Why should leaders hardwire proactive service and next-best-action into 2026 plans?
Executives face rising service costs, impatient customers, and tightening privacy enforcement. Leaders who treat customer service as a strategic engagement channel outperform peers on cost and loyalty by shifting from reactive case handling to proactive alerts and next-best-action decisioning. McKinsey analysis shows AI-enabled customer service can reduce interactions by 40 to 50 percent, increase digital self-service adoption, and cut cost-to-serve by more than 20 percent when executed well.¹ Australian regulators have also raised the bar on consent and electronic marketing, with ACMA publishing a 1 July 2024 Statement of Expectations and escalating fines for breaches of the Spam Act 2003.² ³ These forces create a practical mandate for a proactive system that anticipates needs, chooses the right intervention, and respects consent.
What do “proactive alerts” and “next-best-action” actually mean?
Proactive alerts are timely, relevant notifications that prevent avoidable effort or risk. They include outage notifications, fraud alerts, policy or billing reminders, delivery updates, and contextual self-service prompts that avert inbound demand. Forrester-backed industry research highlights benefits such as lower repeat contacts and higher revenue when proactive communication sits inside an omnichannel strategy.⁴ Next-best-action (NBA) is a decisioning approach that uses real-time context and AI to select the most relevant action for an individual customer across sales, service, and retention moments. Pega describes NBA as AI plus real-time interaction data to deliver hyper-relevant experiences, moving beyond static campaigns to dynamic decisions at every touch.⁵ In short, proactive alerts reduce friction before it happens and NBA selects the right move when a moment arrives.
How does an NBA engine actually decide the “right” action?
A practical NBA engine fuses three components. First, an eligibility and compliance layer respects policies and consent, which is non-negotiable in Australia under the Spam Act and OAIC guidance on APP 7 direct marketing.² ⁶ Second, an estimation layer predicts outcomes for each customer and action. Uplift modeling estimates the causal effect of an action on a specific individual by comparing treatment versus control outcomes, which helps target actions that change behavior rather than those that would have happened anyway.⁷ ⁸ Third, a learning layer uses contextual multi-armed bandits to balance exploration and exploitation in real time. The bandit adapts treatments to customer context and continuously improves payoffs for each segment and individual.⁹ ¹⁰ Together, these layers allow the engine to make decisions that are relevant, testable, and safe.
Where do proactive alerts and NBA fit in the operating model?
CX, contact center, digital, data, and risk all play defined roles. CX sets customer promises and success measures. Contact center leaders instrument channels to send alerts and capture outcomes. Digital and product teams wire real-time events from apps, web, billing, and logistics. Data and analytics teams own uplift and bandit models and create transparent features and bias checks. Risk and legal set guardrails for consent and sensitive use cases, referencing ACMA and OAIC guidance.² ⁶ A cadence of weekly triage and monthly portfolio reviews keeps the decisioning library healthy. McKinsey’s work on the human plus AI contact center reinforces the need for a hybrid model where digital decisioning supports agents, not just customers.¹¹ The structure is cross-functional by design because proactive service impacts every journey.
What journeys produce the fastest impact with minimal risk?
Leaders should start where intent is service-oriented and outcomes are unambiguous. High-yield candidates include billing and payment reminders with compliant consent flags, appointment confirmations and rescheduling prompts, delivery windows and failed delivery recovery, network or service outage updates with expected restoration times, and fraud or security alerts that direct to verified self-service. Forrester-referenced research via Genesys shows reductions in repeat calls and agent costs when proactive communications run inside an omnichannel strategy.⁴ These journeys reduce avoidable contacts, increase digital containment, and protect trust. They also generate clean feedback signals that improve the NBA engine faster than promotional use cases.
How do we measure success without gaming the system?
Measurement must track causal impact, channel health, and compliance. Uplift curves and the Area Under the Uplift Curve (AUUC) capture whether actions change outcomes versus a matched control.¹² Portfolio-level dashboards track avoided contacts, first contact resolution, digital containment, time to resolve, and net revenue retention. Cost-to-serve reduction should align to finance reconciliations.¹ Customer trust and permission health matter as much as efficiency. Track consent capture rates, unsubscribe rates, and complaint rates against ACMA and OAIC expectations.² ⁶ Use pre-registered experiments for sensitive treatments and enforce channel fatigue limits that suppress outreach when customers show saturation. A decisioning review board should own kill switches for any action that drifts from intent or harms equity.
What governance keeps us compliant while staying fast?
Governance starts with explicit definitions. Direct marketing in Australia requires consent and easy, functional opt-out in every message.² ³ OAIC guidance advises adopting APP 7 standards across direct marketing communications and honoring opt-out requests promptly.⁶ ACMA has escalated enforcement and publicized penalties for breaches, including high-profile fines that underscore the cost of getting consent wrong.³ ¹³ The playbook should require tagged consent metadata in every event, channel-specific unsubscribe mechanisms, and suppression logic tied to consent object status. A model risk framework should document applicable use cases, features used, fairness checks, and human escalation paths. The NBA engine should always pass a compliance gate before considering predicted value.
How do we build the platform without boiling the ocean?
Start with a thin decisioning slice. Prioritize one to two journeys with clear events and easy outcome capture. Implement an event stream from core systems, a consent service that any channel can check in real time, and a compact library of actions and treatments. Use uplift modeling for targeting and a contextual bandit to learn the best treatment per context.⁷ ⁹ Deliver alerts in channels with proven reach and clear opt-outs, such as SMS and app push for service notifications that meet consent rules.² ⑤ Deliver agent assist next-best-actions inside the contact center desktop to amplify human judgment.¹¹ Expand from service to retention and cross-sell only when permission and value logic are mature. This sequencing keeps the focus on outcomes, not infrastructure.
What does the playbook look like in practice?
The playbook defines a repeatable lifecycle. Define the customer problem and target state. Map consent and compliance prerequisites with ACMA and OAIC references.² ⁶ Frame the hypothesis as uplift and expected business impact. Build actions and treatments with testable variations. Score eligibility and predicted outcomes in real time. Activate experiments that use bandits to allocate traffic adaptively.⁹ ¹⁰ Observe uplift, contact deflection, and cost-to-serve changes.¹ Publish decisions to customer-facing channels and agent desktops.¹¹ Retire actions with negative uplift and promote those with sustained benefit. Document learnings and update guardrails. This lifecycle keeps the system honest and accelerates learning with every message and call.
What impact should executives expect in year one?
Executives should expect measurable cost reduction, fewer avoidable contacts, and better customer effort scores within 90 to 180 days for service use cases.¹ ⁴ Mature programs report double-digit cost-to-serve savings and significant gains in digital containment when alerts and NBA run as one system.¹ Leaders should also see risk reduction as consent management becomes systematized. ACMA’s recent enforcement activity shows that non-compliance carries real financial and reputational risk in Australia, which proactive governance helps avoid.³ A disciplined cadence that reviews uplift, fairness, and permission metrics ensures scale without surprises. The impact compounds because every decision is a data point that teaches the engine to serve the next customer better.
Implementation blueprint leaders can start this quarter
Leaders can move now with a three-sprint plan. Sprint one wires events, consent checks, and a small action library for two service journeys. Sprint two stands up uplift modeling with a control holdout and launches contextual bandit allocation for treatment variants.⁷ ⁹ Sprint three embeds agent assist next-best-actions and builds an executive dashboard that shows avoided contacts, AUUC, cost impact, and permission health.¹ ¹² Governance runs throughout with ACMA and OAIC compliance gates, unsubscribe verification, and reporting templates.² ⁶ This compact blueprint delivers value early while laying the foundation for scaled NBA across the enterprise.
FAQ
What is next-best-action decisioning in customer service?
Next-best-action is a real-time decisioning approach that uses AI and current interaction data to select the most relevant action for each customer, replacing static campaigns with dynamic, contextual decisions.⁵
How do proactive alerts reduce contact center load?
Proactive alerts prevent avoidable contacts by notifying customers about issues like outages, deliveries, billing, or fraud before they call, which increases self-service use and reduces inbound volume and cost-to-serve.¹ ⁴
Which algorithms power an NBA engine for CX at scale?
Uplift modeling targets customers whose behavior will change because of an action, and contextual multi-armed bandits learn the best treatment per context while balancing exploration and exploitation.⁷ ⁹ ¹⁰ ¹²
Why does consent governance matter for proactive communications in Australia?
The Spam Act 2003 and OAIC guidance require consent, sender identification, and easy opt-out for electronic marketing. ACMA’s 2024 Statement of Expectations and recent fines highlight active enforcement.² ³ ⁶
Which journeys are best to start with at Customer Science scale?
Start with service journeys that have clear events and outcomes, such as billing reminders, appointment updates, delivery notifications, outage communications, and security alerts, then expand to retention and cross-sell.⁴ ¹
Who should own the NBA playbook inside an enterprise?
A cross-functional unit spanning CX, contact center, digital, data science, and risk should own the library of actions, consent policy, uplift and bandit models, and a monthly review that promotes or retires treatments.¹¹ ² ⁶
Which metrics prove value to C-level stakeholders?
Track uplift and AUUC to prove causal impact, cost-to-serve reduction, avoided contacts, digital containment, and permission health such as consent capture and unsubscribe rates aligned to ACMA and OAIC guidance.¹ ¹² ² ⁶
Sources
The next frontier of customer engagement: AI-enabled customer service, McKinsey & Company, 2022, Operations Practice. https://www.mckinsey.com/capabilities/operations/our-insights/the-next-frontier-of-customer-engagement-ai-enabled-customer-service
Consent expectations for businesses using direct marketing, Australian Communications and Media Authority, 2024, Statement of Expectations. https://www.acma.gov.au/articles/2024-06/consent-expectations-businesses-using-direct-marketing
Avoid sending spam, Australian Communications and Media Authority, 2025, Spam Act 2003 guidance and enforcement context. https://www.acma.gov.au/avoid-sending-spam
The untapped benefits of proactive customer communication (Forrester-referenced paper hosted by Genesys), Forrester Research, 2023, Thought Leadership. https://www.genesys.com/en-gb/resources/forrester-paper-the-untapped-benefits-of-proactive-customer-communication
What is Next Best Action?, Pega, 2025, Product and concept overview. https://www.pega.com/next-best-action
APP 7 Direct marketing and Direct marketing guidance, Office of the Australian Information Commissioner, 2025, Privacy Act guidance. https://www.oaic.gov.au/privacy/australian-privacy-principles/australian-privacy-principles-guidelines/chapter-7-app-7-direct-marketing and https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/organisations/direct-marketing
Rzepakowski, P., Jaroszewicz, S., Uplift Modeling in Direct Marketing, Journal of Telecommunications and Information Technology, 2012. https://www.researchgate.net/publication/282845041_Ensemble_methods_for_uplift_modeling (open PDF link on page)
Jaskowski, M., Jaroszewicz, S., Uplift modeling for clinical trial data, ICML Workshop on Clinical Data Analysis, 2012. https://people.cs.pitt.edu/~milos/icml_clinicaldata_2012/Papers/Oral_Jaroszewitz_ICML_Clinical_2012.pdf
Optimizing customer engagement with the contextual bandit and AI for Next-Best-Actions, Pega Customer Decision Hub Docs, 2025. https://docs.pega.com/bundle/customer-decision-hub/page/customer-decision-hub/cdh-portal/contextual-bandit-nbad.html
Optimizing Recommendations with Multi-Armed and Contextual Bandits for Personalized Next Best Actions, WiDS Worldwide, 2025, Workshop video and notes. https://www.widsworldwide.org/get-inspired/video/optimizing-recommendations-with-multi-armed-and-contextual-bandits-for-personalized-next-best-actions/
The contact center crossroads: finding the right mix of humans and AI, McKinsey & Company, 2025, Operations Practice. https://www.mckinsey.com/capabilities/operations/our-insights/the-contact-center-crossroads-finding-the-right-mix-of-humans-and-ai
Nyberg, E. et al., Uplift Modeling with High Class Imbalance, Proceedings of Machine Learning Research, 2021. https://proceedings.mlr.press/v157/nyberg21a/nyberg21a.pdf
Retailer’s huge fine for customer spam, news.com.au, 2024, ACMA enforcement coverage. https://www.news.com.au/finance/business/retail/eyewear-company-fined-15m-for-spamming-customers-with-more-than-200000-emails-in-six-months/news-story/f545c52ebcb7a2e6a3a866b708be9606