What is Personalized Search in Ecommerce and How to Excel

Ecommerce personalized search is exactly what it sounds like: it tailors highly relevant and dynamic search results for each of your customers based on their behavior, search intent, and preferences. A great example is Amazon, which displays recommendations based on what you’ve previously searched, browsed, and purchased. If you often buy books, you’ll see more book suggestions on your homepage and in product recommendations while you shop.

There are four key principles of personalization:

1. Relevance: Delivering content that aligns with what users truly want and need.
2. Recognition: Keeping track of user preferences and behaviors to curate a customized experience.
3. Responsiveness: Providing timely and valuable content that resonates with users at the right moments.
4. Results: Evaluating the impact of personalization efforts and refining strategies for user engagement improvement.

Let’s start with some statistics. A striking 71% of customers anticipate personalized interactions from brands, and 76% feel frustrated when those expectations aren’t met. By addressing the needs of this large segment, you can significantly enhance customer retention, elevate conversion rates, increase average order values, and maximize revenue per user. The more tailored your search results are, the better your metrics will be.

Another important reason why personalized search matters is that a whopping 45% of Gen Z will likely leave a store if the search doesn’t predict what they want or need. Generation Z is set to become a powerhouse in consumer spending! With $360 billion in disposable income worldwide, they’ve become a dominant force in the market, influencing trends and making brands sit up and listen. So, if you want to keep thriving, catering to their preferences is key!

Let’s begin with the common challenges store owners face with search results. Many times, these results can be irrelevant, fail to match the user's search intent, or, in the worst case, lead to a “no results” page. Some store owners can’t help but wonder: does it really matter?

Yes, it absolutely does. The reason why search relevance matters in online stores is that it can significantly enhance the customer experience, reducing bounce rates and increasing sales potential. You might be asking: how personalized search improves relevance in eCommerce? The answer lies in tailoring search results to each user based on their preferences, behaviors, and contextual factors.

For instance, if a customer searches for "t-shirts," the search engine might also take into account related terms like "graphic tees," "casual tops," and "shirts." By including these synonyms, you can broaden the results and decrease the likelihood of customers running into zero or irrelevant results.

By focusing on personalized search, businesses can increase sales with relevant search results. Ultimately, search relevance in eCommerce is crucial; leveraging it can lead to a better search experience enhancement and boost overall sales.

Here are a few more reasons why personalization is so important:

Implementing personalized search can really set you apart from the competition. Why is that? Well, only 15% of customers feel satisfied with their online shopping experiences. What’s even more striking is that just 15% of retailers have embraced full personalization strategies. This highlights an opportunity for you to stand out and draw in customers by adopting personalized search techniques.

Personalization can make a significant contribution to the success of an online shop owner: it can drive customer satisfaction by 20%, boost sales by 10-15%, and increase employee engagement by 20-30%. Plus, 80% of consumers prefer to buy from brands that deliver personalized experiences, which can lower bounce rates - resulting in fewer visitors leaving your site without engaging. By creating a personalized shopping experience, you delight customers and improve overall performance.

Organizations that embrace extensive personalization see significant improvements in customer retention. In fact, 43% of them report enhanced customer outcomes as a result of their dedication to nurturing long-term relationships. This focus on personalization builds loyalty and encourages customers to keep coming back.

The main drivers of personalization are the advancements in technologies, namely artificial intelligence (AI) and machine learning.

Now, let's see how search personalization for online stores works in general using the Serchanise Search & Filter app from Shopify as an example. When a customer visits certain product pages on your website, the app tracks the product type and product category. After the customer has visited more than five product pages, personalization affects the dynamic search results: the more relevant products will appear higher in the results.

For example, the picture below shows the search results page after searching "t-shirt" on the first visit to the website.

The second picture shows the search results for "t-shirt" after the user has viewed 5+ products for women. As you can see, women's t-shirts appear higher in the dynamic search results. It allows customers to find the relevant product faster, resulting in an enhanced customer experience and higher customer satisfaction.

Like any other business owner, you may be wondering what tangible impact personalized search can have on your online shop. Well, just in that case, here’s an infographic with key statistics that highlight its significance.

To effectively personalize and boost the relevancy of your search function, consider using effective techniques such as AI-driven personalized search, search suggestions, geo-targeted search, and product recommendations. Implementing these techniques can enhance your core metrics, enhance user experience, and provide a competitive advantage. Let’s dive deeper into each of them in detail.

AI personalization in eCommerce means using artificial intelligence to offer relevant product recommendations that align closely with individual user preferences and behaviors.

Imagine a customer entering “black jacket” in the search bar and instead of being bombarded with unrelated items like black shoes or shirts, they’re presented with a selection of black jackets that match their style and preferences based on their previous interactions. This is a prime example of AI-powered search results. This personalized approach helps customers find what they want quickly, enhancing the shopping experience.

Some of you may have already faced challenges with your search function, such as:

• Poor Relevance: Generic search engines often fail to account for individual preferences, past behaviors, or synonyms, resulting in a broad range of unrelated products that waste users' time and effort.
• Increased Bounce Rates: If users can’t find what they want quickly enough, they are more likely to leave the site.
• Lower Conversion Rates: Unsuccessful searches diminish the likelihood of making a purchase.

If you face these challenges, use AI-driven personalized search with the Searchanise Search & Filter app. The app with advanced search features for online stores allows you to personalize and provide relevant search results for eCommerce based on user preferences and behavior, filtering out irrelevant content. With adjustable priority settings for four customer actions and the ability to focus on specific elements like category, brand, and product type, Searchanise delivers an exceptional tailored search experience. The benefits of AI in personalized search include, but are not limited to, enhancing the shopping journey and driving sales growth for your online store.

Top product personalization provides tailored search experience by showcasing the most relevant products in sections like “Top Picks” or “Best Sellers.” The products shown are specifically chosen based on each customer’s unique preferences, previous purchases, and browsing history. By carefully curating these selections in your store, you highlight products they are likely to buy.

Imagine a customer searching for “gaming laptops” at an online electronics store. When they return to the homepage after browsing products, they’re presented with a “Top Picks for You” section showcasing highly relevant gaming laptops that match their previous interests. With this approach, customer attention is captured and the shopping experience is improved.
Here are some common challenges merchants face:

• Low Engagement: Generic product recommendations often fail to resonate with users, leading to disengagement.
• Missed Sales Opportunities: Customers are more inclined to overlook generic top products, resulting in potential sales being left on the table.
• Decreased User Satisfaction: Irrelevant suggestions can frustrate users, making them feel that the store doesn’t cater to their needs.

To effectively personalize the "Top Products" section for each user, consider implementing these specific techniques:

• Dynamic Curation: Automatically update the “Top Picks” section based on real-time behavioral data in eCommerce. For example, if a user frequently browses gaming laptops, prioritize those in their recommendations.
• Segmented Recommendations: Group users into segments based on shared preferences or behaviors. This allows you to tailor the homepage experience not just for individual users but also for like-minded groups.

Personalized search suggestions are based on predictive search technology and provide popular suggestions as users type in the search bar. Such a function helps users quickly find products by offering highly relevant suggestions and providing insights into popular search terms and trends. As a result, it enhances the user experience and reduces friction during shopping.

For instance, when a customer begins typing "running s...," the autocomplete feature immediately suggests "running shoes," drawing on their past interactions with the store. These customized suggestions match the customer's intent perfectly, allowing them to effortlessly find exactly what they need without any fuss.

However, you may face some challenges, such as:

• Generic Suggestions: Standard autocomplete features in search bars often provide vague or unrelated product recommendations. This can confuse users and decrease the chances of them locating the specific item they want.
• Slow Search Process: When irrelevant suggestions pop up, users may have to spend extra time refining their searches manually. This not only makes the process more tedious but can also lead to frustration, resulting in higher bounce rates.

To deliver relevant search suggestions, consider implementing the Searchanise Auto-Suggest feature. This tool automatically generates suggestions, ensuring customers see the right options for them. By using this app, you streamline the search journey, allowing users to find what they’re looking for with ease. Refining search queries and delivering accurate results creates a seamless shopping experience.

Geo-located search personalization tailors product displays and offers based on the user's specific location, enhancing search relevance in eCommerce. By pinpointing a buyer's location, you can create a customized search experience that highlights popular or available products and services nearby.

For instance, a traveler uses Airbnb to find a place to stay. When they search for "cozy cabins" around the Alps, the service returns relevant options that are popular in the mountains. This personalized approach makes it easy for them to discover exactly what they're looking for in the dynamic search results, enhancing their experience.

However, you might face some challenges:

• Lack of Local Offers: Customers may not see products or promotions that are available in their region, leading to missed engagement opportunities.
• Missed Sales Opportunities: Regional differences in demand aren’t taken into account, meaning users may not see the products most relevant to them.

To provide local offers and capture sales opportunities, implement some of these techniques:

• Dynamic Inventory Display: Integrate your inventory system with geolocation data to automatically show users only the products available at nearby locations or warehouses.
• Weather-Based Recommendations: Use local weather data to suggest relevant products. For example, if it’s snowing in a user’s area, promote winter gear like coats and boots.
• Cultural Relevance: Tailor product suggestions based on local cultural events or holidays. For example, promote camping gear or essentials during a local music festival in the community.
• Local SEO Optimization: Ensure your product pages and content are optimized for local search terms. This includes using keywords relevant to specific geographic areas.
• In-Store Pickup Options: Highlight products available for in-store pickup at nearby locations, making it convenient for users to purchase online and pick up locally.
• Community Reviews: Display reviews and ratings from customers in the same geographic area to build trust and relevance, making it more likely for users to convert.

Personalized product recommendations tailor relevant suggestions to each user’s search and browsing history. Based on a customer’s past interactions, you can offer highly personalized recommendations.

Let’s say a customer who previously browsed a product page for customized mugs on Etsy left without making a purchase. When they return, the store greets them with customized mugs they’ve seen last time, increasing the chances of conversion.

Here are some common challenges you might encounter in your store:

• Lack of Personalized Recommendations: Without personalized product recommendations based on past activity, users are less likely to make a purchase.
• Decreased Conversion Rates: Customers who aren’t presented with relevant recommendations may leave without buying, reducing overall sales.

To provide personalized product recommendations and drive higher conversion rates, think about implementing the Recently Viewed Widget from Searchanise Upsell & Marketing. This widget serves as a helpful reminder of products customers have previously explored, encouraging them to finalize their purchases. By integrating this app, you can offer personalized product recommendations that align with each shopper’s interests, ensuring they encounter relevant items whenever they browse or return to your store. With this feature, you’ll not only improve conversion rates but also enhance customer experience with personalized search.

Personalized search is no longer just a nice-to-have feature for eCommerce stores - it's a necessity for improving user experience, boosting engagement, and driving conversions. By using techniques like AI-driven personalized search, geo-location, search suggestions, and product recommendations, online stores can ensure that every search interaction is tailored to individual users, fostering satisfaction and loyalty.

Today is the perfect time to maximize the relevance of your online store's search experience and witness the tangible impact on your bottom line. Install Searchanise Search & Filter and Searchanise Upsell today to thrive tomorrow!