



The American Precious Metals Exchange (APMEX) is the leading purveyor of precious metals, serving millions of customers worldwide. The company partnered with E-Nor , a Google Analytics Premium Authorized Reseller, to better understand the customer journey and gain insights to improve marketing initiatives.













The first challenge they tackled was to integrate various data assets by exporting Google Analytics Premium data to Google BigQuery . This was accomplished using both the BigQuery export and the User ID features to connect website behavioral data to the company internal customer profiles. This enabled APMEX to use data more effectively to interact with different types of customers.





In addition, by bringing Google Analytics data into the company’s Customer Relationship Management (CRM) system, they empowered their internal teams to make data-informed decisions on a daily basis. For example, when customers call, site usage information is now available to the customer representative talking them.

“We have found BigQuery data to be immediately actionable. It focuses our marketing efforts, personalizes our onsite experiences, and improves the effectiveness of our sales department. When used in conjunction with our current data systems, there is seemingly no question about our customers that cannot be answered. It’s that powerful.” — Andrew Duffle , Director FP&A, Analytics & Optimization, APMEX, Inc.

As a result of the work mentioned above, APMEX has decreased the average cost per acquisition (CPA) by more than 20% while maintaining the same level of new customer orders.





They have also used Google Analytics Premium data to build a statistical model to target valuable customers earlier in their life cycle. For customers identified in the model, the company has increased email open rates by 58%, email conversion rates by 62%, and revenue per email by 163% as compared to the overall business.





download the full case study. To read more about how APMEX and E-Nor used Google Analytics Premium along with BigQuery in order to make more informed decisions,



