A/B Testing and Marketing Strategies

False (B)

A/B test

<p>precise</p> Signup and view all the answers

<p>It does not provide specific criteria for determining when the spaghetti is done. (D)</p> Signup and view all the answers

<p>The 5% discount will incentivize users who book flights to also book hotels, ultimately increasing the purchasing customer count. (D)</p> Signup and view all the answers

<p>The total number of customers purchasing both flights and hotels before the implementation of the 5% discount.</p> Signup and view all the answers

<p>It includes too many uncontrollable variables and lacks context. (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Noisy Data</p> Signup and view all the answers

<p>noisy</p> Signup and view all the answers

<p>Taylor's exercise routine was inconsistent. (A)</p> Signup and view all the answers

<p>Because the 5% discount allowed TravelSmart to increase base prices of hotels, while still offering a discount. (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Mitigate noisy data</p> Signup and view all the answers

<p>It lacks control and has misleading implications. (C)</p> Signup and view all the answers

<p>Metrics that indicate customer acquisition and significant business goals. (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Total Number of Ticket Sales</p> Signup and view all the answers

<p>macro</p> Signup and view all the answers

<p>Total Number of Ticket Sales = Macro Conversion Views on a Specific Route = Micro Conversion Membership Purchases = Macro Conversion Time Spent on Landing Page = Micro Conversion</p> Signup and view all the answers

<p>Because the macro conversions were unaffected, and money was lost on lower prices. (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>It is important to define and track macro conversions as our Key Performance Indicators.</p> Signup and view all the answers

<p>Ensuring that micro conversion improvements lead to measurable macro conversion increases. (B)</p> Signup and view all the answers

<p>macro conversions</p> Signup and view all the answers

<p>To avoid severe, long-term damage to the business if the product contains bugs or is poorly received. (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>It ensures a more representative sample of the target user base, reducing bias. (C)</p> Signup and view all the answers

<p>biased user sample</p> Signup and view all the answers

<p>fluctuate</p> Signup and view all the answers

<p>Number of distinct users who purchased a ticket or booked a hotel (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>To mitigate risk</p> Signup and view all the answers

<p>treatment</p> Signup and view all the answers

<p>To minimize potential negative impact and allow for adjustments (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Long-term damage to the business</p> Signup and view all the answers

<p>phased</p> Signup and view all the answers

<p>Increase the exposure to 7% of the user base and continue monitoring (C)</p> Signup and view all the answers

<p>Control Group = The group in an experiment that does not receive the treatment. Treatment Group = The group in an experiment that receives the treatment or change being tested. KPI = A measurable value that demonstrates how effectively a company is achieving key business objectives. A/B Testing = A method of comparing two versions of something to determine which performs better。</p> Signup and view all the answers

A method to compare two versions of a webpage or app against each other to determine which one performs better.

A testable statement predicting the outcome of an experiment or study.

Quantifiable measures used to track and assess the status of business performance.

A marketing strategy offering a 5% discount to encourage customers to purchase more services together.

A plan for how to conduct an experiment, specifying how to test the hypothesis effectively.

Statistics that measure how actively users interact with a service, like page views and session times.

The total number of purchasing customers within a specific timeframe.

A discount that can affect user satisfaction and sales without altering net revenue.

A concept indicating too many variables make it hard to draw conclusions from data.

These are factors kept constant in an experiment to ensure valid results.

Elements that may change during an experiment, potentially skewing results.

Data that has a lot of variance, making it challenging to interpret.

An example demonstrating how neglecting control variables can lead to incorrect conclusions.

Situations or events that can influence the outcome of an experiment.

Methods used to reduce the negative impacts of uncontrolled variables.

The process of reducing potential negative impacts before full implementation of changes.

Choosing a group in such a way that every member of the population has an equal chance of being selected.

A group in an experiment that does not receive the treatment or intervention, serving as a baseline for comparison.

The process of interpreting data gathered from an experiment to draw conclusions.

The variation in the proportion of users receiving a treatment over time, rather than a fixed point.

Key customer-accreting metrics of a website, like total sales.

Smaller actions that contribute to macro conversions, like page views.

Total ticket sales, membership purchases, sales conversions.

Views on a route, time spent on a page, referrals.

Monitoring macro conversions is crucial for evaluating experiments.

Indicated no significant change in macro conversions despite more visits.

Experiment deemed unsuccessful due to unchanged sales despite lower prices.

Metrics that are critical for evaluating success in a business.

Assessing micro and macro conversions to gauge overall performance.

Reviewing the results after an experiment to see its impact on conversions.

A key performance indicator measuring distinct users who purchase a ticket or book a hotel.

The group of users exposed to the experiment in A/B testing.

Gradually exposing a small percentage of users to an experiment before scaling up.

The total number of users interacting with a product or service.

Strategies to reduce potential negative impacts during an experiment.

The process of gradually increasing user exposure to an experiment after initial testing.

Assessing the effects of an experiment by comparing treatment and control groups.

Example of deploying an experiment with a large user base for testing.

The knowledge obtained from comparing results of the treatment group and control group.