Pertama Partners

AI Customer Segmentation and Targeted Campaign Creation

Use AI to analyze purchase patterns, build customer segment personas, and generate targeted campaign briefs and promotion strategies. Designed for retailers selling through Shopee, Lazada, and direct-to-consumer channels in Southeast Asia.

RetailIntermediateAI Use-Case Playbooks3-5 weeks

Transformation

Before & After AI

What this workflow looks like before and after transformation

Before

Customer segments defined by broad demographics (age, gender, location) with little behavioral nuance. Campaign messages are one-size-fits-all, leading to low open rates and poor conversion. Promotion calendars built on intuition rather than purchase data. Repeat purchase rates stagnate because high-value customers receive the same messaging as one-time buyers.

After

AI identifies behavioral segments based on purchase frequency, basket size, category affinity, and lifecycle stage. Each segment has a detailed persona and tailored campaign brief. Promotions align with actual buying patterns, improving campaign ROI by 25-40%. High-value customers receive personalized retention offers, and at-risk segments get targeted win-back campaigns.

Implementation

Step-by-Step Guide

Follow these steps to implement this AI workflow

1

Extract and Prepare Purchase Data

1 week

Pull transaction data from your POS, e-commerce platform, and marketplace seller dashboards (Shopee, Lazada). Clean and standardize the data: customer ID, purchase date, items, amount, channel, and any available demographic info. Aim for at least 6 months of history for meaningful patterns.

Purchase Data Analysis Prompt
You are a retail data analyst. I will provide a summary of our customer purchase data covering [TIME_PERIOD] across [CHANNELS]. Analyze the data to identify: top 5 purchasing patterns, average order value trends, repeat purchase rates by channel, and seasonal peaks. Highlight any data quality issues that could affect segmentation accuracy.
Provide aggregated data summaries rather than raw transaction files. AI works best with pre-calculated metrics.
2

Build AI-Driven Customer Segments

1 week

Use AI to cluster customers into behavioral segments based on RFM analysis (recency, frequency, monetary value), category preferences, and channel behavior. Define 5-8 actionable segments such as loyal high-spenders, deal-seekers, lapsed customers, and new-to-brand shoppers.

Customer Segment Persona Builder
You are a customer insights strategist. Based on the following RFM analysis and purchase behavior data for our [BUSINESS_TYPE] retail brand, create [NUMBER] distinct customer segment personas. For each persona, include: segment name, size (% of base), behavioral traits, preferred channels, price sensitivity, and top product categories. Use SE Asian consumer context.
Run RFM analysis in your analytics tool first, then feed the summary into this prompt. Refine personas with your team before building campaigns.
3

Generate Targeted Campaign Briefs

1 week

For each customer segment, use AI to draft campaign briefs with messaging angles, channel recommendations, offer structures, and creative direction. Align campaigns with the SE Asian retail calendar (9.9, 11.11, 12.12, Ramadan, Chinese New Year) and platform-specific promotional mechanics.

Segment-Specific Campaign Brief Generator
You are a retail marketing strategist. Create a campaign brief targeting our [SEGMENT_NAME] customer segment ([SEGMENT_DESCRIPTION]) for the upcoming [CAMPAIGN_EVENT, e.g., 11.11 sale]. Include: campaign objective, key message, offer structure, channel mix (Shopee, Lazada, email, social), creative direction, and success metrics. Budget: [BUDGET].
Generate one brief per segment per campaign. Customize the offer structure based on platform fee structures and margin targets.
4

Implement Promotion Strategy and Measure Results

1-2 weeks

Launch segment-specific campaigns across channels, track performance by segment, and use AI to analyze results and recommend optimizations. Build a feedback loop where campaign outcomes refine future segmentation and targeting.

Campaign Performance Analysis Prompt
You are a retail analytics specialist. Analyze the following campaign results for our [CAMPAIGN_NAME] targeting [NUMBER] customer segments. For each segment, evaluate: ROAS, conversion rate, AOV change, and new vs. returning customer split. Identify the top-performing segment and the underperforming one. Recommend 3 specific optimizations for the next campaign cycle.
Run this analysis within 2 weeks of campaign end to capture post-purchase behavior. Compare against previous campaign baselines.

Get the detailed version - 2x more context, variable explanations, and follow-up prompts

Tools Required

AI assistant for analysis and content generationCustomer data platform or analytics tool (e.g., Google Analytics, CRM)Marketplace seller dashboards (Shopee, Lazada)Email or CRM campaign platform

Expected Outcomes

Identify 5-8 actionable customer segments based on real purchase behavior rather than demographics alone

Improve campaign ROAS by 25-40% through segment-specific messaging and offer structures

Increase repeat purchase rates by 15-20% with targeted retention and win-back campaigns

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Common Questions

A minimum of 6 months of transaction data with at least 1,000 unique customers gives AI enough signal to identify meaningful patterns. If you have fewer transactions, start with simpler segments (new vs. returning, high vs. low spenders) and refine as your data grows. Marketplace seller dashboards often provide demographic and behavioral data that supplements your own records.

No. Feed AI aggregated summaries and anonymized patterns, not raw personal data. For example, share "Segment A: 2,400 customers, avg 3.2 purchases/quarter, avg order MYR 145" rather than individual records. This protects customer privacy and complies with data protection regulations like Malaysia PDPA and Singapore PDPA.

Review segments quarterly at minimum, and refresh after major sales events (11.11, 12.12) when buying patterns may shift. Customers move between segments over time: a loyal buyer may lapse, or a deal-seeker may become a regular. Monthly monitoring of segment migration helps you catch these shifts early and adjust campaigns accordingly.

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