Will Adwords Ads Showing on Desktops Be Less Expensive Than Mobile Ads?

In most cases we see the natural CPC’s for mobile traffic starting off higher than desktop. And also for most of our clients the majority of their traffic is coming via mobile.

While we’re always pumped about more traffic, it’s more high-converting traffic that we really want. So looking at conversion rates and cost-per-lead metrics as main KPI’s in our accounts has lead us to using negative mobile bid modifiers for certain clients in certain campaigns.

For example, here’s the preliminary results from a recent experiment in mobile bid modification for a client in the service business—in elective medical. We’ve been working to lower their CPL below a $50 target for some time, and making steady progress.

More Conversions for a Lower Cost-per-conversion = Good Adwords Optimization!

More Conversions for a Lower Cost-per-conversion = Good Adwords Optimization!

Using mobile bid modifiers (different for each campaign, but averaging -20%), we finally cracked the $50 mark and hit a $48 CPL for 2nd half of this past month.

First Time We’ve Been Able to Drive CPL Below $50 Mark

First Time We’ve Been Able to Drive CPL Below $50 Mark

And a closer look at the campaign level shows the intended effects—mobile CPC’s coming down. And as a side result, more budget running through the higher-converting desktop traffic.

Mobile CPC Comes Down by -17% After -20% Bid Modifier and Overall CPC Is Reduced by Over 13%

Mobile CPC Comes Down by -17% After -20% Bid Modifier and Overall CPC Is Reduced by Over 13%

So as with most things in Adwords, optimizing mobile CPC’s should fit into a bigger picture that includes bottom-of-funnel KPI’s like cost-per-lead and conversion rates. There are other campaigns in this same account where the conversion rates for mobile are 5–6x desktop traffic. Those certainly didn’t get a mobile bid-down. It’s a case by case basis.

One final suggestion. Don’t start a new campaign with any device bid modifiers. Or time-of-day for that matter. Get some clean, baseline data in—then optimize accordingly from there!