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1. 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?

2. A new ecommerce company that sells raincoats for dogs leverages Facebook to drive product sales. The company serves a variety of videos and static images and only targets people who previously visited their website. The company historically spends $50,000 per quarter and always hits its ROAS goal of 3x.

Last quarter, the company aggressively tried to grow its business and tripled spend.

At the end of last quarter, they compared performance between the previous two quarters and noticed the following differences in performance metrics:

• ROAS decreased by 40%

• Average frequency increased by 50%

• CPMs increased by 60%

• Reach increased by 10%

• Cost per click decreased by 15%

• Allocation of budget between creative formats remained the same

An analyst needs to run a test to identify how to scale spend efficiently.

Which variable should the analyst test first?

3. An analyst calculates data averages (norms) of more than 2,500 brand lift tests in the industry, then compares it to recent campaign results:

• Campaign delivered Ad recall lift +8.3pt (the norm Ad Recall is 9.Opt with a standard deviation of 0.6pt)

• Campaign delivered Message Association +7.1pt (the norm for Message Association is 7.8pt with a standard deviation of 0.8pt)

• Campaign delivered Favorability +6.3pt (the norm for Favorability is 4.Opt with a standard deviation of 1.1pt)

• Campaign delivered Purchase Intent +5.9pt (the norm for Purchase Intent is 5.3pt with a standard deviation of 0.5pt)

Which conclusion should the analyst make based on this data?

4. An analyst analyzes 1,000 Facebook video campaigns and calculates there is a correlation of r = 0.6 between view duration of a campaign and additional ad recall lift.

What should the analyst tell the company about the relationship between view duration and ad recall lift?

5. An analyst wants to measure a campaign that is delivering across Facebook and Paid Search. The client wants to understand the causal sales impact of each channel.

Which methodology should the analyst use?

6. A large ecommerce company wants to know which of its two creative strategies is generating the highest number of conversions. It already knows that both strategies generate significant lift compared to a holdout group.

What measurement solution should be used?

7. A startup ecommerce company is beginning to run media campaigns to increase sales. It is having trouble taking action from its media campaigns because of the high number of KPIs it is considering when analyzing results.

The startup company has been in the growth phase, so the focus has been on increasing the number of people who see ads for their products on Facebook. As the company grows, the focus is shifting towards generating sales. For its Facebook campaigns, the business is considering incremental purchases, incremental reach, CPMs, website visits, clicks and impressions.

Which metric should be prioritized to support the company's new Facebook marketing strategy?

8. An analyst for a primarily brick-and-mortar retailer is reviewing measurement results from the last half for all marketing. The media plan was 50% TV spend, with some Facebook (10%), search (10%), print (10%), and radio (20%).

No solution provides the same numbers for a single point in time. The analyst needs to recommend how to allocate budget across channels to maximize sales for the next business quarter.

Which measurement solution should be the primary source of the analyst's recommendation?

9. An advertiser is running an A/B test on Facebook with the goal of finding whether creative strategy A or B achieves the most conversions.

What is the null hypothesis of this test design?

10. An ecommerce brand runs a multi-cell Conversion Lift test. The brand needs to determine if bidding in the Facebook auction based on user value calculated from its LTV model versus demographic targeting improves performance by 10%. The p-value for the test is calculated as p = 0.95.

How should the analyst interpret bidding based on user value?

11. An advertiser is reviewing campaign results in Facebook Attribution and determines that optimizing its campaigns for website content views appears to be more successful than optimizing for clicks.

Details reviewed were as follows:

• A last touch attribution model was used

• Look back window was 90 days

• Campaigns optimized for clicks delivered a cost per view for SO.55

• Campaigns optimized for website content views yielded a cost per view of S0.49

The advertiser wants to assess whether or not this difference is meaningful or due to random chance before adjusting its strategy accordingly.

What action could the analyst take to determine if the difference is meaningful?

12. A food delivery company launches a campaign to attract new subscribers. An advertiser creates two types of video ads: educational and promotional.

The educational video ads focus on increasing people's awareness of the brand. The video ads reach an audience of people, ages 18-50. Previous purchasers and active subscribers are excluded. The ads are also delivered to a Lookalike Audience based on current customers.

The promotional video ads include promotional codes that encourage new subscriptions and are delivered to people who engaged with the earlier ads. The advertiser launches the ads a few weeks before operating in new markets so the company can develop a pool of customers who are ready to receive the product when operations start.

Which metric should be the primary KPI to determine the causal effectiveness of the Facebook ad campaign?

13. An ecommerce brand is interested in understanding which approach delivers the highest ROAS. The advertiser is leveraging two distinct ad sets.

Ad Set A:

Budget: $100,000 Audience: Men, ages 45+ Creative: Product Focused

Ad Set B:

Budget: $120,000 Audience: Men, ages 18-44 Creative: Product Focused

Which two approaches could be recommended to analyze these campaigns? (Choose 2)

14. A beverage brand plans to launch a World Cup campaign to generate awareness across digital, TV and print. It recently ran a marketing mix model to determine the performance of this campaign. The analysis proved that the campaign resulted in a lift in sales. Due to the high cost of World Cup ads, the ROI was $0.15, which is below their historical norms for campaigns.

How should the analysis help contextualize the results?

15. 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?

16. A marketing effectiveness analyst based in an emerging market obtains the following multiple linear regression output for predicting sales as function of price (Price), distribution (Distribution) and advertising (Ads). Some issues occurred with the software, and some of the regression results are corrupted on the output.





What interpretation should be made from the output?

17. A longitudinal data set is missing values.

Which approach should be used to minimize bias in a forecast considering it is a small sample?

18. A taxi company is working on building an understanding of household customer lifetime value. Some of their customers order via digital platforms, some via phone, and some alternate between the two. They currently calculate lifetime value (LTV) by looking at all hashed order data including email addresses for online customers and all phone numbers for phone orders. The results showed that email customers had a yearly LTV of $100, and the phone customers had a yearly LTV of $80. However, the company is aware that a group of people are introducing some noise into the results by ordering via both phone and online.

What solution should the analyst recommend to enhance the ability to create a functional LTV model?