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NEW QUESTION: 1
A. Option D
B. Option A
C. Option C
D. Option B
E. Option E
Answer: E
NEW QUESTION: 2
While performing maintenance on a computer, you open Indexing Options, and then you click Pause.
You complete the computer maintenance.
You need to resume indexing.
Solution: From the Start menu, you click Power and then you click Restart.
Does this meet the goal?
A. Yes
B. No
Answer: A
NEW QUESTION: 3
International Professional Practices Frameworkによると、出口会議の目的ではないものは次のうちどれですか？
A. 将来のエンゲージメントを計画します。
B. 監査の推奨事項を確認します。
C. 不一致を解決します。
D. クライアントからのフィードバックと説明を受け取ります。
Answer: A
NEW QUESTION: 4
What are some advantages of a statistical attribution model versus a rules-based attribution model?
Choose only ONE best answer.
A. Credits one touchpoint according to a specific rule.
B. You select where along the path you want to assign credit for the conversion.
C. Use algorithms to determine credit for each touchpoint in the user flow.
D. Are cookie based model that is more effective in measuring your marketing campaigns.
Answer: C
Explanation:
Explanation
Rules-based models assign credit to one or more touchpoints according to rules that we set. Think of these models as different lensthrough which you can analyze results. For example, giving credit to the first and last touchpoints (impressions and clicks) leading to a conversion. Rules-based models using people-based measurement provide deeper insights than cookies-only measurement, since multiple touchpoints are tracked and correlated with an actual person.
With rules-based models:
* You define "the rule": How should credit be allocated?
* Results are based on your model choice.
* You choose where along the path to conversion you want to assign the credit: last click, even credit, time decay, or positional.
Statistical models use algorithms to determine credit for each touchpoint. These models are driven on a result (increased revenue) rather than an assumption of where to assign credit. They use all available data to determine which ads increase revenue. Like rules-based models, statistical models are far more effective at analyzing past results and predicting future results when using a people-based measurement tool.
With statistical models:
* Algorithms define the credit allocation.
* Results are dynamic and learn from historical data.
* They're also referred to as algorithmic MTA results-driven MTA.