E-commerce

How to automatically optimize your product listing pages
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When you think about improving your ecommerce conversion, which specific web pages come to mind?

Your home page and product detail page (PDP) — the one that provides the copy, specifications, and other information about a single item — are probably the first two. They’re definitely key, but the product listing page (PLP) — also called the category page because it lists items in a category — ranks right up there as well. Why? It’s not just a glorified table of contents; it’s a “gateway” to a bunch of potentially lucrative product detail pages. A PLP done right can quickly shepherd customers toward the products they want so they can efficiently check out.

Whether you’re an ecommerce pro or just getting your feet wet with a new business site, you may find the whole idea of ecommerce web page optimization a bit overwhelming. The good news is that with the right AI-driven technology tools, it’s easy to not only improve your PLPs but automate optimization of them. Here are some key things to know about what’s involved.

Data is your friend

Online shopping patterns are at the heart of AI-generated data. A shopper’s clicks, hovers, and scrolls on a product listing page leave a trail of breadcrumbs. People reveal their preferences as they browse content, clueing us in about what else they might be interested in. You can also gather data at more generalized levels. For example, are most shoppers flocking to beach-oriented products on a summer-themed product listing page and showing less interest in ski gear? Your sort order could reflect that, starting with fun items like colorful beach umbrellas.

Turning data into dollars

AI-driven technologies have been steadily improving the online shopping experience with personalization, and this applies to product listing pages as much as any other. Shoppers enjoy being courted as they move across ecommerce sites: personalized interactions have proven so alluring that 76% of shoppers say they’re irritated when they aren’t treated to these pleasant online experiences. In the case of a PLP, that kind of personalization could take the form of highlighting items that visitors are most likely to be interested in.

One reason personalization is so effective is that machine learning tools are always learning from their input — recording subtle shifts in shoppers’ preferences and refining their output. As data from shopper interactions is continually reviewed, the algorithms spot trends and patterns.

Tools for success

What it all comes down to is this: supported by machine-learning algorithms and data analytics, you can automatically place the right information in the right places on your PLPs, applying personalization that wins over your shoppers.
Product performance analysis is one particularly helpful tool. It delves into underlying consumer behavior and accounts for market trends — It’s the job of PLP algorithms to assess how well a product is performing based on factors such as engagement, conversion rate, and click-through rate. You can identify popular products, underperforming items, and gaps (e.g., people are looking for something you don’t offer.)

Sentiment analysis is one more tool in your AI box; it can add depth to knowledge by deciphering the emotional tone of customer reviews.

Benefits of automation

With high-quality analytics and machine learning, you can automatically:

  • Call attention to popular items
  • In real time, adjust layouts, content, and recommendations to cater to individual shoppers
  • Strategically position items with higher conversion potential
  • Dynamically adjust pricing for optimal profitability. If PLP data reveals that a certain brand of shoes attracts more attention when it’s discounted, your site can automatically discount that brand while, for example, leaving a top-selling handbag brand at full price
  • With advanced analytics, address fluctuations in demand
  • Use AI-driven search tools, which understand user intent, context, and preferences. For example, a shopper looking for a laptop might search for a “fast laptop”. This term wouldn’t link directly to a specific product page but relevant results would be listed on the PLP

How data-driven technology powers up PLPs

Being data driven with your site means:

Reducing manual work

A well optimized ecommerce product listing page automatically boosts popular items without manual intervention. For example, if Kim Kardashian mandates that red dresses are on trend and your site’s sales begin to reflect interest in red dresses, your Dresses category page can automatically let shoppers see more red dresses; no need for a merchandising manager to tweak the line-up.

Streamlining inventory management

Analytics can accurately track your inventory levels and forecast demand. If a particular item is selling fast, with that data on hand, an online store can quickly replenish stock to ensure that the user experience isn’t compromised.

Speeding up time to market

By providing insights about the types of products that perform well and accurately predicting future demand, data-driven PLPs can accelerate the time-to-market process.

Turn your PLPs into conversion machines

Want to raise your PLP conversion game? Algolia’s new AI Browse solution can turn your product listing pages into high-converting assets by letting you use the same APIs and settings you use for search on PLPs. You can also apply Algolia personalization and merchandising to curate results that convert (e.g., rules for a merchandising campaign to be applied both in search results and on category pages).

Want to know more? Contact us for the details today.

About the authorCatherine Dee

Catherine Dee

Search and Discovery writer

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