One of the most common questions we get during a data audit is some version of: "Which tool should we be using?"

It usually comes after a store owner has spent three hours going down a rabbit hole comparing Looker, Power BI, Tableau, and six other platforms — and left more confused than when they started.

So here's our honest take. Not a feature comparison table. Not a sponsored recommendation. Just what we actually suggest to ecommerce stores based on their size, budget, and what they're trying to do.

First — What Both Tools Actually Do

Looker (now part of Google Cloud) and Power BI (Microsoft) are both business intelligence platforms. They connect to your data sources, let you build dashboards and reports, and help you visualize what's happening in your business.

At a high level they do similar things. The differences are in cost, complexity, ecosystem fit, and who they're actually built for.

Power BI — Best For Stores Already in the Microsoft Ecosystem

If your business runs on Microsoft 365 — Excel, Teams, SharePoint — Power BI is the natural extension. It connects seamlessly, your team already knows the interface, and the pricing is hard to beat.

What works:

• Extremely powerful for the price — starts at $10/user/month

• Excellent Excel integration — if your data lives in spreadsheets, Power BI handles it cleanly

• Large community and documentation — easy to find help online

• Works well for stores that need detailed financial reporting alongside sales data

What doesn't work:

• The interface has a steeper learning curve than it looks

• Sharing reports with people outside your organization requires additional licensing

• Not ideal if your data lives primarily in Google tools

Best fit: Stores doing $50K–$500K/month with a team that already uses Microsoft tools and needs detailed internal reporting.

Looker — Best For Stores Built Around Google

If your stack is Shopify, Google Analytics, Google Ads, and Google Sheets — Looker Studio (the free version) is the fastest path to a connected dashboard. It pulls from all of those sources natively with no connectors needed.

What works:

• Looker Studio is completely free — no per-user licensing

• Native Google integrations — GA4, Google Ads, Google Sheets connect in minutes

• Easy to share — anyone with a Google account can view a report

• Clean, simple interface that non-technical team members can navigate

What doesn't work:

• Less powerful than Power BI for complex data modeling

• Limited customization compared to paid BI tools

• Can get slow with large data volumes

• The paid Looker platform (not Studio) is enterprise-priced and overkill for most small stores

Best fit: Stores doing under $200K/month that run primarily on Google tools and need a clean, shareable dashboard without a licensing budget.

What We Actually Recommend

For most ecommerce stores under $500K/month — start with Looker Studio.

Here's why: it's free, it connects to the tools you're already using, and it gets you 80% of the way there without a licensing cost or a steep learning curve. For a store that currently has no dashboard at all, Looker Studio built properly is a massive upgrade.

Power BI makes sense when you outgrow Looker Studio — when your data gets more complex, when you need advanced modeling, or when you're already deep in the Microsoft ecosystem and the integration makes sense.

What we'd caution against: choosing either tool based on a comparison article (including this one) without understanding your specific data sources and what decisions you actually need to make. The best tool is the one that connects cleanly to your data and gets used consistently. A perfectly configured Looker Studio dashboard beats an abandoned Power BI setup every time.

What About Tableau?

Tableau is excellent and genuinely powerful — but it's expensive, complex, and built for data teams, not ecommerce operators. Unless you have a dedicated analyst, we rarely recommend it for stores under $1M/month. It tends to become shelfware.

Not Sure Which Tool Is Right for Your Store?

That's exactly what we figure out during a free data audit. We look at your current stack, your data sources, and what you actually need to track — and give you a straight recommendation with no upsell attached.

When a new client comes to us, their dashboard usually has one of two problems. Either it's completely empty — they're running their store on gut feeling and Shopify's default reports — or it's overloaded with numbers that don't connect to any real decision.

Both are the same problem. Too little signal, too much noise.

Before we build anything, we audit. And every audit starts with the same five metrics. Not because they're the only ones that matter, but because they tell us immediately whether a store has a data problem — and how serious it is.

Here's what we look at first.

1. Customer Acquisition Cost (CAC)

If you don't know what it costs to acquire a customer, you don't know if your ads are working. It sounds obvious, but the majority of stores we audit can't give us a clean CAC number broken down by channel.

They know their total ad spend. They know their total orders. But they can't tell us whether Meta is delivering customers at $18 or $47 — and that difference is the difference between a profitable store and one that's slowly bleeding cash.

What we want to see: CAC by channel, updated weekly.

2. Customer Lifetime Value (LTV)

CAC only makes sense next to LTV. A $47 CAC is a disaster if your average customer buys once and never comes back. It's perfectly healthy if they return three times a year for two years.

Most stores underestimate their LTV because they only look at first purchase value. We look at repeat purchase rate, average order value over 12 months, and churn — and we put that number next to CAC. That ratio tells us everything about the health of the business model.

What we want to see: LTV:CAC ratio of at least 3:1.

3. Conversion Rate by Traffic Source

Your overall conversion rate is almost useless. A store converting at 2.4% sounds fine until you break it down and realize organic traffic converts at 4.1% while paid social converts at 0.8%.

That's not a conversion rate problem — that's a targeting problem. Or a landing page problem. Or both. You can't know until you segment it.

What we want to see: Conversion rate broken down by source, device, and landing page.

4. Cart Abandonment Rate

The average ecommerce cart abandonment rate sits around 70%. Most store owners know this number is high but treat it as a fact of life rather than a recoverable revenue opportunity.

When we see abandonment above 75%, we start looking at checkout flow, payment options, shipping cost transparency, and whether there's any recovery sequence in place. In almost every audit, there's recoverable revenue sitting in abandoned carts that nobody is chasing.

What we want to see: Abandonment rate tracked weekly with a recovery flow in place.

5. Revenue by Product and SKU

Most stores know their total revenue. Very few know which products are actually driving it — and which ones are quietly destroying their margins.

We've audited stores where 80% of revenue came from 20% of SKUs, and the owner had no idea. They were spending ad budget promoting low-margin products while their best performers were barely featured. A clean revenue breakdown by product changes purchasing decisions, ad strategy, and inventory planning overnight.

What we want to see: Revenue, margin, and units sold by SKU, sortable and updated in real time.

What These Five Tell Us

Together these metrics give us a complete picture of where a store is acquiring customers, what those customers are worth, where they're dropping off, and what's actually driving revenue.

If any one of them is missing, unclear, or buried inside a spreadsheet that gets updated manually once a month — that's where we start.

Not Sure Where Your Store Stands?

Book a free 30-minute data audit with lebombo. We'll review your current setup, identify what's missing, and tell you exactly where you're leaving money on the table.

No commitment. No pitch deck. Just a clear picture of your data.

Shopify's analytics have improved a lot over the past few years. If you're on a Basic plan you get sales reports. If you're on Shopify or Advanced you get more — cohort analysis, retail sales breakdowns, custom report builders.

It's genuinely useful. And it's genuinely not enough.

Here's what Shopify's built-in reports consistently miss — and what store owners should be doing about it.

1. Cross-Channel Ad Performance

Shopify shows you revenue. It doesn't show you where that revenue actually came from in any reliable way.

Attribution is broken by default. If a customer clicks a Meta ad on Monday, googles your store on Wednesday, and buys on Thursday — Shopify's last-click attribution gives Google 100% of the credit. Meta gets nothing. Your Meta campaigns look like they're underperforming. You cut budget. Sales drop.

This happens constantly. The fix is connecting your ad platforms — Meta, Google, TikTok — into a single dashboard that shows blended CAC and assisted conversions alongside Shopify revenue. Shopify alone can't do this.

2. Margin by Product

Shopify shows you revenue by product. It does not show you profit.

A product doing $20,000 a month in revenue might be your worst performer once you factor in cost of goods, shipping, and return rate. We've seen it. A store celebrating a bestseller that was actively destroying margin because nobody had done the math.

Connecting your cost data to your revenue data requires either a custom build or a tool like Glew, Triple Whale, or a properly structured dashboard. It won't happen inside Shopify natively.

3. Customer Behavior Between Purchases

Shopify's customer reports tell you who bought and when. They don't tell you what brought them back, how long the gap between purchases was, or which customers are at risk of churning.

For any store with a repeat purchase model — consumables, apparel, subscriptions — this is critical data. Knowing that your average customer returns after 47 days means you can trigger a re-engagement campaign on day 40. Shopify won't tell you that number without significant manual work.

4. Real-Time Inventory Intelligence

Shopify tracks inventory levels. It doesn't connect inventory to sales velocity and forecast when you'll run out.

Running out of stock on a bestseller during a paid campaign is one of the most expensive mistakes an ecommerce store can make. You're paying to send traffic to a product you can't sell. A proper dashboard connects inventory levels to sales rate and flags risk before it becomes a problem.

5. A Single Source of Truth

This is the biggest gap. If your data lives across Shopify, Meta Ads Manager, Google Analytics, and a spreadsheet your logistics team updates manually — you don't have a data strategy. You have four partial pictures that don't talk to each other.

Every time you want to make a decision you're opening four tabs, cross-referencing numbers that don't match, and eventually going with your gut anyway. That's not a Shopify problem. It's an infrastructure problem.

The solution is a single dashboard that pulls everything together — one place where revenue, ad performance, margin, inventory, and customer behavior all live and update automatically.

What To Do About It

If you're on Shopify and hitting these limitations, you have two options:

Option 1 — Use a dedicated ecommerce analytics tool like Triple Whale, Glew, or Northbeam. These plug into Shopify and solve some of these problems out of the box. They come with a monthly cost and varying levels of customization.

Option 2 — Build a custom dashboard connected to all your data sources. More work upfront, but completely tailored to your business and significantly more powerful long term.

Both are valid depending on your store size and how complex your data needs are. The worst option is doing nothing and continuing to make decisions based on incomplete information.

Not Sure What You're Missing?

Book a free 30-minute data audit with lebombo. We'll look at your current setup — Shopify, ads, everything — and tell you exactly what gaps exist and what's worth fixing first.