> For the complete documentation index, see [llms.txt](https://doc.batch.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.batch.com/guides-and-best-practices/batch-ai/how-to-personalize-promotions-based-on-each-customers-price-sensitivity.md). # How to Personalize Promotions Based on Each Customer's Price Sensitivity ## What Is the Promotion Sensitivity Score? Batch AI Predict's **Promotion Sensitivity** score estimates, for each customer in your database, the probability that they need a discount or incentive to make their next purchase. The output is a float between 0 and 1: the closer to 1, the more likely the customer is to require a promotional nudge to convert. Customers with a low score are likely to purchase without any discount; giving them a promotion simply reduces your margin for free. {% hint style="warning" %} **Promotion Recommendation vs. Promotion Sensitivity: which one do you need?** * [**Promotion Recommendation** ](/guides-and-best-practices/batch-ai/how-to-personalize-promotions-with-the-right-discount-level-for-each-customer.md)→ You want to know *which* promotional mechanic **suits** each customer. Focus: offer calibration and margin optimization. * **Promotion Sensitivity** → You want to know *whether* a customer **needs** a discount at all. Focus: identifying who to include in a promotional campaign. {% endhint %} ## Why Use It? Many brands apply promotions broadly because they work on average. But on an individual level, a significant portion of your customer base would have converted without any incentive. Blanket promotions train customers to wait for discounts and erode margins systematically. With the Promotion Sensitivity score, you can: * **Reserve discounts for customers who genuinely need them** to convert, and protect margin on the rest. * **Personalize offer intensity** based on each customer's sensitivity level. * **Reduce promotional dependency** across your base over time by identifying and nurturing low-sensitivity customers separately. ## How to Use This Score in Batch ### 1. Segmentation: Split Your Campaign Audience by Sensitivity Level The core use case: divide your campaign audience into high and low sensitivity segments, and send different messages or offers to each. **How to set it up:** 1. Go to **Campaigns** in your Batch dashboard and create two variants of your campaign. 2. In the targeting step of each variant, apply a condition based on the Promotion Sensitivity score: * Variant A (high sensitivity): `Predictive score [Promotion Sensitivity] ≥ [threshold]` → include a promotional offer or discount * Variant B (low sensitivity): `Predictive score [Promotion Sensitivity] < [threshold]` → message focused on value, newness, or editorial content, without a discount 3. Compare conversion rates and margin across both segments. {% hint style="info" %} You can combine this score with a customer value filter to apply an even stricter margin protection policy on your top customers (who are often the least promotion-sensitive). {% endhint %} {% hint style="info" %} To measure the performance, you can run a **Targeting Comparison Test** between your current promotional strategy (same offer for all) and the segmented approach. Compare conversion rates and overall campaign margin. See the [Performance Measurement Guide](/guides-and-best-practices/batch-ai/how-to-measure-the-performance-of-batch-ai-predict-scores.md) for details. {% endhint %} ### 2. Personalization: Adapt Discount Depth Dynamically Go further by using the score to calibrate the discount level within a single campaign template. **Example logic (thresholds are illustrative):** * High sensitivity score → show a deeper discount block * Mid-range sensitivity score → show a lighter discount block * Low sensitivity score → show a value-based message, no discount This approach lets you run a single campaign while personalizing the offer intensity for every customer. ### 3. Orchestration: Gate Discount Flows by Sensitivity You can also use the score as a condition in your automated journeys in **Automations**: only route customers into a promotional sequence if their sensitivity score crosses a defined threshold. **Example setup:** * Journey entry condition: `Predictive score [Promotion Sensitivity] ≥ [threshold]` * Customers below the threshold receive a non-promotional nurture sequence instead ## Real-World Use Cases | **Industry** | **Example use case** | | ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Retail** | Black Friday campaign: reserve the highest discount level for customers with a strong promotion sensitivity score; send a "new arrivals" message to low-sensitivity customers without a promotional offer | | **Beauty & Cosmetics** | Seasonal sale: exclude high-spending, low-sensitivity customers from the promotional flow and send them an editorial campaign instead, protecting both relationship and margin | | **Travel** | Flash sale: use the score to identify which customers need a discount to book versus which ones respond to urgency or exclusivity alone | | **Media & Entertainment** | Subscription upgrade campaign: offer a discounted rate only to users with high promotion sensitivity; present the full value proposition to low-sensitivity users | ## Related Documentation * [How to Measure the Performance of Batch AI Predict Scores](/guides-and-best-practices/batch-ai/how-to-measure-the-performance-of-batch-ai-predict-scores.md) * [How to Personalize Promotions with the Right Discount Level for Each Customer](/guides-and-best-practices/batch-ai/how-to-personalize-promotions-with-the-right-discount-level-for-each-customer.md) * [Segments](https://doc.batch.com/getting-started/features/customer-engagement-platform/profiles/segments) * [Orchestration Targeting](https://doc.batch.com/getting-started/features/customer-engagement-platform/orchestration/targeting) * [Personalization in Batch](https://doc.batch.com/getting-started/features/customer-engagement-platform/message/personalization) * [Orchestration Analytics](https://doc.batch.com/getting-started/features/customer-engagement-platform/analytics/orchestration-analytics) --- # Agent Instructions This documentation is published with GitBook. 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