“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Elliot Hirsch, CEO at AdYapper.
For digital advertising veterans, talking about how to manage campaign frequency might seem a bit old school. DoubleClick researcher Rick Bruner released a frequency best practices white paper nine years ago – check out the bubble font – which seems like a lifetime ago in this industry.
Media planners will tell you that frequency is already baked into most insertion orders, so many people think of frequency as “covered.” But frequency is a slippery topic, and the more you peel back the layers, the more you find wrong with current practices.
Advertisers that adjust how they manage frequency can cut fraud, create campaign lift and increase transparency. There is a gold mine of helpful data just beneath the common frequency metrics upon which the industry relies and reports.
Today’s Frequency: A Joke
For many digital advertising agencies, ad tech companies and publishers, frequency is a nuisance. It reduces the ability to deliver a campaign quickly and seems totally arbitrary. To keep up with the pressure of delivering campaigns, frequency caps have given way to average frequency numbers, which allow for wiggle room at the margins. Also, in the name of delivering a campaign in full, many players in the ecosystem lift the frequency caps at the end of a campaign.
What this means is that if marketers are going to take frequency seriously, they’re going to have to be clear about it to their partners. If they have an ideal frequency that they’re trying to manage to, there must have been a reason why the number exists. Internal studies, for example, may have found correlation between a frequency figure and high brand recall or conversion rate.
Marketers must justify their frequency numbers to their partners. Do they have the research to show how they arrived at their frequency number? Does their agency know about this information? Are they using it to plan the campaigns? They should be.
Additionally, if frequency is going to matter, it’s helpful to emphasize in RFPs that frequency is integral to campaign success and not just a throwaway metric.
The Value Of The Frequency Curve
Once ideal frequency is established and the importance of frequency is shared with partners, marketers have more power to change what’s wrong – and there’s probably a lot wrong. The best place to start is to dig below the surface of average frequency metrics.
If advertisers want average frequency metrics used across a campaign or publisher buy to make sure they’re hitting their goals, everything might look fine at first. The problem is that average in theory is not average in practice. The data behind the average frequency is rarely a nicely shaped bell curve, where most viewers get about the correct number of ads.
For example, if four to five ad exposures is the ideal frequency and a campaign reports an average frequency of four, it is likely that the vast majority of ads were seen only once. That’s because a few bots probably each saw them 10,000 times. Voila, the average equals four.
The first thing to do to get to the real value of a frequency number is ensure that the average looks like a real bell curve. Look at the curve by publisher, audience data partner and screen or channel, such as mobile, desktop or video.
The easiest thing to do if there is a spike on the high end is to block bots from boosting the average and causing most real people to see too few ads. If the bots can’t be blocked directly, media buyers and partners should discount those data points from the calculation and do a make good. If there are still too many bots, then changing the media plan to blacklist certain partners is a good idea.
Absolute Frequency Is Best
By incorporating independently measured viewable reach, viewable frequency and conversion path data into an evaluation, marketers gain the transparency and granularity necessary to identify artificially inflated frequency and bot fraud, while confirming an optimized frequency range.
The best case for controlling that frequency more completely is to create an absolute frequency by viewer across media buys. This works universally with an integrated data-management platform and demand-side platform like AudienceScience, or it can be done by publisher, which is not as good but still valuable.
Basically, instead of a frequency average, the frequency cap becomes the more important metric. By making sure that partners can’t lift frequency caps, marketers will also protect themselves from many bot issues.
For a better frequency plan to really hold sway, brands must stop creating incentives for their partners to deliver volume over value. When partners force campaigns to deliver in full at the end of a campaign, they take their eye off the metrics that drive value, merely focusing on earning their commission.
Partners need incentives to focus instead on what matters: showing the right number of ads to real humans. That starts with brands.
Follow Elliot Hirsch (@ElliotHirsch), AdYapper (@AdYapper) and AdExchanger (@adexchanger) on Twitter.
