Brands have the opportunity to increase their capability to perform augmented decision making - where a marketer takes analytically driven insight to make a customer 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.   Let's proceed now, and step one level deeper into how marketers can leverage SAS 360 Marketing Decisioning for customer use cases. Keep in mind, 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.   The software design principles for 360 Marketing Decisioning center on three user aspects.   User experience. Composability & flexibility. Embedded AI user assistance.   Let's briefly explore each of these. The user experience benefits from martech-oriented templates, which offer multiple use cases to accelerate from. Although next-best-action (NBA) is our focus in this article, readers should note that SAS supports themes around churn/retention, abandoned baskets, acquisition and more.   Image 1: Martech-oriented Templates   Next, rules allow users to manage real-time decisioning strategies, including eligibility, contact policies, weightings, and message assignments.  Additionally, users can test decision logic, validate flows by comparing results, and highlight failed rules before taking action in production use cases.   Image 2: Customer Eligibility Criteria   Composability & flexibility of the software assist users in a variety of ways. When it comes to working with data, users can define rules using multiple data sources, including real-time channel context, recent events, AI scores, customer data, predefined segments, and external web services. For enterprises with unique business requirements, SAS 360 Marketing Decisioning can be extended using Custom Nodes.   Custom Nodes allow teams to incorporate custom business logic, invoke external services, perform custom calculations, or integrate with enterprise systems directly within the decision flow - giving teams the flexibility to support highly specialized decisioning scenarios while still leveraging the platform's governance, eligibility, arbitration, and execution capabilities.   Image 3: Contact Policies   Arbitration features enable marketers to prioritize eligible messages using formulas that consider propensities, weightages, and business values. The technology's features provide a rule assistant for managing and entering any kind of business rules and/or suppressions.    SAS 360 Marketing Decisioning works by processing data-in-motion customer signals and applying real-time inference from trained AI models, along with business rules and scoring logic to select the next-best action or offer. Marketers can use built-in AI capabilities or import external models, combining propensity scores, priorities and contact rules to orchestrate personalized actions across channels.   Image 4: Arbitration   Embedded AI helpers assist users with intelligent recommendations to optimize decision flows, improve response rates, and achieve business KPIs.    Image 5: Auto-reporting With AI-Helpers & Recommendations   Lastly, performance tracking of message/treatment/action effectiveness across all channels with a dashboard showing response rates, real-time performance, and actionable insights is auto-provided (or can be fed into a brand's 3rd party reporting & visualization preferred software technology). If readers desire more user-level information on using SAS for arbitration and contact policies within decisioning use cases, we recommend reviewing this article.   Learn more about how SAS can be applied for customer analytics, journey personalization and integrated marketing here. For those who want to dive deeper into the current state of the marketing/customer analytics technology ecosystem, check out fresh (and unbiased) research here. ... View more

When connecting one object to another, it would improve usability if users could search for the target object by its ID without first having to select the object type. This would make it easier and faster to locate the correct object, especially when the object ID is already known. We are using Visual Investigator 2025/09, Release: 20251209.1765255106922. ... View more

Hi Team, I have a client requirement to implement usecases on SAS 9.4 AML 7.2 Please find the usecases below. Introduce a “Marked for Review” functionality for entities. During the analysis of alerts, sometimes analysts require some more time or a need to review the alert. This may also be due to unavailability of relevant portals, like GST, CBI, MCA, ED etc. or CBS (Report) at certain times. However, there is no functionality in the SAS-AML at present to do so, leading the analysts to use either manual records or analyse the entity with the available information. Related accounts of the customer listed under customer details section should be bifurcated into ACTIVE and CLOSED. SAS AML application displays list of accounts, however status of accounts, whether it is ACTIVE or CLOSED, is not presently displayed  under: Triage → Entities → Related Accounts   I am not sure how i can perform this. Could you please let us know how this enhancement can be implemented? I would appreciate it if anyone could share the approach and the steps required to achieve this functionality in the SAS AML application. Regards, Shivraj  ... View more

Where relationships are displayed, consider providing a centralized view of all relationships without requiring users to switch between object types. Alternatively, hide object types that have no relationships to the current object. This would simplify navigation and make it easier for users to find relevant relationships.   We are using Visual Investigator 2025/09, Release: 20251209.1765255106922. ... View more

중복된 데이터가 있을 경우  PROC SQL 문을 사용해서 고유한 데이터만을 조회할 수 있습니다.    DATA EMPLOYEE_DATA; INPUT EMP_ID $ NAME $ DEPT $; DATALINES; 1001 KIM HR 1002 Sophia FINANCE 1001 KIM HR 1003 Olivia IT 1002 Sophia FINANCE 1004 Avery HR 1003 Olivia IT 1004 Avery HR ; RUN; DATA 문을 사용해서 EMPLOYEE_DATA 데이터를 생성했습니다.  INPUT 문으로 데이터의 변수와 변수의 형태를 정의했습니다.  emp_id, name, dept 총 3개의 변수를 사용하고, 변수의 형태 중 문자형 변수가 있을 경우 $ 문을 사용해서 문자형 변수라고 지정합니다.  데이터는 공백을 구분되서 각 데이터를 저장합니다.    위 데이터는 총 8개 중 4건이 중복값입니다.          PROC SQL 로 중복 데이터를 제거하려고 합니다.    PROC SQL; CREATE TABLE EMPLOYEE_UNIQUE AS SELECT DISTINCT EMP_ID, NAME, DEPT FROM EMPLOYEE_DATA; QUIT; SAS에서는 SQL 문을 사용하기 위해서 PROC SQL 문을 사용합니다.  CREATE TABLE EMPLOYEE_UNIQUE AS 중복값을 제거한 데이터를 새로운 데이터셋으로 저장하기 위해서 CREATE TABLE 문을 사용합니다.    SELECT DISTINCT EMP_ID, NAME, DEPT  EMP_ID, NAME, DEPT 세개의 컬럼 값이 모두 동일한 행은 중복으로 판단하여 하나만 남깁니다.             ... View more