The final match rates accounted for 99% of form leads and around 80% of call leads, which was considered an excellent result.


The improved visibility into the true performance based on Revenue rather than CPL in Google Ads resulted in a 20% improvement in ROAS, at a time when the market was experiencing a downturn. It also allowed for the scaling of the campaigns, increasing the volume of leads by 30% while maintaining the target CPA.



+20%
ROAS

Boris L.

Mayple Vetted

expert

The Challenge

This customer was running lead generation campaigns that fed the leads into their custom-built CRM, to be contacted by call centre staff. The call centre reps would then convert the leads into sales over the phone, sometimes over several calls. That data would go into the CRM also, but not connected to the original lead. Leads came in via website forms as well as phone calls, so tracking inbound phone call leads was essential as well. They had a call tracking system built by a previous agency that they needed to migrate away from.


The challenge was to be able to feed sales data back into Google Ads so that the campaigns could be optimized to the actual revenue, as opposed to just the CPL. The call tracking also needed to be migrated to another platform.


The Process

The project started off with the migration of the call tracking framework to another provider, after performing a vendor evaluation and presenting the results to the customer.


The most challenging part was extracting all the sales data from the CRM and matching it to click or call data from the various acquisition channels, ie Google Ads, Facebook Ads, tracked inbound calls etc. Only Google Ads needed the sales data fed into it and matched down to the keyword level.


We worked with several teams on this, including the customer's own dev team, the call tracking provider's dev team as well as Google's data and analytics team to design and implement the framework to process this data set and deliver the revenue data to Google Ads, with the highest possible match rate.


The solution involved using BigQuery, with data from the various data sources being pulled into it and matched using unique IDs where possible. Using GCLID's allowed for keyword-level matching in Google Ads.

The Solution

The final match rates accounted for 99% of form leads and around 80% of call leads, which was considered an excellent result.


The improved visibility into the true performance based on Revenue rather than CPL in Google Ads resulted in a 20% improvement in ROAS, at a time when the market was experiencing a downturn. It also allowed for the scaling of the campaigns, increasing the volume of leads by 30% while maintaining the target CPA.



Skills Used

Paid Ads,Paid Ads,Paid Ads,Paid Ads,Email Marketing,Marketing Strategy,Other,CUSTOM,Web Development

Industry

Technology

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