April 28, 2016 | Written by: christine.oconnor
Share this post:
Too often organizations focus on serving their largest, most profitable clients. But how can you ensure that your other, smaller customers won’t slip away? The answer: tapping the stream of customer data that flows into your business, and putting it to work.
Unlike a decade or so ago, organizations today enjoy an unprecedented level of fact-based insights from customer data that can help them retain customers and boost sales by connecting with customers at the right times, with the right messages, all thanks to predictive analytics.
Moving from guesswork to prediction
Predictive analytics has gained a strong foothold among marketers and sales. A recent study conducted by Ventana Research found that 48 percent of marketing departments are now applying some form of predictive analytics. Yet although marketing tops the list of business functions that have begun employing predictive analytics, the vast potential of this powerful capability remains largely untapped. Too often, the sophistication of predictive analytics technology, combined with the quantum leap from the capabilities offered by spreadsheet-based analytics, creates the impression that adopting predictive analytics is a major undertaking beyond the reach of all but the most advanced data scientists.
Predictive analytics is actually easier to understand than many may think. It’s a process that uses historical data and existing data to predict what will happen next. And predictive analytics doesn’t necessarily require deep skill sets for in-house staff. Moreover, predictive analytics can bring enormous value to organizations across a wide range of industries-even within departments from marketing and sales to finance and operations.
And predictive analytics is exciting because it can quickly move project teams from plain guesswork to prediction that is based on a degree of certainty. It shows organizations or project teams where they are now and where they can go, and it enables them to discover trends, patterns and relationships in structured and unstructured data. In addition, predictive analytics can provide the direction necessary to apply insights and predict future events.
Discovering the transformative power of predictive analytics
In a competitive environment marked by empowered, hyperconnected customers, predictive analytics is not a luxury. It’s an essential tool for raising a company to new heights of agility and competitive advantage, especially when it comes to customer retention. Customers frequently buy from a particular business because of high-quality products, outstanding service, or competitive pricing – but it may not always be as clear why they leave. Advanced analytics helps companies in every industry use big data to figure out why some customers defect and how to stop others before they do. Reducing churn rates in this way can dramatically increase profitability and help generate additional revenue through up-sells, cross-sells, and referrals.
For XO Communications, a telecommunications company already well equipped to predict customer churn, the challenge was to understand more about the reasons behind retention risks and place this insight into the hands of a greater range of employees.
Using predictive analytics, data about every aspect of XO Communications’ relationships with its customers is gathered from sales, CRM and customer care systems, and external data sources. Then it created predictive models using predictive analytics software to generate a “churn score” for each customer. Current scores and trend data are automatically integrated into a customer information pack and sent to customer care reps. Accurate churn predictions enable customer care reps to intervene proactively with customers who have the greatest risk of churning – helping to resolve issues quickly, improve service levels, and ultimately increase the retention rate.
Join IBM SPSS on Wednesday, May 18 for a 30-minute webinar to learn how recent advances in predictive analytics technology can help your organization improve customer retention and recurring revenue, even if you’ve never seen or used a predictive model. Mark Grabau, Associate Partner for the IBM Client Center for Advanced Analytics, will share real-world examples of how leading organizations are mining their customer data to predict the likelihood of churn and drive revenue through personalized offers and tailored interactions.Register now.