Metrics and data-driven decisions: a business intelligence guide for marketing

Vanity metrics vs. business metrics

Impressions, followers and "likes" are easy to count, but they don't pay the bills. The metrics that guide decisions are tied to money:

• Revenue and margin by channel, campaign or product.
• Customer acquisition cost (CAC): how much you spend to win a customer.
• Customer lifetime value (LTV): how much a customer brings in over their whole relationship with you.
• Conversion rate: what percentage of visits ends in a purchase or contact.
• Average order value (AOV) and repeat purchase rate.

ROAS, MER and how much you can pay for a customer

ROAS is the revenue attributed to an ad divided by what you spent on it. It's useful, but each platform credits itself with sales in its own way and the figures usually add up to more than your real sales. That's why you should also look at MER (Marketing Efficiency Ratio): total revenue divided by total marketing spend.

The essential thing is to know your break-even ROAS: if your gross margin is 40%, you need a ROAS of 2.5 just to avoid losing money before covering other costs. Without that calculation, a "good" ROAS can actually be a loss.

The foundation: reliable measurement

• Google Analytics 4 with well-defined conversion events.
• Google Tag Manager to manage tags without depending on code.
• Consistent UTM parameters on every campaign, email and social link.
• Pixel and Conversions API on Meta, and enhanced conversions on Google.
• Your store or CRM data as the source of truth for sales.

Without this, any dashboard is decoration. Implementation details in Google Ads and Meta Ads.

How to build a useful dashboard

Tools like Looker Studio let you bring Google Ads, Meta, GA4 and spreadsheets together in one place. A good dashboard:

1. Answers concrete questions, for example: which channel leaves the most margin? Which campaign dropped in performance?
2. Shows few metrics per view: the executive summary first, then the detail by channel.
3. Compares periods (week over week, month over month) and against the goal.
4. Updates itself, so you spend your time analyzing, not copying numbers.

With a data warehouse (for example BigQuery) and automation, the report can even reach your WhatsApp or email every week.

Attribution and incrementality: beware of mirages

No platform sees the customer's whole journey. A person might see an ad on Meta, search for your brand on Google and buy days later. To avoid fooling yourself:

• Compare the platforms' numbers with real sales.
• Watch branded searches and direct sales as signals of impact.
• Run incrementality tests (pausing a channel by region or period, or using control groups) to learn which sales would happen without that ad.

From data to decisions: a working rhythm

• Daily: watch spend and anomalies (conversion drops, tracking errors).
• Weekly: review performance by campaign, search terms and creatives; pause what doesn't work and scale what does.
• Monthly: analyze profitability, CAC, LTV and channel mix; adjust budget.
• Quarterly: review strategy, large tests and goals.

A useful rule: every decision should start from a hypothesis, run as a test, and be evaluated against a metric defined in advance.

Common mistakes when analyzing data

• Deciding with too little data or over periods that are too short.
• Comparing metrics that aren't equivalent across platforms.
• Changing too many things at once and not knowing what worked.
• Chasing vanity metrics.
• Not checking that tracking still works after site changes.

Frequently asked questions