> For the complete documentation index, see [llms.txt](https://help.daasity.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.daasity.com/core-concepts/metrics/retail-and-wholesale-metrics/promotion/post-promo-baseline-lift.md).

# Post-Promo Baseline Lift

**Description:** Sustained increase in baseline sales after a promotion relative to pre-promo levels.

**Formula:** *((Post-Promo Baseline – Pre-Promo Baseline) ÷ Pre-Promo Baseline) × 100*

**SQL Calculation (simplified):**

```sql
(AVG(baseline_sales_post) - AVG(baseline_sales_pre)) / NULLIF(AVG(baseline_sales_pre),0) * 100 AS post_promo_baseline_lift
```

**Why It Matters:** Captures long-term effectiveness of promos in raising everyday demand.

**Metric Type:** Result (Strategic)

**Essence:** Lagging → Leading (predicts future baseline)

**Frequency:** After each promo (usually measured over 4–8 weeks)

**Units:** Percentage (%)

**Suggested Breakdowns:** Product, Retailer, Promo Type

**Questions Answered:**

* Did this promo permanently raise our baseline sales?
* Are we gaining lasting trial and repeat?

**Potential Causes of Worsening:** Pantry loading (baseline dip after promo), deal-seeker churn, ineffective follow-up.

**Tactics to Improve:**

* Target promos to new buyers
* Follow-up loyalty campaigns
* Manage pantry loading

**Related Metrics:**

* Incrementality %
* Lift Sustainability Score

**Related Explores:**

* Longitudinal Promo Analysis

**Related Sources:**

* URMS baseline data
* Syndicated promo performance


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://help.daasity.com/core-concepts/metrics/retail-and-wholesale-metrics/promotion/post-promo-baseline-lift.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.