How to Maximize Your Store's Revenue Using Data-Driven Search and Product Discovery

To take your online business to the next level, it’s important that you provide the smoothest experience possible for your potential customers. But instead of making guesses that may or may not work, what you need is a more targeted approach based on hard data. 

Your site’s search functionality is one of the best ways to harness the data your site collects. Here are 7 ways that you can do so as mentioned below.

1. Boost best-selling products

To get the best out of every customer interaction, ecommerce businesses can leverage the power of product and consumer data. One of the ways you can do this is by boosting products that consumers prefer in a certain category. Boosting best-sellers can be an effective way of offering the most relevant product suggestions to buyers. 

Reinforcement learning can automate and optimize this boosting by learning from data (like clicks, purchases, or other signals). You can automatically adjust the ranking of products in your search results based on real-time user behavior, giving your customers the best possible experience. 

Dynamic boosting takes it a step further. It allows you to set the weights for different signals that determine the ranking and re-ranking of products. 

2. Shine a spotlight on more profitable products

Another way to boost your ecommerce revenue is by highlighting products that offer higher margins. To get started, take a look at your sales data. Which products are selling the most? Which ones have the highest margin? Look for patterns and trends in this data, and use it to identify your most profitable products.

Once you've identified your most profitable products, start optimizing your product search and discovery around them. Make sure these products are prominently featured on your website and in your search results.

This could mean prominently displaying them on your homepage or category pages or giving them featured placement in search results.

3. Use AI to handle long-tail keywords and typos

These keywords are rather specific and often less competitive than broad search terms (or short-tail keywords). Typos are also common in customer searches, so handling them correctly can be key to getting your products seen by potential customers.

While long-tail keywords might not see much search volume individually, taken together, they represent a substantial proportion of your offerings. Instead of manually optimizing for such keywords and the typos associated with them, it makes sense to let an AI-based search engine chew on data and carry out such optimizations automatically.

For long-tail keywords, AI can be used to identify relevant terms and phrases that traditional keyword research tools may miss. For typos, AI can be used to correct them before the search even happens, ensuring that the user finds what they’re looking for. 

4. Provide better product recommendations and search suggestions

AI search can also be used to generate better product recommendations. By understanding a customer’s past behavior, AI-powered recommendations can predict what they might want to buy next and surface the most relevant products for them. This helps to increase revenue by driving more sales of items that are likely to be of interest to the customer.

It analyzes a customer’s search queries and Clickstream data to understand their intent and provide them with more relevant results. This not only improves the customer experience but also improves the chances of them discovering their intended products and making a purchase.

5. Personalize as many aspects as possible

Personalization is the holy grail of site search: users expect almost everything to be tailored for them specifically rather than being generic. 

The more personalized the experience, the better chance you have of showing relevant results to each individual visitor, and the more likely they are to find what they're looking for (and purchase it).

There are a few different ways to personalize the search and discovery experience:

• Offering personalized recommendations based on past behavior.
• Allowing customers to filter results by their preferences.
• Making it easy for customers to save items for later or add them to a wishlist.
• Keeping track of customer behavior and using it to improve the search algorithm.

6. Carry out data-driven merchandising

Between customer behavior data, product data, and market data, there is a wealth of information that can be used to guide your merchandising decisions. But sifting through all of this data can be daunting. That's where AI search engines come in.

By analyzing data about your customers, you can figure out what items they are most likely to buy and make sure those items are prominently displayed in your store. You can also use data to create targeted marketing campaigns that will attract new customers to your store. AI search engines can adjust (or re-rank) results in real time based on changes in customer demand, ensuring that businesses always have the most up-to-date information.

7. Use data to evaluate your product catalog and implement dynamic re-ranking

Too often, retailers rely on intuition and opinion to make changes to their product catalog and search results ranking rather than using data to drive those decisions.

This strategy might work in the short run and for smaller product catalogs, but as your business grows, relying on data to change the search results ranking serves well to boost your ecommerce performance. 

One way to use data to improve your product catalog is by evaluating which products are being returned or have low sell-through rates. This information can help you make decisions about which products to rerank in your search results or remove from your catalog altogether. AI-driven dynamic re-ranking—which allows you to automatically adjust the ranking of search results and products in real-time based on a number of pre-defined criteria—is a powerful technique to boost your sales and customer experience. 

There are many potential factors that you can use to dynamically re-rank your search results, but some of the most common include:

• Search terms: Adjust the ranking of results based on the specific search term used.

• Product attributes: Re-rank products based on factors like price, availability, or customer reviews.

• User behavior: Modify rankings based on how users interact with your site, such as which results they click on or how long they stay on a product page.

• Purchase history: Use past purchase data to give preference to certain products or categories.

Conclusion - Data-driven search is a powerful tool for revenue generation

By understanding what your customers are searching for and providing relevant results, you can increase your store's revenue in more ways than one. Product discovery remains a key part of this process. By understanding what products are popular with your customers, you can ensure that they are able to find the items they are looking for quickly and easily. 

AI and ML-powered search engines are incredibly potent in delivering this. Search, and discovery solutions like Zevi use neural search and advanced machine learning algorithms to understand customer behavior and search intent. This results in higher search relevance combined with a seamless user experience. To explore this for your business, book a demo with us today.