Offer Shoppers Product Even Before They Knew They Wanted Them
In e-commerce, shoppers interact through actions like searching, browsing, adding to the cart, and purchasing. The path to purchase varies with each user. Therefore, e-commerce platforms strive to deeply understand each customer to maximize lifetime value.
Often, today’s platforms will still take a one-size-fits-all approach. Every shopper experiences the same generic recommendations regardless of their unique preferences that evolve over time. This lack of personalization leads to shallow engagement, lower conversions, and missed revenue. It significantly caps customer lifetime value potential. Without the essential tailoring to fit an individual's interests, key opportunities are missed, causing problems for the platform such as:
- Low conversion rates from ineffective on-site navigation and guidance
- High abandonment when checkout processes don’t adapt to user needs
FirstBatch User Embeddings solve these issues through AI-powered real-time hyper-personalization that enables:
- Minimal cross-selling as customers receive irrelevant suggestions
- Recommendations tailored to personal tastes and unique individual needs
- Customized navigation and guidance based on session intention
- Shopping experiences adapted to the user’s evolving interests
- Optimization of messaging and offers according to user traits
With User Embeddings, platforms can finally deliver on the promise of transformational personalization which was long envisioned but never quite realized - until now. The result is deeper engagement, higher conversion, and maximized lifetime value across the entire customer journey.
Let’s focus on two of the common issues in e-commerce. E-commerce platforms often struggle with two key issues that limit revenue:
Low cross-selling engagement resulting from insufficient personalized recommendations and exploration of new products. This leaves money on the table.
High cart abandonment rates as customers lose interest or get distracted before completing their purchase. Abandoned carts represent lost sales.
Our built-in algorithms help e-commerce sites tackle these problems through better personalization.
Increase Cross-Selling with "Discovery Journey"
The "Discovery Journey" algorithm serves users a progression of random, sampled, and biased batches to balance discovery with personalization.
Key stages:
- Random Discovery: Users explore products without personalization constraints.
- Broad Sampling: Users get variety from across catalog categories.
- Personalized: Users see recommendations aligned to completed interactions in real time.
- Exploration: Fine-tuned exploration parameters foster continued discovery.
By gradually personalizing, "Discovery Journey" keeps shoppers engaged across their sessions, resulting in higher cross-selling conversions.
Reduce Abandonment with "Cart Recovery"
To prevent abandonment proactively, we built an algorithm to keep customers engaged throughout each step while moving them efficiently toward purchase.
With our “Cart Completion” algorithm, you can tailor the experience to each user from their first interaction:
- Discovery Phase: Users explore the catalog and add items to their cart.
- Browsing Phase: Variety across categories sustains interest in continuing the session.
- High Intensity: Key products are identified to highlight the value and the needs to be met.
- Dedicated Phase: Recommendations stay focused on maximizing purchase likelihood.
By serving relevance in the moment at each stage, Acme reduced distractions and friction, enabling customers to smoothly complete purchases in a single visit.
With our built-in algorithms tailored to e-commerce, you can boost your KPIs by serving personalized experiences that optimize the shopping journey.