PERSONALIZATION SOFTWARE

Comprehensive omnichannel personalization software powered by machine learning to deliver individualized customer experiences across any touchpoint

A PERSONALIZATION ENGINE DRIVEN BY THE INDUSTRY’S MOST ADVANCED MACHINE LEARNING

Kibo’s Customer Experience Profile (CXP) gathers online and offline data in real-time and is layered with powerful machine learning to create individualized experiences

Customer Experience Profile

Kibo’s Customer Experience Profile (CXP) collects, connects, and enhances customer data from online, offline, device, and touchpoint sources to create an individualized profile for each of your customers.

The CXP is then layered on top of a proprietary personalization engine powered by machine learning to optimize engagement and brand loyalty across any channel.

Read About Machine Learning for Retail

Delivering relevant recommendations even for first time shoppers is easy with Kibo’s multi-layer machine learning framework

Big Data + Machine Learning

An effective personalization engine is all about layers, not rules.

  • Unique Personalization Engine – Proven composite layered framework of high-performance machine learning algorithms designed to engage users
  • Multi-layer Approach – Leverage product and item attributes, collective behavior, individual browsing behavior, and individual preferences to drive conversions, average order value lift, and improved engagement at scale
  • Patent-Pending, Self-Learning Individualization Algorithms – Provides the most relevant recommendations for both known and unknown customers across any touchpoint
Assess Your Personalization Maturity

Collective Intelligence Algorithms

Kibo’s personalization engine utilizes collective intelligence algorithms to aggregate data across your customers to provide the most basic recommendation strategies on your homepage or category pages.

  • Top Sellers – Drive promotions by surfacing the most sold items to your customers
  • Trending Items – Drive continued momentum of the most recently engaged items on your site
  • Most Popular – Drive conversions by recommending the most historically engaged items on your site
Read House of Antique Hardware's Story

Drive increased attach rates to orders with personalization

Contextual Algorithms

Enhance the customer experience with contextual algorithms that leverage interactions such as search terms, clicks on products detail pages (PDPs), and past purchases to drive personalized recommendations.

  • Search Term – Collect onsite search terms and combine with historical customer engagement data to provide relevant in-the-moment product recommendations
  • Bought Together – Display items that are typically purchased together to drive higher attach rates to orders
  • Item Attribute Affinity – Spark a customer’s interest with products that have an affinity based on similar metadata
Read the Musicroom's Story

Create product recommendations based on your most popular items .

Personalized Algorithms

Personalization algorithms layer contextual and customer segment data to predictively place product and content recommendations in front of the customer.

  • Trending Items by Segment – Predict and recommend the most relevant products by aggregating the most engaged items for a known segment of customers
  • Most Popular Items by Segment – Similar to trending items, but looks at a longer time horizon to personalize results
  • Segment Personalization – Infer group preferences by segment and apply these preferences to your customer’s recommendations to drive targeted conversion by demographics
Watch the Sun & Ski Testimonial

Leverage intent, latent preferences, and customer inferred preferences to recommend products to each individual

1:1 Individualized Algorithms

Take your omnichannel personalization beyond segments and rise above your competition with 1:1 individualized recommendations. Kibo’s personalization engine drives conversions and lifts engagement by combining individualized algorithms and customer data in the CXP with customer click streams to predict intent.

  • Intent Pattern Detection – Analyzes individual browsing patterns to predict intent
  • Latent Preferences – Combines individual customer preferences with item affinities to deliver the most appropriate recommendations throughout the entire buying journey
  • Customer Inferred Preferences – Delivers highly targeted recommendations by comparing an individual’s preferences and interests against recent customer interactions with products
  • Buy It Again – Predicts an individual’s next purchase by analyzing prior purchase and replacement patterns to recommend the right product at the right time
Read the Press Release

The personalization strategy has increased online sales by 8-10% in a year.

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