Designing AI systems starts with a data audit

Great AI outcomes come from great inputs. Before we talk about agents, personalization, or automation, we start with a data audit: a practical, end-to-end review of the systems that create, move, and store your customer data.

Most teams already have the raw ingredients: product events, subscription data, support tickets, and acquisition context. The gap is that those signals live in different places, with inconsistent identity, naming, and quality. A data audit is how we close that gap quickly and safely.

Our goal is not to build a perfect “enterprise data platform.” It is to centralize the highest-leverage signals, model them into clear business definitions, and make them deployable across activation and product experiences.

Framework

The Wallabout Data Audit Blueprint

Sources → Collection → Warehouse → Modeling → Activation

Wallabout

Data sources

Product events, identity, transactions, support signals, marketing events, and content interactions.

Collection layer

Consistent instrumentation and routing (often via RudderStack) so every downstream tool gets clean data.

Consolidated warehouse

A single home for durable storage, identity resolution, freshness checks, and access patterns.

Centralized models

Reusable traits, audiences, and lifecycle states that match your business definitions, not a vendor UI.

Activation-ready profiles

Marketer-friendly user profiles and eligibility rules you can deploy across channels and product.

Governance + QA

Ownership, naming, and quality gates so teams trust the data and can ship faster with fewer regressions.

Blueprint

Example: workout app data blueprint

Generic sample data showing what we centralize and how we query it with prompts.

Wallabout

Source systems (generic example)

Mobile app events

workout_startedworkout_completedplan_selectedsubscription_started

Website events

pricing_viewedsignup_startedemail_capturedblog_read

Acquisition channels

utm_source / utm_campaignpaid_search_clickinfluencer_referralapp_store_page_view

Customer support

ticket_createdticket_reasonrefund_requestedcsat_score

Consolidated Data Warehouse

Warehouse tables

centralized

fct_events

event_time · user_id · anonymous_id · event_name · properties

dim_users

user_id · email · signup_time · device_os · country · marketing_opt_in

fct_subscriptions

user_id · started_at · plan · status · trial_days · is_upgrade

fct_support

user_id · ticket_id · created_at · reason · resolution_time_hours · csat_score

Prompt examples

Analytics prompt

What is our upgrade rate from free trial to paid in the last 14 days? Break it down by acquisition channel (utm_source) and device OS. Highlight the biggest movers week over week.

Outputs: upgrade_rate, cohort sizes, channel + OS breakdown, and key changes.

Journey prompt

Build me a customer journey for new users from signup to first workout to subscription. Identify the 3 highest-leverage drop-off points and propose one messaging intervention for each.

Outputs: funnel steps, drop-off rates, recommended interventions by step.

Support insight prompt

Which support ticket reasons are most predictive of churn within 7 days? Include the top 5 reasons and suggested product or messaging fixes.

Outputs: reasons ranked by churn correlation, suggested fixes, and cohorts impacted.

What “centralized” actually means

• One identity spine: unify anonymous and logged-in behavior into a single user record
• One event vocabulary: standard names and properties so analyses and prompts are reliable
• One truth for key outcomes: upgrades, retention, churn, and support pain live in durable tables
• One place to iterate: change a definition once and have it propagate everywhere

Framework: The Wallabout Data Audit Blueprint

• Inventory: map data sources, identities, and key events across the stack
• Consolidate: centralize the signals that matter into a warehouse
• Model: define traits and lifecycle states that match how your business actually works
• Activate: deploy the same profile logic across channels and product experiences

Our data audit looks at your full stack

• First-party sources: product events, identity, transactional systems, and customer support signals
• Marketing stack: ESP/CDP, push/SMS providers, preference centers, attribution, experimentation
• Analytics + storage: warehouses, BI layers, schemas, event conventions, and access patterns
• Operational reality: ownership, data quality, freshness, and what teams actually trust day-to-day

From there, our goal is to centralize what matters, model it into something marketers and systems can reliably use, and make it ready to deploy across different activations (email, push, in-app, paid, and beyond).

TrailGuide: modeling data with AI prompts

We use TrailGuide to translate messy, distributed signals into usable, consistent profiles. It helps us turn business questions into structured models using AI prompts, then ship those models into channels where they can drive targeting and personalization.

Example: modeled traits from the workout app

• lifecycle_state: new, activated, committed, at_risk, retained
• time_to_first_workout_hours: derived from signup_completed to workout_started
• workout_frequency_7d: completed workouts in the last 7 days
• upgrade_intent: pricing views + checkout started + plan comparisons
• support_risk: open ticket + reason category + resolution time

• Create reusable audience and profile definitions (not one-off segments)
• Generate normalized traits and preference signals from raw events and tables
• Build marketer-friendly user profiles we can market against and personalize with
• Deploy the same profile logic across multiple activations without re-implementing it each time

The result is an AI-ready foundation: centralized, modeled, and built to support real deployment, not just dashboards.

Once the foundation is in place, everything downstream speeds up: your analytics questions become promptable, your experiments become measurable, and your activation logic becomes reusable across channels.