The problemShopify says 80 orders. Meta says 60. Google says 45. Which is right?
In shortData analytics consulting means an analyst defines the numbers your decisions depend on, cleans and joins data from your store, payments, CRM and ad accounts, and answers specific questions such as which channels or customers make money. We do it for small and mid-sized businesses with no data team, from $2,000 at a fixed quote, over 2–4 weeks.
Data analytics consulting starts where most small businesses get stuck: three tools, three revenue numbers, and nobody sure which one to trust. Usually that is not a data problem but a definitions problem. "Revenue", "order" and "conversion" mean different things in your store, your ad accounts and your CRM, so every report starts an argument instead of ending one.
We start with the decisions you need to make, write down the handful of numbers that inform them and which system owns each one, then build the cleaning, joins and reports that produce those numbers the same way every time. The work is for businesses of roughly 5 to 100 people without a data team, in the tools you already use, and every query is handed over.
Maintained by Haider Ali, Founder & CEO · Updated
The reporting problems owners bring to us most, and what they cost when nobody fixes them.
Budget drifts to whichever platform claims the most sales, and weeks go into making numbers agree that never will.
What we doWe write down which system owns each number, compare by order ID over the same dates and timezone, and track the gap each week instead of chasing a match. The gap gets explained, not hidden.
Lost weeks reconciling by hand, and numbers you cannot defend when your accountant or a partner asks.
What we doA written reconciliation bridge (order date vs payout date, refunds, fees, tax, shipping, gift cards) built once as a query that gives the same answer every month.
Product costs, ad spend, shipping and payment fees sit in different tools, so a product, channel or discount can lose money on every order while revenue looks fine.
What we doA contribution-margin model that joins orders, ad spend, shipping and fees, using the cost that applied at the time of each order, and refreshes on its own.
Hours every month, copy-paste errors in the numbers, and a report that arrives late when you need it most.
What we doThe recurring report rebuilt to refresh itself from readable SQL or a scheduled query, with expected timing differences labeled apart from real exceptions.
If phone, sales rep or invoice orders never reach the storefront analytics, decisions rest on a slice of the business and nobody questions them.
What we doThe data audit maps every source of orders and revenue, names the source of truth for each number, and labels clearly which view covers what.
The Monday report shows blanks, broken tracking goes unnoticed for days, and connector subscriptions keep climbing.
What we doPipelines you own: scheduled queries into a free or cheap store such as BigQuery's free tier, Postgres or Google Sheets, plus an alert when a source stops updating.
We would rather tell you now than take the project and disappoint you.
| Starts from | $2,000 for a focused question, at a fixed quote |
|---|---|
| Typical timeline | 2–4 weeks |
| Best for | Businesses of about 5–100 people with no data team and reports that disagree |
| Not for | A dashboard for its own sake, with no decision behind it |
| What you get | Metric definitions, a data audit, cleaned and joined data, a written findings report and self-refreshing reports |
| What you own | Every query, script and report, in tools that are free or cheap at your size |
| Works with | Shopify, Stripe, your CRM, ad accounts, Excel, Google Sheets, Looker Studio and Power BI |
| How pricing works | A short look at your data, then a fixed quote. Business-wide reporting setups are quoted at $5,000–$15,000 |
Worth knowing: A metric nobody trusts is worse than no metric, because people quietly go back to gut feel while the dashboard keeps running. The cheapest analytics win is usually writing down what each number means, where it comes from and who owns it. It takes a day and settles most report arguments. What it will not do is make Shopify, Meta and Google agree; they count differently by design.
Each key number written down: what it means, how it is calculated, which system it comes from, and who owns it.
What you collect, where it lives, how complete it is, and where it disagrees with itself.
Customers, orders and campaigns matched across systems, so you can follow a customer from first click to repeat order.
Contribution margin after product cost, ad spend, shipping, fees and discounts, not just revenue.
Retention and cohort analysis, customers at risk of leaving, and forecasts from your own history.
The weekly or monthly report rebuilt to update automatically, from SQL you can read.
A warning when a source stops updating or a total jumps for no reason, before the Monday meeting.
What the data says, what changed and why, how confident we are, and what we would do about it.
A 22-second look at the work: Shopify, Meta and Google sales counts joined under one definition, then a chart by channel that refreshes itself. From $2,000, 2–4 weeks.
Shopify: 80 orders. Meta: 60. Google: 45. Which? Budget drifts to the loudest platform. Weeks lost reconciling by hand. Real profit by product: unknown. There’s a better way. Data analytics consulting: one definition, one number. Metric definitions agreed first. Profit by product and channel. Reports that refresh themselves. From $2,000 · 2–4 weeks · fixed quote. Get a fixed quote. pixelandoak.com/services/data-analytics-consulting
Because each one counts something different, on purpose. A common example: the store records 80 orders in a week, Meta reports 60 purchases and Google Ads reports 45. None of them is broken. The usual causes are:
The fix is not forcing a match. It is naming the store or ERP as the source of truth for orders and revenue, treating ad platforms as directional, and tracking the gap each week so a sudden change stands out.
Whether the report lives in Excel, Google Sheets or Power BI, the steps are the same:
If your workbook is hitting Excel's limits or crashing, that is a sign the data belongs in a small database, which is a data warehouse question rather than a reporting one.
| Option | How you pay | Good for | Trade-off |
|---|---|---|---|
| In-house analyst | A full-time salary, plus tools | Daily questions at a business that can keep one busy | Hard to justify for a small team; knowledge sits with one person |
| Freelancer | Hourly or per task | A one-off analysis with a clear brief | Quality varies, and the knowledge often leaves with them |
| Large consultancy | Large engagement fees | Enterprise data programs | Built for big companies, priced to match |
| Pixel & Oak | Fixed quote from $2,000 | Small businesses with specific questions and no data team | Not a full-time team: Haider directs AI agents and signs off every finding |
Chosen to fit what you already run. If your team lives in Excel or Power BI, the work ends up there.
SQL, Python, pandas and DuckDB
BigQuery, Postgres, Snowflake, ClickHouse
Looker Studio, Power BI, Metabase
Google Sheets and Excel, where the team already works
GA4, Search Console, Stripe, Shopify, CRMs, ad platforms
Scheduled queries, n8n, Python jobs
Not sure whether analytics help is what you actually need? Send over what you are trying to fix and we will tell you straight — including if a smaller job would do it.
Ask us first →An hour on what you need to decide and which numbers would change the decision.
We look at the sources, map where every order and dollar is recorded, and quote a fixed price.
The data prepared and the questions answered, with work in progress shared as we go. Haider reviews every finding before it reaches you.
A written report, the queries behind it, and automatic reports with alerts where they are useful.
AI agents from our Engineering, Quality & Security and Growth & Marketing divisions do the work, in parallel. Haider Ali, founder and CEO, agrees the plan with you and signs off everything before it reaches you.
Real work that uses the same skills. Every card links to something you can open and inspect yourself.
The clicks and impressions on our case studies come straight from Google Search Console, and numbers too small to mean anything are left off.
Collected and checked dataEvery provider's prices read from its own website, turned into comparable plans, and dated.
It is hiring an analyst to turn the data you already collect into answers you can act on. In practice that means defining your key numbers, cleaning and joining data from your store, CRM, payments and ad accounts, answering specific questions such as which channels make money, and setting up reports that refresh on their own.
A focused engagement answering a specific question starts at $2,000. Setting up metric definitions, cleaned data and automated reporting across a business is quoted at $5,000–$15,000, depending on how many sources are involved. The quote is fixed after a short look at your data.
Start with a few questions that would change a decision. Which marketing channels bring customers who stay? Which products carry the margin once returns, discounts and fees are counted? Where do customers drop out between inquiry and sale? Which customers are about to leave? Each answer is worth more than a dashboard of fifty charts.
For orders and revenue, trust your store or accounting system, because it records actual payments. Treat ad platforms and Google Analytics as directional: good for comparing campaigns, not for counting sales. Then track the gap between them each week, so a sudden change tells you tracking broke.
Yes, for exploring a clean, joined export with clear column definitions. It goes wrong when fed scattered exports where "revenue" means three different things, because it will confidently analyze the wrong number. Get the definitions and joins right first; then AI tools become genuinely useful for quick questions.
Often not. Many businesses get the answers they need from exports and a well-built spreadsheet or a small database. A warehouse makes sense once you are joining several sources every day, or the data has outgrown spreadsheets.
Usually none. We work in the tools you already have where possible. If something new is needed, it is typically free or cheap at small-business scale, such as Looker Studio, Metabase or BigQuery's free tier.
Usually 2–4 weeks. The first days go on the decision workshop and the data audit, the middle on cleaning, joining and analysis, and the last week on the findings report and handover of the queries and automatic reports. A single focused question sits at the short end.
Yes, under an NDA, with access limited to what the analysis needs, personal details removed or masked where they are not needed, and our copies deleted when the work ends.
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