Hypothesize

Question 1

A strong hypothesis includes answers to what five questions?

Answer 1

Who, what, where, when, and why

Question 2

The “Who” in a hypothesis refers to what?

Answer 2

The audience

Question 3

The “What” in a hypothesis refers to what?

Answer 3

Behavior of that audience

Question 4

The “Where” in a hypothesis refers to what?

Answer 4

Location (of action)

Question 5

The “When” in a hypothesis refers to what?

Answer 5

Ad schedule

Question 6

The “Why” in a hypothesis refers to what?

Answer 6

The rationale for the anticipated audience behavior or perspective

Question 7

True or False: A test hypothesis is a proposed explanation for an observation you can test, usually in the form of an if/then statement.

Answer 7

True

Question 8

What two kinds of variables must be identified to create a test hypothesis?

Answer 8

Dependent and Independent Variables

Question 9

The alternative hypothesis says that the test hypothesis is correct or incorrect?

Answer 9

Correct

Question 10

The null hypothesis says that the test hypothesis is correct or incorrect?

Answer 10

Incorrect

Question 11

What measurement approach measures how channels work together to generate business outcomes?

Answer 11

Cross-channel reach reporting

Question 12

What measurement approach is limited by things like metrics varying by channel, reporting being channeling, or reach not always correlating with brand and conversion business outcomes?

Answer 12

Cross-channel reach reporting

Question 13

What measurement approach determines credit for each touchpoint on a consumer’s path to conversion?

Answer 13

Attribution

Question 14

What measurement approach includes limitations like potentially being costly to access, cross-device limitations, or often only including digital channels?

Answer 14

Attribution

Question 15

What measurement approach is a data-driven statistical analysis that quantifies the incremental sales impact and return on investment of marketing and non-marketing activities, measuring both offline and online sales across channels?

Answer 15

Marketing Mix Modeling

Question 16

What measurement approach can be limited by things such as struggling to capture incremental sales where an increase is minimal, requiring collaboration between modelers and an econometric model, and not inferring causality (only correlation)?

Answer 16

Marketing Mix Modeling

Question 17

What measurement approach tests multiple campaigns against one another to see which tactical approach produces the best results based on your KPIs?

Answer 17

A/B Test

Question 18

What measurement approach may be limited by factors such as not including control groups or not always being appropriately powered with adequate conversion numbers in each group?

Answer 18

A/B Test

Question 19

What measurement approach shows how channels work together to generate business outcomes?

Answer 19

Cross-channel reach reporting

Question 20

What measurement approach shows how much credit each touchpoint in the consumer’s path to conversion should get?

Answer 20

Attribution

Question 21

What measurement approach is a data-driven statistical analysis that qualifies the incremental sales impact and return on investment of marketing and non-marketing activities, measuring both offline and online sales across channels?

Answer 21

Marketing mix modeling

Question 22

These limitations stand for what measurement approach?

• Metrics vary by channel
• Cross-channel reach reporting can be challenging because not all channels share touchpoint/reach data
• Limitations vary by specific solution. In general, limitations arise when metrics are not comparable or like-for-like data is not available
• Reach does not always correlate with brand and conversion business outcomes

Answer 22

Cross-channel reach reporting

Question 23

These limitations stand for what measurement approach?

• Statistical attribution models can be costly to access in some cases, while others are limited to specific channels
• Cross-device limitations on cookie- or web-based systems
• Many tools do not have access to the full path to conversion (for example, in-app impression data is often not shared across publishers)
• Often only includes digital channels

Answer 23

Attribution

Question 24

These limitations stand for what measurement approach?

• Can struggle to capture incremental sales where an increase is minimal
• Requires collaboration between modelers and an econometric model
• Doesn’t help with in-channel optimization
• Does not infer causality, only correlation
• Can be time-intensive to implement

Answer 24

Marketing mix modeling

Question 25

These limitations stand for what measurement approach?

• Some A/B tests do not include control groups, only randomized test groups. In these cases, they do not measure causality or the incremental value of a strategy, and therefore are not recommended when strategy A has a different baseline conversion rate than strategy B (such as audience)
• Not all tests are appropriately powered with adequate conversion numbers in each group

Answer 25

A/B test

Question 26

These limitations stand for what measurement approach?

• Tests may not be set up with sufficient statistical power (for example, budget is too low, test duration is too short, not enough conversions)
• Accurate power calculations can be challenging to carry out where there is a lack of historical data from similar activity on which to base the calculation
• Control groups may become contaminated by media running outside the experiment that targets the audience being measured
• Treatment variable may not always be isolated
• Tests may not run long enough to capture the full purchase cycle or longer-term impact of the strategy being measured
• Experiments may not always capture the full impact of a strategy (for example, offline data may not be available to upload)
• Not all experimental approaches manage outliers and variance equally

Answer 26

Randomized control trial (RCT)