Required Datapoints
- Baseline Brand Recall % (Control Group)
- Brand Recall % Among Exposed Group (Post-Video)
- Survey Sample Segmentation (e.g., ICP, geography, campaign theme)
Post-Video Brand Recall Lift measures the increase in brand recall among viewers after watching a specific video campaign, compared to a control or pre-exposure baseline. It helps quantify the brand impact of video content.
Post-Video Brand Recall Lift is a key indicator of brand campaign effectiveness and creative resonance, reflecting how well viewers remember your brand after watching a piece of video content.
The relevance and interpretation of this metric shift depending on the model or product:
A high lift suggests clear messaging, brand alignment, and effective targeting. A low or negative lift indicates audience mismatch, weak creative, or forgettable storytelling.
By segmenting by cohort — such as audience type, channel, message type, or video length — you can tailor content strategies to double down on top-performers and fix forgettable creatives.
Post-Video Brand Recall Lift informs:
These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome
Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.
Activities commonly tied to improving or operationalizing this KPI.
| Activity | Description |
|---|---|
| Campaign Measurement | Campaign Measurement focuses on the systematic collection, evaluation, and interpretation of both quantitative and qualitative data related to go-to-market campaigns. It turns signals into decisions, interventions, and measurable follow-up. Relevant KPIs include Post-Video Brand Recall Lift. |
| Brand Lift Studies | Brand Lift Studies focuses on conducting structured research to evaluate how marketing initiatives, product launches, or sales campaigns influence customer perceptions, awareness, and attitudes toward a brand. It helps teams translate strategy into repeatable execution. Relevant KPIs include Post-Video Brand Recall Lift. |
| Creative Testing | Creative Testing involves systematically designing, deploying, and analyzing variations of marketing or product assets—such as ads, landing pages, product messaging, or in-app experiences—to determine which creative elements most effectively drive user engagement, conversion, or other key performance indicators. It improves performance by removing friction, testing changes, and scaling what works. Relevant KPIs include Post-Video Brand Recall Lift. |
| Audience Panel Surveys | Audience Panel Surveys focuses on systematically collecting feedback from a representative group of users, prospects, or customers to gain a deeper understanding of their perceptions, needs, and experiences with a product or service. It helps teams translate strategy into repeatable execution. Relevant KPIs include Post-Video Brand Recall Lift. |
This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:
This KPI is classified as a lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.
This role is directly accountable for the KPI and is expected to drive progress and decisions around it.
These roles contribute directly to performance and typically partner on execution, reporting, or optimization.
These roles should stay informed on changes in the KPI because it influences adjacent planning, coordination, or outcomes.
These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.
cube('BrandRecallLift', { sql: `SELECT * FROM brand_recall_data`,
measures: { baselineBrandRecall: { sql: `baseline_brand_recall_percentage`, type: 'number', title: 'Baseline Brand Recall %', description: 'Percentage of brand recall in the control group before video exposure.' },
postVideoBrandRecall: { sql: `post_video_brand_recall_percentage`, type: 'number', title: 'Post-Video Brand Recall %', description: 'Percentage of brand recall among the exposed group after video exposure.' },
brandRecallLift: { sql: `post_video_brand_recall_percentage - baseline_brand_recall_percentage`, type: 'number', title: 'Brand Recall Lift', description: 'The increase in brand recall percentage after video exposure compared to the baseline.' } },
dimensions: { id: { sql: `id`, type: 'string', primaryKey: true, title: 'ID', description: 'Unique identifier for each survey response.' },
surveySampleSegmentation: { sql: `survey_sample_segmentation`, type: 'string', title: 'Survey Sample Segmentation', description: 'Segmentation of the survey sample, such as ICP, geography, or campaign theme.' },
surveyDate: { sql: `survey_date`, type: 'time', title: 'Survey Date', description: 'The date when the survey was conducted.' } }});Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema