Contents
- Overview
- Benefits
- RFMR User Segments
- Cohort Analysis
- Discount Code: Customer Acquisition
- Discount Code: Performance
- Geographic Segmentation
Overview
By interpreting historical data on your returns, you can now monitor and make informed decisions on your company's financial performance, operational efficiency, and customer behavior trends.
The Analytics page facilitates data visualization from which you can gain invaluable insights into your returns, revenue, and your customers.
On your Customers tab, the new Customer Segments dashboard will help you better understand how your customers behave and will enable the creation of better-targeted campaigns and communications.
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Note: The Customer Segments Dashboard is currently only live for a select number of merchants in a beta stage, to be released publicly in 2023. |
Benefits
- Understand how discount-acquired customers perform, seeing trends, and tracking individual discount code usage.
- Allow merchants to have a clear view of what their customers’ cumulative revenue is based on cohort analysis.
- Provide geographical segmentation by CLTV (Customer Lifetime Value)and # of customers per state.
- Provide the ability to create custom segmentations based on Recency, Frequency and Monetary scorings
Each section on this dashboard can be downloaded by clicking on the vertical ellipsis button located on the top right corner. Data can be exported as a CSV (Comma-separated Values) file.
RFMR User Segments
Understanding the Data
Customer Segments place customers in different groups based on their value according to the developed RFMR (Recency, Frequency, Monetary) model.
RFMR Model
This model is used in Marketing analysis where a business’ customer base is segmented by purchasing patterns or behaviors. It evaluates the following:
- Recency: How long ago your customers made a purchase.
- Frequency: How often your customers make a purchase.
- Monetary: How much money your customers spend.
Your RFMR User Segments are grouped as follows:
- High-Value Users: These are your ideal customers who buy frequently, spend a lot of money, and have purchased recently.
How they could be retained: Promoting new products and offering rewards programs.
The following are the pre-determined RFMR scores applicable to High-Value Customers:
Frequency: 5
Monetary: 5
Recency: 5
- Inactive Users: Your Inactive Users are your Inactive customers who used to spend a lot of money and buy frequently but have not been active recently.
How they could be reactivated: Offer discount codes and promotions.
The following are the pre-determined RFMR scores applicable to Inactive Customers:
Frequency: 4,5
Monetary: 5
Recency: 2
- Price-Conscious Users: Your Price-Conscious customers who buy frequently and recently but are price conscious.
How they could be targeted: Offer discounted items, sales, and product alerts.
The following are the pre-determined RFMR scores applicable to Price-Conscious Customers:
Frequency: 5
Monetary: 2
Recency: 1,2,3,4,5
- Custom Segment: You can create a custom segment to zone in on a specific group of customers. For this Custom Segment, define your own parameters by designating a Recency, Frequency, and Monetary score for the group.
Each number represents 20% of the total possible score, with 5 being the most desired shopper behavior. When 5 is selected in every column/score, the resulting segment will reflect your high-value customers.
- As an example, a recency score of 5 would mean the customer has purchased recently vs. a recency score of 1, the customer has purchased a long time ago.
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Note: Changing the filtering values will only impact your Custom Segment; the numbers for the other user segments will remain the same. |
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METRICS |
DEFINITION |
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# shoppers |
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AOV (Return-adjusted) |
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Last Order |
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# Orders |
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Return Rate |
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Note: These are average values, and as such you will want to prevent a customer from being included in multiple / overlapping segments by entering values outside of the user segment range. |
Use Cases
- Reward High-Value users with early access to new collections, referral codes and loyalty rewards.
- Poll High-Value users to better understand what they’d like to see in future collections.
- Ask inactive customers how you can improve, and re-engage them with new product announcements and promo code.
- Exclude price-conscious users from select High-Value-only sales.
- Entice price-conscious customers with limited time discounts or last-chance inventory.
- Tailor communications with price-conscious customers to emphasize benefits and value over features and cost.
Cohort Analysis
Understanding the Data
Analysis of the CLTV (Customer Lifetime Value) of users segmented by acquisition cohort.
A cohort is a group of people who have something in common. A cohort analysis would look at such a group as a whole for statistical purposes.
This gives the merchant the ability to analyze the average revenue generated from ir users by acquisition cohort to be compared to the CAC (Customer Acquisition Cost)for a specific period. Over time you can see how sticky the users acquired in each of the cohorts are via the activity rate section.
This heat-map table shows the number of customers newly acquired per cohort along with the cumulative revenue per user.
For example Cohort “2021/07” shows the revenue gained from 66.0k shoppers who started in July of 2021. Month 1, they generated a revenue of $119. Month 2 they generated an additional $7, bringing the total revenue generated to $126.
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METRICS |
DEFINITION |
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Months Since Joined |
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Shoppers |
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1 to 12 |
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$ amount |
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Values in the above heat-map are encoded in colors. Those are as follows:
- Tints: Underperforming cohorts
- Shades: Overperforming cohorts
Among the factors that could show underperforming / overperforming cohorts are:
- Natural seasonal effects of their business
- For tints: Ineffective marketing campaigns that led to acquisition of low LTV customers.
- For shades: effective marketing campaigns that led to acquisition of high LTV customers.
- Cumulative revenue per new user as it compares to CAC
Use Cases
- Identify cumulative lifetime revenue for each cohort to understand the stickiness of each segment and recurring revenue. In other words, when you acquire customers today, you know how much revenue you can expect them to generate for you a year from now.
- Identify at which point the customer relationship becomes profitable comparing customer acquisition cost (CAC) and average monthly customer spend.
- Identify the maximum amount you should spend on marketing and still be profitable by comparing CAC and LTV.
- Demonstrate expansion revenue to investors for cohorts that increase spend month over month.
- Identify the highest converting months to help optimize marketing spend and seasonal promotions.
Discount Code: Customer Acquisition
Understanding the Data
This section provides a general view of discount codes.
The image below shows the evolution of customer acquisition for the store segmented by discount/non-discount acquisition, their average lifetime revenue, number of orders placed and returned value rate.
- Click on any data point to view further details on new customers acquired.
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METRICS |
DEFINITION |
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New customers acquired via discount code |
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New customers not acquired via discount code |
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New customers by discount code / non-discount code acquisition |
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Discount Code: Performance
Understanding the Data
This section provides a more granular view of discount codes.
On the right-hand side of the graph, you can track discount codes performance in terms of:
- Discount code and type (percentage or fixed amount)
- Average order discount
- New/Recurrent shoppers using the discount code
- Average order revenue (return-adjusted) and returned value rate
- Number of orders
- Discounted amount/percentage
- Average shopper lifetime revenue and number of orders
- Median usage time from launch
Discount code types: further analysis
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METRICS |
DEFINITION |
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Percentage-based discount codes |
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Fixed-amount discount codes |
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Note: Not considering Returnly-generated gift cards, only merchants’. |
Use Cases
- Determine the deepest discount you can offer and still be profitable by comparing cost of acquisition and average lifetime revenue.
- Determine which discounts attract one-time buyers versus those that drive customer loyalty over time to optimize marketing efforts based on customer retention.
Geographic Segmentation
Understanding the Data
This provides merchants insight on the customer lifetime value (CLTV), or the total value generated by one customer’s experience with your business, segmented by geographical location.
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METRICS |
DEFINITION |
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CLTV |
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Number of shoppers |
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- Click on any state to view details on either Customer LifeTime Value or Number of customers.
Use Cases
- Ramp up marketing efforts or invest more heavily in community activation in high-converting markets.
- Tailor seasonal promotions to the customer’s climate.
- Invite price-conscious users located near a brick-and-mortar to return or exchange in-store to save on shipping costs.
- Host local pop-up to re-engage inactive users or invite High-Value users to sneak peek at new collections.
- Partner with local influencers to promote your brand and drive community engagement.
