Brands will accelerate post-pandemic customer engagement, above all digital –

Algonomy, announced new features and product updates to enhance their unified platform capabilities in their latest Spring ’21 release. The new version is specially designed for retailers and brands that are in a post-pandemic recovery cycle. Algonomy’s unified platform for digital customer engagement integrates supply-on-demand data across the value chain with algorithmic decision making.

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

Sarath Jarugula, Product Manager at Algonomy commented: “The digital age is about building relevant experiences throughout the customer journey. Additionally, extend personalized interactions to all customer touchpoints, inbound and outbound. In this release, Algonomy customers can continue to better leverage their technology investments across the enterprise. To improve their customer engagement, conversion and retention metrics. “

Go down the road without a code

New capabilities include composite AI frameworks, code-free ML frameworks, visual AI algorithms, increased control and governance over customer data. The company says this provides better orchestration capabilities in marketing, commerce, and merchandising.

Algonomy reports that the code-free ML framework has been deployed throughout the solution. The Configurable Strategies feature is a step towards self-service machine learning. Enable non-technical users to quickly create, test, and iterate new personalization strategies. Users can choose from a predefined library of algorithms to create new strategies, test their hypotheses, and meet their unique needs. This year, additional controls have been added to apply category diversity to attributes. These include Best Sellers, New Releases, Best Selling Attributes, Best Deals, and Category and Brand Affinity. A buyer’s affinity with a category or brand can be used so that the resulting recommendations match their preferences. Additional user attributes to add, replace, delete values ​​are now available, providing great flexibility for 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 for testing these hypotheses – on the ecommerce site or for email promotions. The personalized campaign leveraging personalized categories and brand affinities generated 356% more revenue than the fallback campaign. Likewise, the branded pages on our e-commerce site saw a + 3% conversion rate for key categories “

Key capabilities and features

  • New OOTB connectors. Algonomy Connect can help brands leverage their business investments with Shopify Plus, VTEX, Adobe Magento, SAP Hybris, SFCC / Demandware and more. Brands can sync product catalogs, inventory and prices to always keep them up to date, thanks to our unique real-time streaming catalog integration.
  • DeepRecs Visual AI enhancements provide deep learning to replicate a store-like personal experience on digital commerce properties. This helps shoppers find visually similar products and get full recommendations based on product images. This is done without the need for behavioral data.
  • Configurable policies have additional controls to apply category diversity to policies.
  • Contact Center Personalization (EA only) provides online shopper behavior, intent signals, search data, affinities, cart content. In addition to past purchases from sellers and agents for personalized interaction and support.
  • Social Proofing (EA only) engages buyers using real-time view and purchase data. This provides urgent messages on digital commerce properties, resulting in an immediate increase in conversion rates and reduced abandonments.

Customer analysis

Customer Analytics now offers a brand new Data Studio. This gives data scientists and analysts secure access to the company’s complete customer data. To build models, perform exploratory analysis, and create dashboards in an easy-to-use interface. Customer journey orchestration improvements in online and offline journey automation, and an expanded set of journey analysis capabilities.

The universal control group allows the creation of a control group at the program level. Algonomy says this allows for more effective measurement of multiple campaigns and multiple journeys across longer-term marketing goals. The platform includes a new Criteo integration allowing marketers to deliver automated dynamic and personalized ads to customers across all channels.

Planning and analysis of goods

  • New size assortment planning for multiple size strategies – single and multiple size packs, each fill pack, and hybrid size planning. Granular store recommendations, better user control and automatic scaling, modeling for new stores and new classes of plans with no 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 provides an improved user interface and offers a broader set of pricing and markdown strategies. New updates on cross-price elasticity and offer recommendations add depth to fashion merchandising analytics and algorithmic decision-making capabilities.

Enterprise Times: what does it mean for businesses?

Artificial intelligence, machine learning, and algorithms are creating new opportunities for hyper-personalization in digital commerce and marketing. Empower brands to fine-tune the delivery of personalized messages to consumers based on data-driven intelligent past behaviors.

Algonomy’s decision to fully embrace the no-code ML framework makes good sense. Any solutions that automate and learn from digital consumer behavior, refine messages and campaigns will be well received by marketers.

The company claims that its ML algorithms are also integrated with various capabilities such as contact center recommendations, social verification. On the CX side and style / size / offer recommendations in merchandise planning. DeepRecs used deep learning models.
In addition, Algonomy is committed to developing more algorithmic orchestration capabilities for customer engagement and real-time personalization.

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Aldrich Stanley

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