Over the years, commerce has evolved drastically, from physical stores to digital carts and mobile checkouts. Today, it is conversations that drive transactions. Conversational commerce blends messaging, voice, and AI capabilities to turn customer service interactions into real-time sales opportunities. Mordor Intelligence Report estimates that the conversational commerce market will grow from $11.26 billion in 2025 to $20.28 billion by 2030, at a 12.47% CAGR. This shift positions contact centres as active revenue engines, not cost centres.
what is conversational commerce?
Conversational commerce enables customers to discover, evaluate, and purchase products through natural conversations across chat and voice channels. It combines AI-powered tools, data-driven insights, and integration layers to deliver contextual, real-time engagement at scale.
Key components that allow businesses to turn customer service into sales opportunities include:
- AI assistants and chatbots: These act as frontline sales and service agents, handling enquiries, guiding purchases, and qualifying leads continuously.
- Natural Language Processing (NLP) modules: NLP interprets intent, sentiment, and context, enabling human-like conversations across text and voice.
- Machine Learning (ML) engines: ML models learn from interactions, improving recommendations, responses, and conversion outcomes over time.
- Orchestration layer: This layer manages conversation flow, switching seamlessly between automation and human agents when needed.
- Analytics engine: Real-time and historical insights track intent, drop-offs, and revenue signals to refine engagement strategies.
- Integration connectors: Pre-built connectors link CRM, order management, inventory, and payment systems for end-to-end execution.
why leverage contact centres for revenue generation?
Contact centres already sit at the intersection of customer intent, trust, and timing. Turning customer service into sales opportunities depends on aligning these interactions with commercial outcomes. A combination of changing customer behaviour, technology maturity, and operational priorities is driving this shift.
meeting expectations for instant, conversational engagement
Customers expect immediate responses across channels they already use. Messaging and voice platforms now dominate everyday interactions, making conversational buying feel natural rather than intrusive.
enabling seamless omnichannel continuity
Customers move fluidly between chat, voice, email, and apps. Conversational commerce platforms maintain context across channels, preventing repetition and friction during purchase journeys.
converting service interactions into revenue moments
Service conversations reveal intent signals. AI chatbots for sales help identify upsell, cross-sell, and renewal opportunities during issue resolution, without disrupting the experience.
improving efficiency while scaling revenue
Automation reduces handling time and operational costs. At the same time, it enables consistent revenue engagement across high-volume interactions.
differentiating in saturated markets
Personalised conversations, dynamic offers, and real-time support create a competitive advantage where products and pricing look similar.
Technology underpins the scalable transformation of contact centres into revenue generation engines. Infosys BPM combines AI, analytics, and domain expertise to embed conversational commerce within customer service outsourcing solutions, enabling revenue-focused engagement across digital and voice touchpoints.
scaling conversational commerce: best practices
Scaling conversational commerce requires disciplined execution across data, technology, and people. Each step builds towards turning service conversations into measurable sales outcomes.
- Establish a unified customer data foundation: Centralise customer profiles, interaction history, and behavioural data to enable consistent, contextual conversations.
- Design seamless, end-to-end shopping journeys: Map discovery, support, and checkout into a single conversational flow that removes friction at every stage.
- Enable omnichannel continuity by default: Preserve context as customers move between channels, devices, and agents without restarting conversations.
- Balance automation with human empathy: Use AI for speed and scale, while routing complex or emotional interactions to skilled agents.
- Build on modular, API-first architectures: Leverage modular platforms to support rapid integration with CRM, commerce, and payment systems as needs evolve.
- Embed security and compliance by design: Protect customer data and transactions across channels while meeting regional regulatory requirements.
- Continuously optimise using AI-driven insights: Analyse intent patterns, conversion drop-offs, and revenue signals to refine conversational strategies.
- Strengthen teams through training and governance: Equip agents to collaborate with AI tools and maintain consistent commercial messaging.
- Future-proof with emerging interaction modes: Prepare for voice-first, multimodal, and immersive experiences as customer behaviour evolves.
Technological advancements and evolving customer expectations will continue to accelerate this shift. Autonomous AI shopping agents will negotiate and transact independently. Generative AI will enable hyper-personalised, multimodal journeys. Predictive and invisible commerce will anticipate needs before customers ask. Context-aware AI ecosystems and immersive technologies will turn conversations into a long-term strategic advantage.
conclusion
Conversational commerce is redefining how organisations generate value from customer interactions. Contact centres now influence revenue, loyalty, and differentiation in equal measure. By aligning AI-driven conversations with commercial intent, enterprises can transform service touchpoints into scalable growth engines. As conversational technologies mature, organisations that act early will shape customer expectations rather than react to them.
Frequently Asked Questions:
How is conversational commerce different from traditional contact center “support”?
It turns service conversations into guided purchase journeys, not just issue resolution.
It combines AI assistants, intent/sentiment understanding, orchestration, analytics, and CRM/commerce integrations so customers can discover, evaluate, and transact in-channel.
This enables revenue capture at the moment of highest intent and trust.
What risks should leaders mitigate when turning service into sales via AI chat and voice?
The key risks are data security, inconsistent offers, and uncontrolled handoffs between bots and humans.
Leaders should require secure integrations with CRM/OMS/payments, clear routing rules, and governance for approved messaging to avoid compliance and brand risk.
This protects trust while scaling conversion.
Which KPIs prove the contact center is acting as a revenue engine (not a cost center)?
Track conversion rate from service-to-sale, revenue per conversation, and containment-to-conversion quality.
Pair these with operational metrics (AHT, transfer rate, repeat contacts) to ensure revenue uplift is not created by degrading experience.
This links conversational programs to profitable growth.
