Home Data-Driven Thinking Decades Later, Frequency Still A Challenge For Marketers

Decades Later, Frequency Still A Challenge For Marketers


LisaBarnesData-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 Lisa Barnes, brand media solutions engineer manager at 84.51°.

The Rule of Seven states that a consumer needs to be exposed to a marketing message seven times before taking action to purchase the brand or product. While much has changed in advertising and media since this concept emerged in the 1930s from the entertainment industry, it’s widely agreed that multiple messages are required to change a consumer’s behavior.

The explosion of new advertising mediums that now exist, however, has made frequency much harder to understand and optimize. As a researcher, the No. 1 question I hear is, “What is the optimal frequency for my campaign?

With all the measurement solutions now available to help marketers understand campaign execution, this should be easy to address, right? But, there are a number of confounding challenges that have continued to keep frequency optimization just out of reach for most advertisers.

All Impressions Are Not Created Equal

These days, just about any campaign includes a wide variety of channels and formats, including banner, online video, search, social, mobile, TV, CRM and direct mail. Each channel and format creates a unique experience for the consumer and therefore a different level of engagement with the ad.

For instance, banner blindness causes consumers to block out ads on their screens to only focus on content. For that reason, banner ads require a much higher frequency of exposure before they’re able to influence a purchase decision, compared to a high-engagement ad format like TV. However, the cost of serving a banner ad is remarkably less than the cost of serving a TV commercial so finding the optimal balance of frequency and cost is imperative.

Complicating matters further is the issue of fraud. Advertisers will lose $7.2 billion globally to bots in 2016, according to a 2015 ANA study. So how is an advertiser to know whether their impressions are reaching their desired targets?

There are some platforms and third parties providing insight into viewability and other metrics that help solve some of this, but there is no silver bullet. This leaves us in a scenario where even if we’re able to determine through research what frequency is optimal, we’re unable to execute that with confidence in market.

Cross-Channel Frequency Is Difficult To Manage


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Additional complexity arises when attempting to link a single consumer across channels to enable overall campaign frequency management.

Advertisers should consider running a campaign on both mobile and desktop. Each format uses unique identifiers, such as devices and cookies, to link consumers to the data used to target media to them. If two different publishers are used to execute the mobile and desktop portions for a target demographic of women 18 to 34 years old, there is no way of knowing what the overlap will be between the two platforms. Each publisher will have a different pool of cookies and devices available to reach.

Estimating the overlap incorrectly could result in overserving or underserving the campaign. Demand-side platforms (DSPs) and publishers that offer cross-device functionality within their owned platforms help to solve for some of this, but campaigns that require multiple publishers or channels that cannot be reconciled, such as TV, remain problematic.

Consumers And Messages Behave Differently

Advertisers spend considerable time thinking about their desired target for a specific campaign message because they know that tailoring their message to the right audience is critical to the success of a campaign.

However, what is often overlooked is the need to tailor the frequency with which that audience is exposed to that message in order for it to influence their purchase decision. A best practice for optimal frequency levels is typically two to three impressions per week, but optimization is rarely quantified for the specific group of consumers being targeted.

For instance, current buyers of a brand likely need fewer impressions – perhaps just one to two per week – as the message is simply serving as a reminder to purchase. On the other hand, converting a lapsed consumer back to a brand likely requires a much higher frequency – perhaps three to four per week or more – as the hurdle to influence brand choice is greater.  With all the complexities of frequency management, audience-specific frequency often gets deprioritized but this level of precision is necessary for campaign success. 

The story about frequency can seem pretty grim but there are steps advertisers can take to improve. Mixing up the medium and message is a good place to start. Advertisers should test and learn across channels, formats and messages to identify differences and optimize what is within their realm of control. They need to hold their media accountable for driving incremental sales rather than click-through rate or other online metrics wherever possible to ensure they’re optimizing toward the results that matter.

Advertisers must also balance reach and frequency. It’s better to reach the consumers most likely to respond to an ad at the optimal level of impressions rather than reach the most consumers at a suboptimal frequency. In this way, the same budget allocated differently can drive dramatically better results.

Finally, it’s important to focus on the bigger picture. Since we can’t sit our desired audiences down and force them to consume media, even if advertisers can identify an optimal frequency level, they’ll never hit all their desired targets the exact desired number of times. Advertisers need to focus on channels and channel combinations that allow them to get to a frequency distribution that makes sense by working toward averages and caps that drive the best results in totality.

Follow Lisa Barnes (@LisaBarnes87) and AdExchanger (@adexchanger) on Twitter.

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