Paddle
Identifying the right customer

The challenge: Identifying the right customer

As a FinTech company focused on SaaS, Paddle needs a clear idea of which markets and territories to go after and how many accounts are in-market that are true reflection of its ICP. With stringent rules on who can become a customer, it was a challenge for the company to reflect these requirements when quantifying future opportunities and coordinating its go-to-market efforts. Paddle needed a platform to accurately detect and refine a data set for both ideal and bad fit customers.

A limiting sales criteria 
Unlike most industries, the in-house rules for the right account are extremely stringent. Due to the nature of the FinTech sector, not only does a customer need to match Paddle's product capabilities, there’s also a necessary legal component placed within the process of identifying the right customers. This is a huge responsibility alongside trying to win accounts.

Despite clarity on who Paddle is unable to support from a product and legal standpoint, mapping out the market of "right customers" still proved to be a challenge. Historically, Paddle tried to identify customers using technographics and industry filters which led to false positives and negatives from mislabelled software companies, or poor technographic accuracy and coverage with other providers.

When and if Paddle has been able to identify a software company, the qualification becomes stricter; accounts are only eligible if its product is sold via online checkout which Paddle's infrastructure is built to support.

GoodFit data shows that 70% of software companies in-market today sell via offline invoices. As such, Paddle's team was losing time and money researching, marketing and selling to a lion's share of an ill-fitting market.
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We have strict criteria of who we can sell to; the checkout is the single most important thing for our product. Before using Goodfit we relied on  poor industry filters, often spending a lot of effort on accounts we actually couldn’t service.
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- Tharshan Thavaharan, Associate Director, GTM Strategy and Operations

Unclear Market Estimations
‍Not only did Paddle have specific rules to adhere to, it needed a greater understanding of how many accounts meeting the criteria existed, and how many net new accounts would emerge year-on-year.

Without this visibility, it was difficult to confidentially hire or scale commercial teams and a challenge to determine the product needed to adapt to expand its ICP and total addressable market moving forward. This means stagnated numbers, unusable accounts and under-serviced team members.

This is where Goodfit came in.

The solution: A truly refined approach

Defined customer criteria
For Paddle, industry filters and technographics weren't plausible in identifying accounts, BDRs were the first touchpoint in finding customers and very often the deciding fit came down to professional gut instinct; this isn’t scalable or referenceable.

Using Goodfit, Paddle worked to create scalability in two ways. First, they defined both good and bad fit accounts with the following criteria:
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- What needs to be true for an account to be accepted?
- What needs to be true for an account to be rejected?
- What are the data points that define these two?

A lot of people think having more is better. If you have hundreds of thousands of  accounts in your CRM that’s not necessarily a good thing in reality you know most of them are a bad fit. It’s much better to be refined and focused on a set number of accounts that are truly right for the company. This is what yields the best results.
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- Tharshan Thavaharan, Associate Director, GTM Strategy and Operations

AI-led Scalable Account Model
Now with the criteria defined, Goodfit built two natural language processing models (NLPs), the first to identify software companies, the second to identify whether a software company was selling offline via invoices. Now having trained the models with 1000s of accurately labelled companies, these NLP models flag when an account was a software company and used an online checkout at 80% accuracy.

Working with Goodfit, Paddle is now able to supply accounts to its Sales team with a trustworthy and benchmarked level of accuracy.
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The NLP models have truly helped to map the market for us, filters and technographics simply didn't work. Now, having actually gotten our hands on the true list of companies we can sell to, we were able to initially identify around 20,000 ICP accounts. Over the course of the year, the market has grown and GoodFit has provided an additional 9,000 net new accounts.
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- Tharshan Thavaharan, Associate Director, GTM Strategy and Operations

Improved Accuracy and Predictability
Using the NLP GoodFit were able to map the market of qualified customers for Paddle who "passed" both models. In the 12 months prior to working with GoodFit, only 30% of accounts being worked by the commercial team met the necessary product and legal requirements. In the month following GoodFits implementation, GoodFit grew this rate to over 70%.

Now with improved accuracy, clear mapping and net new accounts being created, working with Goodfit has provided overall trust in market understanding and account distribution for Paddle moving forward.
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Using Goodfit we were able to get back to basics of what needs to be true for an account to be the right customer and have the ability to reach out to them in an impactful way. This gave us focus, structure and a clear list of accounts to target.
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- Tharshan Thavaharan, Associate Director, GTM Strategy and Operations

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