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Adobe Target Announces New AI-enriched Enhancements for Multi-channel Personalization

Adobe’s advanced customer journey mapping solution for hyperpersonaliztion, Adobe Target, has announced next-gen Artificial Intelligence-driven enhancements for smoother customer experience (CX) delivery. Adobe users can now use AI-powered personalization within their Cloud to scale CX efforts across all major industries.

AI-powered Personalization will Improve Digital Acceleration across Industries

Adobe Target, a powerful CX management solution within the Adobe Experience Cloud, helps you achieve the highest ROI from your omnichannel CX activities. The solution is now powered by AI enhancements to make your marketing communication more personalized across the web, mobile apps, social media, email and other digital channels. Customers consume so much information digitally that it is impossible to maintain a consistent branding and communication standard across all channels. In order to differentiate the CX efforts from it competitors, Adobe Target users leverage its AI-powered solutions.

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As part of the latest announcements, Adobe Target has announced the following enhancements:

AI motion controlled Automated Personalization

Automated Personalization or AP works on advanced machine learning algorithms specifically built for personalization. It has been found that ML algorithms can greatly improve the personalized experiences and increase the conversion rates for digital experiences. AP in Adobe Target activates every marketing communication with the customer using Random Forest Algorithm. This ML algorithm delivers powerful visitors profile matching capabilities and then automatically  personalizes content for an improved lift.

Automated Personalization builds a database of visitors and their activities across multiple touch-points. This information is them fed to Machine Learning algorithm training model to create Random Forest Algorithms for each customer experience and offer. After AP tests the ML algorithms for a threshold quality check, Adobe Target then filters the campaign toward driving a meaningful personalized traffic with minimum human intervention or analysis. Multiple self-learning algorithms are available in AP that can be combined with other AI and data science techniques such a Thompson Sampling and Multi-armed Bandit or MaB.

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ll these come together within Adobe Target to deliver rank-ordered offers on a homepage and a ranked product page design on a PDP.

Auto-Target

Part of Adobe Premium package, Auto-Target (AT), like AP, is again an ML-based advanced CX and content personalization solution to drive conversions. Users leverage this ML solution to create the most tailored experience for an individual user. Its Random Forest Algorithms merges with the MaB to build a model specifically purposed to self-learn traffic trends without over-exploitation. These models are efficient in recreating and rebuilding newer algorithms every 24 hours based on visitor’s online behavioral and preferences’ data. Advanced trend spotting involved MaB merged with performance enhancing technique, called the Thompson Sampling.

Automated personalization activity provides more personalization than Auto-target for the same volume of traffic per experience fed to train and build the respective Ml algorithms. On the other hand, Auto-target allows lot of opportunities with A/B workflow for building continuously improving self-learning augmentative ML models based on real-world conditions.

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Real-world Applications

  • User-defined incentives-based rewards’ program signup
  • AI-based propensity score evaluation
  • Current loyalty status and geo-location
  • Data noise wipe-out

Adobe has also announced new beta in its Adobe Real-Time CDP attributes and high-value segments for mapping with Automated Personalization and Auto-Target activities. This expands the data available for Adobe Target’s algorithms to evaluate for more precise 1-to-1 personalization execution.

[To share your insights with us, please write to sghosh@martechseries.com]

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