Calculate how much each subscriber is worth and budget your marketing spend more effectively.
Focus efforts on retargeting, discount programs, customer retention, and reducing churn, by measuring indicators like customer Lifetime Value (LTV).
McKinsey’s research suggests that 15% of online shoppers subscribe to an ecommerce service and are willing to continue buying from those brands they subscribe to. LTV measures the desire of whether they want to continue engaging with your brand or not.
Set up cross-domain tracking on Google Analytics to track customer flow on your ecommerce site selling subscriptions.
Set basic Google Analytics settings to see when customers visit your online store, the subscription they choose, and checkout to complete the purchase.
Link steps in the customer lifecycle to bring together a unified view of the customer in Google Analytics. Look for differences between customers who order once and those who commit to a subscription.
Differentiate among one-time orders, first-time subscription orders, and recurring orders. For example, first-time buyers aren’t sure which flavor of soda or subscription product variant. These customers usually have a higher order value (AOV), as they test multiple products before committing to a subscription.
Track each subsequent recurring payment to the same pre-checkout user journey, including the marketing campaign from which they came, along with other custom dimensions in Google Analytics to determine lifetime value.
Manually add the platform such as Shopify, and Customer ID as the User ID when the original order is sent. Download the CSV customer list from Shopify on a weekly or monthly basis, and upload it as a user import via Google Analytics data import, with LTV as a custom dimension. Use an automated tracking solution, like Littledata, Elevar, or Glew, to attribute traffic. Integrate your billing tool, like ReCharge or Bold, to the platform.
Export data to Google Sheets to see which traffic sources drive the highest LTV.
Use a user ID such as Shopify Customer ID to tie all orders to one customer, and push it to a custom dimension in Google Analytics to ensure the most accurate LTV calculations.
Alternatively, calculate historic LTV, the sum of gross profit from all historical purchases for a single customer. Or run a predictive analysis of transaction history with shopping and behavioral indicators to predict the LTV of a single customer.
Slice your data to understand LTV by traffic source, and see which traffic sources drive a higher lifetime value for a subscriber.
- Query the data from Google Analytics using Google Sheets.
- Use pivot tables to prepare the data for Data Studio by selecting all the data and selecting Pivot Table from the Data menu.
- Add the custom dimension with the User ID and
ga:sourcemediumin the Pivot Table setting in the Row section.
- Import the sum of all transaction revenue/source/customer into Data Studio.
- Create a new data source and import the Google Sheet with the pivot table.
- Modify the aggregation type for transaction revenue to be able to view the median LTV per channel. Use median to limit the impact of data outliers.
- Add Source Medium as a dimension and Transaction Revenue to the Metric section.
- Repeat the process, but use the Default Channel Grouping dimension from Google Analytics instead of Source Medium to be able to view the LTV per channel.
Export the automated data, Customer ID, and LTV in custom dimensions to calculate the median LTV based on all customers.
Use prerequisite data that includes Customer ID linking Shopify and Google Analytics, and Client ID, to calculate Customer Acquisition Cost (CAC).
Determine how successful your acquisition campaigns are to see whether you’re acquiring one-time buyers or loyal subscribers. Aim for a 3:1 LTV/CAC ratio to ensure your cost acquisition is one-third of your lifetime value. Most often, a higher ratio like 3:2 isn’t profitable in the long term, and a ratio like 5:1 usually means limited growth.
Conduct a consistent campaign tagging for traffic sources outside of Google Ads, and use the Cost Data Upload feature to upload the costs from other Ad Networks like Facebook, Bing, and AdRoll, to get all data for accurate LTV/CAC.
Tag each of your campaigns with UTM parameters when using Google Analytics; it has all the data from each customer touchpoint like platforms and devices. For example, reporting CAC only on Facebook data is an incomplete approach as you only get to see a part of what works or doesn’t within your marketing strategy.
No LTV guarantees that a channel is profitable or helpful on your path to scale. Similarly, when accounting for CAC plus product and shipping costs, even a customer with twice the LTV may not be profitable.