Tradition can be a good thing when it facilitates workflow, adds deeper meaning to otherwise ordinary activities and enriches the corporate culture. But unless the tried-and-true also evolves with the times, sticking with the old way of doing things can also be a restrictive hindrance. This is especially true for established banks operating in an increasingly digital world.
Traditionally, banks have stored customer information by account type (i.e., checking, savings, mortgages, etc.). The result – a siloed mindset – may have worked in a retail banking world, but it is causing problems in the increasingly omni-channel, digital ecosystem, in which consumers hold much more power, and poor data visibility and counter-predictive analytics can sink even the most progressive-minded institution.
Overcoming this mindset starts with buy-in from senior leadership (see our paper Digital Banking: Time to Rebuild Your Organization). But it ends with an integrated, enterprise-wide customer journey database, in which internal and external inputs are continuously analyzed, delivering insights to all banking departments and stakeholders across marketing, compliance, call center and digital operations.
The payoff is immense: Total client visualization, campaign discovery, cost reduction, revenue optimization, client acquisition and, ultimately, predictive analytics, which results in better anti-money laundering, risk data aggregation, counterparty exposure and fraud prevention checks.
These are the kinds of results we’ve seen in our experience helping leading banks successfully transition to what we call “one-customer” banking.
New Performance Indicators
The need for centralized customer analytics is rooted in the accelerating digitization of the banking world and customers’ unrelenting desire to transact and interact digitally. Every action that customers make online – whether engaging with the bank, posting on social media, downloading an app, even walking down the street with a location-based smartphone – creates a digital identity for that individual, what we call a Code Halo™.
Forward-thinking businesses – including banks – are harnessing Code Halos to become more customer-centric, informing and accelerating both revenue-boosting and cost-cutting initiatives. To that end, centralized analytics offer banks a plethora of new customer insights that can be best tracked through the following five performance indicators:
- Key operational indicators (KOI). Transactions gleaned from all the cross-digital channels of activity between customers and the bank (i.e., mobile, ATM, Web, IVR, etc.).
- Key lifestyle indicators (KLI). Indicators describing propensities to spend and act based on analysis of all transactions across all accounts and data siloes, both operational and geographical (i.e., car lease payments to Ford, green fees to Bermuda, student loan payments, final mortgage payments, ATM fees on a cruise ship, etc.).
- Key market data indicators (KMI). Data harvested from public or “pay” Web sites (i.e., FICO score, criminal records, court reports, bankruptcy databases, etc.).
- Key social indicators (KSI). Information, sentiment, volume and velocity data harvested from social media sites such as Twitter, Facebook, etc.
- Key financial indicators (KFI). Consistently updated calculated values indicating various financial risk or portfolio/holding metrics.
By combining and analyzing these data types into one ecosystem, banks can develop a holistic view of their customer’s journey, from which all bank stakeholders can benefit.
Notable Use Cases
Whether the goal is to cut costs or boost revenues, centralized customer analytics deliver. For instance, by combining KLIs with KOIs, the bank might notice an 18% increase in mobile use among customers aged 65 and older over the last 12 months. This could signal an opportunity to launch a mobile marketing campaign targeting this demographic to encourage them to use mobile when banking to reduce operational expenses.
Or by combining KLIs and KOIs, the bank could detect an increasing trend ofhigh net worth customers going directly to a service representative rather than using mobile or Web capabilities. This would signal that the bank’s digital capabilities were insufficient or that something else was causing cost inefficiency and diminished customer satisfaction.
Centralized customer analytics also helps banks reduce “digital leakage,” the term we use to describe low-value, low-complexity transactions that could have been handled digitally had the technology been up to snuff. A rule of thumb is that it costs $8 to $10 to service every call made to the bank. At hundreds of thousands of calls per month, and a digital leakage rate of 25%, that could add up to $2 million per month in added expense, not to mention an increase in customer frustration. By aggregating information throughout the digital customer journey, however, banks can detect patterns and trends that reveal where the digital experience breaks down, and take action to fix it.
A centralized approach to customer analytics also helps banks boost revenue. By analyzing the same pool of centralized data, banks can develop insights that lead to new types of products and services. For instance, they can combine KLIs and KOIs with customer account data to predict what the customer might be interested in purchasing in the near-term, and respond with relevant offers at just the right time. If the bank notices an increase in a customer’s recent deposits, it could automate the offer of a high-spending credit card. Or if another customer is paying off multiple student loans, an automated offer for loan consolidation could be triggered.
Combining KLIs and KSIs to create “next best action” offers is another example. For instance, a bank could detect whether someone is planning a vacation or researching websites for a new set of golf clubs. Using that insight, the bank could offer a credit card with special offers for vacations or big purchases. Or it could note a customer’s window shopping habits towards the end of his car loan to advertise special financing rates on a new car.
These examples are the essence of Code Halo thinking — deriving insights (and revenues) from the collision of multiple Code Halos. The key for all of these cost-cutting and revenue-optimizing endeavors is that the data points — the KOIs, KLIs, KMIs, KSIs and KFIs — all come from a single, integrated data ecosystem to be used by any department, whether it’s sales and marketing, compliance, customer service or digital services.
What to Look For
Large cross-silo enterprise data projects are fraught with political, logistical and technical hurdles. The most significant technical issue is not the analytics but the data. Challenges include data quality, lineage, normalization and aggregation, as well as stakeholder coordination and massive corporate overhead.
For this reason, we recommend using a pre-wired, pre-integrated analytical ecosystem that pulls only key data from silos, and shares data and analytics across the functional infrastructure. Such a system should use compliant industry ontologies at its core to mitigate heavy lifting and expedite the design, implementation and integration of the ecosystem, especially if it’s a drop-in appliance.
To develop a centralized customer analytics, banks will also need to do the following:
Create an integrated customer journey committee of senior staff members, representing each of the operational and geographical siloes.
Build an institutional customer journey mission statement that represents the needs of the overall bank, while serving the individual needs of the silos.
Task the chief data officer with creating a convergence environment, in which key data from across the enterprise is made available to a centralized customer journey analytics environment.
Expose that data to the four functional customer journey servicing solutions: digital experience, marketing and campaigns, customer service and analytics.
Cross-pollinate that data across each functional solution to create powerful capabilities in the white space between them.
Leverage all that data into other solution silos, such as anti-money laundering and risk data aggregation. It’s already there – use it again rather than creating another silo.
Apply predictive analytics across the convergence environment to identify new campaign and product possibilities, lower customer churn and enhance the customer experience.
Because digital channels operate in isolation in most banks, organizations generally have no insight into how each channel is operating in relation to others. Are frustrated customers leaving the Website and calling the branch? Are mobile deposit customers experiencing technical problems and driving to a branch or the ATM?
Without a holistic view of each channel, banks will never find answers to these or similar questions. Hence the need for centralized customer analytics.
To learn more, read Creating One Customer Journey Ecosystem that Meets All Banking Needs, visit our banking practice, or see how to apply Code Halos to your business.