As consumers become more digitally connected, their buying journeys are becoming increasingly complex: The path to purchase is no longer a linear funnel; rather, it's a circuitous journey that continues long after the transaction has been completed.
Today's consumers have more touch points with more brands than ever before; and so, to capture and retain their business, marketers must have a deep understanding of consumers and their intentions at every stage of the customer lifecycle.
Luckily, customers' increased digital engagement with brands has also allowed organizations to amass more customer data, creating the opportunity to glean actionable customer insights through predictive analytics, a form of advanced analytics that uses both new and historical data to forecast future activity, behavior, and trends.
Predictive analytics has become much more prominent over the past few years as organizations look to harness their data: Gartner estimates that by 2020 predictive and prescriptive analytics will attract 40% of enterprises' net new investment in business intelligence (BI) and analytics.
Today's marketers should apply predictive analytics at every stage of the customer journey, from raising awareness, to educating prospects, to completing the transaction, to enhancing customer service and beyond. Doing so will help marketers anticipate their customers' needs and desires at every moment, so that they personalize engagement with each customer.
However, many marketers are likely left wondering how to leverage predictive analytics. What data systems and services need to be in place? And how, exactly, can predictive analytics be applied at various stages in the customer journey?
Laying the Foundation for Personalized Engagement
To effectively harness the power of predictive analytics throughout the entire customer journey, organizations must invest in a customer relationship management (CRM) platform that supports advanced analytics and integrations with other applications.