# Which personalization engines handle A/B testing and recommendations without a data science team?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I'm looking to pin down which personalization engines handle A/B testing and product recommendations without a data science team, meaning the no-code promise actually holds, where marketers build the tests, and the recommendations tune themselves. </p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">What that requires, practically, from the<a class="a a--md" elv="true" href="https://www.g2.com/categories/personalization-engines"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/personalization-engines">Personalization Engines</a> category:</p><ul>
<li>Visual test setup with the statistics handled for you, no significance math by hand</li>
<li>Recommendation algorithms that self-optimize rather than expose models to configure</li>
<li>Templates a marketer can deploy without a sprint ticket</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">A few that fit from my read on G2: </p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/dynamic-yield/reviews"><strong>Dynamic Yield</strong></a> (4.5, 155+ G2 reviews): reviewers rate its product recommendations 94% and A/B testing 91%, exactly this thread's pairing. Tests are built in a visual editor with the platform calling significance for you, and its recommendation strategies self-select per visitor rather than asking anyone to tune a model. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/nosto/reviews"><strong>Nosto</strong></a> (4.6, 235+ reviews): the most self-serve of the set. Its recommendations come pre-trained for ecommerce (cross-sells, bestsellers, browsing-history recs) and start optimizing from install, and its templates drop into standard storefronts like Shopify without a sprint ticket.  </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/bloomreach-bloomreach/reviews"><strong>Bloomreach</strong></a> (4.6, 735+ reviews): powerful without demanding modelers, recs and weblayer templates are configured, not coded, and testing is built into its campaign flows so a marketer can experiment inside journeys they already run.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/optimizely-web-experimentation/reviews"><strong>Optimizely Web Experimentation</strong></a> (4.2, 405+ reviews): experimentation-first heritage, and its visual editor plus its stats engine are exactly the "statistics handled for you" criterion, arguably the most rigorous version of it in this set. </li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Which feature turned out to quietly need a data person after all, targeting rules, result interpretation, or the product feed itself?</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p>

##### Post Metadata
- Posted at: 21 days ago
- Author title: Tech Consultant
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;The no-code promise always sounds cleaner than it ends up being in practice. My guess is targeting rules end up needing someone data-savvy eventually even if the initial setup is drag and drop. Has anyone actually run one of these long term without ever looping in an analyst, or does that person just show up later under a different title?&lt;/p&gt;

##### Comment Metadata
- Posted at: 20 days ago
- Author title: SEO Content Specialist





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