How to underwrite eCommerce merchants effectively
We break down the data you need, how to access it, and the metrics to calculate.
It’s safe to say that the retail landscape has undergone a significant transformation in the last few years. One notable change has been the growth of eCommerce which is forecasted to reach 24.5% of global retail sales by 2025. There are now approximately 7.1 million businesses operating online and many of these businesses are in need of innovative funding solutions to help them manage cash flow, cover expenses, and increase sales.
However, underwriting eCommerce merchants is far from straightforward. When it comes to determining creditworthiness, using the right data and knowing what to do with that data is what separates market leaders from market stragglers. Read on to understand:
- What data you need to underwrite online merchants effectively.
- How to extract the most value from the data.
- How you can access the data.
Step 1: Access the right data from the right sources
Before building out an underwriting model, it’s critical to consider what data you’ll actually need. Underwriting eCommerce businesses is very different from underwriting brick-and-mortar stores for the following reasons:
- eCommerce businesses sell across multiple platforms, websites, and stores.
- They are heavily reliant on operating online and using online platforms to manage their business.
- Their business models can vary significantly. For instance, many use subscription-based billing.
- They often have considerable marketing spend
To illustrate the importance of this consideration, let’s take the example of a fictional US eCommerce business called Cometica House. They sell physical beauty and wellness products in multiple geographies and platforms. They also sell to retailers and in a few physical stores in the US. In addition, for some of their products, they offer subscription-based pricing with customers billed monthly.
Pulling a small amount of accounting data from Cometica House’s accounting platform or simply analyzing their P&L and Balance Sheet will not provide comprehensive insight into their sales, the popularity of their products, or overall growth.
Tap into the merchant ecosystem 🌐
It’s essential to access the data you need from all the systems merchants use to run their businesses, and these days, there are more than ever. Even the smallest merchants use multiple platforms. According to Blissfully, the average small business relies on 102 different apps to handle its operations.
To truly understand performance, you need to be thinking about everywhere merchants sell and all the systems they use to get paid. A business similar to Cometica House will likely use platforms like Shopify, Etsy, Amazon, and WooCommerce. If they have physical stores, they’ll also accept in-person payments using systems like Square and track those transactions in accounting platforms like Xero. They could well manage their recurring billing via the subscription management platforms such as Chargebee, use HSBC as their bank, and use Facebook and Google to run their marketing campaigns.
Using data from all of these platforms allows you to get a holistic view of merchant creditworthiness and make more informed decisions. For instance, without access to their Etsy data, you could stand to miss a considerable portion of their sales.
Step 2: Build out your underwriting model
Now you have a good idea of where merchants are operating, it’s key to understand which data types you should be using and what to do with them, including the best metrics and ratios to calculate.
Understanding current sales 📈
As a first step, you’ll want to understand the current sales of a business. This includes metrics that indicate customer profitability like gross profit on each sale, how much it costs to acquire customers, and each customer’s lifetime value (LTV).
Many eCommerce businesses’ operating expenses vary based on numerous factors. Therefore, understanding their gross profit margin is key to understanding their pure profit. You can achieve this with access to their accounting data.
By looking through a merchant’s Profit and Loss statement, you can dive into:
- The income per channel.
- Overall operating income.
- Operating expenses broken down by sales, materials, and labor.
This information can then be used to calculate gross profit and gross profit margin:
Gross profit margin = gross profit ÷ net sales
This metric also makes it much easier to compare merchants and identify market trends.
Revenue growth rate
Commerce data, including data from the PoS, eCommerce marketplaces, and payment systems, also hold key indicators of current sales. For example, this data can be used to calculate total sales and the percentage change between specific periods.
Using sales data to calculate revenue growth rate helps you understand a merchant’s revenue and can provide some indicators of their future performance. To get the best sense of how many orders they are fulfilling, it may also be helpful to consider using metrics like the number of orders and average order value in your underwriting model.
Identifying red flags 🚩
The following indicators can predict unstable or poor performance in the future.
Firstly, it’s essential to understand how happy their customers are and the quality of their products. To determine this, you can use commerce data, including payments, orders, and customer information.
For example, orders may be flying off the shelves, but the merchant may also have considerable disputes and refunds. Refund rate is a great indicator of customer satisfaction and can be calculated by considering total orders and refunds side-by-side:
Refund rate = Number of refunds / Number of orders
You can also look at other indicators to spot this, including dispute rates, chargeback rates, and customer reviews.
If customers aren’t returning, this could be an indicator of poor product quality, which may impact future sales. You can calculate customer retention rate by digging into customer information and transactional data over time:
Customer retention rate = Total customers at the end of the period – Total new customers during the period ÷ Total customers at the start of the period.
Projecting future sales 🔮
Now you have a good understanding of current sales and how to identify red flags, you can predict future performance. This will help you determine how much the business will grow in relation to the competition and how quickly they can realistically pay back your loan. After all, you’ll want to deploy your capital most effectively by selecting the merchants with the highest growth potential.
Using all the data points detailed above allows you to create a sales forecast by digging into current and historical sales. You can also carry out further analyses. Find out how below.
Monthly recurring revenue
Subscription billing is growing in popularity, and regular, recurring payments are a highly reliable indicator of future revenue. It’s a great indicator of future sales because it is repeatable, the same amount each time, and often is predictable in that the duration is a set time frame or rolls on.
By pulling recurring revenue data from the subscription management platforms such as Stripe Billing, Recurring, or Chargify, you can get real-time subscription data without searching through invoices and customer IDs to match transactions.
Lenders can pull the subscription information, the cost of the subscriptions, and sum the revenue from subscriptions to work out exactly how much monthly recurring revenue they generate and how much they are likely to earn in the future.
Having access to the data and metrics outlined above enables you to build out a clear understanding of how well a merchant is performing and allows you to benchmark merchants compared to other businesses.
Step 3: Consider how merchants will repay your loan
Monitor for repayments
Having ongoing access to real-time merchant data from all the platforms they use to run their business means you can determine how much they can afford to repay on a daily or weekly basis. For example, if your repayments are taken as a percentage of sales, you can easily monitor if they will make those sales to repay the loan on time.
Monitor ongoing performance
As a final step, it’s essential to be aware of any threats that may impact a merchant’s ability to keep up with repayments. For example, you can use real-time data to see if a merchant is taking on too much debt or has any other existing liabilities on their books. Equally, a sudden spike in refund rates or a sudden increase in churned monthly recurring revenue can indicate an inability to repay.
The path to get there
Perfecting your underwriting model may seem straightforward, but the path is not easy. If you were to attempt this alone, you’d need to build integrations to all of the major platforms merchants use, and as we outlined above, this can be as many as 100 per merchant.
Building and then managing each of these integrations is a massive constraint on your time. In addition, it will undoubtedly take focus away from your core roadmap, which is especially important if you are getting started and trying to stand out in an increasingly competitive market.
Then, you’ll need to consider how to enrich the raw data from each platform. This involves standardizing data then categorizing each data type. It’s a huge step in the underwriting process that is often underestimated. The length of time the process takes can be the difference between a merchant working with you versus working with one of your competitors instead.
Finally, you need to decide which metrics to calculate. This process will not only impact your Product, Data, and Engineering teams, it also places additional strain on already time-poor Underwriters and Account Managers.
How Codat helps
We built the Lending API to solve all of these challenges for you. The Lending API enables you to collect your merchants’ real-time data from the different platforms they already use, via a single link journey and collect this data on an ongoing basis.
The API automatically transforms your merchants’ raw data into actionable insights in the correct format for credit decisioning. Data is standardized, categorized, and all of the financial metrics, risk ratios, and eCommerce metrics are auto-calculated. Our models are trained on data from over 100,000 SMBs with a 99% categorization rate, so that you can approve more merchants faster and with greater confidence.
Over the past five years, we’ve helped many lenders from Pipe to Uncapped and Wayflyer underwrite tens of thousands of small businesses. To learn more about how we can help you, sign up for a free account or contact a Codat specialist by filling out the form below.
Get started today.