Case Study

How I increased Amtech’s Google Ads revenue by 42% in 2 months

Client: Amtech

Vertical: Medical Supplies

Account type: B2C Ecom

Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Mitch Cartwright
Google Ads revenue
Return on ad spend
Conversion rate

The background

Founded in 2003, Amtech is a New Zealand-based medical supplier dedicated to sourcing and providing cutting-edge healthcare products.

Amtech is a business that services two different avatars - B2B clients who buy wholesale and retail purchases who are purchasing for self use. Ads That Convert was commissioned with the primary goal of increasing the revenue of the retail side of the business.

The challenge

The types of products that are sold by Amtech are price inelastic. People will shop around for the lowest cost version of the product. These are utility products - people know they need them and they will buy them, the main variable is who they are going to purchase from.

One of the benefits of this is that you don’t need the ads and PDP to convince to generate demand. However, it will mean that people will use Google’s pricing shopping comparison to filter for the lowest priced items.

The brand is also 20 years old and so there are thousands of brand searches every month - we wanted to ensure we were hitting new customers, not returning customers so we could continue to grow the customer base.

One final challenge is the verticle we are working in. Due to the nature of the medical products that are being advertised we face restrictions on customer lists and given the error ‘Not eligible due to a sharing, privacy, or other policy issue’.

The solution

Starting with the foundations - the account was built from the ground up. The website uses a custom CMS so there are no plugins to get Google Merchant Centre integrated in a few clicks - we built the feed from scratch which allowed for a ton of flexability.

To prevent spending on exisiting customers (both wholesale and retail) we knew we had to blanket negate brand search terms - they are highly likely to be wholesale customers who aren’t who we’re trying to target via ads.

We then configured tracking as previously there was no conversion or revenue tracking in Google Analytics. To be certain that we could optimise towards retail purchases (rather than wholesale purchases) we used a custom tracking set up with Google Tag Manager and varaibles in events triggered with dataLayer.push(). 

With this we now could see where different types of sales were coming from - this allows us to feed retail purchases to Google Ads conversion tags so that the automated bidding (Target Return-On-Ad-Spend) has clean data to optimise towards. This tactic gives the bidding systems the data required to increase/decrease bids depending on the type of user that is searching.

The incremental revenue from here came from ongoing optimisations based on the data that was collected in the coming weeks. By directing spend to the highest ROI products, we were able to scale daily spend from $400/day to $800-$1200/day.