{"id":186,"count":0,"description":"","link":"https:\/\/tenjin.com\/ja\/glossary\/incrementality\/","name":"Incrementality","slug":"incrementality","taxonomy":"glossaries","parent":0,"meta":{"status":["1","1"],"order":["0","0"],"glossary_term_description":["
\r\n\r\nDefinition:<\/strong>\r\n\r\nIncrementality<\/strong> is a measurement framework used in mobile marketing to determine the additional impact a campaign has generated beyond what would have happened organically without it.\u00a0<\/span>\r\n\r\nIt isolates the true causal effect of marketing activity on conversions, revenue, or any other outcome, separating genuine campaign-driven results from activity that would have occurred regardless of whether the campaign ran.<\/span>\r\n\r\n<\/div>\r\n\r\n

What is Incrementality in Marketing?<\/b><\/h2>\r\nIncrementality in marketing is the practice of measuring whether a campaign actually caused a result, rather than simply correlating with it. It answers one of the most important and most difficult questions in marketing: would these conversions<\/a> have happened anyway, even without this campaign?<\/span>\r\n\r\nThis distinction matters enormously. Why? Because standard attribution<\/a> models assign credit to the last touchpoint<\/a> or the most recent campaign a user interacted with before converting. However, attribution credit is not the same as causal impact.<\/span>\r\n\r\n\u00a0A user who was already highly likely to install<\/a> your app, make a purchase<\/a>, or subscribe may have done so regardless of whether they saw your ad. If your campaign is taking credit for conversions that would have happened organically<\/a>, your reported ROAS<\/a> is overstated and your budget allocation decisions are based on incomplete information.<\/span>\r\n\r\nIncrementality solves this problem by introducing a counterfactual. Instead of asking what happened when users saw the campaign, it asks what would have happened if they had not. The difference between those two outcomes is the incremental lift,<\/strong> the true measure of what your campaign actually contributed.<\/span>\r\n\r\nFor mobile marketers operating in an environment where attribution is influenced by privacy frameworks, incrementality is a foundational measurement discipline.<\/span>\r\n\r\n\r\n\r\n\r\n

How Does Incrementality Work?<\/b><\/h2>\r\nIncrementality works by comparing the behavior of two groups<\/a>: a test group that is exposed to a campaign, and a control group that is not. The difference in outcomes between the two groups, measured under controlled conditions, represents the incremental impact of the campaign.<\/span>\r\n

Step 1: Define the Question<\/span><\/h4>\r\nBefore an incrementality test, clearly define what you want to measure. A clearly defined question will point to actionable results.\u00a0<\/span>\r\n\r\nExample:<\/b> Are you testing whether a specific campaign drives incremental installs? Whether a retargeting channel drives incremental purchases? Whether a particular creative drives incremental subscriptions?\u00a0<\/span>\r\n

Step 2: Split Your Audience<\/span><\/h4>\r\nDivide your target audience into two groups. The test group is exposed to the campaign as normal. The control group, sometimes called the holdout group, is withheld from the campaign entirely. The two groups must be statistically equivalent at the start of the test to ensure any difference in outcomes can be attributed to the campaign rather than pre-existing differences between the groups.<\/span>\r\n

Step 3: Run the Campaign<\/span><\/h4>\r\nRun the campaign for the test group while keeping the control group unexposed. The duration of the test needs to be long enough to collect sufficient data for statistical significance but focused enough to reflect current market conditions. Campaigns and user behavior both change over time, so an incrementality test that runs too long risks measuring a moving target.<\/span>\r\n

Step 4: Measure the Difference<\/span><\/h4>\r\nAt the end of the test period, compare the conversion rates, revenue, or other outcomes between the test and control groups. The difference between the two groups represents the incremental lift of the campaign.<\/span>\r\n

Step 5: Assess Statistical Significance<\/span><\/h4>\r\nThis is where confidence intervals become critical. Before acting on the results of an incrementality test, you need to establish statistical significance from random variation.\u00a0<\/span>\r\n\r\nA confidence interval tells you the range within which the true incremental effect is likely to fall. And, a result is statistically significant when it reaches a 95% confidence level, or a 5% probability that the observed difference occurred by chance.<\/span>\r\n\r\nActing on results that have not reached statistical significance risks making budget decisions based on noise rather than signal.<\/span>\r\n\r\nExample:<\/b> A subscription app runs an incrementality test on a prospecting campaign across a new ad network<\/a>. The test group of 50,000 users is exposed to the campaign. An equivalent control group of 50,000 users is held out. After four weeks, the test group shows a subscription conversion rate<\/a> of 3.2% versus 2.1% in the control group. The incremental lift is confirmed as statistically significant at a 95% confidence level. The campaign is proven to be driving genuine additional subscriptions beyond organic baseline, and budget is increased accordingly.<\/span>\r\n

How to Calculate Incremental Lift<\/b><\/h2>\r\nIncremental lift is the core output of an incrementality analysis. It expresses the additional conversion rate driven by the campaign as a percentage of the control group baseline.<\/span>\r\n\r\nFormula:\r\n<\/b>\r\n
Incremental Lift = (Test Conversion Rate - Control Conversion Rate) \/ Control Conversion Rate<\/span><\/pre>\r\n
Worked Example<\/b><\/h3>\r\n