Marketing Analytics: What's Working?

Half Your Marketing Works—AI Helps You Find Which Half

There is a famous quote in marketing: "Half the money I spend on advertising is wasted. The trouble is, I don't know which half." That was true for a long time. But with AI-assisted analytics, you can get much closer to answering that question. Stop guessing which marketing works. AI helps you measure, analyze, and optimize for better ROI.

The goal is straightforward: spend more on what works, spend less on what does not, and test new ideas with enough rigour to know which category they fall into.

Campaign Performance Analysis

The foundation of marketing analytics is measuring how each campaign and channel actually performs. This means going beyond vanity metrics like impressions and clicks, and focusing on what matters: leads, conversions, and revenue.

Multi-channel analysis:

Campaign data:
- Channel A: Spend $X, Leads Y, Conversions Z
- Channel B: Spend $X, Leads Y, Conversions Z
- Channel C: Spend $X, Leads Y, Conversions Z

Calculate for each:
- Cost per lead
- Cost per conversion
- Conversion rate
- ROI
- Which to scale, which to cut?

AI is particularly helpful here because it can calculate all these metrics instantly and also spot relationships you might miss. For example, it might notice that Channel A has the lowest cost per lead but also the lowest conversion rate from lead to sale—meaning its leads are cheap but low quality. Without that second layer of analysis, you might scale the wrong channel.

Tip: When comparing channels, always compare them on the same metric. Cost per lead is meaningless if one channel's leads convert at 20% and another's convert at 2%. Cost per paying customer is usually the most useful comparison.

Attribution Modeling

Attribution is one of the trickiest problems in marketing. A customer might see your Google ad, read your blog post a week later, receive an email, and then finally buy. Which of those touchpoints deserves the credit? The answer matters because it determines where you invest.

Understanding customer journey:

Customer touchpoints before purchase:
[List typical journey: ad → blog → email → purchase]

Help me understand:
- Which touchpoint deserves credit?
- First-touch vs last-touch attribution
- Multi-touch attribution model
- How to measure each channel's true impact

AI can explain the different attribution models in plain terms:

• First-touch attribution gives all the credit to whatever first brought the customer to you. Good for measuring awareness channels.
• Last-touch attribution gives all the credit to the final touchpoint before purchase. Good for measuring closing channels.
• Multi-touch attribution spreads the credit across all touchpoints. More accurate, but harder to set up.

For most small and medium businesses, the practical approach is to use last-touch attribution as your primary model (because it is simple and available in most analytics tools) while keeping an eye on first-touch data to make sure your awareness channels are not undervalued.

A/B Test Analysis

A/B testing is how you make marketing decisions based on evidence instead of opinion. You run two versions of something—a landing page, an email subject line, an ad—and see which one performs better. The catch is knowing when you have enough data to declare a winner.

Statistical significance:

A/B test results:
- Variant A: 1000 visitors, 50 conversions
- Variant B: 1000 visitors, 65 conversions

Is this statistically significant?
Confidence level?
Should I declare a winner or keep testing?

Common mistake: Calling a winner too early. If you look at your test after 100 visitors and one variant is ahead, that is almost certainly random noise. AI can tell you exactly how many visitors you need before the result is trustworthy—typically at least 95% confidence level. Ending tests too early leads to decisions based on randomness, not real differences.

Another common mistake: Testing too many things at once. If you change the headline, the image, and the call-to-action all at the same time, you will not know which change made the difference. Test one element at a time so the results are clear.

Budget Optimization

Once you know which channels perform best, the next question is how to allocate your budget. This is not as simple as putting everything into the highest-ROI channel, because most channels have diminishing returns—the more you spend, the less efficient each additional dollar becomes.

Channel allocation:

Monthly marketing budget: $10,000
Current allocation:
- Google Ads: $4k (ROI: 2.5x)
- Facebook: $3k (ROI: 1.8x)
- Email: $2k (ROI: 4x)
- Content: $1k (ROI: unknown)

How should I reallocate for maximum ROI?
Consider: diminishing returns, testing budget, brand building

AI can model different allocation scenarios and estimate the impact. A good rule of thumb is to allocate 70% of your budget to proven channels, 20% to channels you are optimizing, and 10% to experimental channels you are testing. This balances short-term results with long-term learning.

Conversion Funnel Analysis

Your marketing funnel is a series of steps from first visit to paying customer, and at each step some people drop off. Finding where the biggest drop-offs happen—and fixing them—is one of the highest-leverage things you can do.

Finding leaks:

Funnel stages:
- Visitors: 10,000
- Sign-ups: 500 (5%)
- Trials: 200 (40% of signups)
- Paid: 40 (20% of trials)

Analyze:
- Which stage is weakest?
- Industry benchmarks?
- Where to focus optimization?
- Estimated impact of 10% improvement at each stage

AI can run the maths on improvement scenarios instantly. For example, improving your visitor-to-signup rate from 5% to 6% would add 100 more sign-ups, which flows through to 8 more paid customers (assuming the rest of the funnel holds). That one percentage point improvement at the top of the funnel can have a meaningful impact on revenue—often more than optimizing later stages where the numbers are smaller.

Cohort Analysis

Cohort analysis groups customers by when they signed up and tracks their behaviour over time. This reveals whether your product and marketing are getting better or worse at retaining customers, and it highlights the impact of specific changes.

Customer behavior over time:

Monthly cohorts (customers by signup month):
[Data showing retention/revenue by cohort]

Analyze:
- Which cohorts perform best?
- Retention trends
- Changes that improved/hurt retention
- Lifetime value by cohort

Practical example: If customers who signed up in March retain at 60% after three months but customers who signed up in June retain at only 40%, something changed between March and June. Maybe you changed your onboarding flow, attracted a different audience, or raised prices. AI can help you correlate cohort performance with the changes you made so you can understand what is actually driving the numbers.