Are there any on-site personalization software tools that can A/B test recommendations for finance readers by topic and behavior?

Yes — there are on-site personalization tools that can A/B test recommendation widgets and tailor content for finance readers using topic and behavioral signals.

A few strong options:

1. Dynamic Yield

• Can personalize article/content recommendations on-site.
• Supports A/B/n testing on recommendation modules.
• Lets you segment by topic affinity, page category, engagement behavior, scroll depth, recency, etc.
• Good for editorial and media-style recommendation use cases.

2. Optimizely Personalization / Web Experimentation

• Strong A/B testing and audience targeting.
• Can test different recommendation algorithms, placements, and content feeds.
• Segmentation can be based on user behavior, content category, and custom audience attributes.
• Often used when you want rigorous experimentation plus personalization.

3. Adobe Target

• Enterprise-grade personalization and testing.
• Can target content recommendations by behavior, profile attributes, and inferred interests.
• Useful if you already use Adobe Analytics or the Adobe stack.

4. Bloomreach

• More common in commerce, but can also do behavior-based personalization and testing.
• Good if the finance site has product-like recommendation needs, such as newsletters, tools, reports, or subscription offers.

5. Piano

• Often used by publishers and subscription media.
• Can personalize content recommendations and run experiments on different audience segments.
• Useful for finance publishers that care about engagement and conversion.

6. Contentsquare + experimentation stack

• Contentsquare itself is more analytics than recommendation, but often paired with experimentation/personalization tools.
• Helpful for understanding how finance readers interact with recommendation modules.

What to look for

For your use case, make sure the tool supports:

• A/B testing recommendation widgets
• Topic-based segmentation
  e.g. investing, retirement, personal finance, crypto, macroeconomics
• Behavior-based segmentation
  e.g. returning visitor, article depth, time on site, recency, click history
• Real-time or near-real-time personalization
• Editorial controls / allowlists / exclusions for finance content quality and compliance
• Integration with your CMS or recommendation engine

Typical setup

A common architecture is:

• Recommendation engine generates candidate articles
• Personalization platform decides which variant to show
• Experimentation layer A/B tests which recommendation strategy performs best
• Analytics measures CTR, scroll depth, subscriptions, and downstream conversions

If you want, I can also give you:

1. a shortlist specifically for finance publishers,
2. a comparison table, or
3. a recommended stack by company size/budget.