In today's crowded e-commerce landscape, businesses can no longer compete on product selection and price alone. Customers have come to expect personalized, tailored shopping experiences that meet their individual needs. Search is the primary way most customers navigate e-commerce sites and discover products, so optimizing on-site search with personalization is key to gaining a competitive advantage.
According to the Braymard Institute, up to 72% of e-commerce sites fail to meet customer expectations for their on-site search capabilities¹. Improving on-site search is a massive opportunity for e-commerce businesses to serve their customers better and drive real business results.
Personalized search powers engaging, customized experiences
As machine learning and AI continue to advance, customers now expect e-commerce sites to tailor their experience based on individual interests and behaviors. Personalized product recommendations, curated search results, and predictive search all create an engaging, streamlined experience that keeps customers on site longer and converts more sales.
Per McKinsey & Company, e-commerce sites that personalize their experience see revenue increases of 10-15%.² Personalized search, in particular, offers three key benefits:
- Higher conversion rates. By surfacing the most relevant products for each customer, personalized search has been shown to increase conversion rates by up to 50% or more.³
- Improved customer loyalty. A customized experience builds trust and loyalty, leading customers to return to the site again and again. Loyal customers also spend more and refer others.
- Lower bounce and abandonment rates. Personalized results reduce frustrating searches, irrelevant items, and dead-end pages, keeping customers actively engaged on site for longer.
Optimizing search for a seamless omnichannel experience
Today's consumers interact with e-commerce sites through many different channels, including mobile apps, voice assistants, and more. Optimizing your search experience for each channel allows you to serve your customers consistently wherever they shop.
Key capabilities for omnichannel search include:
- Mobile-friendly and voice-ready. Ensure your site is fully responsive, content is optimized for small screens, and metadata supports voice interfaces and featured snippets. A majority of searches now happen on mobile devices.
- Cross-channel personalization. Use a customer's full browsing and purchase history across channels to personalize their experience in each channel. For example, suggest mobile app or voice purchases based on recent web orders.
- New search pattern optimization. Track how users search in each channel and optimize to rank for emerging phrases like "Find me..." or "Show me..." in voice interfaces. Then tune your search algorithms accordingly.
- Unified data architecture. A shared data platform across web, app, voice, and other interfaces enables cross-channel personalization and consistent experiences. Synchronize data, product catalogs, recommendations, and search algorithms in one system across endpoints.
Best Practices for Personalized On-Site Search
Improve product metadata
Well-optimized product metadata, including titles, descriptions, and keywords, ensures your items rank highly in search results and provides enough context for customers to determine relevance.
Best practices include:
- Customer-focused. Use words and phrases customers would naturally search for to describe each product. Keep terms broad enough to rank for various searches but specific enough to convey what the product is.
- Insightful. Write product descriptions that give customers a good sense of key features, specifications, and benefits to determine if the item meets their needs before clicking. Descriptions should be 3 sentences or roughly 150 words.
- Optimized. Include important keywords, especially brand and product names, prominently in titles, and URLs if possible. Place key phrases at the beginning of descriptions for maximum impact.
- Actionable categories. Group products into logical, easily navigable category trees. Category names should also contain relevant keywords to help items rank in searches.
- Enhanced attributes. Supplement product metadata with additional structured data for attributes like color, size, price range, etc. to provide more filtering and sorting options for customers.
- Mobile-optimized. Keep titles short and format metadata to appear clearly on small screens with large font sizes and minimal clutter. We’ll go into further detail with in-depth recommendations below.
Personalize recommendations and results
Advances in machine learning now allow e-commerce sites to implement tools like ontology edition and taxonomy management to tailor their recommendations and search results towards individual customers for a personalized experience.
Options to consider include:
- Behavioral personalization. Track customers' browsing and purchase history to recommend related products they're most likely interested in. For search, show results tailored to a customer's past interactions.
- Predictive personalization. Machine learning algorithms can analyze behavior of customers with similar attributes to recommend new products a customer is predicted to engage with or search for based on the interests of similar groups.
- Geospatial personalization. For stores with physical locations, recommending and prioritizing products that are in-stock at a customer's nearest store drives higher conversion rates, especially for larger purchases. Store inventory feeds can populate geo-personalized search.
- Customer profiling. Ask new customers a few questions about their interests and attributes to begin tailoring their experience from first visit. Enhanced profiles with demographic, interest, and other data can power more personalized recommendations and results over time.