Using data to refine your online ad strategy and target buyers more effectively

Posted by Meg Bernazzani on May 1, 2018

Data is a window into buyer behaviour—use it to refine ads and drive sales

As a dealer, you should go beyond high-level metrics like Click Through Rate (CTR) to create a more rounded view of the path buyers travel to your website and the actions they take when they get there. As we discovered in our article on measuring data, the key data you need is surprisingly easy to find and analyse. In this piece, we’ll learn more about the valuable insights this data provides and how you can use it to attract more serious, sales-ready buyers.

Data helps you understand how to target buyers over browsers

Online ads may help drive consumers to your website, but are those consumers genuine buyers or casual browsers? More detailed analysis of what they’re doing when they get to your site can add a great deal to your understanding of ad performance.

Analytics platforms provide a lot of useful information here: they can tell you how long somebody spends on your site, how many pages they visit, and any actions they take (like downloading model specifications). Time spent on your website is a positive gauge of interest—a car is a significant investment and a serious buyer will research it thoroughly.

What has this got to do with your original ad? Everything. It shows whether or not your ad strategy is delivering genuine buyers, and gives you the information you need to start refining it. To get this data, you should:

  • Measure how consumer behaviour changes when you move more ad spend from paid search to social media (and vice versa).
  • Compare stats on visitor behaviour to those from a campaign you ran this time last year.
  • Find out what time of day your ads seem to have the most effect.

By capturing this data over a number of months, you begin to see what works best for you, in terms of the placement, timing, and content of your ads, and their role in wider marketing campaigns.

Continuous improvement leads to better results

Using these metrics means continually refining your ads, and drilling down further towards what you really want, which is conversions (like filling out a form on your website) and, ultimately, sales. You should certainly measure conversion rates and sales too, but there are a couple of caveats:

  • Online ads don’t necessarily work immediately. They may drive awareness rather than action, and you might not see an effect on the bottom line for two or three months, especially given the long and winding road a typical car buyer travels.
  • Your ad might be perfect, helping to drive genuine buyers to your website, but your website is pushing them into the arms of another dealer. We won’t address website optimisation here (that’s a topic for a whole other article), but it’s worth bearing in mind as you gradually refine your ad strategy.

Again, using historical data can help you optimise your ad performance. Comparing ROI metrics between campaigns—how many extra leads, form fills, or sales you generated during the relevant time periods, and at what cost—can give you a good indication of the strength of your ads, regardless of other factors. With those metrics in place, you can direct ad spend more efficiently.

And you can certainly go much further, especially on the attribution side. Attribution modelling can help determine how much credit to give each touchpoint throughout the entire customer journey. One step further on in sophistication, data-driven attribution uses your own data to help you work out the optimal combination of, say, paid search, display ads, email, and social.

But if you’re still using CTR as your sole measure of ad performance, there are plenty of easier steps to take first. Basic data, gathered automatically by common analytics tools and displayed in simple graphs and charts, reveals patterns in performance that can intelligently inform your advertising strategy. Regular analysis of this data can lead to more efficient ad spend, and, ultimately, extra sales.

Topics: analytics, Attribution, data