200-101試験無料問題集「Facebook Certified Marketing Science Professional 認定」

A CPG advertiser wants to determine how much ROI is provided by Facebook compared to other channels, like TV and online publishers. The advertiser runs a digita deep dive marketing mix modeling.
Refer to information from the test:



An analyst working for a financial services company is reviewing Facebook campaign results to assess how many new credit card signups can be attributed to its Facebook campaign. The analyst is comparing attributed results in Facebook Ads Manager with those in Google Analytics and needs to explain why these are different.
What are two key differences between the platforms that can provide a reasonable explanation for this outcome? (Choose 2)

A fashion retailer has recently developed a new creative strategy and has also seen a decrease in sales. It is interested in learning what may have contributed to the decline, and has kept its media buying strategy consistent year-over-year.
What test design should be used to address this business challenge?

A car manufacturer discovers that the purchase journey is typically one-year long, involves several media channels and is followed by a dealership visit that ends with a purchase at the dealership. Historically, sales are mostly influenced by the quality of the customer service experience. The manufacturer has a KPI of driving incremental customers to its website.
What measurement solution should be used?

A snack company ran a preliminary simple linear regression analysis to determine channel contributions to sales. The model, coefficients, and data set are as shown. All numerical values are rounded.
Sales(week) = BO (Intercept) + B1 f(FB Video) + B2 f(FB Display) + B3 f(TV) + B4 f(Digital Video)

What are the attributed sales from Facebook for Week 2?

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?