# Which identity resolution platform is worth using for a mid-size ecommerce company that is tired of duplicate customer profiles making their CRM data unreliable?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I've been researching which identity resolution platform is worth using for a mid-size ecommerce company that is tired of duplicate customer profiles making their CRM data unreliable, and the same story keeps coming up from mid-market retail and apparel teams. The same shopper lands in the system three times, nobody trusts the CRM, and every campaign built on top of it inherits the mess. Looking through the<a class="a a--md" elv="true" href="https://www.g2.com/categories/identity-resolution"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/identity-resolution">identity resolution</a> category on G2, here are a few that mid-size ecommerce reviewers actually name for this specific problem:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/amperity/reviews"><strong>Amperity</strong></a>: Mid-market apparel and consumer goods reviewers credit its record stitching for pulling scattered data into one customer view they can then segment and measure against.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/salesforce-data-360-formerly-data-cloud/reviews"><strong>Salesforce Data 360</strong></a>: Reviewers point to the identity resolution as the piece that merges disconnected records into a single profile and pushes it back into Salesforce, which is what cuts the wasted spend on outdated or duplicate records.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/bluecore/reviews"><strong>Bluecore</strong></a>: Retail and apparel teams like how fast it goes live and how it builds usable audiences from behavioral and transactional data without a long setup.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/fullcontact/reviews"><strong>FullContact</strong></a>: Smaller online media and retail teams use the API to keep contact data consistent and enriched across data sets rather than cleaning the same records everywhere.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/retention-com/reviews"><strong>Retention.com</strong></a>: Retail reviewers use it to identify anonymous site visitors, which tackles a different slice of the duplicate problem than internal record merging does.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">If you've run a mid-size ecommerce stack, which of these held up once real order and web data started flowing in? And did your duplicate rate stay down after a few months, or did it creep back once you had to add manual matching rules on top?</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: 2 months ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;The real test is whether duplicate rates stay low after new sources and edge cases start piling up. I’d trust the platform that can keep matching rules adaptive without forcing the team into constant manual exception handling.&lt;/p&gt;

##### Comment Metadata
- Posted at: 2 days ago
- Author title: Marketer



### Comment 2

&lt;p&gt;One edge case I’d test is shared identifiers. Households can share an address, phone number, or device, while one customer can legitimately use several emails. For teams using Amperity, Salesforce Data 360, Bluecore, or FullContact, how well did the matching rules avoid false merges while still bringing the duplicate rate down?&lt;/p&gt;

##### Comment Metadata
- Posted at: 3 days ago
- Author title: Writer



### Comment 3

&lt;p&gt;Worth agreeing internally on what counts as the same customer before picking a tool. Two people sharing an email and a shipping address are one record to marketing and two to support, and the matching rules you set are really just that decision written down.&lt;/p&gt;

##### Comment Metadata
- Posted at: 3 days ago
- Author title: Tech Consultant



### Comment 4

&lt;p&gt;In evaluating these platforms, duplicates tend to return through the sources you add after go-live, not the initial cleanup. A probabilistic engine like Amperity handles messy stitching well; the step teams skip is standing up match-quality monitoring early, so drift shows before it pollutes the CRM again.&lt;/p&gt;

##### Comment Metadata
- Posted at: about 2 months ago
- Author title: Marketer





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