(Image credit/Pixabay/Gerd Altmann)Algonomy, announced new features and product updates to enhance their unified platform capabilities in their latest Spring ’21 Release. The new release is specifically designed for retailers and brands who are in a post-pandemic recovery cycle. Algonomy’s unified platform for digital customer engagement integrates data from supply to demand across the value chain with algorithmic decision-making.

Sarath Jarugula, Chief Product Officer at Algonomy (Image credit/LinkedIn/Sarath Jarugula)
Sarath Jarugula, Chief Product Officer at Algonomy

Sarath Jarugula, Chief Product Officer at Algonomy commented, “The digital-first era is all about staging relevant experiences across the entire customer journey. Furthermore, extending personalised interactions to all customer touchpoints, both inbound and outbound. In this release, Algonomy customers can continue to better leverage their technology investments across the enterprise. To improve on their metrics for customer engagement, conversion and loyalty.”

Going down the no code route

The new capabilities include composite AI frameworks, no-code ML frameworks, visual AI algorithms, greater control and governance over customer data. The company says this delivers better orchestration abilities across marketing, commerce and merchandising.

Algonomy says the no code ML framework has been deployed throughout the solution. The Configurable Strategies feature is a step towards self-serve machine learning. Enabling non-tech users to quickly build, test and iterate new personalisation strategies. Users can pick from a pre-built library of algorithms to create new strategies, test their hypotheses and serve their unique needs. This year, additional controls have been added to apply category diversity to attributes. These include Top Sellers, New Arrivals, Attribute Top Sellers, Best Offers and Category and Brand Affinity. A shopper’s affinity to a category or brand can be utilised so the resulting recommendations match their preferences. Additional user attributes for add, replace, remove values are now available, providing high flexibility to marketers and merchandisers.

According to Rob Hitchman, Digital Product Owner at John Lewis, “Our merchandisers and marketers always have new ideas. Configurable Strategies is a very handy tool to test these hypotheses – on the eCommerce site or for email promotions. The personalised campaign leveraging custom category and brand affinities achieved 356% more revenues compared to the fallback. Similarly, brand pages on our eCommerce site have seen +3% conversion rate for key categories”

Key capabilities and features

  • New OOTB Connectors. Algonomy Connect can help brands leverage their commerce investments with Shopify Plus, VTEX, Adobe Magento, SAP Hybris, SFCC/Demandware and more. Brands can sync product catalogues, inventory, pricing to keep them always updated, with our unique real-time streaming catalogue integration.
  • DeepRecs Visual AI enhancements offer deep learning to replicate store-like personal experience on digital commerce properties. This helps shoppers find visually similar products and get complete-the-look recommendations based on product images. This is achieved without the need for any behavioural data.
  • Configurable Strategies features additional controls to apply category diversity to strategies.
  • Contact Centre Personalisation (EA only) provides online shopper behaviour, intent signals, search data, affinities, cart contents. In addition to past purchases to sales associates and agents for personalized interaction and assistance.
  • Social Proofing (EA only) engages shoppers using real-time view and purchase data. This provides urgency messaging on digital commerce properties, resulting in immediate lift in conversion rates and reduced abandonments.

Customer analytics

Customer Analytics now features an all-new Data Studio. This offers data scientists and analysts security-controlled access to full-enterprise customer data. To build models, perform exploratory analyses, and build dashboards in an easy to use interface. Customer Journey Orchestration enhancements in online and offline journey automation, and an extended set of journey analytics capabilities.

Universal Control Group allows the creation of a programme level control group. Algonomy says this enables more effective measurement of multiple campaigns and multiple journeys across longer-term marketing objectives. The platform features a new Criteo Integration enabling marketers to push automated dynamic and personalised ads to customers across channels.

Merchandise Planning and Analytics

  • New size-pack assortment planning for multiple size strategies – single and multiple size packs, each fill-in packs, and hybrid size planning. Granular store recommendations, greater user control and autoscaling, modelling for new stores and new plan classes without history.
  • Style Intelligence uses Visual AI to rank and recommend fashion products/trends and integrates with the attributes creating the assortment plan.
  • Product Lifecycle Pricing offers UI enhancement and features a wider set of pricing and markdown strategies. New updates on cross-price elasticity and offer recommendations add to the depth of fashion merchandising analytics and algorithmic decisioning abilities.

Enterprise Times: What this means for business?

Artificial Intelligence, machine learning and algorithms are creating new opportunities for hyper-personalisation in digital commerce and marketing. Providing opportunities for brands to fine-tune the delivery of personalised messaging to consumers based on data-driven past intelligent past behaviours.

Algonomy’s decision to fully embrace the no code ML framework is sensible. Any solutions that automates and learns from consumer digital behaviour, fine-tunes messaging and campaigns will be welcomed by marketing folks.

The company says its ML algorithms are also built into various capabilities like contact centre recommendations, social proofing. On the CX side and style/size/offer recommendations in merchandise planning. DeepRecs used deep learning models.
Furthermore, Algonomy are committed to building more algorithmic orchestration capabilities for customer engagement and real-time personalisation.


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