You Can Spend Less on Retargeting to Get More from Your Ad B…

When you picture this year’s holiday buying season, you probably envision mobs of shoppers edging their way through the aisles. Or maybe you visualize crowds in a freezing parking lot at three AM on Black Friday waiting for a retailer to open its doors.

That’s only half the story. Actually, it’s less than half. A Deloitte survey of 4,000 Americans shows that for the first time ever, consumers plan to do 51 percent of their holiday shopping spending online. So, a more accurate picture of holiday shoppers might be someone sitting at home in their pajamas tapping on their iPad.

For marketers, this means that competition for online shoppers will be more intense than any year. That doesn’t mean you should throw money at this problem though. The way to win the holidays is to execute surgical, data-backed media buys while everyone else is tossing money around like, well, holiday shoppers.

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Be a smart holiday advertiser.

With the holidays representing 20-30 percent of annual sales for retailers, according to the NRF (National Retail Federation), it’s a critical time — so critical, that many brands will tolerate wasted advertising spending. Perhaps it’s better to hit too many people than too few people, so what’s a few thousand wasted ad impressions?

While this approach may be accepted as standard practice, it sidesteps the fact that a mechanism to avoid overspending already exists. It’s called incrementality.

Borrowed from science and medicine, the incrementality concept is based on randomized control trials that measure the effects of one variable by observing what happens to a similar group that’s not exposed to the variable. The difference between the exposed group and the control group can tell you a lot about the success of your treatment within the exposed group.

In advertising and marketing, it’s the same idea: If you run a campaign and then pick a randomized control group that’s not exposed to ads — the holdout audience — then you can find out how much incremental revenue resulted from the campaign. For instance, a sporting goods retailer could target 1,000 ideal users with a new holiday campaign and then withhold the ads from 200 similar users. If a larger percentage of those 1,000 people made purchases than the 200 people in the control group, then the sporting goods store can assume people were influenced by their advertising.

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Taking action in your campaigns.

Once you’ve decided to pursue incrementality, the first step is to split your users into randomly selected test (they see ads) and control (they don’t see ads) groups. Group assignment can be done upon initial site visit, and then you only show ads to the users in your test group. Alternatively, holdout group users can be selected at the time ads are served.

How you do A/B testing after that is up to you, but an important end result of incrementality is that it helps you avoid delivering ads to consumers who were predisposed to purchase anyways. For instance, when eBay pulled the plug on its search advertising in 2015, it saw no drop in sales. The reason may be that people who were going to search “Lands’ End sweater eBay” were already going to go to eBay anyway. Instead of wasting their ad spend on users already moving through their purchase funnel, eBay likely opted to target users who were circling the top of it but not converting. This is a strategy you can easily replicate with your own ad spend this quarter.

It can be hard to tell who’s already going down the funnel on their own, but signals do exist in the form of behavioral data from individual users, such as how many add to carts, site visits and purchases they’ve had over time.

For example, many advertisers use JavaScript pixels to track consumer behavior either through a combination of an ad tech/measurement partner, Facebook or Google Analytics, with one typically being the source of truth. Of course, a trusted partner platform is extremely helpful as well. With this data, many advertisers can, in conjunction with their ad buying platform, decipher which consumers have high organic purchase rates and then potentially exclude those consumers from future ad campaigns.

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Holiday hazards

In addition to neglecting incrementality, marketers often err during the holidays by putting too much emphasis and ad spend between Black Friday and Cyber Monday.

Unfortunately for advertisers, Black Friday and Cyber Monday fall during Q4 when CPM and CPC rates are typically at their highest — and this creates a slippery slope when it comes to overspending. With CPM and CPC rates high because of competition, and low conversion rates during certain weeks in the holiday season posing a threat, there’s potential for retailers to waste an obscene amount of ad spend at the most important time of the year.

Instead of throwing more money at the problem, consider leveraging that potential spend to acquire new prospects who aren’t familiar with your brand, potentially reaching users who are more likely to make an impact on your bottom line.

Retailers don’t have to succumb to holiday season hazards. They can win the holidays by gathering data on, and allocating ad budget to, users who are more likely to be influenced by ads — while at the same time letting those frequent purchasers find their own way to the buy button.

Optimizing for incrementality using the tactics outlined above will help advertisers avoid holiday overspending, grow revenue profitability and turn Q4 into an opportunity, not a threat.

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