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Control group

As explained in First-Party Data Activation

A comparison group in an experiment that does not receive the tested intervention or receives a designated baseline condition.

A control group is a comparable set of customers or leads that does not receive the tested intervention, or receives a designated baseline such as a generic message. Comparing its outcomes with a treatment group helps separate causal uplift from seasonal trends, competitor promotions, and other external factors. section 7.7.3

Use control groups to protect the credibility of experiments:

  • Assign eligible participants randomly to treatment or control groups; use stratified randomization when important subgroups may behave differently. section 3.2.1
  • Keep longitudinal participants in their original groups, include originally assigned participants in analysis, and wait through the planned duration. section 3.2.1
  • Export only treatment members to marketing destinations when possible; keeping control members unexposed reduces contamination risk. section 3.2.1
  • Consider spillover from word-of-mouth and use geo-experiments or matched-market tests when individual-level separation is unreliable. section 3.2.1

Testing an added SMS sequence

A treatment group receives an automated SMS sequence alongside existing email, while the control group receives only email. Higher conversions or revenue reveal the incremental effect of SMS. section 7.7.3

How First-Party Data Activation covers it

How deep it goes
Explains baselines, random assignment, subgroup balance, exposure control, spillover, retention, and planned statistical analysis.
Examples
Uses generic versus personalized email, segmented versus broad audiences, and email-only versus email-plus-SMS campaigns.
What it assumes
Assumes defined audience criteria and measurable outcomes while teaching baseline comparison, assignment, exposure, and analysis safeguards.
What you can do after
Evaluate/understand incremental conversions, revenue, engagement, or return on investment attributable to an activation strategy.
How it connects
Connects experiments with treatment groups, randomization, statistical analysis, personalization, segmentation, and incremental impact.

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This explainer was generated by Manning's knowledge graph from the text of First-Party Data Activation. The section links point to the parts of the book it draws on.