Banks have an increasing opportunity to employ and leverage analytics as customers continue to seek increased digital engagement. Combining data, analytics, and decision management tools together enriches executive insights, quantifies risk and opportunity, and makes decision‑making repeatable and consistently executed.
Analytics, and the broad, umbrella phrase automated intelligence can be confusing; there are many different subfields of the phrases. AI is the ability of a computer to do tasks that are regularly performed by humans. This includes expert models that take domain knowledge and automate decisions to replicate the decisions the expert would have made, but without human intervention. Machine learning models extract hidden patterns and rules from large datasets, making decisions based purely on the information reflected in the data.
Financial institutions can use this technology to better understand their data, get more value out of the information they already have and make predictions about consumer behaviors based on the data.
For example, having identified the needs of two consumers, digital marketing analytics can identify the consumer with the greater propensity-to-purchase or which consumer has the more-complex needs to determine resources allocation. These consumers may present equal opportunity, or they may vary by a factor or two. It’s also important to employ analytic tools that extend beyond determining probability to recommending actions based on results. For example, a customer could submit necessary credit information that is sufficient for a lender to receive an instant decision recommendation, increasing customer satisfaction by reducing wait time.
While there are countless ways banks can benefit from implementing analytics, there are eight specific areas where analytics has the most impact:
- Measuring the degree of risk by evaluating credit, customer fraud and attrition;
- Measuring the likelihood or probability of consumer behaviors and desires;
- Improving customer engagement by increasing the relevance of engagement content as well as reaching out to customers earlier in the process;
- Providing insight into the success or failure in the form of marketing, customer and operational key performance indicator;
- Detecting and measuring opportunity in terms of customer acquisition, revenue expansion and resource/priority allocation;
- Optimizing pricing;
- Improving decisions based on credit, campaign, alerts or routing escalation; and
- Determining intervention or corrective next action to reduce abandonment.
Each of these capabilities has numerous applications. In a digital economy, the entire customer journey and sales cycle becomes digitally concentrated. This includes using personal financial goal planning, market segmentation, customer relationship management data and website digital sensory to detect opportunities based on consumer intent, fulfillment, obtaining customer self‑reported feedback, attrition monitoring and numerous engagement methods like education or offers. Using analytics adds considerable value to each of these processes — it drives some of them completely. Actionable analytics are key. They drive outcomes based on expert models and data analysis, to scale, to a large set of consumers without increasing the need for additional employees.
Looking at actual business cases will underline the benefits of analytics in relation to propensity‑to‑purchase (PTP), email campaigns and website issue detection. When two different customers visit a bank’s website, the bank can use analytics to detect and measure each user’s navigation for probable interest and intent for new products based on time on page, depth of navigation and frequency signals within a given timeframe. If one person visits a general product page and only stays for 15 seconds, that person has a lower PTP than the other visitor who navigates to specific product and pricing information and remains there for 40 seconds.
The bank can route probable leads to either human‑based or automated engagement plans, based on aggregated data, segmentation, product intent, and in the case of an existing customer, current products owned.
A recent college graduate may be interested in debt management solutions, whereas a more-established empty nester may be in the market for wealth management and retirement planning. Based on user preferences and opportunity cost, these customers can be properly engaged with offers, education and helpful tools through email campaigns, texts, third‑party marketing or branch or contact center personnel.
In today’s banking environment, financial institutions must find new ways to increase efficiency, improve business processes and scale to consumer volume. Analytics support financial institutions in forecasting, risk management and sales by providing data points that help them increase performance, predict outcomes and better solve business issues.