More Plans, Faster Growth?

Executive Summary

If you’re a monetization analyst, a new plan launch isn’t exciting. It means rerunning profitability, rebuilding plan mix, updating forecasts, and breaking historical comparability across your dashboards.

Every time I hear the team created a new product_id or price_id in Stripe, or launched a new plan, I know what it means: over the next few weeks, I’ll basically need to redo everything. Did you know that some companies end up with more than 100 product IDs (active or inactive)? On top of maintaining the taxonomy of product and price IDs, I also need to make sure the plan mapping stays consistent so analytics and reporting remain accurate.

After a few years of watching businesses grow, and watching the number of plans grow along with ARR and the customer base, I developed a hypothesis: the more diverse the plan catalog, the faster the growth.

This [secret] hypothesis is based on 26 apps I currently support, which isn’t statistically meaningful in any way. I also couldn’t find any studies or analyses on this, so I was happy to collaborate with ChartMogul, one of the largest analytics platforms for SaaS and subscriptions, and use their data to either prove or reject the idea. And magic happened! ✨

Below is my analysis of data from 8,000+ companies, including historical growth rates and plan data provided by the ChartMogul team. The dataset is anonymized, and it covers a wide range of businesses, from mature enterprise SaaS to small, fast-growing apps. The goal is to test whether fast-growing companies tend to maintain a more diverse plan catalog. I’ll look at how the number of plans relates to growth, and whether more plans are associated with faster growth, or whether fewer plans correlate with slower growth. This post walks through the data, the methodology, and what we learned.

Pricing Plans: More Options, More Problems

Before diving into insights, let’s talk about plans.

There are countless pricing plan options today - monthly, annual, quarterly, weekly, lifetime, and more. Some companies offer a single plan, while others maintain 20, 50, or even more. Pricing can be a simple flat fee, or it can scale with seats, active users, transactions, revenue, or other usage-based metrics:

Working with plan data isn’t easy

Consistent plan management is a pain for most SaaS businesses. Plans are typically created in billing or platform systems such as Stripe, Adyen, the Apple App Store, or Google Play, each of which processes payments, stores subscription data, and exports data differently.

If you offer multiple payment options (for example, iOS and Android apps), you need to merge very different data sources into one cohesive report. That report has to use consistent definitions for ARR, “active” customers, churn, and more, while still allowing segmentation by plan, billing period, price, and other attributes.

This almost always requires a sophisticated, flexible plan-mapping layer, for example, mapping a product_id from Apple to a price_id from Stripe, and translating price_564738589 or IDS_5668 into readable values like pro_49.99_7d_trial or lite_69.99.

If you’re working with intelligent AI systems, you also need to provide plan context, such as which plans are active in each region, which prices users see, how and when discounts are applied, which funnels each plan belongs to, and more.

All in all, reporting on something as simple and essential as plan mix isn’t easy. It requires infrastructure and ongoing maintenance. As an analyst, I can’t emphasize enough how much easier this becomes with tools designed to handle the complexity of subscription data. As you add more plans, it's important to keep plan taxonomy and configuration as clean and consistent as possible to make your analysis and reporting accurate.

Getting started: definitions, data, and methods

Definitions and metrics

• Plan mix: The set of active pricing plans a company offers and how customers are distributed across those plans.
• Plan length: The plan billing period (monthly, annual, 3-month, 6-month, etc.).
• Customers: Active paying customers currently subscribed to a plan.
• Tenure: Company age as observed in ChartMogul, specifically, the number of years ChartMogul has data for that company.
• ARR: Annual run rate as reported by ChartMogul.
• ARR growth per year (derived): ARR growth ÷ tenure.
• Customer growth (derived): Net new customers per month.
• Growth score (derived): A combined ARR and customer growth score per organization, weighted 60/40 toward ARR to avoid cases where customer growth is high while ARR is zero.

Data sample overview

• Total sample: 8,193 organizations.
• Organizations with at least one recurring plan: 6,875.

After normalization and removing organizations with missing or incomplete data, the final sample included 4,279 companies with the following ARR split:

Here is the plan mix across all these companies:

Overall, 54% of companies offer 10 or fewer plans, and 46% offer 11 or more plans.

I didn’t directly model plan mix, but it still matters when interpreting growth. The number of active plans usually increases as a company matures, improves customer segmentation, and becomes more advanced in how it monetizes. Teams can also expand monetization through add-ons, one-time transactions, and ads in freemium products, in addition to subscription plans. My analysis is limited to plan mix only, focusing on how many recurring plans are offered, their lengths, and their distribution.

Methodology

There are multiple ways to define growth. I started with ARR growth by calculating ARR growth per year for each company, then clustering companies into 5 segments (using k-means) based on their ARR growth.

Next, I did the same analysis for the number of customers, because in SaaS, revenue growth does not necessarily imply customer growth. Given the breadth of ChartMogul’s dataset, which includes SaaS companies of many types and sizes, we have cases of “rabbits” and “elephants,” and an entire zoo (in the sense of Christoph Janz’s 5 ways to build a $100M business). To account for this, I also created a new customers per month growth metric. As expected, customers' growth looks different from ARR growth:

In our sample, 47% of companies grow by more than +$270K ARR per year, but only 17% add more than 10 customers per month. This suggests either that the dataset is skewed toward companies serving large customers (“whales”), or that a meaningful share of ARR growth comes from expansion revenue, which often correlates with offering more plans!

To build growth signals that were comparable across companies of very different sizes and ages, I used both ARR growth per year (ARR growth divided by tenure in years) and subscriber growth per month (the net change in paid subscribers divided by the number of observed months in the dataset). I then combined these into a single score, weighting the metric toward ARR (60/40). This gave us a simple “velocity” measure: not what happened in one particular month, but the average rate of growth over the observed window.

Next, I bucketed companies into 5 clusters:

• Early breakout (“On fire!”): explosive growth, with more than +$330K/year in ARR and 6K–7K new customers per month.
• Fast-growing on track (“Winning the market”): strong growth, with around +$270K/year in ARR and 1K+ new customers per month.
• Mature but accelerating (“Hold” / “Don’t touch anything”): steady compounding, with an average of +$165K/year in ARR growth.
• Stagnant incumbents (“This doesn’t work”): low momentum, with less than roughly +$1.5K/year in ARR growth and fewer than 10 new customers per month.
• Declining (“Pivot deck incoming”): companies in contraction, with zero or negative ARR growth.

Caveats: Buckets were assigned using thresholds on a combined growth score (ARR growth per year + subscriber growth per month). There was one override: if both ARR growth and subscriber growth were non-positive, the company was labeled "Declining" regardless of its combined score. "Early Breakout" was reserved for companies that were not only high-scoring but also relatively young, since compounding rapidly early in a company’s life is meaningfully different from compounding rapidly after a decade of iteration.

What we learned from analyzing the plan data

1. More plans tend to reflect higher and faster growth.
2. Expanding plan offerings is positively correlated with higher revenue and larger customer growth. Companies that introduce additional plans often create more pricing and packaging paths, which can support both monetization and acquisition.
3. There are exceptions. Early breakout companies often demonstrate the fastest growth regardless of plan strategy.
4. Adding more plans may influence not only the magnitude of growth, but also the speed at which companies scale.

Let’s break these down.

1. The larger the ARR is, the more plans the company offers

But remember, correlation isn’t causation. The fact that high-ARR organizations tend to offer more plans doesn’t necessarily mean that adding plans increases revenue. Plan complexity may simply follow growth: as companies scale, they introduce tiers for segmentation, enterprise packaging, regional pricing, and bundles. In other words, plan count can be a result of momentum. To be more confident that plan count is a driver, we’d need to compare growth patterns across companies with different numbers of plans.

2. More plans correlate with higher growth across all company stages

Companies have very different growth rates, shaped by many factors, most of which we can’t quantify in this analysis. What we can do is bucket companies into groups based on growth (early breakout, fast-growing, mature, stagnant, and declining) and check whether the relationship between growth rate and plan count remains strong within those groups:

Clearly, both the average and the median number of plans are higher for fast-growing companies, regardless of company size or starting ARR. To be cautious, I report 2 plan-count metrics - one normalized and one non-normalized, because the distribution includes influential outliers. The relationship holds across most groups, with the exception of early breakouts, which is also expected: these are newer breakout companies with less tenure, so obviously, less time to expand their plan catalogs.

3. The more plans companies add, the higher the growth rate

Now, let’s see if adding more plans reflects a better growth rate:

The same pattern holds across most segments. Companies that continue adding more plans—including additional products and/or price points—tend to demonstrate higher growth:

The takeaway is that growth is influenced not only by having a diverse set of plans early on, but also by continuously introducing new products and pricing options over time. So interesting how fast-growing companies tend to add significantly more plans (more than 10) than stagnant or declining companies.

4. More plans impact the speed of growth

It’s also important to analyze growth in the context of tenure and speed.

For example, Company A with $1M of total ARR growth over 60 months grows at about $16.6K per month. But Company B may have $300K of growth in 6 months, which is $50K per month. Company B is growing faster, so it should be ranked higher on velocity.

That’s why this analysis focuses on growth velocity, not just overall growth. The goal was to understand whether offering more plans helps companies not only reach higher ARR, but also reach it faster.

To do this, I defined 5 personas (segments) based on: (a) ARR growth, (b) tenure, and (c) ARR growth speed:

• arr_growth_total = Max ARR – Min ARR
• tenure_months ≈ (Max Date – Min Date) / 30 days
• arr_growth_per_month = arr_growth_total / tenure_months

The last metric is the primary clustering factor. It reflects how quickly ARR is increasing, while still accounting for the magnitude of growth.

Because the sample is heavily skewed with many outliers, I did not use k-means. I wanted to compare growth speed vs number of plans across equal-sized groups, so I used percentile segmentation to create 5 clusters. I named them using a fast-cars theme (rumor has it the ChartMogul team likes Formula 1):

As you can see, the same pattern holds in this segmentation as well: companies with more plans don’t just grow more, they grow faster.

5. Other interesting findings

In addition to the plan and growth relationship, I also learned that:

• Growth is highly non-linear. Most companies cluster in slow-to-moderate growth, while breakout growth is rare. “Stagnant” is the most common failure mode.
• The strongest breakouts show both signals. ARR and subscriber growth reinforce each other. Seeing strength in only one is less convincing. The healthiest companies tend to be strong on both dimensions.
• Early breakouts are an exception. In the earliest breakout phase, companies appear largely “immune” to plan count. Plan strategy doesn’t show a clear relationship with either revenue or customer growth, and these orgs behave like outliers.
• High plan counts can also indicate price testing. And while more plans can reflect experimentation and iteration, this analysis is based on active plans only (not historical or retired variants).

Final thoughts

I’ve been looking forward to this analysis for a long time. Thank you to the ChartMogul team for supporting the work and sharing their data.

Plan expansion alone doesn’t create growth. But across this dataset, companies with broader plan catalogs tend to show stronger and faster growth. The healthiest businesses add subscribers without lowering revenue per customer. And good plan structure often makes this possible.

Plan mix, pricing structure, subscriber growth, and ARR are tightly linked—companies that grow faster tend to offer more plans that fit different customer types and needs, supporting better segmentation and more effective monetization.