Fix Inefficient Audience Segmentation with Gemini-Powered Marketing AI

Connect Gemini to Your BigQuery Marketing Dataset with Clear Context

To get useful AI-driven segmentation, Gemini needs more than raw data – it needs context. Start by ensuring your key marketing tables (events, sessions, transactions, campaigns) are in BigQuery with consistent user identifiers. Then document the schema in plain language: what each table represents, primary keys, and how events map to key funnel steps.

When you interact with Gemini (e.g. via Vertex AI, the Gemini API, or a connected notebook environment), always include this context at the start of the conversation. Provide table names, column descriptions, and example queries that follow your internal conventions. This dramatically improves the quality and safety of the SQL Gemini will generate.

System / setup prompt for Gemini:
You are a senior marketing data analyst.
You write safe, BigQuery-compatible SQL for segmentation.

Schema context:
- Table: events (user_id, event_name, event_time, source, campaign_id, revenue)
- Table: users (user_id, signup_date, country, device_type)
- Table: campaigns (campaign_id, channel, objective, cost)

Always:
- Use fully qualified table names: `project.dataset.table`
- Filter out test data where country = 'TEST'
- Return SQL only, no explanation.

With this setup, every subsequent prompt from marketers will yield more reliable, production-ready queries for segmentation.

Use Gemini to Generate and Refine RFM-Style Segments

A pragmatic first use case is to have Gemini build RFM (Recency, Frequency, Monetary)-based clusters that go beyond simple thresholds. Ask Gemini to compute RFM scores, propose segment definitions, and write the SQL. Marketers can then iterate on these definitions using natural language instead of hand-coding every change.

Prompt to Gemini:
Write a BigQuery SQL query that:
1) Calculates for each user_id in `project.dataset.events` over the last 365 days:
   - recency_days (days since last revenue > 0 event)
   - frequency (count of revenue > 0 events)
   - monetary (sum of revenue)
2) Assigns R, F, M scores from 1 to 5 using quintiles (5 = best).
3) Creates a segment_label with values like 'champions', 'loyal', 'at_risk', 'new_high_potential'
   based on R, F, M combinations.

Once the base query works, use Gemini to adjust segment thresholds, add channel-specific logic (e.g. champions by paid search vs. organic), or create separate segments for different product lines. This quickly gives you a robust segmentation backbone that can be mapped to Google Ads or CRM audiences.

Identify High-Value Cohorts by Intent and Channel Mix

Go beyond pure purchase history by having Gemini surface high-intent, high-value cohorts based on behavior patterns and channel mix. For example, you might want users who engaged with a specific category, responded well to email but not to display, and showed recent intent signals.

Prompt to Gemini:
Based on the schema context, write SQL that:
1) Identifies users who in the last 30 days:
   - Viewed product pages in category 'premium'
   - Had at least 2 sessions from email and 0 sessions from paid display
   - Did not purchase yet
2) Calculates predicted value proxy:
   - Number of premium product views
   - Total time on site (if available)
3) Outputs a table `project.dataset.premium_intent_email_fans` with user_id and the above metrics.

With this in place, you can sync the resulting table as a custom audience to Google Ads and run tailored creative for these cohorts, such as premium-focused upsell campaigns or email-to-search retargeting flows.

Automate Segment-to-Campaign Mapping with Structured Prompts

Once you have AI-generated segments in BigQuery, use Gemini to help design personalized campaign strategies for each cluster. Provide segment profiles (size, value, key behaviors) and ask Gemini to propose channel mix, messaging angles, offers, and basic measurement plans.

Prompt to Gemini:
You are a performance marketing strategist.
Given the following segment description, propose a campaign strategy.

Segment: at_risk_high_value
- Size: 12,300 users
- Last purchase: 90–180 days ago
- Historically high AOV and margin
- Primary channels: paid search, email
- High engagement with how-to content

Please outline:
1) Core value proposition
2) Recommended channels and budget split
3) 3–4 message angles
4) Offer strategy (discounts, bundles, content)
5) Primary KPI and expected benchmarks.

This doesn’t replace human judgment, but it speeds up planning and ensures every new segment quickly gets an appropriate activation plan instead of sitting unused in a data table.

Embed Gemini into a Repeatable A/B Testing Loop

To avoid one-off experiments, build a loop where Gemini helps design, monitor, and interpret A/B tests for different segments. First, standardize how you log experiments (e.g. an experiments table in BigQuery with variant, segment, KPI metrics). Then use Gemini to generate queries that evaluate performance by segment and suggest next steps.

Prompt to Gemini:
We run experiments logged in `project.dataset.experiments` with:
- experiment_id, segment_label, variant, impressions, clicks, conversions, revenue

Write SQL to:
1) Compare conversion rate and revenue per user for variant A vs B
   within each segment_label.
2) Flag segments where variant B outperforms A with at least 95% confidence.
3) Return a summary table sorted by highest uplift in revenue per user.

Use the results in a follow-up prompt to Gemini to get a natural-language summary for marketers and recommendations on where to roll out winners or design follow-up tests.

Operationalize Segments into Google Ads and CRM Workflows

Finally, turn your Gemini-generated segments into operational audiences. Use scheduled queries in BigQuery to refresh segment tables daily or weekly, and connect them to Google Ads, DV360, or your CRM/CDP using built-in connectors or lightweight pipelines.

Define clear naming conventions (e.g. seg_rfm_champions_search_high, seg_intent_premium_email_loyal) and document which campaigns each segment is mapped to. Gemini can help you generate and maintain this mapping as a structured spec that both marketing and data teams understand.

Expected outcome: Teams that follow these practices typically see more focused spend (10–20% budget reallocated from low- to high-value segments), faster time from segmentation idea to live campaign (from weeks to days), and measurable lifts in conversion or ROAS once the first wave of AI-driven segments is deployed and iterated.