WorkMagic
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About
WorkMagic is a marketing experimentation and measurement platform that empowers e-commerce brands to measure the true impact of marketing efforts. It uses incrementality-based attribution and MMM, fully automated incrementality testing, measures the marketing halo effect, and has seamless and easy onboarding. WorkMagic automates the entire testing process, from eligibility checking to test design, hold-out setup, and result calibration. It provides insights about the optimal daily ad spending for each marketing channel, as well as the budget allocation strategies to drive sales, iROAS, and profitable ad spending.
Platform
Task
Features
• geo incrementality testing
• measurement platform
• marketing experimentation
• budget allocation insights
• easy onboarding
• measures marketing halo effect
• automated incrementality testing
• incrementality based attribution model
FAQs
What is incrementality testing?
Incrementality testing is a controlled experiment to measure the true impact a marketing campaign, channel, or other tactic like a promotion has on a key outcome, like sales or revenue.
Why are incrementality tests important?
Incrementality tests help marketers understand the true effectiveness of their marketing efforts and optimize their spend by identifying which channels and tactics are driving incremental results
How do you conduct an incrementality test?
To conduct an incrementality test, you typically split your audience into a test and control group, expose the test group to the marketing activity, and compare the outcomes between the two groups
What are some common incrementality test designs?
Some common incrementality test designs include geo-experiments, where you compare results across different geographic markets, and A/B tests, where you randomly assign users to test and control groups
What are the benefits of running incrementality tests?
The main benefits of running incrementality tests include better understanding of marketing effectiveness, optimizing marketing spend, and making data-driven decisions
What are some challenges with incrementality tests?
Some key challenges with incrementality tests include ensuring sufficient sample size, avoiding contamination between test and control groups, and accounting for external factors that may impact results.
How long should an incrementality test run for?
The ideal duration for an incrementality test depends on the metric being measured, but generally tests should run long enough to capture a meaningful number of conversions and account for seasonality.
How do you analyze results from an incrementality test?
To analyze incrementality test results, you typically calculate the difference in outcomes between the test and control groups, assess statistical significance, and extrapolate the findings to estimate total incremental impact
How do you account for external factors in an incrementality test?
To account for external factors, you can use techniques like matching test and control groups on key characteristics, using historical data as a baseline, and conducting sensitivity analyses
How do you ensure statistical validity in an incrementality test?
To ensure statistical validity, use proper randomization, ensure sufficient sample size, account for multiple comparisons, and conduct robustness checks
How do you incorporate learnings from incrementality tests into your marketing strategy?
Use insights from incrementality tests to optimize marketing spend, shift budget to higher-performing channels, and inform future test hypotheses and marketing plans
How do you ensure data quality for an incrementality test?
To ensure data quality, use robust tracking and attribution, validate data across sources, monitor for data issues, and conduct data audits and reconciliations
What are some common pitfalls to avoid with incrementality tests?
Some common pitfalls to avoid include contamination between test and control groups, not accounting for seasonality, insufficient sample size, and not having a clear hypothesis and success criteria
Which is the best attribution model?
There is no universally "best" attribution model, as the ideal choice depends on a business's specific goals and customer journey. However, models like data-driven attribution (DDA) are increasingly favored for their ability to provide insights based on actual user behavior and interactions across multiple channels.
What is Data Driven Attribution (DDA)?
Data-Driven Attribution (DDA) is an algorithm-based approach that assigns credit to various touchpoints in a customer's journey based on historical data, rather than relying on predefined rules or models like last-click attribution.
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