The long-term value of a good customer decision far surpasses the short-term ROI.
Sometimes we get so focused on short term analytics, simulation, optimization, decision modelling, 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.
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, analyse 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:
- 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.
- 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.
- 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:
- 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,
- 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.
No one knows what the future holds; but as the saying goes, chance favours 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
FICO (NYSE: FICO) powers decisions that help people and businesses around the world prosper. Founded in 1956 and based in Silicon Valley, the company is a pioneer in the use of predictive analytics and data science to improve operational decisions. FICO’s South African offices are headquartered in Illovo, Sandton.
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Learn more at www.fico.com.