If you shop on e-commerce frequently, you've noticed changes in the research and decision experience. Recommendation, search, and service resources evolved rapidly, and part of that acceleration came from the adoption of Artificial Intelligence in areas that previously relied on manual configurations.
In 2024, AI will become the infrastructure for critical stages of the journey: product discovery, personalization, service, and measurement. This scenario changes the consumer expectation pattern, which begins to compare the experience of a store with that of other brands and platforms that already operate with AI on a large scale.
Below, see e-commerce trends for 2024, based on recurring points in industry reports (such as The Future of Commerce) and what already appears in the operation of digital retailers.
1) Omnichannel as an operational requirement
In 2024, e-commerce performance depends on consistency across channels and the control of friction in the journey. The purchase can start on the marketplace, continue on the site, go through the social network, generate contact on WhatsApp and end at the physical store. This sequence is already part of buying behavior in several categories.
The challenge is not just to “be present” on multiple fronts. The central point is to reduce ruptures between channels, ensuring that data, offers, history, and service accompany the person.
Stocks that enter the radar in 2024
- Integrated pickup and return (buy online, pick up in store; store return with traceability).
- Social commerce as an acquisition and conversion channel, with direct connection to catalog and service.
- Context-guided service, with continuity of conversation and a unified history.
2) Search for voice and intelligent assistants with a more consistent role
Speakers and assistants are already part of everyday life, and the trend for 2024 is the most recurring use in product inquiries, lists, and replacement. Even when the purchase doesn't finish on the voice device, searching for voice influences discovery and consideration.
This movement requires practical adjustments: catalog structure, well-filled attributes, objective descriptions, and content organization for conversational queries.
Practical implication
- Optimization of pages and categories for direct questions (e.g., “which lightweight running shoe”, “best family airfryer”).
- Standardization of attributes, variations, and specifications, since the voice depends on minimal ambiguity to work well.
3) “Search online, buy offline” with more demanding measurement
The hybrid journey isn't new, but the requirement for tracking is increasing. Many people search digitally, compare reviews and complete the purchase at the store. In 2024, the issue is no longer about recognizing behavior and is about measuring impact and attribution with less signal loss.
With more trackable events and better analytical models (including AI), the company is able to investigate in greater depth which points accelerate conversion and which generate abandonment.
What tends to become a priority
- Unified customer data platform (CDP) to consolidate interactions, consents, and events.
- Clear attribution rules and incremental tests to separate correlation from causation.
- Integration of store data (POS) with campaigns and CRM to close the measurement cycle.
4) Curated personalization: explicit consumer expectation
Personalization ceases to be “differential” and becomes part of the perceived contract when the consumer agrees to share data. In 2024, the expectation is to receive concrete benefits: recommendations with context, coherent offers, communication on the preferred channel, and timing compatible with intention.
AI strengthens this scenario because it reduces the operating cost of personalization at scale, provided that there is data governance and well-defined business rules.
Examples of personalization that impact conversion
- Recommendations based on previous purchases, browsing, and stated preferences.
- Loyalty benefits defined by propensity (discount, gift, shipping, points) and not by a single rule.
- Messages on channels chosen by the customer, segmented by behavior and stage of the journey.
Operational risk
- Inconsistent personalization (recommending an unavailable product, insisting on a rejected category, repeating a message) reduces trust and increases unenrollment.
5) Generative AI applied to service, search, and product content
Generative AI tools gained scale in 2023, and 2024 tends to consolidate uses with more measurable ROI. The practical point is to connect AI to the context of the operation: catalog, policies, logistics, orders, exchanges, and customer data (with consent and controls).
Use cases with direct application in e-commerce
- Knowledge-based chatbots with access to order status, with handoff to human when necessary.
- Site search assistant that interprets intent and filters by need (not just by keyword).
- Generation and improvement of product descriptions with editorial standards, attributes, and search terms.
Criteria for avoiding noise
- Defined sources of truth (catalog, policies, FAQ, CRM).
- Conversation log and response auditing to adjust basis and intent.
- Language guardrails to avoid inventing deadlines, conditions, and availability.
Orchestration of omnichannel e-commerce campaigns
Several of the above trends depend on an orchestration layer: audience definition, offer decision, channel choice, frequency, and measurement. When this is fragmented, the experience loses consistency and personalization falls to generic rules.
One solution for this control is a omnichannel campaign orchestrator, which consolidates data, activates journeys and allows you to govern communication by event and context.
Example in practice
O MKT Suite enables incremental revenue with CRM operations. Within the platform, it is possible to define audience and offer with the support of AI algorithms and behavior-oriented automations.
