# What identity resolution tools are the most accurate at connecting offline point-of-sale data with online customer behavior for a retailer running both channels?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I'm researching which identity resolution tools are the most accurate at connecting offline point-of-sale data with online customer behavior for a retailer running both channels, and I'd like input from people who have actually reconciled store and web data. The reason a generic answer falls short here is that in-store purchases and online sessions rarely share a clean key, so the match quality is the whole game. Based on what omnichannel retail reviewers say in 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, here are five that come up for stitching offline and online together:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/amperity/reviews"><strong>Amperity</strong></a>: Reviewers credit its machine-learning, probabilistic stitching for resolving one identity from online, offline, transactional, and behavioral data. For retailers, how accurate did the store-to-web match stay once you fed it messy POS records?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/treasure-data-customer-data-platform/reviews"><strong>Treasure Data Customer Data Platform</strong></a>: One reviewer specifically calls out the offline and online reconciliation it provides to understand the full customer journey across stores and web. Did that reconciliation actually improve your media efficiency and in-store attribution?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/tealium-customer-data-hub/reviews"><strong>Tealium Customer Data Hub</strong></a>: A retail reviewer describes it becoming their single source of truth across touchpoints. Was the profile stitching reliable for anonymous in-store shoppers, or only for known ones?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/liveramp/reviews"><strong>LiveRamp</strong></a>: Retail reviewers highlight tying offline sales data back to online ad exposure for an omnichannel view. Did the offline-to-online match hold up well enough to plan spend against?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/meiro/reviews"><strong>Meiro</strong></a>: An apparel reviewer describes connecting online and physical retail store data into one consistent profile. How well did it handle customers who shop across both without a loyalty ID?</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For retailers running both channels: which of these gave you a match you actually trusted between the register and the website, and how are you measuring accuracy beyond the vendor's stated rate?</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: about 1 month ago
- Net upvotes: 2


## Comments
### Comment 1

&lt;p&gt;I recommend selecting a group of loyalty customers recognizable in both channels and then measuring precision against that group.&lt;/p&gt;

##### Comment Metadata
- Posted at: 21 days ago
- Author title: Marketer





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