Reimagining Recommendations and real-time Omnichannel Marketing for DNVB

Support easy buying with individualized, behavioral-driven experiences that remove the friction from commerce across the customer lifecycle.
Deepen loyalty while increasing revenue and repeat purchases with complementary offers and recommendations that complete, enhance, or replenish.
Balance manual curation and automated optimization by combining the best of real-time AI-driven decisioning and the most advanced set of merchandising tools and configurations on the market.

Yes. Personalization should respect your business and merchandising commitments. Through a simple user interface, merchandisers can set custom weights for different attributes such as brand, category, price, newness, and more. In addition, personalization delays can be set to impact how quickly you want to influence search results.

The personalization software is equipped with an Experience Optimizer (XO) that selects the winning strategy based on the Unified User Profile Service (UPS), the chosen KPI — RPV, AOV, Conversion Lift, etc., shopper’s stage in the funnel. This AI-driven decisioning by XO ensures the highest performing strategy is chosen, and the most relevant products are shown to every shopper, without the need for manual merchandising.

One can build out real-time segments by filtering using historical data or visitor schema for marketing orchestration.

Yes, one can choose pre-built segments for common use cases like churn, cart abandonment, order value etc. These can also be modified based on attributes for use in specific targeted campaigns.

Yes, absolutely. We provide the industry’s only Experience Browser (XB) that provides instant transparency into why an experience was chosen for a given individual. The XB overlays right on top of your website, so you don’t have to leave your browser tab, and can audit AI decisions with a single click, and dig into the user profile, strategy evaluation, rules, and the process of recommendation selection.

You can use our advanced recommendation software DeepRecs™, which leverages product data to contextually recommend new, seasonal, and long-tail products that would’ve otherwise remained buried due to lack of historical events. DeepRecs NLP uses deep learning algorithms to analyze catalog descriptions, reviews, and other textual data to infer relationships between products. DeepRecs Visual AI extracts features from product images and identifies relevant items for a personal shopper experience, in fashion & apparel and furniture verticals.

Yes. Recommend allows for open-time personalization of emails. It lets you personalize the contents of an email based on when the email is opened — updating messaging and recommendations based on the latest activity, context, and purchases, while also checking the latest inventory levels. The aim is to eliminate redundant experiences, be it on emails or on digital channels.

Yes, we understand that you may have specific business objectives that require manual merchandising, e.g. promote high margin brands or ensure certain products are never recommended. While Recommend constantly optimizes and selects the best performing products, we also allow you to boost, restrict, or set manual rules to help you meet your business goals. However, we suggest respecting the AI engine and avoid setting up too many restrictive rules that can end up creating a sub-optimal experience for the shoppers.

The Advanced Merchandising feature adds an extra layer of intelligence and product knowledge to product recommendation — providing a scalable method of managing cross-sell, up-sell, and complete-the-bundle recommendations without having to manually merchandise every SKU.
Manual merchandising leaves recommendation slots unfilled when products go out of stock and new products either are not recommended or do not have recommendations until the merchandiser is able to get around to adding them. Advanced merchandising automatically fills slots when empty or swaps in new items.