SAP centralizes commerce data for AI personalization

SAP introduced the Advanced Success Plan for customers using SAP Customer Experience solutions to centralize commerce data and operationalize AI-driven personalization across data, decisioning and delivery layers.

At the data layer, the plan requires unified, real-time customer profiles that respect consent settings. These profiles combine completed transactions, browsing activity, historical engagement, customer service records and loyalty interactions so machine learning models receive broader behavioural inputs. SAP conducts data readiness assessments to measure baseline quality and to map the integrations needed to feed those profiles.

The decisioning layer converts aggregated data into executable directives. Machine learning evaluates incoming data streams to select the next product to display, the most relevant promotional offer and the optimal moment to contact a customer. Governance rules define when automated algorithms act and when human operators must intervene. Administrators receive parameters for control and override to keep automated outputs within defined business limits.

Delivery covers how personalized experiences are presented across channels. SAP Commerce Cloud serves as the storefront execution engine, using an AI-assisted recommendation system to surface trending items, related catalogue products and complementary accessories at targeted moments in the shopping flow. Personalised messages move from the storefront into email, mobile push and loyalty interfaces through SAP Engagement Cloud, which is powered by Emarsys. The plan includes orchestration to align messages with a customer’s live context across those touchpoints.

SAP identifies common barriers that prevent the three layers from working together: poor data quality, integration faults between storefronts and profile databases, and a lack of testing frameworks in marketing teams. To address those issues, the Advanced Success Plan deploys integration mapping, adoption accelerators and structured testing workflows that let teams define hypotheses, run A/B tests and apply successful changes into platform configurations.

On the engagement side, send-time optimisation analyses individual behaviour to dispatch messages when a user shows the highest probability of engagement rather than following fixed schedules. Emarsys tools include AI-assisted campaign translation and omnichannel orchestration that let marketing teams build dynamic automated journeys that update based on response metrics. SAP says the native integration between Commerce Cloud and Engagement Cloud shortens deployment timelines and links commerce activity with engagement data.

The plan frames personalization as an ongoing operation rather than a one-off implementation. Outcome-based governance sets target KPIs such as conversion lift, repeat purchase volume, open and click-through rates, and average order value. Prescriptive playbooks outline steps to enable recommendations, configure send-time logic and deploy next-best-action algorithms. Role-based training is included for data engineers, product owners and campaign managers.

Operational monitoring is part of the plan. Telemetry systems run automated adoption checks to flag underperforming configurations and issue alerts recommending tuning before revenue is affected. SAP tracks integration and adoption milestones across commerce and engagement environments so financial benefits can be measured against observable storefront and communication metrics.

SAP describes the goal of the Advanced Success Plan as moving organizations away from isolated point solutions and toward an integrated operating model measured by ongoing performance against stated KPIs.