CUSTOMER LIFETIME VALUE ANALYSIS IN BIGQUERY

Customer Lifetime Value Analysis in BigQuery

SQL-based customer LTV modelling and segmentation in BigQuery

Historical CLV calculation by cohort and channel
Repeat purchase rate and order frequency analysis
Predictive CLV model (probabilistic or regression-based)
Customer value tier segmentation query

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About This Service

What Is Customer Lifetime Value Analysis in BigQuery?

Customer Lifetime Value (CLV/LTV) is the most important metric for sustainable ecommerce growth — it determines how much you can profitably spend to acquire a customer. We build CLV models in BigQuery: historical LTV by acquisition channel, cohort-based retention analysis, predictive LTV estimation using purchase frequency and average order value, and customer value tier segmentation. This gives you a defensible number to set CAC targets by channel.

Quick Facts

Delivery 2–5 business days

Pricing Fixed upfront quote

Access Needed GTM + relevant account

Support 30 days post-delivery

Certification Google-certified specialist

Output Written documentation

What You Get

Every Customer Lifetime Value Analysis in BigQuery Engagement Includes

Historical CLV calculation by cohort and channel

Repeat purchase rate and order frequency analysis

Predictive CLV model (probabilistic or regression-based)

Customer value tier segmentation query

CLV by acquisition source/channel

Payback period analysis by channel

Our Process

How We Deliver Customer Lifetime Value Analysis in BigQuery

Audit & Review

We examine your existing setup, ad accounts, and analytics to understand exactly what needs to be built or fixed.

Scope & Price

You receive a clear, fixed-price quote with defined deliverables before any work begins. No surprises.

Build & Test

We implement and verify everything in GTM Preview mode, DebugView, or the relevant platform debugger before going live.

Deliver & Document

We launch, confirm all tracking fires correctly, and hand over full written documentation of everything built.

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Why Adslytics

The Difference a Specialist Makes

Google-Certified Specialists

Every project is handled by a Google-certified tracking specialist. Not an account manager. Not a junior contractor. The person on your project is the expert.

Fixed-Price, No Surprises

You see the exact price before work starts. We scope every project fully before quoting — so you know what you are getting and what it costs, with no ongoing meter running.

Full Written Documentation

Every delivery includes documentation of what was built, what triggers each event, and how to verify it. Your team can maintain it without coming back to us.

Direct Access to Your Specialist

You communicate directly with the person doing the work. No ticketing systems, no account managers relaying messages. Direct Slack or WhatsApp contact throughout.

Verified Reviews

What Clients Say

"We had tracking spread across multiple tools with conflicting numbers and no clear source of truth. After the full tracking audit and rebuild we have clean, consistent data across GA4, Google Ads and Meta. Decision-making is so much faster now."

Jessica Howard

Head of Marketing

"Every time we made a site change something would break in our analytics. The specialist rebuilt our entire tracking layer properly, documented everything and gave us a QA checklist for future deployments. No broken tags since."

Marcus Webb

E-commerce Director

"Three months of messy data after a platform migration had left us unable to trust any of our reports. The audit identified every issue, fixes were applied within two days and we finally had reliable numbers to work with again."

Lucia Gomez

Growth Manager

FAQ

Frequently Asked Questions

Real answers to questions we hear from clients before starting a Customer Lifetime Value Analysis in BigQuery project.

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Your order history with customer identifiers, timestamps, and order values. This typically comes from your Shopify/WooCommerce data in BigQuery, enriched with GA4 acquisition source data.

Predictive CLV models use purchase frequency and average order value to estimate future revenue. Simple models use BG/NBD probability distributions; we can implement these in BigQuery SQL.

If customers acquired via Google Ads have a 12-month CLV of $350, you can afford a much higher CPA than if you're optimising toward first-order value alone. We build queries that show CLV by acquisition channel.

Yes — by identifying the point at which customers typically churn, you can trigger retention marketing at the right moment. BigQuery cohort analysis reveals these inflection points.

Monthly or quarterly is typical. We set up scheduled queries that refresh LTV figures automatically, so your strategy is always based on recent data.

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