our business decisions isn’t an exact science yet, but leading-edge analytics technology available today – underpinned by AI and ML – is delivering remarkable ROI and uncanny accuracy for forward-thinking companies who are investing in them.  These companies are dramatically improving their efficiency ratios by using combinatorial analytics to reduce costs and maximize revenue – including identifying innovative new revenue strategies personalized for both new and existing customers – using simulation, predictive and prescriptive analytics. 

The benefits of this approach are easy to understand, but game-changing in their ROI:

  1. Simulating and optimizing decisions before they are put into production gives highly accurate forecasts about their prospects for success, with dashboards displaying predicted and compared-to results.
  2. Firms maximize the success rate and ROI of their decisions, by iteratively simulating, fine-tuning, and perfecting them prior to launch to ensure predictable, optimal results.
  3. Companies attain highest possible degree of certainty that new programs will perform as desired before putting them into production.

This approach uses historical data to predict future events; spotting hard-to-see trends in the data, they tell you what is very likely to happen – x, y, and z – at some defined date or event. But prescriptive analytics go much further; they tell you – given that x, y, and z are very likely to happen at some defined date or event – what steps you need to take immediately to attain the optimal outcomes when they do.  It’s one thing to know that a customer is at risk of defecting; it’s another to know what will persuade them to stay… and how to convince them to invest even more. Predictive analytics might help you improve near-term customer satisfaction; prescriptive analytics will help you improve long-term customer retention.

Here are a couple of examples of companies who are cracking the code by centralizing analytics, rules and optimization execution in a single “Customer 360” platform, manageable by self-reliant business users:

  1. Example 1: By centralizing all in-house customer information and enhancing it with new external data sources, a traditional bank was able to maintain a holistic view of each customer in real-time.  Now, they scan for customer behavior that signifies an opportunity for a specific product or service, and launch a pre-designed offer personalized to that customer’s immediate needs. Decisions, strategies and offers are now based a logical, integrated, and higher-ROI methodology, pre-validated by simulation and improved by post-program iterative learning.
     
  2. Example 2: Another bank is perfecting the identification and targeting of high-potential “customer moments”: situations and events when timing-wise, the customer would statistically most receptive to overtures about a product or service. Rather than have each line of business marketing their own product to the customer – credit cards, loans, insurance, etc. – on an every-department-for-itself basis, the bank tracks every single point of interaction with every customer, allowing the creation of enterprise-wise strategies, and making data analytics a priority to everyone… not just the IT team.

Getting Started

What are the steps are companies like these taking to achieve this level of foresight, and attain the long-term success that follow?  They start by instilling higher levels of collaboration and efficiency across all lines of enterprise via an integrated platform that unifies analytics, business strategies, audit and compliance… all working together in a three-step progression to 1) raise higher customer satisfaction, 2) improve customer retention, and 2) achieve maximum profitability over the customer lifecycle. 

Beyond just simulation and optimization, here are steps companies can take to ensure the best possible outcomes for their future:

  1. Unify and mobilize the enterprise: A scalable decision platform across the enterprise optimizes and monetizes the use of people, data and analytics. By leveraging all information from across the enterprise and beyond, a platform fills-in-the-blanks in decisioning and ensures better decisions at every point of customer actions across the lifecycle, improving over time.
     
  2. Ensure a personalized user experience: Personalization empowers companies to offer customers consistent, tailored experiences that are reached by, and infused with, customer intelligence. By creating personalized customer strategies, companies are able to accurately predict and effectively serve their clients’ immediate and future needs. 
     
  3. Leverage business users’ domain expertise: Disruptive companies are bringing the heat of the marketplace into their planning by encouraging and empowering business users to create and manage the strategies, rules and analytics that drive decisions and actions – without IT intervention. This enables companies build and test a wider range of scenarios, faster than ever before, and fosters a market-driven approach to ensure long-term efficacy.
     
  4. Optimize and maximize the use of knowledge assets: This enables companies to leverage and re-use connected decision assets to improve decisions across the customer lifecycle, while making them transparent and explainable. This gives companies the ability to create customized, target decisioning strategies that are consistent, transparent, and expandable as needed over time.
     

No one knows what the future holds; but as the saying goes, chance favors the prepared mind. Knowing what sorts of strategies to plant today, in order to harvest their fruits tomorrow, is a sure-fire way to ensure future competitiveness.  Armed with the right historical data and the most accurate prescriptive analytics, companies can now be better prepared than ever for whatever lies around the bend in their customers’ journeys.

For more information on the FICO Centralized Decision solution, please visit www.fico.com/CentralizedDecisioning  For educational material on digital transformation and digital disruption, please visit www.fico.com/DTeducation  For information on digital transformation in the financial services industry, including detailed research and reports from Arizent and American Banker, please visit www.fico.com/ABresearch

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