Too Many Tools

As the number of banks continues to shrink, their technology choices seem to multiply. Banks have invested a lot of coin in the improvement of customer experiences these last 10 years. Investments range from new email systems, rewards platforms, CRMs, and intranets to mobile and online banking tools, websites, web applications, in-branch tools, kiosks, and even (cue the groans)…cores. Yet, banks (and especially community banks) still seem lost in one area of investment: how to systematically manage their data to influence marketing, sales and service. Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all (Source). That is a whole lot of flying blind!

The data investment is complicated and evolving as we speak. Do you skill up a current IT employee, buy a new data visualization software, hire a database architect, sunset the old MCIF, rely on an add-on widget to your CRM? The answer could be yes, all of the above. And that is frightening for many. But, that fear (and the inaction to-date) is thanks to a “vendor before strategy” mentality that has consumed many areas of banking for years. In areas like customer data where there is limited expertise in-house, the first instinct to find a tool that “does it” will lead your company down a rabbit hole. Three years will pass before you discover how deep you dug without coherent thinking.

If there is one activity in which you will fail fast if you focus on vendors, it is data management and how it informs customer intelligence. You see banks…

…YOU CAN’T TOOL YOUR WAY TO EFFECTIVE CUSTOMER INTELLIGENCE.

The list of vendors is daunting. Every new financial technology solution today claims to be a “data-driven, machine learning, AI-enabled” wonder app. New data management and visualization companies spring up overnight. But, don’t waste your time and look at a single company if you haven’t done the following strategic exercise. Until then, and only then, can you examine solution providers and how to connect them to build your data infrastructure.

“Getting Started” Strategy For Data Intelligence

DEFINE THE GOALS. The first step to effective customer intelligence (and every other project) is to define your goals. The challenge here is one of breadth. “Connecting every system that contains non-Core customer information” is a nice goal, but may not be realistic for your bank at this time. Goals should involve organizational, governance, analysis, and deployment strategies. Here are some good examples of strategic data goals:

  1. Connect our three largest personal banking engagement platforms (e.g. mobile/online banking, Bankcard rewards platform, email marketing system) to core data and determine our most engaged and disengaged customer segments.
  2. Create an encryption and anonymized customer identifier algorithm to securely transfer information to solution providers for data analysis.
  3. Deliver customized client lists to branch staff based on likely client attrition and “share of wallet” gaps.
  4. Develop a marketing source to conversion attribution database to showcase the monthly effectiveness of our most important marketing channels.
  5. Connect customer online/mobile behaviors with product choices and loyalty.

Your goals may look different, but the important step in defining customer intelligence goals is identifying your most important systems to connect, how you will securely manage and manipulate the information with outside help, the three or four behaviors you want to define and track (e.g. attrition, engagement, relationship penetration, etc.), and how you want to deliver the intelligence to a larger audience. Without these clear definitions, you won’t make it beyond aging spreadsheets in a network file folder. And you’ve already been there.

DRAW YOUR SYSTEMS. Get your whiteboard ready. Actually have five whiteboards ready because you need to draw out the systems, their level of connectivity and the critical data points you want to pull from each. It may feel overwhelming, but IT, Retail, Compliance, Marketing, Customer Service, Digital Channel, and other team leads probably need to be in the room together. And you’ll need to have a prepared list of your banking systems and as many report samples as you can find.

This is not a REPORTING EXERCISE. The goal is not to develop more reports. Reports can be built once you have the data canvas painted. This is a discovery exercise where you aim to find new data combinations that may be fruitful to client sales and service.

If one of your data sources is a vendor SQL file, ensure you have defined what components you want to extract from the raw file. If another source is an email marketing software, determine how to extract engagement and develop a separate scoring system before loading or connecting into a new database. If one of your solutions is a transactional feed from a third-party merchant services company, work with the vendor to determine the best possible output file on your customers.

Keep your focus on compatibility and connecting information and keep a list of questions for further investigation. This is really tough, but goes back to your data strategies. What insights are you trying to gain and what actions are you ultimately trying to take based on those actions? The data “bridges” you are creating should be built with those same strategic goals in mind.

Why do I keep harping on this? Because in these type of projects lend themselves to tangential thinking. As you gain access to a new data set from a system where you previously had smaller view, your mind will race. Mine does at least. Try to keep your focus and map only the systems that meet your immediate strategic goals. Keep a list of all the fun thoughts you have on the side for your advanced strategies in the future.

SETUP THE PROTOCOL. After drawing out your systems and determining the best connective approaches, you need a strategic process in place and understanding of how the data is moving and managed day-to-day, week-to-week or month-to-month. Your protocol goes along with the “dictionary” described below and helps you maintain a deep understand of your data sources, what they provide, how often they provide it, and any limiting factors to the information. This feels like an “ITish” log, but it should be written in a common, understandable language for the entire organization alongside the technical system requirements, security, programming, or server information.

CREATE THE DICTIONARY. Too often overlooked early in data development, but one of the biggest inefficiencies in data-driven organizations. The “data dictionary” is your best friend in the long run. It contains every element of your organization’s data defined in simple terms, not systematic terms. The information can be divided in the categories and labeled to fit your organization or group and include the specific assets held in your data warehouse or software solution. You could also link any protocol information from this file to help your analysts or outside vendors. Here is a quick table showing a rough outline of a mock bank marketing data dictionary.

Data Dictionary Bank Example

 

Advanced Strategies in Customer Intelligence 

As your organization becomes more data-driven, your company at all levels will crave more information. More refined, more specific, more actionable. Advanced strategies might include automations, visualizations, and predictive reporting that builds a more responsive sales and service model in your organization. Once you’ve established a connected network of data, built transformative information, and begun to share these insights across the organization, the need for faster, more efficient and automated insights will arise. Here are a few advanced strategies that may become strategic priorities within a few years of a more data-driven approach.

Scoring

Lead scoring is available in most marketing, CRM, and sales solutions today; still, it is severely misunderstood and underutilized without the context of additional data sources. Your future data warehouses, lakes or single-source applications can handle as complicated a scoring algorithm as you can create. You might score HELOC prospects based on a combination of mortgage, external transfers, time since purchase, relationship status, home to loan value, survey and/or additional data points. You could examine cash flow, transaction volume, debt scores, and credit card usage to score a prospective treasury services business client. The possibilities are only as limited as the sources you connect. Scoring also requires a level of diligence and training to ensure your team understands the logic behind the scoring algorithms.

Automated Messaging 

Today, most community banks have automated messaging in some form. These could originate in Core systems as NSF or overdraft notices or stem from online banking systems that send custom service alerts. Some even have automated emails that welcome new clients to the bank or introduce them to a product or service with a click of a button.

The future of automated messaging is really about addressing behaviors as they occur. As you begin to explore your customers’ behaviors, you will see a need for new and different sales and services messages. No longer will the spring time HELOC promotion be sufficient. You’ll know which customer was ready to make a HELOC decision in October, long before the marketing campaign dropped.

Timeliness demands automation. A message to someone who has not used your mobile app in 4 weeks after using it weekly is achievable. A message offering an extra reward balance transfer benefit to a client who, just moments ago, transferred funds to a competitor bank credit card is achievable. A message to a client who made sizable extra payments on a loan after a years of standard payment behaviors deserves an immediate communication. None of these scenarios can wait for the next campaign.

Personalization 

Personalization is one of those in vogue words right now. 15 years ago, we called it “1:1 marketing.” And never really did it. Now, the computing power, data and knowledge are catching up. Personalization is as strong as the data that powers it. It could include scripts on your website that recognize the last product page a user visited and present a unique homepage call to action as a result. It might be a section of an email from your marketing automation system that is dynamic based on the usage (or lack of usage) of mobile banking features. It could be a robot universal banker that picks up the beacon signal from your customer’s cell phone and greets them with a warm allusion to a recent life event based on purchase behaviors it immediately calculated from your transactional record accessible from its software….

…Sorry, got carried away there.

It’s a great time to be data-driven and a good strategic foundation will go a long way to setting the tone for your organization’s approach in the years to come. Be prepared because the waves of data won’t stop rolling in any time soon.

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