As more and more customers use Info Markers in the Performance Charts view to add context about their systems such as configuration changes, A/B tests, and feature rollouts, we noticed that multiple markers within a short time frame could be difficult to identify and read due to text overflow.

To improve clarity, we have updated the UI so that Info Markers are now displayed in the detail view, providing significantly better readability and greater flexibility.

We hope you enjoy this improvement and continue adding markers to your charts.

We've improved trend visualization for KPIs.

Based on user feedback, we introduced a trend line that better reflects how key metrics evolve over time. Instead of only comparing the first and last value in a time series, the new trend line uses linear regression to calculate the overall trajectory over the defined period.

This makes it easier to identify whether a KPI is generally increasing, decreasing, or remaining stable. By visualizing the true underlying trend rather than just connecting the endpoints, this update helps teams quickly assess metric development, ignore short-term outliers, and understand whether actions are having the desired impact.

Today, we’re excited to announce that we have quietly launched our new a model we have been working on for the past 12 months.

In ecommerce, product data usually follows a clear structure. Whether for SEO, GEO, PIM systems, or catalog management, products are organized, enriched, and standardized. User queries, however, are not.

This gap creates a major opportunity: aligning user queries with a structured representation can unlock significant value for ecommerce and retail players and their users.

Structured queries are easier and faster for humans to scan. This is especially visible in autosuggestions: when suggestions follow a recognizable structure, users understand them more quickly and learn the underlying query patterns over time. As a result, they can formulate, refine, and expand their searches more effectively. This led to significant uplifts across the board in user engagement.

Ecommerce engagement data is often extremely sparse, making it difficult to identify meaningful trends, behavioral shifts, and intent patterns. By structuring queries and especially by normalizing their formulation we can dramatically reduce the long tail of events and shift engagement mass toward the short head and mid-tail.

This significantly strengthens behavioral feedback loops such as learning-to-rank, embedding-model fine-tuning, and causal analytics.

We continuously strive for higher computational efficiency. Query understanding is a key part of this.

Query Understanding combined with hybrid search based on embeddings has become the de-facto standard for product discovery, but most base models are still primarily trained on structured content. This content structure often does not align with the language and structure of real user queries, which makes it harder for models to learn, adapt, and generalize.

By aligning user-query structure with product-content structure, we can substantially reduce computational effort through better caching and more reusable representations. Having more structurally aligned labels reduced our query-understanding API calls by roughly 20% whilst increasing recall over 10%.

To our knowledge, we are the first to have successfully achieved this kind of task at this scale. Therefore a big thank you to the whole team.

We’re excited to bring even more downstream capabilities to our customers, powered by our new .

We’re excited to introduce the new Query Explorer.

Until now, SearchInsights has focused on surfacing the most impactful data points related to search performance. However, feedback from our customers has shown that search and intent data can support many additional use cases beyond search optimization.

Topics such as seasonal demand analysis, keyword analysis, and category topic mapping help different teams across your organization work more efficiently and make better-informed decisions.

That's why we're happy to introduce the new Query Explorer view: In addition to the top 5.000 top records, you can browse in the New Query Explorer through all data to get deeper insights, without limitations. A more flexible way to access your raw query data, apply powerful filters, uncover deeper insights, and draw meaningful conclusions.

We are very proud to officially announce the release of SmartSuggest 2.5. Our Auto-suggestions always created significant impact by modeling intent, preventing search failures, and guiding users toward high-value discovery.

With this release we push the boundary one step further on our path from Autocomplete to Intent Modeling:

Traditional keyword based product suggestions ignore evolving intent and fail on ambiguous queries. SmartSuggest combines intent similarity with behavioral signals, continuously improving product suggestion relevance.

For broad intent, SmartSuggest recommends relevant categories or contextual scopes before search execution preventing unfocused result pages and bounce.

Intent often starts before typing. SmartSuggest now delivers pre-suggestions based on the current or last recent user context (e.g., category page, campaign entry, viewed product), with safe fallbacks when context is limited or unknown.

SmartSuggest transforms auto-suggestions into an intent-driven conversion engine, combining real-time learning, guided discovery, and merchandising control to reduce search failure and accelerate purchase decisions, effectively turning the suggestion bar into the most efficient shopping assistant in the store.

As you may already know, we’ve been working intensively behind the scenes to stay ahead of the competition in the areas of Auto-Complete and Auto-Suggestions. Very soon, we’ll unveil what this means and how it will help you further enhance user experience and product discovery.

Before introducing these bigger updates, we’ve focused on aligning this feature with our new UX & UI guidelines, already rolled out across several other views.

Our main goals were clarity and transparency. To achieve this:

Many of our users’ tasks start with data sourced directly from searchInsights.

Until now, most tables were limited to the top 1,000 records due to latency constraints we didn’t want to exceed.

Thanks to improvements in our underlying stack, we’ve been able to raise this limit to the top 5,000 records without significantly affecting latency.

This means you can now access and work with 5x more data directly in the searchHub UI, helping you deliver tasks with even greater effectiveness and efficiency.

Since its launch, has become one of the most valuable data sources for assortment planning and demand analysis. The feedback from our customers has been outstanding, and we wanted to give something back.

Starting today, we’re introducing. This new feature allows customers to track how demand in their assortment evolves, making it easier to see whether specific marketing campaigns or category-related changes have actually driven improvements.

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!