in Sweden from 7 - 8 October 2025. Pre-book a meeting to connect with our
product experts.
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Welcome to Office Hours –
Your go-to place for everything DXP
Join us every Wednesday at 1:30 PM CET / 12:30 PM BST for an informal, virtual Q/A session.
400+ Brands Have Elevated Digital Experiences with Our Personalization Solutions
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Passcode: 313954
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Why attend?
No agendas, no presentations—just a space to connect, ask questions, and get the most out of your experience with our products.
Monthly Recap
Our October Office Hours were packed with valuable discussions as clients, including several first-timers, joined us to explore best practices, share insights, and dive deeper into the capabilities of Algonomy’s tools. Some clients initially joined just to observe but soon engaged as topics sparked their interest. This month, MVTs and Reporting remained hot topics, and we also covered advanced merchandising strategies and holiday preparation tips for maximizing results during peak shopping events.
Here’s a look at what came up this month:
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Holiday Preparation
As holiday shopping kicks off, clients are keen on optimizing their use of Recommend™ for events like Black Friday and seasonal sales. One useful tip is to leverage the Recently Viewed algorithm to display products that the shopper has previously shown interest in and that are currently on sale. By filtering the recommendations to include only recently viewed items that are discounted, you ensure that each shopper sees relevant sale items tailored to their browsing history, maximizing both personalization and the impact of your promotions. These recommendations can be prominently placed on the homepage, category landing pages, or cart pages to capture shopper interest and boost conversions during high-traffic times.
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MVT Reporting and the Power of a Well-Tuned Boost Rule
Our reports are designed to support client needs, offering easy data downloads for further analysis. One client shared their practice of exporting raw visit data from MVT reports to analyze alongside their own data, creating a more comprehensive view. During the session, this sparked a Product Feature request to add external user and session IDs to the raw data export. Currently, the export includes only Algonomy’s global ID, which can make it more challenging for clients to align with their internal data. Adding these fields will simplify their workflow and streamline the integration process.
Additional MVT-related questions included inquiries about Unqualified Visits and Flip Floppers:
- Unqualified Visits refer to users assigned to a test group but not exposed to the actual test, excluded by default from test results. However, clients can include these visits by selecting a checkbox, though it’s typically uncommon to do so.
- Flip Floppers are users who see more than one test treatment. These are also excluded by default but can be included via checkbox if needed.
One client shared a successful MVT test that other clients might find insightful: In their control group, all shoppers saw a boost for “own brand” products, while the winning treatment only boosted the “own brand” when the seed product was from the same brand. This approach resulted in a +9.5% lift in RPV (99.7% confidence) and +7.41% lift in CVR (99.7% confidence).
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Reporting Insights
This month, newer users joined Office Hours with general reporting questions, so our consultants began with a quick overview of the reporting capabilities, emphasizing that clients can always reach out to their CSM/CSA for dedicated training if needed.
Specific questions also arose, such as how to determine which products are being recommended on item pages. To explore this, we reviewed several key tools, including the Site Analytics Report, strategies being triggered, and how to review these strategies within the portal. One client also wanted to analyze the margin impact for shoppers using recommendations versus those who don’t, so our consultants took the opportunity to introduce them to the Cohort report, which offers visibility into user behavior trends.
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Advanced Merchandising
Advanced Merchandising sparked lively discussions around when and how to apply it effectively. Advanced Merchandising offers powerful tools, allowing clients to easily manage recommendations—sometimes even overriding relevancy or personalization. While this flexibility can enhance user experience, our consultants highlighted the importance of monitoring performance regularly.
We recommend using the Advanced Merchandising Report, which allows clients to assess the performance of each rule and identify which rules may need adjustments or deactivation. This balance ensures that merchandising remains both effective and aligned with overall relevancy and personalization strategies.
Conclusion
As Office Hours continue, we’re thrilled to see clients leveraging these sessions to deepen their understanding of Algonomy’s tools and collaborate on strategies that enhance their results. Whether you’re joining to ask questions, learn from peers, or hear best practices, each session offers practical insights and support.
We look forward to seeing more of you in the next session—don’t miss out on the chance to connect, learn, and optimize your experience with Algonomy.
This September, we relaunched Algonomy Office Hours with the goal of offering clients direct access to our consultants for real-time discussions and insights. After a successful first month, we’re excited to share some of the key topics raised by our clients and the collaborative discussions that took place.
These sessions have proven to be a valuable opportunity for both clients and the Algonomy team to tackle specific product questions and explore best practices for optimization.
Here’s a quick overview of what we covered in the last few weeks:
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Best Practice Strategies for No-Hit Search Recommendations
A client not currently using our FindTM solution asked about best practice strategies for recommendations when a search returns no results. Our consultants discussed how Algonomy’s MVT tool can beused to test different strategies in these scenarios. We highlighted dedicated Solr-based recommend strategies that trigger relevant product or category recommendations based on the search term, even when no exact matches are found in the catalog data.
These strategies are complementary to a search engine: while the search engine returns products based on the indexing of data in the product catalog, the Solr recommend strategies provide alternative products based on behavioral data, such as products that were viewed or purchased after searching for the same term.
Additionally, personalized fallback strategies were recommended, as these tend to perform well across a variety of verticals. However, given the complexity of variables like shopper behavior, catalog data, and industry verticals, one always has the option of running MVT tests to determine the best-performing configuration.
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Portal Reporting Frequency
One of the first questions came from a client who wanted to better understand how frequently reports in the Algonomy portal are updated.
We explained that, while some reports are real-time, the majority of analytics reports for products like RecommendTM, EngageTM, DiscoverTM, and FindTM are based on daily roll-ups. For our European clients, these reports are typically ready by the early morning, following nightly data processing.
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Find Score Composition and Fine-tuning
Another key discussion revolved around the composition of FindTM scores and how products are sorted in search result sets.
One particular area of interest was the tie parameter, which allows users to adjust the importance given to the frequency of a term in the product catalog data. This parameter helps fine-tune search results, offering a more customized search experience depending on the user’s specific goals and the nature of their catalog.
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Handling Brand Restrictions and Boosting Custom Attributes
In one of the later sessions, a client faced a challenge when trying to apply brand restrictions using a combination of a “match brand” and “other brand” rule, which currently requires more than one rule.
Another client had a similar issue with boosting the custom attribute “gender” to prioritize values like “Male” or “Female,” alongside the value “Unisex.”
In response to this, we raised a Product Feature request, which is already being prioritized. We’re excited to see how this feedback will help enhance the platform to better support use cases like these.
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Explore our personalization resources—best practices, case studies, and more—to stay ahead of the curve.
