Shopify Analytics Consulting

Shopify analytics consulting for brands that have outgrown Shopify reports and packaged dashboards. We put Shopify, Amazon, ad and email data in one Snowflake warehouse, so finance, marketing and operations see the same revenue and margin.

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Where the numbers come from
  1. 1

    Shopify and every other channel

    Shopify, Amazon, TikTok Shop, Meta, Google Ads and Klaviyo

  2. 2

    Pipelines with checks

    Loaded on a schedule, with record counts compared to each source

  3. 3

    Snowflake

    Your own warehouse, in your own account

  4. 4

    dbt models

    Orders, refunds, fees and costs, each defined once

  5. 5

    Reports people open

    Sigma, Power BI or Tableau, all reading the same tables

Sound familiar?

If one of these is true, this work usually pays for itself.

  • Shopify, finance and the ad platforms each show a different revenue number for the same month.
  • You sell on Amazon or TikTok Shop too, and no report shows margin across all of them.
  • Refunds, fees and discounts land in the wrong month, so the monthly numbers keep moving.
  • A packaged tool covers marketing, but finance still rebuilds the P&L in spreadsheets.

What we build for Shopify brands

  1. 01

    Shopify to Snowflake pipelines

    Orders, line items, refunds, discounts, payouts and customers loaded on a schedule, alongside Amazon, TikTok Shop, ad and Klaviyo data.

  2. 02

    Order, refund and fee models

    dbt models that count each order once, put refunds on the right date, and carry platform fees and costs to the order line.

  3. 03

    One product list across channels

    A SKU map across Shopify, Amazon and TikTok Shop, including bundles, so product margin adds up.

  4. 04

    Margin, retention and channel reports

    Contribution margin by channel, repeat rate and LTV by first order, with reports your team can check against Shopify.

  5. 05

    Answers in plain English

    Optional: a question tool on top of the models, so leadership can ask a question and get the number without waiting for an analyst.

Start with the decision, then trace the data

  1. 1

    Start with the numbers that disagree

    Bring the Shopify, finance and ad reports that do not match. We trace each one to its source and write down the definition.

  2. 2

    Build a few sources and one set of reports

    Shopify plus the next most important channel first, reconciled against each source before anything else is added.

  3. 3

    Hand it to your team

    The code, definitions, checks and operating notes stay with you, in your own Snowflake account.

What the work changed

2 weeks to minutes

One model for a multi-brand ecommerce portfolio

Cross-brand questions answered in minutes instead of two weeks, from 120 dbt models. One Shopify migration reconciled 10,105 records after 53 tests passed.

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27% lower average daily warehouse use

Snowflake and Power BI support for a DTC apparel brand

After the refresh schedule changed, average daily Power BI warehouse use was 27% lower than in the measured comparison period.

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Free tools and guides

More in the DTC reporting guides and free tools.

Start with the problem

You don't need a finished requirements document. Start with a number the team doesn't trust, a report that takes too long, or a warehouse bill that keeps climbing.

Discuss your data problem