Traditionally, credit decision-making occurred at the back end of the process with credit marketing bringing the customer into underwriting when they needed to get approved for credit and vice versa for getting approved for credit before marketing. The credit decision-making process happened there as well; however, sometimes it was done backwards.
With artificial intelligence, the front end of the process has now been altered with credit decision-making being both an ongoing and an evolving part of the customer experience. Customers can have their approval, credit limit adjustments (increases or decreases), pricing changes, and/or risk assessments done without them knowing about it. So customer experiences continue to change long after the initial marketing effort stops.
Credit decision-making also changes how marketing can operate. As decisions are fluid, marketing can no longer focus on just acquiring new customers; rather, marketing must consider how to retain an existing customer and how each individual customer interacts with the company throughout their entire customer journey (beginning with marketing and acquisition) based on underlying decision-making principles already provided via one or more of the previously mentioned credit decision-making points. Thus, many credit decision-making processes influence how marketing companies communicate with potential customers. As a result, what would appear to be a traditional marketing journey can actually be controlled by the logic of credit decision-making verses being a traditional marketing journey.
The impression of ownership for the customer journey has changed as a result of Artificial Intelligence; customers will no longer view marketing, underwriting and product management as separate entities, but rather, as an integrated whole.
Since the advent of AI, there no longer are distinct lines of responsibility throughout the journey. Rather than have one designated point in time where ownership for the customer journey was established (e.g., marketing acquires customers, risk decides if they qualify, product is responsible for connecting the two), multiple points have developed. These multiple points allow many different parties to have input into what a customer sees, when they see it, and what they can expect to qualify for at each point during the journey.
As a result, customers now view their experience as one long continuous series of events instead of a series of handoffs between functions (such as marketing to risk and risk to product). The way that customers experience their overall journey has changed as a result of instability created by AI; rather than view themselves as having separate roles in an overall process, marketers see themselves as having a role in creating a window of opportunity in their customers’ lives.
When customers receive messaging, are given eligibility, and receive their outcomes as part of a single transaction during their journey, the combined effect is that customers have a positive experience. If any of these elements is not aligned during the customer’s journey, particularly between agreeing to purchase and completing the purchase, customers can feel an increased level of friction from this inconsistency than an otherwise positively constructed journey.
The ”fallout” begins from a gap in functionality between AI-driven decisioning systems, which work to optimize both accuracy and minimize risk, and marketing systems, which are focused on optimizing engagement and increasing conversion. When these systems operate independently, the real-time updates in AI-driven decisioning can produce different outcomes, resulting in less consistent and seamless customer journeys.
The solution is not to merge the two functions, but rather to establish a common framework for the flow of information between each function’s outputs and the resulting customer-facing experiences. While marketing strategies should consider how real-time credit logic introduces variability and represents a constraint on determining eligibility for products or services, AI-driven decisioning needs to understand how its outputs impact customer-facing experiences. As a result, to maximize effectiveness, the customer journey should be designed as an integrated system, as compared to a series of hand-offs.
In addition, the definition of relevance is also being redefined. Personalized experiences are typically established through the use of demographics, behavior and intent signals. However, the introduction of AI-driven decision-making provides a new dimension to personalizing experiences based on eligibility, i.e., whether or not a customer is eligible for a particular product or service at a given point in time. When handled effectively, this will provide seamless and satisfying customer experiences; whereas, if not managed correctly, will create inconsistent and opaque experiences.
The organizations successfully managing this phenomenon typically view decision-making as a component of the overall user experience, rather than merely as an operational engine. Models produce output which has downstream implications related to how people perceive something, rather than just how well it performs.
Thus, the questions of who owns the customer journey become much clearer. The customer journey has transitioned from being owned by one function to being created throughout the journey continuously via systems collaborating together.
AI has not usurped the journey; instead, it has forced the organization to stop managing the customer journey within silos.
(Authored by Abbhinav R Jain, Co-founder & Chief Financial Officer, AdCounty Media)
