Understanding Sales Metrics Over Time

KPI > Sales > KPI Trend > Trend

Summary

The report provides key insights into your business’s performance over time, 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 Sales Amount 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) or Conversion Rate, it may indicate issues such as ineffective promotions or issues with the website.
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 or operational changes.
- 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 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 Sales Amount is 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 Conversion Rate during a specific month, it may point to issues like website performance or 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 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 the Sales Amount is declining: Check the Number of Orders and Average Order Value. If the Number of Order is declining check Session, Conversion Rate, Number of Customers, and Order per Customer. These will explain to you why sales are declining.

Glossary

`Average Order Value (AOV)`

The average amount a customer spends per order. It is calculated by dividing the total sales amount by the number of orders.

Average Order Value =

\frac{Sales Amount}{Number of Orders}

`Average Orders per Customer`

Average number of orders placed by each customer. It is calculated by dividing the total number of orders by the number of unique customers.

Average Orders per Customer =

\frac{Number of Orders}{Number of Customers}

`Average Products per Order`

The average number of unique products purchased per order. It is calculated by dividing the total number of unique products present in orders by the total number of orders placed.

Average Products per Order =

\frac{Products in Order}{Number of Orders}

`Average Unit Price`

The average selling price of a single unit of a product. It is calculated by dividing the total sales amount by the total number of products sold.

Average Unit Price =

\frac{Sales Amount}{Quantity Sold}

`Average Unit Price (Discounted)`

The average selling price of a single unit of a product specifically for orders where a discount was applied, either at the header level or at the line item level. It is calculated by dividing the total discounted sales amount by the total quantity of products sold in those discounted orders.

Average Unit Price (Discounted) =

\frac{Sales Amount from Discounted Orders}{Quantity Sold in Discounted Orders}

`Average Unit Price (Non-Discounted)`

The average selling price of a single unit of a product specifically for orders where no discount was applied, either at the header level or at the line item level. It is calculated by dividing the total non-discounted sales amount by the total quantity of products sold in those non-discounted orders.

Average Unit Price (Discounted) =

\frac{Sales Amount from Non-Discounted Orders}{Quantity Sold in Non-Discounted Orders}

`Average Units per Order`

The average number of units sold (Quantity sold) per order. It is calculated by dividing the total number of units sold by the total number of orders.

Average Units per Order =

\frac{Quantity Sold}{Number of Orders}

`Average Units per Product`

Average number of units sold per product with a given order. It is calculated by dividing the total quantity of products sold by the total products in order.

Average Units per Product =

\frac{Quantity Sold}{Product in Order}

`Average Days to Repeat`

The median number of days it takes for a customer to make a repeat purchase within a year after their initial order. It calculates the median of the days to repeat the order. It specifically considers only those orders where the number of days to repeat is less than 365.

`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

Orders per Customer

The average number of orders placed by each customer. It is calculated by dividing the total number of orders by the total number of unique customers.

Order per Customer =

\frac{Number of Orders}{Number of Customer}

`Number of Orders`

Total count of orders placed by customers. It is calculated by counting the number of orders.

`Number of Customers`

Total count of unique customers who have made at least one purchase. It is calculated by counting the number of unique customers.

`Sales Amount`

Total revenue generated from the sales. It is calculated by summing sales amount values.

`Sessions (Google Analytics)`

The total number of sessions recorded in Google Analytics.