At Saxo, the Danish bookseller, a customer can reach the end of an audiobook without buying anything. There is no basket to enlarge, no checkout to optimize. The customer is a subscriber. The useful thing to do at that moment is suggest another book. It sounds obvious. Deciding which book, for which listener, at precisely that moment is the problem Algonomy gets paid to solve.
- Choose what each shopper sees across search, recommendations, and marketing.
- Give retail teams controls for stock, margin, and business goals.
- Connect customer demand with merchandising and replenishment planning.
Saxo uses Algonomy’s Recommend and Find across its bookstore and streaming experience. A reader browsing a store and a listener finishing a series need different interventions. The company’s case study describes a recommendation placement called Inspiration that appears when a member finishes a book. The clever part is the placement: a moment when the customer has a reason to welcome a suggestion.
Elsewhere on Saxo’s website, a small patch of digital real estate was unusually productive. Its Add to Cart placement generated approximately 21.3% of recommendation-attributed cross-sell sales from 5.5% of placement views. That imbalance prompted a three-week A/B test in February 2026. The lesson is wonderfully practical: look for the place where attention already becomes action, then experiment there.
Two companies, one missing connection
Algonomy’s roots go back to Manthan, founded by Atul Jalan in 2004. Its current name arrived in January 2021, when Manthan and RichRelevance completed their merger. Manthan brought retail analytics. RichRelevance brought personalization. Their combination addressed a familiar organizational inconvenience: understanding a customer and doing something useful with that understanding can live in separate systems, managed by separate people.
The merger’s proposition was to bring customer data, decisions, and delivery closer together. A profile would inform an action while the interaction was still happening. That placed Algonomy in the market for enterprise customer experience software, alongside broader suites such as Adobe and Salesforce. Its pitch concentrated on retail’s specific decisions, from what appears on a product page to how customers move across channels.
The checkout is only the beginning
Start with the products a shopper can feel. Recommend chooses products and content. Find personalizes search. Discover changes product listing pages. Audience Manager builds segments; Campaign coordinates marketing journeys. Beneath the activity sits rCDP, the real-time customer data platform. These tools serve marketers, merchandisers, and commerce teams whose everyday problem is turning customer information into something visible and useful.
In May 2025, Algonomy announced three further offerings. Ensemble AI assembles outfits. Social Proof Messaging tests nudges such as popularity and low-stock messages. Active Content personalizes marketing content when it is opened. Each takes a task that could absorb substantial manual attention and makes it repeatable. The appeal is operational as much as statistical: a merchandiser can manage more decisions without preparing every variation by hand.
purchases
+ retail rules
campaigns
Matas, the Danish beauty retailer, shows how that expands. Its relationship dates to 2018. The program puts recommendations across web, mobile, and email, including cart cross-sells and replenishment reminders. One particularly sensible detail: subscribed products can be de-boosted so shoppers discover something beyond purchases already arriving automatically. Personalization includes knowing when to stop suggesting the obvious.
“All I care about is relevance on the customer’s terms”
Peter Hestbaek / SVP of Digital Sales, Matas
The published Matas case reports a 36% year-over-year rise in sales attributed to recommendations. That describes a growing recommendation channel; it does not establish that total company sales rose 36% because of the software. For a buyer, the distinction matters. Attributed revenue shows where purchases touched a feature. A controlled comparison helps establish what the feature added.
At Martin & Servera, the Swedish foodservice wholesaler, the opportunity was sponsored products. It used Recommend with Data Science Workbench to build retail media on its existing setup. Supplier campaign files supplied search terms, products, dates, and ranking scores. Custom strategies placed the products; a campaign planning tool completed the workflow. The business retained supplier relationships and campaign control while avoiding a separate advertising platform.
The portfolio also reaches behind the storefront. Order Right forecasts demand and plans replenishment at product-and-store level, considering factors including lead times, pending orders, and shelf life. Vendor Link supports supplier collaboration. These products address an awkward truth about selling: an appealing recommendation has limited value if the retailer cannot supply the item.
A budget with people in it
Algonomy sells enterprise software with services around it. A company-published 2022 economic study gives a concrete historical example: its composite retailer budgeted $100,000 for the first year of Recommend and $28,000 for initial implementation services. Those are model assumptions drawn from customer research, rather than a current price list. Additional products, connectors, websites, and internal labor changed the economics.
The same research describes legacy tools that lacked flexibility to test strategies. That is a useful account of what became inadequate first: the ability to change the experience. Buyers still need enough audience, catalog variety, and implementation capacity to justify the effort. A narrow assortment offers fewer meaningful choices; a team unable to maintain integrations or evaluate experiments has less room to improve outcomes.
A bigger stage, a smaller first step
On July 30, 2026, ADA announced it had completed its acquisition of Algonomy. The combined business operates under ADA, with existing customers retaining access to Algonomy products, solutions, and teams. ADA framed the deal around bringing retail decisioning into its broader data and AI offering, extending the combined reach to 34 markets.
For a retailer, the useful starting point remains small. Pick a decision worth improving. Define the available products and constraints. Compare alternatives. Measure the outcome that matters to that moment. Saxo’s listener needs a next listen. A replenishment planner needs an order that fits local demand. Algonomy’s business rests on making those next choices easier to execute, and easier to learn from.