Why Optimization in Marketing Matters More Than Ever

March 14, 2017 — by Michael Neiss

Read any marketing trade publication and “optimization” is a term that regularly pops up in the context of digital ad campaigns. But what does it mean to really “optimize” a campaign—and why should you consider it as part of your long-term marketing strategy?

Why you should use optimization

Any time you launch a campaign, you have a goal in mind. If you can measure this goal, you can optimize to it.  You just need to understand which dials to turn—which users to target, which sites to target them on, what time of day is most effective and more.

As more and more data becomes available, it is quickly becoming impossible for one person to manage this process by hand. There are multiple formats, individual images and messages to consider. What team—let alone individual—could look at every impression multiple times a day and score it? You need machine learning—the science and practice of designing computer algorithms that find patterns in large volumes of data—to manage the scale of the opportunity. A machine learning algorithm not only improves how well opportunities are assessed and continually improves upon itself over time; it also saves time and efficiency for employees and companies, freeing up traders to do less manual and more strategic work.

How to overcome the challenges of optimization

As the first demand-side platform, we’ve seen hundreds of advertisers, thousands of campaigns and trillions of impressions over the last decade. During this time, we’ve often encountered certain scenarios that pose a challenge to traditional optimization:

  • Short-lived campaigns: You have a campaign that won’t be active long enough to generate learnings. For example, a retailer may want to run a short promotional campaign to highlight an upcoming sales event.
    • Solution: Seed the new campaign with data from previous campaigns the advertiser has run. There’s no need to start from scratch every time!
  • Rare events: You care about a certain event but it doesn’t happen frequently enough. For example, a cruise operator wants to maximize new bookings through their website, but cruises are expensive and so there are relatively few merit events from which to learn.
    • Solution: Move up the sales funnel and choose a proxy for the event you care about. Users may not book a new cruise very often, but a repeat visit to the itinerary page may be a reliable signal of intent. You can also optimize towards audience members that look like the users you want to reach. Look-alike models like this are even more powerful when they utilize second-party data, which augments an advertiser’s first-party data with behavioral data from non-competitive advertisers to build a richer profile of each user.

How to be smart about optimization

Every advertiser faces a fundamental choice when setting up a new campaign: Is it more important to spend the budget in full or to stay within a certain performance threshold?  Most advertisers probably aspire to the second goal, but it can be difficult to have high conviction in performance KPIs that rely on last-touch conversion credit.  In other words, what does it really mean if a user who clicked an ad or saw an ad went on to buy something?  Would that purchase have happened anyway?

Answering questions like these and understanding the relationship between last-touch conversion credit and real, causal marketing outcomes requires marketers to think about incrementality—something we will cover in our next blog post.