8 minute read
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.
Common Mistakes to Avoid
While personalized on-site search offers significant rewards, it also brings opportunities for missteps that can hinder your results and frustrate customers if not addressed. Avoid these common mistakes:
Lack of mobile-friendliness
If your site and search experience are not optimized for mobile devices, you lose the opportunity to engage the growing segment of customers shopping on the go and risk poor visibility in mobile search results. Best practices for mobile-friendly search include:
- Responsive, fast-loading design. Ensure your site and search pages render quickly and scale to any screen size. Mobile visitors will abandon slow sites.
- Short metadata. Keep titles, descriptions, and keywords under 60 characters to display in small spaces. Prioritize the most important terms.
- Simplified categorization. Group products into broader categories that translate easily to mobile, rather than long, multi-level menus. The most popular or relevant subcategories for smaller screens are the only ones that need to appear initially.
- Voice search readiness. Optimize metadata, especially page titles, to rank in voice search results. Voice interfaces require different search patterns, so monitor how customers search your site by voice and optimize accordingly.
- Mobile app integration. If you have a native mobile app, ensure search in the app provides a seamless experience and taps into the same personalized recommendations and machine learning capabilities as your web search. Synchronize data and algorithms across web and app for a consistent customer experience in each channel.
Lack of ongoing testing and iteration
Personalized search requires an ongoing optimization loop to continually refine experiences based on how customers engage. Without regular testing and iteration, your search personalization will quickly become ineffective. Key practices include:
- Search analytics. Monitor key metrics like conversion rates, bounce rates, search volume, and average order value for search pages and results to pinpoint opportunities to improve. Look for pages or searches where these metrics lag to determine the biggest impact areas.
- User experience testing. Regularly test your search experience from a customer perspective to find pages that are hard to navigate, unclear, or lack personalized recommendations. See the experience through the eyes of different customer segments to ensure personalization is tailored and relevant for each group.
- Algorithm testing. Machine learning models that power personalized recommendations and results must continue learning and optimizing to keep providing the best, most relevant experiences. Monitor the performance of your algorithms and regularly test new models and logic to achieve the highest metrics.
- Search query analysis. Analyze the actual search terms customers use, especially those that produce few or no results, to gain insights into their intent and discover new ways to optimize search relevance and personalization. Update metadata, algorithms, and the search experience based on trends in search queries and customer feedback.
- Iterate and update. Use insights from analytics, testing, and query analysis to make regular updates to your search personalization, machine learning models, metadata, and site experience. Even small changes can significantly impact key metrics if made consistently and based on the latest customer data. Continual iteration is the only way to stay ahead of rising customer expectations.
If you only personalize part of your on-site search experience or for some customer groups, you miss opportunities to maximize the benefits of tailored search. Strive for personalization that:
- Spans the full journey. Personalize all aspects of search, from initial query to results pages to product details. Don't stop at recommendations without optimizing search visibility and relevance throughout.
- Incorporates all customer data. Tailor each search based on a customer's full browsing and purchase history across all visits, devices, and channels. Limited data provides an incomplete view that hinders personalization.
- Extends to all groups. Many sites only personalize for registered or repeat customers. Identify personalized search opportunities for new and guest visitors as well, such as using geo-personalization or basic behavioral data collection within legal limits of privacy regulations.
- Meets rising expectations. As personalization rapidly becomes the norm, customers expect increasing levels of tailored, relevant experiences. If your search personalization does not continue advancing, it will fall behind expectations and ultimately feel static or generic in comparison to competitors.
- Applies cross-channel. For a seamless customer experience, personalize search across web, app, voice, and any other interfaces. Allow customers to pick up where they left off in one channel when switching to another, using login and shared data to persist personalization across endpoints.
Personalizing your on-site search experience offers significant benefits for both customers and your business. Optimized search drives key metrics and results, such as higher conversion rates, lower bounce and exit rates, and deeper customer insights.
To evaluate the impact of your e-commerce search personalization and continue optimizing, implement CRM tools to measure and monitor essential metrics such as conversion rate, bounce and exit rate, search volume, average order value, and customer insights.
Using AI-driven solutions like Lettria, you can leverage product catalog optimization through personalized search, metadata optimization, and recommendation personalization is critical for e-commerce businesses to stay competitive and deliver the best customer experience.
At Lettria, we’ve already worked with European retail giants like Leroy Merlin on product catalog optimization, deploying ai-driven solutions to overhaul their website’s search feature. Discover our AI-driven approach and get in touch today to see how our solution can work for you.