Improved Bulk Redirect Management

Managing search redirects is critical for many of our customers, with some overseeing thousands of active redirects. Since introducing our AI-powered redirect suggestions which automatically detect and recommend missing redirects the need for efficient oversight has only grown.

To support this, all bulk updates are processed through our Import Guide, which automatically checks for validity issues, duplicates, and potential conflicts. However, as many updates still require human review, managing these changes at scale has become increasingly complex.

With this release, we’ve significantly improved the bulk update process. Users now have clearer visibility into upcoming changes and more control over how updates are applied, making large-scale redirect management simpler and more transparent.

Our customers rely heavily on our Redirects feature and have accumulated tens of thousands over time. To make managing them even more efficient, we recognized that a mass-edit function (such as activate/disable or delete) could significantly reduce manual effort in many cases.

That’s why we’ve introduced an edit column, allowing you to manage and edit multiple redirects with a single click.

We hope this small improvement makes your workflow smoother and helps you keep redirecting with ease!

Since the beginning searchHub focuses on search intent rather than search queries and relevance. While search queries are often vague relevance depends on context and personal interests and intent represents the most stable representation.

In order to further help you optimizing this core process we are thrilled to announce our the new view which offers direct interactive insight into how the intents (search demand) distributes across your assortment.

Together with our beta-testers we identified the following impactful benefits:

Understand what customers are truly looking for not just by keyword, but by product category providing a clearer picture of demand trends across your catalog. Example: Instead of just seeing “black dress,” you see growing interest in “evening wear > cocktail > sustainable fabrics.”

Align product placements, promotions, and inventory with real search patterns. Optimize your assortment based on live category-level demand rather than assumptions or seasonal schedules.

Inform category page curation, filters, and product recommendations with actual query data—improving relevance and reducing bounce.

Spot emerging interest in niche or long-tail categories before they spike, enabling proactive buying, pricing, and marketing strategies.

Feed your personalization engines, search tuning, and recommendation systems with structured, intent-rich query-to-category mappings—improving performance across the board.

As discovery moves from the homepage to conversational AI, social platforms, and deep links, category-level query data helps ensure every touchpoint is aligned with real customer curiosity.

To ensure reliable operation, we’ve introduced a preferred method for whitelisting searchHub’s Redirect Validation Bot. This allows it to bypass security mechanisms such as firewalls or bot protection systems and verify all active redirects without interruption.

Previously, the Redirect Validation Bot may have been blocked by such systems, potentially impacting its ability to perform comprehensive checks. To address this, the bot now uses a static IP address , making it easier for customers to whitelist.

If the static IP does get blocked, the bot will still attempt to operate using fallback IPs to maintain coverage.

You can find more information on how to take advantage of this new capability and further implementation details here:

We don't try to flood you with features that often add complexity without real usable value, instead we focus on what truly matters: .

That’s why we’re excited to introduce our new smart, integrated UX starting with the launch of "My searchHub" and "Mapping Insights", supported by powerful new contextual guides. All designed to make your work easier, more efficient, and deliver even greater results.

A fully redesigned interface that puts clarity and actionability first.

At the top, you'll find a concise overview of your key Search & Discovery KPIs, followed by actionable insights, complete with targeted, contextual recommendations to help you continuously optimize with searchHub.

Just below, you’ll find our contextual recommendations (Tasks), designed to highlight the most impactful improvements automatically detected by searchHub. Each action is streamlined—clicking takes you straight to the relevant optimization task or view.

As the primary transparency layer, the Mapping Insights view gives you direct visibility into how smartQuery works for you making it a cornerstone of trust and control. To maximize its value, we’ve made it faster, more streamlined, and even more insightful:

Now you can understand and refine your query mappings more efficiently than ever.

Most search blind spots can be addressed in multiple ways each with its own trade-offs. To help you choose the most effective path, we’ve introduced contextual guides across key views. These guides:

Unlock smarter, faster decision-making - right where it matters most.

We've relocated the system status overview to the header for clearer separation between system updates and performance insights making it easier to navigate and focus on what matters.

Since we introduced expectation ranges for all relevant search KPIs to detect and analyze significant short-term fluctuations, it became evident that we also needed a seasonal expectation range. This would incorporate long-term data to help distinguish temporary variations from genuine long-term trends.

Implementing this was a complex challenge, but we are excited to announce the launch of the Seasonal Expected Range view for your data!

We analyze historical data from previous years, remove extreme outliers, and smooth the data based on extracted seasonal effects. This smoothing process is performed using the Whittaker-Eilers method.

We then calculate confidence intervals and overlay them with the current year's data (the time frame of interest).

By doing so, we effectively filter out short-term outliers if they align with expected seasonal patterns. The following chart illustrates this approach using sample search CTR data.

Three years ago, we introduced our Query Interpretation Service, designed to detect true shopper intent by breaking queries down into distinct concepts.

Since then, we’ve been hard at work integrating a taxonomy classification mechanism capable of mapping shopper intent to our shopping taxonomy with over 95% precision.

After processing billions of queries and interactions, we’re thrilled to announce the launch of our Automatic Query Taxonomy Classifier, now available through the Keyword Interpretation view.

In the coming weeks and months, we’ll roll out several new features that leverage this powerful capability. For now, we’re excited to make it accessible to our users. Stay tuned!

In any e-commerce search system, analytics and insights are essential for driving continuous improvement. Historically, while SearchInsights has focused on analyzing search paths, we have placed too much emphasis on the final stage of the funnel—conversion. However, our data highlights the importance of focusing on the preceding step: add-to-basket.

Add-to-basket data provides a powerful signal of user intent, revealing which products resonate most with shoppers after being discovered through search. By analyzing this data, we can uncover behavioral patterns, prioritize high-performing products, and enhance the relevance of future search results. Furthermore, comparing add-to-basket rates with checkout rates offers valuable insights into whether issues in the final stage of the funnel require attention, even if they are not directly tied to search performance. This approach ensures a more holistic understanding of the customer journey and identifies opportunities for optimization at every stage.

Ranking also plays a pivotal role in determining the order in which products are displayed, balancing factors like relevance, popularity, and business objectives such as inventory clearance or promoting high-margin items. Significant effort is invested in improving ranking performance in search. However, many commonly used metrics for evaluating ranking quality in e-commerce search, such as MRR and NDCG, are heavily rooted in traditional web search use cases and may not fully capture the unique needs of e-commerce.

To address this gap, we set out to provide our customers with a clearer, user-focused perspective on their ranking quality—ultimately judged by their shoppers. This led to the introduction of EngagementRank. EngagementRank measures how far your current ranking deviates from the optimal ranking, while accounting for biases such as position and presentation effects. A score of 100% represents a perfect ranking, while lower values highlight areas for improvement. This metric ensures a more accurate and actionable evaluation of ranking performance tailored to the e-commerce context.

We are excited to announce an update to the data table CSV export functionality: category affinity raw data is now included! This enhancement provides valuable insights into category preferences, distributed as frequencies across various taxonomy levels. The specific taxonomy levels included in the export depend on your tenant configuration, ensuring the data aligns with your setup.

With this addition, you can now leverage detailed category affinity data to analyze user behavior and refine strategies for product categorization, personalization, and search optimization. This update makes it easier to extract actionable insights and improve decision-making based on user preferences.

Our performance charts have become the go-to tool for search managers to identify micro- and macro-trends and benchmark search performance effectively. However, search is complex. Internal changes, external factors, deployments, and unexpected hiccups can all impact the charts, often leading to spikes, dips, or other notable patterns.

To help provide clarity, we’ve introduced annotations to our performance charts. This new functionality allows you to add additional context—either systematically or manually—to document what might have caused these changes.

There are two types of annotations:

With annotations, you can now better understand and explain the dynamics behind your performance data.