Understanding Ad Campaign Metrics Over Time

Acqusistion > Ad Campaign > KPI Trends

Summary

The report provides key insights into your business’s performance over time from Ad Campaign perspective, allowing you to track the progress of critical metrics and compare the current year’s performance against the prior year.

Questions the report answers

How the business is performing over time and how it is performing compared to the prior year?
What trends or patterns are emerging in KPIs over time?
What might be driving changes in key metrics over time?

How to read the report

Understanding the Comparison Between Years

The report allows you to compare the Selected Year (blue line) against the Prior Year (gray line) for each KPI. This comparison helps you quickly assess whether your business is improving or underperforming in specific areas.

If the blue line is consistently above the grey line: This indicates that your selected year performance is better than the prior year for that specific metric. For example, if your Revenue from Ads is consistently higher this year, it suggests that your sales strategies are more effective.
If the blue line is below the grey line: This indicates that your selected year performance is worse than the prior year for that metric. If you notice this in metrics like Average Order Value (AOV) from Ads, it may indicate issues such as ineffective promotions.
Crossing lines: If the blue line starts below the grey line but eventually crosses above it (or vice versa), this indicates a turnaround or change in performance relative to the prior year.
- A positive shift (the blue line starts below the grey line but eventually crosses above it) suggests improvement due to recent actions like marketing campaigns.
- A negative shift (the blue line starts above the grey line but eventually crosses below it). A negative shift may suggest that a successful strategy from the prior year is no longer working or promoting a review of your current approach.

Spotting trends over time:

Look for patterns or spikes in the data. For example, if the metric Revenue from Ads in certain months, this could indicate seasonality or the impact of a successful strategy or tactic.
Identify declines or dips in KPIs. For example, if you see a drop in Average Order Value from Ads during a specific month, it may point to changes in customer behavior.
Compare performance during key periods like holiday seasons, sale events or campaign launches to see how these initiatives affected key metrics. For example, did the Number of Orders spike during a specific event.

Analyzing Supporting Metrics

Each KPI is influenced by supporting metrics to understand these you refer to the KPI Tree - Ads Campaign report. KPI Tree will help you understand the relationship between these key metrics. By reviewing these supporting metrics, you can gain deeper insight into why your high-level KPIs are trending up or down. For example, If Revenue from Ads is declining: Check the Number of Order and Average Order Value. If the Number of Order is declining check Clicks and Clicks to Order Conversion. These will explain to you why sales are declining.

Glossary

`Average Order Value from Ads`

The average revenue per order generated from all advertising platforms. It is calculated by dividing the total ad revenue by the total ad orders.

Average Order Value from Ads =

\frac{Revenue from Ad}{Number of Orders from Ads}

`Ads Cost`

The total spend on advertisements across all advertising platforms. It is calculated by summing the costs associated with each ad platform.

`Clicks`

The total number of clicks generated from all advertising platforms. It is calculated by summing the clicks across all ad platforms.

`Clicks to Order Conversion`

The percentage of ad clicks that result in an order across all advertising platforms. It is calculated by dividing the total number of ad orders by the total number of ad clicks.

Clicks to Order Conversion =

\frac{Number of Orders from Ads}{Clicks} \times 100

`Click through Rate (CTR)`

The percentage of ad impressions that resulted in clicks across all advertising platforms. It is calculated by dividing the total number of ad clicks by the total number of ad impressions.

Clicks through Rate (CTR) =

\frac{Clicks}{Impression} \times 100

`Conversion Rate (Google Analytics)`

The percentage of website sessions tracked by Google Analytics (GA) that result in a purchase. It is calculated by dividing the number of orders by the number of sessions.

Conversion Rate =

\frac{Number of Orders}{Sessions tracked by GA} \times 100

`Cost per 1000 Impressions (CPM)`

The average cost per thousand ad impressions across all advertising platforms. It is calculated by dividing the total ad cost by the total number of ad impressions and then multiplying by 1,000.

Cost per 1000 Impressions (CPM) =

\frac{Ads Cost}{Impressions} \times 1000

`Cost per Click (CPC)`

The average cost incurred for each click on advertisements across all advertising platforms. It is calculated by dividing the total ad cost by the total number of ad clicks.

Cost per Click (CPC) =

\frac{Ads Cost}{Clicks} \times 100

`Impressions`

The total number of times advertisements were displayed to users across all advertising platforms. It is calculated by summing the impressions from all ad platforms.

`Number of Orders from Ads`

The total number of orders generated from all advertising platforms. It is calculated by summing the orders across all ad platforms.

`Revenue from Ads`

The total revenue generated from all advertising platforms. It is calculated by summing the order values across all ad platforms.

`Return on Ad Spend (ROAS)`

The revenue generated for every dollar spent on advertisements across all advertising platforms. It is calculated by dividing the total ad revenue by the total ad cost.

Return on Ad Spend (ROAS) =

\frac{Revenue from Ads}{Ad Cost}