New vs Returning

Customers > New

Data Source: ERP, Tadpull Pixel (i.e. site behavior)

Note: Only data from customers who have purchased one time is included. 

The goal of the new customers screen is to gain insight into first time customers purchasing preferences and site behavior to better understand a) how to target prospective customers and b) how to convert first time customers into returning customers. 

The aggregate metric section outlines key metrics such as what percent of the customer base is first time customers and how much a first time customer spends on average. There is a chart below which can be used to visualize these metrics over time. 

The gateway product section highlights which products and pairings of products are most commonly bought in a first purchase. This information is helpful to note when deciding what items to feature in acquisition campaigns targeting prospective customers. 

The chart displaying the distribution of customer's average order value is helpful to get a better sense of the range of first order totals. If customer's order values typically fall within a certain price range, this information can be used to set ideal free shipping thresholds to better convert site visitors or set ROI targets for acquisition campaigns. 

The top performing acquisition channels table helps inform which channels the customers are typically coming through directly before placing an order. It's important to note they may have interacted with other channels before making a purchase. 

Looking at the site behavior metrics for first time customers can help inform how long they typically interact with the site before making a purchase.

Metrics:   

• Total Revenue: Total revenue from first time customers
• % of Customers: % of first time customers out of total customers
• Count: Count of total first time customers
• Average Order Value: Average order total of every purchase
• Discount Rate: % of orders bought on discount
• Units Per Transaction: Average number of units on an order
• Gateway Products: Items most commonly purchased in a first order 
• Time on Site: Average one-time customers spent on site
• Pages/Session: Average pages visited per session
• Days to First Purchase: Average number of days from first visit to first purchase
• Channel Interactions: Average number of channels visited before first purchase
• Order Value: Customer’s cumulative order value to date  
• Last Updated: The date the customer was last updated in the ecommerce platform (e.g. Shopify, BigCommerce, etc.)

Charts:

• One-Time Order Value Distribution: Average order value by custom ranges
• Top Performing Acquisition Channel (First Interaction): Most popular channels for customer acquisition and their total revenue

Customers > Returning

Data Source: ERP 

Note: Only data from customers who have purchased two or more times is included.

The goal of the returning customers screen is to gain insight into repeat customers purchasing preferences and site behavior. This information can be used to convert one-time customers into repeat customers as well as grow customer loyalty.

The aggregate metric section outlines key metrics such as what percent of the customer base is repeat customers and how much a returning customer spends cumulatively. There is a chart below which can be used to see these metrics over time. 

The popular products section highlights which products and pairings of products are most commonly bought by returning customers. This information is helpful to note when deciding what items to feature in retention campaigns. 

The average customer lifetime value chart shows the predicted lifetime value for a returning customer and how that predicted value changes over time. Ideally, over time, your customer lifetime value increases. 

The purchase frequency and AOV by purchase number charts help inform how many times people are purchasing and what their typical order value is for each purchase. The average time between purchases chart shows how much time typically passes in between purchases. 

The average predicted lifetime value is a predicted revenue value that each customer will spend in their entire lifetime of purchases with a company. The following data isused in a predictive model to determine the average predicted lifetime value:

• Recency (when the purchases were made)
• Frequency (how many purchases were made)
• Monetary (how much money was spent)

Metrics: 

• Total Revenue: Total revenue from repeat customers
• % of Customers: % of repeat customers out of total customers
• Count: Count of total repeat customers
• Discount Rate: % of orders bought on discount
• Cumulative Order Value: Average cumulative order value for repeat customers
• Units Per Transaction: Average number of units on an order
• Popular Products: Items most commonly purchased based on quantity sold
• Predicted LTV: a customer’s predicted monetary value to your company over their lifetime (need to add hover def)
• % Chance Active: Predicted probability that a repeat customer will make another purchase in the future (need to add hover def)
• Last Updated: The date the customer was last updated in the ecommerce platform (e.g. Shopify, BigCommerce, etc.)

Charts:

• Average Customer Lifetime Value Over Time: The predicted lifetime value for returning customers over time 
• Number of Repeat Purchases: A breakdown of the number of customers who have purchased a specific number of times
• AOV by Purchase Number: The average order value for customers for each specific purchase number (helps identify if customers spend more or less as they continue to make repeat purchases)
• Average Time Between Purchases: The average number of days between purchases for each purchase number.