Jump Personalizer

JUMP Personalizer

Tailor the user experience by plugging AI into your digital service

Contextual recommendations for your users tailored to their consumption habits

Use personalized recommendations adapted to different user consumption scenarios to increase user satisfaction and engagement:

Recommendations by user similarity and product similarity

Provide different recommendations to your users based on similar products and products consumed by similar users

Recommendations by day of the week

Make recommendations to your viewers depending on whether it’s a work day or weekend, a Monday, Wednesday or Friday, etc.

Recommendations by time of day

Make user recommendations based on what time it is: morning, evening, night time, 9am or 12pm, etc.

Recommendations by device

Give your users recommendations based on the device they use: a smartphone, a web browser, a game console or a Smart TV, etc.

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Whitepaper: Ignite your digital consumers passions or die!

Offer an ultra-personalized experience by configuring individualized digital content

Enhance your digital service personalization, easily fine-tuning the recommendation engine according to your personalization strategy.

Weighted personalization by product category

Configure your recommendations to prioritize a specific product category when recommending content to your users

Personalized curated content

Showcase new products, seasonal products or campaign related products as selected by your curators

Product blacklists

Hide the products that your curators flag up as unsuitable for user groups

Recommendation grid configuration

Configure your recommendation ribbon messaging and formatting for various recommended content items.

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Whitepaper: What can Artificial Intelligence (really) do for your consumer digital service?

Recommendation performance tracking through performance analysis of recommended products

Assess your digital service recommendation performance and fine-tune the recommendation strategy and configuration through repeated iterations.

Recommendation impressions

“How many people did my recommendations reach?”

Recommendation impressions CTR

“What percentage of people showed interest in my recommendations?”

Recommendation conversion rate

“How many people consumed the content I recommended?”

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Whitepaper: What is data-driven user experience?

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