Modash helps brands find creators, manage relationships, run campaigns, monitor content, and pay their influencer partners. Alongside its core platform business, Modash also sells creator data through its API.
The business is global and almost entirely recurring revenue, with customers split roughly equally between the US and the rest of the world.
Founded in 2018, Modash started using ChartMogul early in its journey when Founder Avery Schrader signed up in 2020. As the business grew, ChartMogul evolved from a simple reporting tool into the system CFO Nadim Taoubi and the leadership team use to connect product behavior to revenue and decide which initiatives are worth pursuing.
It’s been extremely reliable. As a CFO, it’s a tool I use on a daily basis.
From Stripe billing data to company-wide reporting
Each day, Nadim checks the top-level indicators in ChartMogul: how the company is growing, how many new customers have been added during the month, and what is happening with churn.
We don’t really go into Stripe to check the data. We get everything from ChartMogul.
Analysis that goes beyond tracking MRR, ARR, or a company-wide churn rate happens at least weekly. Nadim uses cohorts and segmentation to examine how performance changes over time and how different customer groups respond when Modash changes its product or customer experience.
Modash segments retention by customer profile, contract value, product category, and activation—including industry, ICP fit, Shopify status, and how many parts of the platform a customer uses.
That allows Nadim to move from seeing that churn changed to identifying which customer groups drove it, how their product usage differed, and what Modash could realistically do about it.
Adding product context to subscription data
Modash imports customers from Stripe to ChartMogul, with additional data coming from its own backend and other commercial systems.
With everything in one place, each customer record is enriched with information that can’t be found on an invoice: what kind of customer they are, which parts of Modash they use, whether they match the company’s ICP, and where they are in the activation journey.
That context matters because Modash is not a single-purpose product. Its platform supports several stages of an influencer program:
- Finding and evaluating creators
- Communicating and managing relationships
- Monitoring campaigns and creator activity
- Paying creators
Each area has its own activation point. A customer who only searches for creators behaves differently from one who also manages outreach, monitors an active campaign, or runs creator payments through the platform.
Modash’s analysis showed that customers using at least two areas of the product are more likely to remain customers and more likely to expand.
Each of these sub-products has different activation. We know that if we push customers to use two out of the four, they’re more likely to stay and more likely to expand.
From “happy churn” to repeatable activation
Initially churn was a challenge for Modash, but not always because customers were dissatisfied.
The product was initially built around creator discovery. A small e-commerce brand might use Modash to find 20 creators for a campaign, complete the search, and then no longer need the product. The customer received the value it came for; the use case simply had a natural endpoint.
If you have discovery only and you’re a small shop, it’s most likely that you’ll churn because you don’t need Modash anymore after month three or four. You’re done.
Many customers would later reactivate when they needed to find another group of creators.
As Modash expanded beyond discovery, it added features customers could use throughout the life of a campaign. The business began shifting from a tool for a one-off task to a platform embedded in the customer’s regular workflow.
By segmenting retention according to product usage in ChartMogul, the team found that the first three months were critical.
Customers who failed to adopt at least two parts of the platform during that period were significantly more likely to churn.
The first three months are critical. If we don’t manage to get the customer using these two services or products, there’s a risk we’ll lose them within the first three months.
The team also identified specific milestones linked to stronger retention, including sending emails through the platform and launching a first campaign. Rather than defining activation as a login or completed search, Modash could tie it to the point when a customer began running its influencer program through the platform—and measure how deeper campaign management affected retention over time.
Modeling which retention initiatives are worth pursuing
Finding a behavior associated with stronger retention is only the first step. Nadim also needs to understand whether changing that behavior would have a meaningful impact on the business.
Take Shopify customers, for example. Modash can compare the net MRR churn of customers who have sent emails through the platform with those who have not. Nadim then models what would happen if more Shopify customers reached that activation milestone.
I’m comparing the Shopify customers who have never sent an email with the ones who have. I take the net churn from ChartMogul and ask: if 100% of them used email, what would be the impact?
This helps Nadim separate an interesting correlation from an initiative worth investing in. If changing the behavior would reduce company-wide churn by just 0.1 percentage points, it may not justify significant Product, Sales, or Customer Success effort. A larger modeled impact gives the team a much stronger case for prioritizing it.
Business is a lot about priorities. Many things could have an impact, but you want to focus first on what will have the biggest impact.
For more detailed scenarios, Nadim exports the customer segments, cohort performance, and net MRR churn from ChartMogul and adjusts the assumptions in Excel and Claude. That allows him to model changes within a specific group rather than applying the same improvement across the entire customer base.
Putting retention into operation
The same retention data is also used to manage Customer Success performance.
Modash assigns larger customers to individual Customer Success managers and tags each account by portfolio in ChartMogul. The company can then track each portfolio’s retention and use that performance to calculate bonuses.
If you select a portfolio, you can see all of the customers that person has. We follow retention on the portfolio and calculate their commission.
Modash has a dedicated Customer Success platform, but calculates compensation from ChartMogul because the subscription and retention data is more current. Part of each manager’s bonus is therefore tied directly to the recurring-revenue expansion of the customers they manage.
Access to ChartMogul is open across the company. Finance and leadership use it most heavily, while Customer Success uses it to manage portfolios. Engineers may not open it every day, but they can still see how product adoption connects to retention and expansion.
Engineering is not using it on a daily basis, but they still have access because we want them to understand the impact they have on the business.
For Nadim, confidence doesn’t come from having another dashboard. It comes from being able to trace a change in churn back to a customer segment, identify the product behaviors behind it, and estimate whether changing those behaviors would have enough financial impact to justify the work.