Today, organizations need to run at market speed and differentiate themselves by anticipating risks and opportunities in real time. With the competition growing more fierce, they have a much shorter time to act and react. The good news is that predictive technology is becoming accessible to non-data scientists and statisticians.
Though predictive analytics is not brand new, the technologies that help organizations make sense of their data have become easier to use. Moreover, leading organizations recognize they can use predictive analytics for more than just customer insight. Compliance, security, fraud detection, risk management, and optimization of operations are just a few of the areas benefiting from predictive analysis.
In fact, a Ventana research study revealed that 86 percent of organizations say they saw a positive impact from predictive analytics on their business, and 68 percent said they realized a competitive advantage.
We want more businesses tapping more of these predictive capabilities, so we’re launching a 20 tips campaign – a tip a day over four weeks – that introduces real-life scenarios of customers using predictive analytics to overcome business challenges.
Each week of #SAP20tips will zero in on a different topic:
• Week 1: customer relationship management (CRM)
• Week 2: marketing
• Week 3: risk and fraud
• Week 4: operations
And each week, I’ll write a blog on the chosen theme. So let’s get started with CRM.
CRM and predictive should be best friends. Why? Predictive analysis adds a layer of intelligence on top of your CRM system, making it smarter.
With predictive analytics, you can mine huge amounts of customer behavioral data, automate it all, and augment it with social media and third-party information.
Each customer is unique and wants to be treated as such, so the same experience and offers don’t fit all. Predictive analytics helps you personalize the customer experience by answering key questions – like how to increase value, target customers more accurately, and better track behavior and lifecycle – so you can perfectly time special offers and promotions.
When I speak to customers, they explain how the ability to build predictive models quickly is crucial. They can’t afford to wait weeks, because customer loyalty can change on a dime and behavior is a moving target. Equally important is the ability to manage a large number of variables, thereby increasing the accuracy of their predictive models and increasing the uplift of their targeted marketing efforts.
Predictive analytics also helps with another big CRM headache: customer churn. How do you prevent customers from leaving by offering greater customer experiences? And, how can you spot the ones on the edge of churning before it’s too late?
If you’re interested in learning how predictive analytics can help you answer these questions and make your CRM smarter, make sure to follow #SAP20Tips and @SAPAnalytics over the coming week for the daily tips posted to STAY AHEAD: 20 Tips from SAP Predictive Analytics.
Week 1 – Originally published on the Analytics blog and republished with permission.