Most D2C brands have data everywhere and insight nowhere. We build the analytics infrastructure that consolidates your Shopify, ads, email, and subscription data into unified dashboards — and the analytical models that turn raw numbers into decisions you can act on.
A typical D2C analytics build consolidates: Shopify (orders, customers, products, inventory), Klaviyo (email and SMS performance, list growth, flow attribution), Meta Ads, Google Ads, TikTok Ads, and Pinterest Ads (spend, impressions, clicks, conversions), ReCharge or Skio (MRR, churn, subscriber LTV), Yotpo or Okendo (review volume, ratings), and Google Analytics 4 (on-site behaviour). We connect these via API-based ETL pipelines into BigQuery, then build Looker Studio dashboards on top.
We use your Shopify order history to build BG/NBD (Beta-Geometric Negative Binomial Distribution) and Gamma-Gamma models — the academically validated approach to predicting individual customer purchase probability and future spend. These models are retrained monthly on new order data. The outputs are pushed back into Klaviyo as customer attributes for segmentation and used to set channel-level CAC targets for paid media based on predicted 12-month payback rather than last-click ROAS.
Cohort analysis groups customers by their first purchase month and tracks their behaviour over time — how many made a second purchase, what they spent in months 3, 6, and 12. This reveals the true quality of your customer acquisition over time, which aggregate metrics hide. A brand doubling revenue might be acquiring increasingly poor-quality customers with declining retention — something only cohort analysis reveals. We build monthly cohort models for every client as the foundation of retention strategy decisions.
A standard unified dashboard (Shopify + 2–3 ad platforms + Klaviyo) built on Looker Studio with manual data connector setup takes 3–4 weeks. A full data warehouse implementation with automated BigQuery pipelines, GA4 integration, and advanced analytics models including cohort retention and predictive LTV takes 8–14 weeks. We deliver in phases — a working v1 dashboard in the first 3 weeks so your team starts getting value immediately.
Indirectly yes — the content and structural decisions that analytics informs (which products to prioritise in content, which questions customers ask most, which categories drive highest LTV) directly shapes an AEO content strategy. We also build analytics tracking for AI citation monitoring — using Perplexity API and ChatGPT query analysis to measure how often your brand appears in AI-generated answers for relevant queries, treating GEO as a measurable channel alongside organic and paid.
Our analytics team builds the dashboards, models, and data infrastructure that D2C brands need to make decisions with confidence — not intuition.