According to a study by Temenos, third of all retail bankers fear that today’s more demanding, informed and digital consumers are more likely to switch institutions and in greater numbers. Many financial institutions are responding to this dynamic climate by making significant investments in two key areas: digital marketing and data analytics. By coupling digital marketing strategies with better data analytics, banks and credit unions can target consumers at the right time with the right message, as well as empower frontline representatives with real-time customer intelligence.
Unfortunately, many banks and credit unions are wrangling with dated and disparate sources of data, like it was still 1999. To better understand consumers and their needs and values, all data must first be pulled together into a singular database. This includes any data lurking in systems, MCIFs, CRM systems, and other sources that contain relevant details and information.
A well-integrated marketing database should also include controls to ensure the data is accurate and remains current. Marketing data decays at an average rate of 2% per month, which means up to 25% to 30% of your contact list will go bad every year. People move, addresses and emails change, names are misspelled, and households are split.
What About Data-as-a Service?
Data-as-a-Service (DaaS) is based on the concept that data can be provided on-demand regardless of where that data resides. DaaS asserts that quality data can be made available in a centralized place — cleansed, enriched and offered it to different users on different systems, irrespective of where they are in the organization or on the network.
In the past, most financial institutions stored data in a self-contained repository, for which software was specifically developed to access and present the data in a digestible format. This often meant that both the data and the software needed to interpret it were bundled into a single package. As the number of bundled software+data packages proliferated and required interaction among one another, yet another layer of interface was required. All these various systems and interfaces often tended to encourage vendor lock-in, and significantly increased the amount of resources required to manage and maintain them. In addition to routine maintenance costs, a cascading amount of software updates were required as the complexion of data changed.
DaaS allows financial institutions to maximize the benefits of data analytics without worrying about the source of data, storage of data, and/or the specific software package or platform used to crunch the data. This untethered independence enables DaaS providers to tap various types of data: proprietary, in-house data, third-party data and real-time behavioral data. Some of the more common sources of data include:
- Web Mining – Data compiled by mining the open web. This includes automated processes of discovering and extracting information from Web documents and servers, including mining unstructured data. This can be information extracted from server logs and browser activity, information extracted about the links and structure of a site, or information extracted from page content and documents.
- Search Information – Data available as a result of browser activity tracking search and intent behavior. This data also identifies digital audiences through onboarding (matching consumers to their online IDs).
- Social Media – The average global Internet user spends two and a half hours daily on social media. A vast array of data is available on personal preferences, likes, “check-ins”, shares, and comments users are making.
- Crowd Sourcing – This is collective intelligence gathered from the public. Data is compiled from multiple sources or large communities of people, including forums, surveys, polls, and other types of user-generated media.
- Transactional – Data that is created when organizations conduct business, and can be financial, logistical or any related process involving activities such as purchases, requests, insurance claims, deposits, withdrawals, flight reservations, credit card purchases, etc.
- Mobile – Mobile data is driving the largest surge in data volume. It isn’t only a function of smartphone penetration and consumer usage patterns. The data is also created by apps or other services working in the background.
Then all these different data streams can be connected to addressable identities. That’s the beauty of DaaS — it structures this almost overwhelming cacophony of data into something manageable and practical — steady streams of qualified prospects (including your own customer base) who are actively searching for products and services they or their competitors sell. Banks and credit unions can use this data to deliver marketing campaigns through multi-channel programs or customized ads and messaging can be sent directly to channel partners and end users through a digital marketing platform.
Here are a few examples of how DaaS can boost marketing effectiveness.
- First Time Home Buyers – Build a list of the bank’s customers and prospects. Suppress those that are current homeowners, leaving non-homeowners with specific traits (age, head of household, HHI, etc.). Monitor the resulting file for mortgage activity with a specific FICO level indicator (e.g. 680+). When a “hit” is identified, the bank would immediately be notified and a fair offer of credit can be made to the potential homeowner.
- Refinance Variation – Secure a list of property building permits on specific bank customers and high end prospect records. When a substantial amount of work is being done at a property, this could designate the opportunity for a refinance.
- Investments – Scrape public record data in bank’s footprint to ID consumers and businesses that have experienced a significant liquidity event.
- Life Events – Monitor life triggers, such as marriages, divorces, and new births. Identify pre-movers. Identify people looking to sell their car.
- Business Banking — Scrape public record data in bank’s operating footprint to ID businesses that are moving, have moved, or have received certain levels and kinds of funding. Review of other financial activity that may be leading indicators of growth or decline.
Marketing has changed almost beyond recognition in the last decade and banks and credit unions must learn new techniques to keep up in an age of bigger and faster data. They have more options than ever to set themselves from the competition. The winners will be those that apply the right data to uncover new customer insight, identify new markets, and take the leap into the new era of marketing.
Bob Orf is CEO of DataMentors, a data quality, data management and business intelligence provider. Bob co-founded DataMentors in 1999, after a success in building OKRA Marketing Corporation, where designed and developed the company’s data householding and access systems. You can connect with Bob via email.