An advertiser recently ran a month-long campaign on a new media platform. This campaign targeted customers who had purchased from the advertiser in the past year. Of the 10 million customers targeted, 3 million were reached. The average frequency for the campaign was three impressions over the month. The advertiser spent $100,000 on this media buy.

After the campaign, an analyst from the media platform noticed that customers who received six or more impressions were twice as likely to purchase than those who received three or fewer impressions. To increase the number of users who receive six or more impressions, the analyst recommends that the advertiser double their spend. The goal is to increase the frequency from three to six in order to drive a significant increase in incremental return on ad spend.

What primary concern should the advertiser's in-house measurement team have about this conclusion?

A large gaming company is developing a measurement strategy designed to shift its planning and buying on Facebook. The company believes that its ability to iterate on testing will provide more long-term growth than the short-term sales boosts from individual campaigns. The conversion rate is high enough that they should have NO issues with statistical power at any size of test/holdout groups. It is planning on running multi-cell Conversion Lift tests on a monthly basis.

What test/holdout proportions should the company use for each test cell?

A quick-serve restaurant brand is launching a Conversion Lift test to understand the effect of media in surrounding neighborhoods. It offers the option of paying in cash, credit card or through its app. One week after the Facebook campaign launches, these are the results:

• The daily budget for each ad set was spent in full

• The majority of people reached live within two miles of any given location

• 90% of transactions were made in cash

• Offline event sets were only uploaded at the end of the campaign resulting in a total of 10,000 conversions

What should the analyst consider when the test is complete to determine if the results are reliable?

An analyst reviews Conversion Lift test results mid-flight and has the option to take action immediately. The conversion cycle for this advertiser is 14 days, and the advertiser is running a multi-cell Conversion Lift test with equal budgets between both cells:

• Strategy A: Auction buying; Automatic Placements

• Strategy B: Auction buying; Facebook News Feed only

After the first day, the results are as follows:

• Strategy A: Automatic Placements: 12 conversions

• Strategy B: Facebook News Feed only: 14 conversions

What should the analyst recommend?