Upsell & Cross-sell Recommender
Agentic automation that auto-presents compliant, personalized offers in under 5 seconds during customer interactions, lifting accessory and consumable attach rates by 30% or more and protection plan conversions by 15% or more while maintaining full audit compliance.
Challenge
Support teams handle heavy chat, voice, and email volume but miss monetization because recommendations depend on agent memory and comfort. Agents bounce across CRM, ERP, catalog, warranty, pricing, and commerce systems; context signals such as model, age, warranty, symptoms, sentiment, resolution state, and purchase history are not assembled in real time. Offers arrive late or feel generic, driving low attach rates and compliance risk. Each targeted offer can take 3–5 minutes of lookups and data entry. Leadership needs a consistent, auditable upsell process that runs 24/7 across human and AI channels.
The objective: Lift accessory and consumable attach rate by ≥30% and protection plan conversions by ≥15% within 90 days; auto-present compliant, personalized offers in less than 5 seconds; maintain ≥95% policy compliance with full audit.
Solution: How AIP changed the operating model
Learning and setup
Powered by the Aftermarket Intelligence Platform (AIP), the agentic solution applied its predictive recommender, NLP intent and sentiment, policy and entitlement, and ontology reasoning models. Training data came from product catalog and compatibility matrices, SKU taxonomy, historical service transcripts with outcomes, prior purchases and order lines, warranty records, pricing and promotions, agent workflows and A/B results, and offer acceptance logs. This enabled the AI agent to interpret product model and serial, configuration, age and install date, warranty tier, symptom codes, resolution state, sentiment and intent, purchase history, cart contents, inventory, price, promotion eligibility, and region or language.
Workflow orchestration
The AI agent analyzes live conversation events and case milestones, enriches context from CRM, checks ERP for warranty and pricing, validates compatibility in catalog and CPQ, confirms stock in the order system, and prepares a quote in commerce. It mirrors the steps a service agent follows while logging every decision to analytics and writing outcomes back to CRM and ERP. Orchestration branches by policy and intent—for example, suppress protection plan offers when active coverage exists, delay offers during high frustration sentiment, or cap offers to top two items to maintain compliance and tone.
Execution and resolution
The AI agent parses the live transcript, extracts entities and sentiment, evaluates eligibility and policies, expands compatible add-ons via the ontology, then scores and ranks offers. It generates reason codes and a concise script, presents the top one to three offers at the right moment, creates a quote or cart, applies promotions, reserves inventory, collects payment when enabled, sends confirmation, updates connected systems, and records outcome signals for learning. Responses complete in seconds. Exceptions—such as out of stock, price mismatch, low confidence, or policy conflict—are routed to human agents with full context.
