As competition for Aussies’ attention intensifies online, rethinking the way you use data can make all the difference. Here, Stephen Benjamin, performance marketing manager at the National Roads and Motorists' Association (NRMA), and Juha Lauronen, digital activation manager at the organisation’s media agency, Spark Foundry, share how they collaborated to increase online bookings by fuelling a new Search strategy with machine learning.
Between fighting for safer road regulations and offering services such as roadside assistance and car repairs, the NRMA has offered a helping hand to Aussie drivers for over a century. Over time, we’ve seen one thing hold true: When it comes to car maintenance, time is of the essence. You can’t be on the road with faulty brakes or flat tyres, so the NRMA and Spark Foundry have been working together for almost a decade to quickly connect our members with reliable mechanics.
Whenever Aussies needed the NRMA’s car repair services in the past, they’d typically hop on the phone to chat with a representative. But when we took a closer look at the NRMA’s car servicing business in late 2019, we realised we could do more to help Aussies easily find the help they need — after all, each call centre representative can only assist one Aussie at a time.
Because the NRMA has been around for as long as commercial radio, we’ve seen our fair share of technological advancement, and we know how important it is to keep up. When we noticed how many more mechanics were advertising online, we looked for ways to connect with drivers on Search before our competitors. To better guide Aussies towards helpful tools on the NRMA’s website, we decided to test how we could use automation to reinvent the way our members book the services they need.
Changing lanes with our data strategy
The NRMA’s online booking platform for car repair services already allowed Aussies to schedule a service by filling out a simple five-step form — no need to wait around on the phone. The challenge for our teams was figuring out how to make our online resources more visible for drivers when they searched for help online.
First, we needed to understand what kinds of online experiences inspired members to make a decision. While the NRMA has been active on Search since 2005, we’d been chalking up conversions to the last ad that people clicked. But considering how today’s consumers find information and inspiration all over the web as they explore and evaluate their options, we knew that our last-click approach gave us a skewed picture of what actually drove our consumers’ decision-making.
This time around, we used a data-driven attribution model to get a more comprehensive look at how Aussies interacted with the NRMA online before booking a service. Gathering insights about the most impactful touchpoints helped us figure out which ads and keywords were most relevant and engaging between Aussies’ first search and final booking.
Gathering insights about the most impactful touchpoints helped us figure out which ads and keywords were most relevant and engaging between Aussies’ first search and final booking.
Making the most of machine learning
By using the technology at our fingertips, we found we needed to do more than invest in branded search terms alone to get the visibility we wanted. We started prioritising generic keywords such as “car services” to improve our chances of catching drivers’ attention when they started their research, but we faced a lot more competition compared to branded queries. Auction-time bidding helped us automatically adjust our bids in response to real-time contextual signals such as device and browser type to make sure we showed up on the most relevant open-ended searches.
We also wanted to train our machine learning algorithms in Search Ads 360 to prioritise driving online leads rather than calls. To do so, we set a weighted default conversion goal by giving online bookings double the value of a call so our automated tools would hone in on the most valuable actions.
With our revamped approach, we boosted online bookings by 23% year over year (YOY) between October 2019 and February 2020. This growth was unprecedented for us, and the fact that we were able to lower cost per acquisition (CPA) by 17% made this success even sweeter.
Pushing forward with a test-and-learn mindset
Ever since our latest experiment with automation, we’ve continued using data-driven attribution and auction-time bidding to connect interested Aussies with car loans, battery replacement services, and bookings at the NRMA’s parks and resorts. After discovering new ways to make automation work for us, we were able to uncover more paths for growth — and we’re excited to see how we can better serve Aussies and our 2.6 million NRMA members as we continue our journey with machine learning.
