LiftIgniter Real-time Personalization Platform

5 / 5
1 review

Who Uses This Software?

Enterprise customers eRetailers Media companies Consumer Brands Any company that wants to personalize their customer experience via AI, with extensive content to optimize on web, mobile & email

Average Ratings

1 Review

  • 5 / 5

  • 5 / 5
    Ease of Use

  • 5 / 5
    Customer Service

Product Details

  • Starting Price
    Not provided by vendor
  • Deployment
    Cloud, SaaS, Web
    Mobile - Android Native
    Mobile - iOS Native
  • Training
    Live Online
  • Support
    24/7 (Live Rep)

Vendor Details

  • LiftIgniter
  • Founded 2014
  • United States

About LiftIgniter Real-time Personalization Platform

LiftIgniter is the leading Real-Time Personalization platform utilizing Augmented Intelligence to help enterprises transform their customer experiences across every touch point. Leveraging powerful machine learning algorithms, LiftIgniter helps clients deliver end to end real-time personalized experiences and true customer centricity. We empower Marketing teams by combining massive machine learning processing and scale with human insights for continuous A/B testing through Augmented Intelligence

LiftIgniter Real-time Personalization Platform Features

  • A/B Testing
  • Abandoned Cart Saver
  • Account Based Marketing
  • Behavioral Targeting
  • Campaign Segmentation
  • Content Analytics
  • Contextual Targeting
  • Customer Profiles
  • Experience Management
  • Recommendation Engine
  • Website Personalization

LiftIgniter Real-time Personalization Platform Reviews Recently Reviewed!

Liftigniter Article Recommendation Engine

Nov 06, 2018

5 / 5
Ease of Use

4 / 5
Features & Functionality

5 / 5
Customer Support

5 / 5
Value for Money
Likelihood to Recommend: 10.0/10 Not

Pros: The AI driven article recommendation engine was very easy to integrate and delivered impressive user engagement and conversion improvements in a very short time (1 to 2 weeks).

Cons: There are situations where the AI recommends articles that appear not the most optimal. However, it's appears to be delicate to try to manually influence the AI driven recommendations without negatively impacting overall performance.