Measuring Paid Media’s Impact on New Customer Acquisition Beyond Platform Attribution

In performance marketing, one of the biggest challenges brands face is understanding the true effectiveness of their paid spend on acquiring new customers.

The attribution dilemma (we all know & love… maybe)

Most brands rely on platform-reported metrics from Meta, Google, and TikTok to gauge success.

The problem?

These platforms are incentivised to make themselves look good. Duplicate conversions with other ad platforms, delayed conversions, and self-attribution bias all inflate reported results.

Larger brands often turn to advanced attribution software like Triple Whale and ActiveCampaign to get a more comprehensive view of performance. While these tools provide real-time deeper insights at the business level, they come with very high costs.

Our Approach: Looking Beyond ROAS

When we audit accounts, we take a different route. 

Instead of blindly trusting platform-reported ROAS, we analyse the correlation between daily media spend and first-time order data pulled directly from Shopify (or your eCommerce platform). This method helps us:

• Validate the effectiveness of the past & present paid media strategy beyond platform-reported ROAS
• Determine if brands are over-leveraging existing audiences in paid media.
• Make more informed media mix & budget allocation decisions moving forward
• Identify diminishing returns on spend and optimise for efficiency

If your paid ads aren’t heavily contributing to bringing in new customers, your marketing efficiency will decline over time. Simple as that.

Understanding Statistical Correlation

To quantify this relationship, we create a statistical correlation graph using Google Sheets or Excel. This gives brands a clear, data-backed view of how their paid media spend influences new customer acquisition.

How Correlation Works:

Correlation measures the relationship between two variables (in this case, ad spend and first-time orders) and ranges from -1 to 1:

📈 1 → Perfect positive correlation (spend goes up, new customers go up).
⚖ 0 → No correlation (spend has no impact on new customers).
📉 -1 → Perfect negative correlation (spend goes up, new customers go down).

By applying this to media spend, we can determine whether increasing the budget actually leads to more first-time customers, or if it’s just inflating ad platform vanity metrics.

Running the Analysis: Step-by-Step

1. Extract First-Time Orders
   • Pull first-time orders by day for the time-period you want to review (e.g., last 180 days) from Shopify or your eCommerce platform.
2. Extract Daily Marketing Spend
   • Gather daily ad spend from Meta, Google, and other platforms for the same time-period split by day. Consolidate it into one sheet with the following columns: Day | First-Time Orders | Total Media Spend
3. Step 3: Create a Scatter Plot
   1. Select your dataset (columns: Day, First-Time Orders, Total Media Spend)
   2. Go to Insert → Scatter Plot (or XY Chart).
   3. Set the axes correctly:
      1. X-Axis: Total Media Spend
      2. Y-Axis: First-Time Orders
   4. Add a trendline to visualise the relationship between spend and first-time orders.

Interpreting the Findings

Strong correlation (above 0.6) → Paid media is likely a significant driver of new customers. Scale spend confidently as long as the business is meeting MER/profitability targets.

Weak correlation (below 0.3) → Other factors (organic traffic, referrals, seasonality) might be doing more heavy lifting than you realise. 

Diminishing returns → If spend increases but new customer growth stalls, it’s time to rethink budget allocation.

For the brand above we analysed, the correlation coefficient was 0.648, indicating a strong relationship between increased ad spend and new customer orders. However, correlation isn’t static. It shifts over time due to factors like:

• Changes in media mix, like shifting budget between Meta and Google).
• Targeting adjustments, like over-investing in retargeting site traffic and customers).
• Budget reallocations, like moving budget from standard shopping to Performance Max).

To track these shifts, it’s important to segment the data into different timeframes so you can measure its impact on the correlation.

Case Study: Catching a Declining Correlation & Fixing Strategy

For this client, we also tracked marketing efficiency trends and noticed a decline over time. To investigate further, we segmented new customer data into two different timeframes and found:

• Earlier period r = 0.648
• Recent period r = 0.513 (a 20.8% decrease)

This decline in correlation mirrored a drop in MER (Marketing Efficiency Ratio), leading us to have confidence in our technical analysis of their Meta Ads account and diagnose the cause.

The Budget was subtly shifted over time towards targeting existing customers, as opposed to new ones. In-platform performance looked great, but paid wasn’t contributing to driving new customers like it used to. As such, the brand started to suffer the consequences. 

We saw a similar trend when auditing Cycology, and after implementing new customer acquisition strategies, we increased first-time orders by ~26% YoY.

What Else Could Be Impacting Correlation?

While correlation analysis provides valuable insights, it’s important to factor in external influences:

Sale Events & Promotions
Black Friday and flash sales artificially boost first-time orders. Running the analysis excluding sale periods gives a more accurate baseline.

Seasonality & Demand Shifts
Industries have peak/off-peak seasons (fitness surges in January, swimwear in summer). Ensure findings hold year-round, not just in seasonal spikes.

Budget & Strategy Changes
Large budget shifts can skew correlation, especially if platform algorithms need time to adjust. Breaking the analysis into pre/post periods can provide clarity.

Platform-Specific Performance
Google and Meta often perform differently. One platform may continue driving new customers while other plateaus. We recommend breaking down spend by channel to see where efficiency drops.

Market Conditions & Competition
Economic downturns, competitor price drops, and external trends can all influence demand. sometimes causing misleading fluctuations in correlation.

Actionable Takeaways

By implementing this correlation analysis, you can make smarter investment decisions. Here’s what you can do with these insights:

• Scale spend where correlation is strong → If paid ads are actually driving new customers and your MER is on target, don’t be afraid to invest aggressively.
• Identify diminishing returns → If scaling budget isn’t increasing first-time orders, it’s time to optimise targeting, creative, or diversify channels.
• Improve creative & messaging → If ad spend rises but new customer acquisition stalls, revisit audience targeting and creative strategy.
• Move away from vanity metrics → Stop obsessing over platform-reported ROAS and shift focus to business-level growth metrics to look at performance more holistically.

Ready to Get a True Measure of Your Paid Spend’s Impact?

If you’re running paid ads and want to ensure your budget is driving meaningful customer acquisition, not just inflated revenue metrics, reach out. 

We help brands cut through the platform noise and optimise for real growth.