HYPV

A pre-launch prototype of a predictive-AI powered hyperpersonalization engine for enterprise online retail

enhances large-scale high traffic retail stacks with deeply integrated predictive targeting capabilities — includes a safe, API-led upgrade path for legacy platforms

Built ground-up for maximum precision:

combines deep product discovery and MVT features within a unified bare metal platform
able to hypervariate all aspects of discovery, code/algos and layout enabling unlimited automated MVT targeting of entire UX surface-area
entirely unique experiences can be created by cohort such as :high_ltv or :female_30_up without any restriction on what can be varied
all targeting compute goes through same pipeline which can financial goal-seek for macro scenarios to increase push on aged-stock or to prioritise margin over GMV
generates a fresh denormalized database for each deployment with all variance baked in — which allows exotic testing scenarios such as an A/B of two completely different category taxonomies
uses wide data windows to gen pre-calculated recsys caches alongside realtime refinement calcs to render extremely precise hybrid-sets with merch promoted objects interlaced
operates as light-touch discovery and augmentation for platforms like Shopify+ or extends to handling trickier scenarios like upgrading legacy internal ecommerce apps — where HYPV can also polyfill wider missing functionality — compatible with any frontend tech such as React or native mobile apps

Distinctly different from off-the-shelf SaaS add-ons (e.g. Segmentify, Algolia or Optimizely) — HYPV consolidates all aspects of hyperpersonalization into a single engine which can be modified without limitation to optimize for the exact UX requirements of the specific audience and desired client outcome — no one-size-fits-all

All targeting is tuned by HYPV engineers per client — not as a one-time setup, but as an ongoing partnership where we actively share ownership of continuous UX performance optimization

4D Data Model

Advanced MongoDB layer that stores, as equals, any variate clone of an object and every change to those objects (similar to Git branches + version history) — enabling ultimate ML control over UX personalization

Behaviour & Segmentation

All actions and patterns exhibited by an enduser and the interactions with objects such as products or categories for a moving view of their intent — including weight of interactions and velocity &mdash This rich behavioral data fuels dynamic micro-segmentation, enabling hyper-targeted experience

Granular Object Measurement

All objects (categories, brands, page types etc.) are measured in date bracket periods with dozens of metrics available for each type, providing deep insights for continuous optimization

Render-time Targeting

Control and candidate variations of any object are elected at runtime from fast targeting caches usually in less than 50ms for all selections

Asynchronous Hybrid Target Caches

Built offline every couple of hours to ensure fast recsys and other augmentations — often using multiple compute sources and interlacing additional merchandising promotions

External Integrations

Pulls in metrics from all external sources Google, Meta and TT Ads to compute and visualize ROAS/attribution — also from Lighthouse, Klaviyo and any other sources

Anomaly Detection

As a byproduct of the intelligence accumulated from the personalization activity, any anomalies are visualised in realtime

Specifically aimed at retailers that are limited in how much they can vary UX on behavior cues, are able to benefit from unlimited complex MVTs running concurrently and can support highly granular segmentation strategies

HYPV targeting can be up and running as an augmentation API in weeks with minimal client resource or disruption as most resource effort involved is vendor-side — pricing is based on resource plus a license fee model, with license costs permanently waived for all trial clients

The optimization process does not stop after integration — contact@uncommerce.com

Get Launch Updates

Important Information

1. HYPV engine and tooling are intended for enterprise-scale retail situations with a minimum of 5K+ products, 4M+ sessions/month and high potential for repeat visit frequency. A lot of compromises have been made to maximize targeting for those scenarios, in particular around signal collection volume, CMS ease-of-use and proficiency level required of teams involved.

2. To be viable a client product range needs to have some obviously distinct audience divisions as is common with groceries and fashion sectors or marketplaces. For specialist sites it needs scenarios like fishing with clear sea vs coarse audience behavior splits and high repeat traffic — other sectors like jewellery with less pronounced segmentation are unlikely to benefit.

3. It is a core system component with SaaS elements, not just a simple add-on application. Our expert resources work in close partnership with client operational teams and share ownership of improving performance, ensuring it runs effectively and continually improves over time.