Next best action marketing has gained a great deal of momentum in recent years. That’s because companies are increasingly focused on gathering, analyzing, and acting on insights for appropriate actions to take with individual customers.
For instance, let’s say a bank customer applies for a new credit card. Bank officials can use transactional, lifecycle status, and other information about that customer along with analytics to help them determine that the next best action is to offer that customer a home equity loan at a lower rate than the credit card he was hoping to get.
The use of analytics informs banking leaders that a home equity loan would allow the customer to consolidate and pay off his existing credit card debt at a lower rate, thus freeing up additional disposable income.
In this scenario, the customer benefits because he’s reducing his debt, paying a lower monthly fee, and improving his credit rating. The bank also benefits because it has demonstrated that it is acting in the customer’s best interest, helping to strengthen the relationship with the customer, and increase the customer’s lifetime value.
Next best action isn’t just for marketing – business and functional leaders can also use real-time operational and market data with analytics to help identify the next best action to take on behalf of their business units or for the enterprise.
For example, financial services companies can use analytics to detect and prevent economic crimes that are taking place internally. It’s a widespread problem, the rate of global fraud – including misappropriation, procurement fraud, bribery and corruption, and accounting fraud – grew 37% in 2014 with financial services companies at greatest risk among industries, according to a study by PwC.
Risk managers, CFOs, and other organizational leaders can use analytics to identify anomalies with internal reporting as well as suspicious incidences of check, online banking, and wire fraud. Many cases of these types of fraud are low-value and high-volume. The use of analytics can help identify patterns and recommend preventive actions to stem fraud which ultimately will strengthen profit margins and customer trust.