Business insights with Stripe x AI data analysis
Is Stripe's default dashboard enough for analysis?
In recent years the business environment has changed rapidly, and a data-driven approach is essential to success. Especially in this day and age, with online transactions normalized and payment processing an integral part of the process, the integration of data analysis tools with Stripe is a powerful way to give your business a competitive edge.
Of course, Stripe has a very useful data aggregation and visualization dashboard by default. (Morph also uses Stripe for billing!).
But by calculating your own indicators from Stripe data and then combining them with other data for analysis, you can start to get a much more in-depth picture of your business.
Benefits of integrating data analysis tools with Stripe
Is it really worth the effort to add Morph data analysis tools to the provided Stripe dashboard? Absolutely. With increased analysis, you can:
- Track sales data in real-time: tracking sales data in real time allows you to identify trends and adjust your strategy immediately, not the next day.
- Analyze customer behavior: more granular customer information, such as how customers are choosing to pay and which plans they are choosing, can be analyzed with Morph. This allows you to understand customer behavior patterns, change targets, and optimize campaigns.
- Optimize revenue: Morph can be used to optimize revenue through detailed analysis of sales data. For example, you can identify products and services with low revenues and consider ways to improve them.
- Share internal information securely: simply, the Stripe Dashboard is something people have access to or don’t. The dashboard shows all of the information to whomever has access to it. But you might want, for example, the marketing team to have access to sales numbers but not customer payment information. By linking Stripe to Morph, you can show or hide data on a per-item basis, so you can share data securely without sharing more data than you need to.
How to integrate Stripe with Morph
Now we will actually explain how to link Morph and Stripe. Please refer to the video below for a step-by-step tutorial!
Case Studies Using Morph and Stripe
In this section, we will show you how the integration between data analysis tools and Stripe was used in actual case studies.
Subscription Business Growth: An In-Depth Analysis of Monthly Recurring Revenue
The default dashboard in Stripe can also produce Monthly Recurring Revenue (MRR), but it is not possible to graph it alongside the number of new subscriptions per month, etc.
By using Morph, a detailed analysis of MRR can be easily represented graphically.
For the items to be retrieved from Stripe, we selected Stripe Import Invoices.
- customer： customer ID
- total： sales in-tax
- period_start： Start date per subscriber
- period_end： End date per subscriber
Use these items to calculate MRR, like so:
When creating this graph, the prompt was run twice.
Total ${stripe_invoices.total} by month and graph it by ${stripe_invoices.period_start}.
The graph should be a bar chart.
After February 2023, determine whether ${stripe_invoices.customer} existed in the previous month and separate the colours for true and false.
Tip: instead of trying to process all the prompts at once, divide the prompts into multiple executions so that you can see how far the prompts have been executed, making it easier to formulate a correction policy in case a prompt fails.
We were able to show those in red as continuing customers from the previous month and those in blue as new customers who signed up during the month.
This provides a visual and intuitive understanding of how much the number of new contracts contributes to sales.
In this example, we could see that sales did not increase that much from May to June, but new subscriptions increased significantly. In other words, you can see that there was a lot of churn.
Integrated analysis of sales and expense information
Along with Stripe data, cost data can be linked from accounting software to allow for comparative analysis of sales information and costs associated with sales and marketing.
Analyzing sales efficiency is important to ensure that product growth is efficient and sustainable. Comparing new customer sales to acquisition costs can help understand sales efficiency.
We will use the Invoice table we used earlier and a new table of expenses extracted from our accounting software.
We ran the prompt twice here as well.
👇Prompt to tally monthly newly acquired Annual Recurring Revenue
Determine whether ${stripe_invoices.customer} existed in the previous month, sum ${stripe_invoices.total} by month only for False values, multiply by 12 and then graph.
👇Graph the monthly expenses and dock them with the graph created in the first prompt.
Aggregate ${stripe_invoices.period_start} by month and ${stripe_invoices.total} and display them side by side on the same graph.
The two graphs were then combined and displayed.
Analyzing new sales and marketing expenses is a very important metric, but we were able to easily compile the totals in a couple of minutes.
What do you think? We hope you can see how easy Morph makes aggregating and visualizing data from Stripe.
If you’d like to try Morph for your own business to enable flexible data aggregation and speed up the growth of your product, you can try Morph for free today. Sign up at the link below!
