Contextual search for ecommerce: Where intent meets action

Key takeaways from this article

• Move beyond keyword-based search and use behavioral data, preferences, and first-party cookies to infer intent and deliver relevant results instantly.
• Guide, don’t just filter. Ask needs-based questions that tailor results from the start of the session.
• Use AI to personalize and adapt. AI tools like Zoovu’s Data Platform maps user intent to products, to optimize recommendations.
• Upsell with purpose. Context-aware suggestions (e.g., recommending rubber boots after a rain jacket purchase) improve conversion without annoying users.

It’s easy to treat conversation design as a set-it-and-forget-it task by mapping questions to answers and assume customers will convert.

But ecommerce isn’t that simple.

Shoppers search by brand, feature, use case, or SKU. They make mistakes. They change intent mid-session. The journey from search to discovery isn’t linear, and it rarely follows the path you expect.

To support sales, product growth, and lead generation, conversation design needs to evolve. The best assistants go beyond basic FAQ responses. They anticipate needs, infer intent, and guide customers using workflows and search built for discovery.

At the center of it all is contextual search.

Contextual search: The foundation of intent-driven ecommerce

Most ecommerce platforms still rely on outdated keyword-based search engines. When customers search in natural language, asking for “running shoes for a marathon” or a “laptop for gaming”, they’re met with irrelevant, or often overwhelming results. This disconnect leads to search abandonment and lost sales.

Contextual search changes that.

A contextual search engine uses signals like device type, entry point, behavior, and search history to understand what a shopper actually means, not just what they type. Platforms like Zoovu take this experience further through a combination of Natural Language Processing (NLP), ontology-based structuring, and large language models (LLMs).

Zoovu’s AI organizes your product catalog into a semantic structure, connecting attributes like size, model, compatibility, and usage. This structure enables query-independent matching, so even vague or subjective searches return meaningful results. When a shopper types “laptop for working remotely,” Zoovu identifies relevant specs like battery life, weight, display size and delivers precise, high-converting options.

Even with limited user data such as a first-time visitor, Zoovu leverages first-party cookies to determine behavior, infer intent, and adapt search results in real time. This allows for session-based personalization that continuously improves as the shopper browses.

With contextual search, you don’t just filter products, you guide decisions.

Examples of first-party cookies in ecommerce

First-party cookies power contextual search by collecting key product insights and behavioral data without relying on third-party tracking. These are the foundation of any contextual search plugin or AI-based tool that delivers relevant, personalized product recommendations:

• Recent searches: Help to filter out irrelevant results that technically match keywords but don’t align with actual intent.
• Shopping cart contents: Inform follow-up offers and context for cross-selling opportunities.
• Product category views: Help define session-based intent.
• Quiz responses or configurator inputs: Inform decision downstream product discovery logic.

These signals contribute to a single platform view of each customer. Essential for delivering simpler experiences and secure experiences at scale.

Ask to understand: Guided selling unlocks context

Clicks can’t reveal true needs but guided questions can.

Zoovu’s guided selling assistants are a conversation tool designed to surface needs-based insights. Rather than asking “Do you want 16GB or 32GB RAM?”, they ask: “Are you gaming, traveling, or working remotely?”.

This shift supports faster decisions and reduces friction for the individual user. It also helps enterprise retailers and partner applications deliver smarter, more targeted product discovery. Microsoft, for example, uses guided selling across hundreds of retail partners to help users find their ideal Surface computer while delivering care with quality at scale.

5 goals you can achieve with contextual search and conversational AI

1. Increase engagement by guiding the journey

When abandonment is high, it’s often not a product issue, it’s a relevance issue.

Shoppers aren’t just overwhelmed by too many options, they’re not seeing the right ones.

Guided selling tools like quizzes help cut through the noise. They narrow choices based on need, then send that input like preferences, use cases and constraints into a central decision engine.

This data powers real-time product recommendations across channels.

Example: A shopper looking for “wireless headphones for workouts” gets filtered results based on their quiz input (sweat-proof, in-ear, under $100), not just a long list of wireless SKUs.

The result: lower abandonment, higher relevance, and improved product discovery across ecommerce and digital service workflows.

2. Turn conversations into qualified leads

Value-led prompts like “Want a custom skincare plan? Answer three questions” shift shoppers away from static filters toward dynamic, personalized experiences.

Instead of browsing endless SKUs, customers get tailored recommendations based on real input, turning casual interest into high-intent engagement.

GE HealthCare’s self-service configurator is a strong proof point for B2B ecommerce companies onboarding a contextual search tool as well. It automated a complex product selection process, cutting resolution time by 80%, and improved CPQ performance.

3. Upsell and cross-sell, contextually

Context is the difference between recommending boots and recommending the right boots.

If a shopper adds a rain jacket, then later searches for “black boots,” Zoovu’s contextual system connects the dots. It surfaces rubber boots first, not just any black pair.

This kind of relevance isn’t guesswork, it’s powered by AI that maps user behavior to product attributes in real time. The result: smarter recommendations, faster decisions, and stronger performance across every category.

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4. Deliver truly personalized product recommendations

Instead of letting customers apply filters post-search, a contextual system proactively guides them through search.

Start by asking preference-based questions: “Do you prefer matte black or silver?” Or use goals: “Are you commuting or hiking?”

These answers can be mapped to product attributes in real time—material, weight, battery life, or lens compatibility—and used to re-rank results based on intent, not just keywords.

Canon does this well with its camera finder. Instead of browsing dozens of models, shoppers answer a few quick questions about use case (travel, sports, portraits), experience level, and feature preferences. The system then narrows the selection to a few high-fit recommendations, making the path to purchase shorter, smarter, and more relevant.

5. Push promotions that actually make sense

Contextual merchandising goes beyond guesswork. It uses real-time shopper signals to surface the right bundles, at the right moment.

If a shopper is comparing options, show a curated lookbook or complementary set. If they’re showing purchase intent, present a bundled offer that increases cart size without relying on discounts.

With tools like Zoovus, merchandising teams can build, test, and scale these experiences across product lines, configuring logic once and applying it everywhere.

The result: smarter bundling, higher AOV, and merchandising strategies that adapt in real time without added operational complexity.

Search that learns: How AI powers contextual discovery

Zoovu’s intelligent platform is driven by four pillars:

• Semantic enrichment: Converting technical specs into searchable context.
• Real-time recommendations: Automatically adapting to session data, performance at scale, and dynamic user inputs.
• Predictive flows: Adjusting pathways based on quiz or configurator input for a proactive care experience.
• Ontology-based analysis: Mapping relationships between related terms, product categories, and user behaviors to improve application designer logic.

Why contextual search is a competitive advantage

According to recent industry analysts, 85% of ecommerce brands still fail to deliver guided discovery experiences. Most rely on static filters or outdated search tools—missing the upside of a truly intelligent experience.

That gap is where tools like Zoovu drive growth. By reducing friction and delivering context-aware search, merchandising and bundling, they enable smarter discovery across industries like retail, manufacturing, electronics, and more.

Ready to compete with context?

If your ecommerce experience is still running on traditional keyword search, you’re not just outdated, you’re missing revenue.

Contextual search, enhanced by AI, conversational selling, and product discovery tools, is the key to modern ecommerce. It creates a bridge between business processes, unlocks product insights, and enables secure, personalized, and scalable operations across millions of customers using your platform.

Experience it yourself. Book a demo and transform your ecommerce journey with contextual search.