Customer Decisioning, Offer Treatment Prioritization & Risk Assessment
Brands today are complex ecosystems of decisions that must be executed with increasing levels of automation - due to their competitors digitally transforming and influencing customer expectations. In response, there is a need to change how decisions are made. Organizations have the opportunity to increase their capability to perform augmented decision making - where a human takes analytically driven insight to make a decision (such as within a call center, website or mobile app). Automation within decision making is when an algorithm (or algorithms) with business rules make the decisions without human intervention (such as next best offers/actions/experiences). With each passing year, the acceleration of the scale, speed and complexity of customer 1:1 decisions is increasing.
Decision intelligence is a practical discipline used to improve decision making. This is performed by explicitly understanding and engineering how decisions are made based on how outcomes are evaluated, managed, and improved via customer feedback. Decision intelligence helps brands reduce technical debt, increase visibility, and improve the impact of business processes. Further, it enhances the sustainability of decision models through relevance and transparency.
Organizations are allocating time and money into DataOps and ModelOps, yet do not receive the full value of these investment. AI and machine learning help brands make better decisions, whether that is deciding to accept a loan application or what pricing discount to offer. DecisionOps help organizations get the most value from their data engineering and data science investments by creating streamlined, efficient processes around model management and decisioning.
A customer journey in its purest form represents a series of brand-orchestrated connected experiences addressing an individual's desires and needs — whether that be completing a desired task or traversing the end-to-end journey from prospect to customer to loyal advocate. When you reflect on this, the customer experience is the totality of cognitive, affective, sensory, and behavioral consumer responses during all stages of the consumption process including pre-purchase, consumption, and post-purchase stages.
Customer decisioning is best used to drive real-time actions in three contexts.
- To drive the ideal next journey-based interaction that a customer or prospect should have with your brand.
- As part of a cross-channel marketing initiative that unifies an experience across customer-facing channels.
- To enable personalization that delivers customized messages based on an individual's profile and observed behaviors while respecting experiential privacy.
These capabilities come together to enable your brand to create a robust decisioning cycle that deliver analytically driven customer-focused decisions. Users create, manage and deploy decisions via an optimization engine which are made available to a wide variety of endpoints for incorporation into operational systems and processes. A continuous learning cycle ensures the best decisions are being made and governance workflows ensure the orchestrated treatments can be trusted and understood.
SAS blends DataOps, ModelOps, DecisionOps & marketing orchestration to support offer treatment prioritization requirements for a wide variety of journey-based use cases. To enable data-driven decisions at scale, the analytics life cycle must be highly operational, automated and streamlined. By connecting all aspects of the analytics life cycle – brands can turn critical questions into trusted decisions.
Chapter 1: Customer Offer Treatment Prioritization
The first demo video below will feature a fictional financial services company comprised of multiple business units (savings, lending, wealth management, etc.) and operating in numerous geographies. The primary objective will be to showcase a mutual value exchange across the full customer journey by optimizing orchestration capabilities and using customer-directed engagement models to guide personalization strategies.
Secondarily, SAS recognizes brands must adapt to a mix of cross-functional and cross-brand goals. The use of machine learning and prescriptive analytics will be shown in support of how marketing teams can generate and prioritize single and cross-brand journeys. Data monitoring, machine learning and AI help surface alerts & address needed optimizations that govern which inbound and outbound interactions a customer should receive in a given time period. The intention is for brands to prove the value of marketing in a volatile business environment, connecting strategies to marketing and customer outcomes.
Chapter 2: Customer Offer Treatment & Risk Assessment
When it comes to use cases, SAS provides brands the ability to make decisions across the entire customer lifecycle and within each discrete customer journey. The result is a diversity of use case applications across credit services, fraud prevention, claims processing, next best action, and personalized marketing.
For our second demonstration example, we will cross-pollinate traditional customer engagement with risk & fraud detection. This will begin by transparently highlighting how SAS enables decisioning, orchestration and channel delivery services in support of a customer digital loan application experience. It will pivot and conclude on exemplifying erroneous customer behavior that will trigger the risk assessment value proposition.
Keep in mind, when thinking about risk, we are not working on a happy path problem, but trying to protect against exception cases, making security as tolerable as possible for good customers. However, we are not solving a fraud problem; it is really a customer experience problem and the goal is to minimize inconvenience for good customers. Digital identity is less about who we physically are, and much more about what, where, when, and why we do those things.
We look forward to what the future brings in our development process – as we enable marketing technology users to access all of the most recent SAS analytical developments. Learn more about how SAS can be applied for customer analytics, decisioning, journey personalization and integrated marketing here.
