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How Do You Identify Next Best Product Recommendations?

Identify next best product recommendations by combining customer signals, product eligibility, constraints into transparent, governed models and AI agents.

Explore Financial Services Solutions Explore the FI AI Agent

Identify next best product recommendations by linking customer intent, eligibility, and profitability in a governed decision framework. Start with a unified customer view, define eligibility and compliance rules, and use propensity and ranking models to score products for each customer in real time. Then constrain recommendations with risk, suitability, and capacity rules, test them via experiments, and operationalize in channels and AI agents so bankers and customers see clear, explainable suggestions.

What Matters for Next Best Product Recommendations?

Clear objective — Decide whether “next best” is optimizing funded accounts, relationship depth, risk-adjusted profit, or customer satisfaction.
Rich customer signals — Combine balances, transactions, channel behavior, life events, preferences, and engagement with previous offers.
Eligibility and suitability — Apply product, risk, and regulatory rules (KYC, affordability, age, geography) before ranking any candidate offers.
Propensity and uplift modeling — Use models to estimate likelihood to take a product, incremental response, and expected value for the bank and customer.
Real-time decisioning — Serve recommendations in milliseconds at key moments: onboarding, service calls, digital logins, and lifecycle events.
Governance and explainability — Make it clear why a product is recommended, track performance and fairness, and give Risk and Compliance confidence.

The Next Best Product Recommendation Playbook

Use this sequence to move from generic offers to governed next best product (NBP) recommendations that grow funded accounts and deepen relationships.

Align → Discover → Design → Build → Orchestrate → Activate → Learn

  • Align on outcomes: Decide where NBP will matter most first—digital banking, contact center, branches, or outbound—and which metrics it will move (funded accounts, CLV, risk-adjusted margin).
  • Discover journeys and signals: Map high-impact journeys (onboarding, renewals, life events) and inventory available data: balances, transactions, applications, interactions, and preferences.
  • Design the decision framework: Define candidate product sets, eligibility rules, ranking logic, optimization goal (e.g., CLV or revenue), and guardrails around risk and customer experience.
  • Build models and rules: Create propensity and value models by segment, embed business rules, and define how AI agents will explain and present recommendations to bankers and customers.
  • Orchestrate across channels: Connect your decision engine to marketing automation, CRM, digital banking, and contact center platforms so the same logic shows up everywhere.
  • Activate and test: Launch controlled tests, compare NBP performance to business-as-usual offers, and refine models, rules, and creative based on what works.
  • Learn, govern, and scale: Monitor KPIs, review models with Risk and Compliance, document changes, and gradually expand NBP to more products, segments, and journeys.

Next Best Product Capability Maturity Matrix

Capability From (Ad Hoc) To (Optimized NBP) Owner Primary KPI
Data & Signals Channel-specific lists and basic segmentation Unified customer view with behavioral, product, and interaction signals Data & Analytics Signal Coverage %
Decision Logic Manual offers and static rules Combined rules, propensity, and value models with clear optimization goals Analytics / Product Offer Response / Conversion Rate
Eligibility & Compliance Manual checks after offer Embedded eligibility, suitability, and risk rules in the decision engine Risk / Compliance Exception Rate / Complaints
Channel Orchestration Different offers by channel Consistent NBP logic across digital, branch, contact center, and outbound Marketing / CX Cross-Channel Consistency
AI & Agents Static playbooks AI agents and tools that surface and explain NBP to bankers and customers Digital / Innovation Agent Adoption & Impact
Experimentation & Measurement One-off A/B tests Always-on testing with attribution to funded accounts, CLV, and risk Analytics / Finance Incremental Revenue / NBP Uplift

Client Snapshot: Next Best Product → More Funded Accounts

A regional bank implemented next best product recommendations for digital banking and contact center, combining eligibility rules, propensity models, and agent guidance. Result: 18% lift in funded accounts from onboarding journeys, 25% increase in cross-sell among engaged customers, and higher satisfaction scores as offers became more relevant and transparent. See how this connects to our broader financial services and AI work: Financial Services Solutions · FI AI Agent

Treat next best product as a decisioning capability, not just a model: unify data, codify rules, build explainable models, and let AI agents surface the right offer at the right moment in every journey.

Frequently Asked Questions about Next Best Product Recommendations

What are next best product (NBP) recommendations?
Next best product recommendations are data-driven suggestions that identify which product or offer is most relevant for a specific customer at a specific moment, given their needs, behavior, eligibility, and potential value.
How do you identify the next best product for a customer?
You combine eligibility rules with propensity and value models, then rank candidate products based on the likelihood to accept, expected value, and constraints like risk, suitability, and capacity. The top product (or small set) becomes the next best recommendation.
What data do you need for next best product recommendations?
You typically need account and product holdings, balances and transactions, digital and branch interactions, response history, demographic and life-stage information, stated preferences, and product eligibility criteria.
What is the difference between next best product and next best action?
Next best product focuses on which product or offer to present, while next best action can include non-sales interactions, such as education, service outreach, or proactive risk mitigation. Many banks use both as part of the same decision framework.
How do AI and agents help with next best product?
AI and agents can process complex signals, score and rank products in real time, and surface explanations and talking points to bankers or customers. They can also learn from outcomes and continuously improve recommendations.
How should we start if we are new to next best product?
Start with a narrow scope—one or two journeys, a limited product set, and clear eligibility rules. Build simple models, test against control groups, prove value, then expand to more products, channels, and AI capabilities.

Turn Next Best Product into Real Growth

We’ll help you design decisioning, models, and AI agents so every interaction can surface the right product for each customer.

Explore Financial Services Solutions Explore the FI AI Agent
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