The Next Best Action for Banks - Chapter 4: Optimizing Channels
As convenience, availability and proximity become increasingly important to consumers, banking and FinTech companies are recognizing the need to develop hyper-personalized omnichannel strategies that support 1:1 engagement with customers.
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According to a survey conducted by Aspect Software, businesses that adopt omnichannel strategies achieve 91 percent greater year-over-year customer retention rates compared to business that do not.
The scramble to meet the expectations of customers accustomed to seamless, cohesive experiences across channels, regardless of brand or device begins with an understanding that today’s customer journey transcends traditional channels. Consumers are now channel agnostic. With this as their starting point, banks can set about gaining visibility into their customers’ behavior across channels, whether in the branch, online, in-app or on social media.
In another installment of our series aimed at helping banking and financial services organizations better understand how they can leverage Data Science to become more customer-centric, we’ll explain the necessary steps for building a marketing automation stack that utilizes customer data to create relevant omnichannel engagements.
Achieving a unified, omnichannel view of an organization’s customer can be complex. It requires integration of multiple data sources from household-level data, purchase history, attribution sources and insights from multiple digital properties and channels. The key to bringing this all together is to utilize a customer data platform. A customer data platform, or CDP, serves as the plumbing for all systems and marketing tools to connect, segment and route essential data. It is the foundation of a marketing automation stack.
By integrating all data sources, it is possible to identify behavioral patterns that help develop customer segments and profiles. Based on these segments, communication can then be personalized, providing the ability to foster relationships by personalizing messaging in relevant, meaningful ways to deliver content, promotions, offers and communication at exactly the right place and time. This segmentation allows for the streamlining of advertising and marketing efforts and spend, leading to higher ROI and LTV per customer.
Marketing automation stacks can utilize several different tools to help enrich customer segments and optimize engagement. The tools below make up a foundational automation stack.
Customer Data Platform (CDP) – The CDP is a central, unified customer database that is accessible to other systems. Data is pulled from multiple sources, cleaned and combined to create a single customer profile. This structured data is then made available to other downstream marketing systems.
Analytics Platform – Often companies will add an additional analysis platform that structures data in a way that is relevant and easy to use for their needs. This eliminates the need to perform analysis in the data lake, which often requires support of a data scientist. Marketing teams can use this analytics platform to easily build cohorts or triggers that can kick off automated campaigns in downstream tools such as the customer engagement platform (CEP) or customer relationship manager (CRM).
Customer Engagement Platform (CEP) – CEP software helps businesses manage complex customer relationships. The CEP is often lighter weight than a CRM in that the customer profile lives elsewhere, and uses logic from the CDP and/or analysis platform to inform campaign sends.
Now, let’s look at a few important steps to consider when building a marketing automation stack.
Step 1: Understanding the data sources and destinations
The first step of the process begins with looking at what data is available today by taking inventory of all data sources, systems and repositories to understand how they connect and then determine any hurdles in unifying this data. The big questions are:
- Where is this data coming from?
- Where is it stored?
- What needs to happen to create a loop?
At this stage, it’s essential to identify and work with data governance and compliance teams to ensure necessary regulatory and compliance requirements are adhered to while developing this plan.
Step 2: Determine what’s missing from the customer data
Utilizing the information collected in the first step, data can then be layered over customer profiles and channels to understand where the gaps are. In most cases, companies have a central repository (warehouse or lake) that everything is being sent to. This data will travel through the CDP into the marketing automation stack.
Once any gaps have been identified, a plan can be developed for what is needed to improve or fill out data profiles, for example:
- Visibility into mobile app or web behaviors.
- Attribution intel.
- Product profile details.
- Marketing campaign engagement.
At this point, it is important to understand data gaps and functional gaps within the systems to then plan what an ideal data model looks like and what tools might be necessary to fill the system gaps.
Step 3: Develop the data model
Working with an organization’s data stewards, a data model can now be developed. The data model will consider what a user’s identity looks like and what properties are associated with that user. Another key component of the data model is a tracking plan for all channels implemented with the CDP.
Once the data model and tracking plan are complete, it's time to transition into a more robust data dictionary that holds the definitions for business objectives. At this stage, the team is either implementing with existing systems or beginning the process to identify third party solutions for the tools mentioned above.
With well documented sources and destinations, the data flowing through an organization’s marketing automation stack creates a visibility loop. More complex setups may include both a data warehouse, where a structured analysis of the customers can be used to create cohorts and triggers that kick off engagements across channels, and a data lake, where an unstructured analysis can be performed on the customer data to send additional attributes downstream to the data warehouse.
Companies successful in developing strong omnichannel engagement retain an average of 89 percent of their customers, compared to 33 percent for companies with weak omni-channel customer engagement (Aberdeen Group). It’s not difficult to derive actionable insights from customer data and analytics, but it does require careful planning and a small arsenal of third party tools. The insights gained by understanding the intersection of marketing communications, online channels and branches can be used to inform many aspects of a customer engagement strategy. With a robust, well-built marketing automation stack, omnichannel strategy can be optimized and automated for a full 360-degree view of the customer, enabling the delivery of relevant engagements and increased customer retention.