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ChartMogul

chartmogul

This blog post is about how retired its last pieces of infrastructure on DigitalOcean, marking its migration to AWS as complete. ChartMogul The journey was not your regular AWS migration as it involved moving our infrastructure from classic VMs to containers orchestrated by Kubernetes. In a series of articles, we will share our experiences about: Our journey to AWS EKS (Kubernetes managed service). Some of the most critical roadblocks we encountered. Our current stack and tooling. Our infrastructure plans going forward, hoping they can be helpful to the entire community. Life With DigitalOcean Since our inception in 2014 and up to mid-2021, our entire infrastructure has run on DigitalOcean droplets (self-managed cloud virtual machines). We needed a cloud provider to get us off the ground quickly, reliably, and cost-effectively. DigitalOcean made a lot of sense and was a great choice. We are where we are because of them. That choice gave us the freedom to focus on product building without worrying about scalability and infrastructure complexity – aspects that typically kick in at a later stage. Every aspect of our infrastructure was provisioned, configured, and managed in-house. We used configuration management and Infrastructure as Code tools (Saltstack and Terraform) to manage things. We kept growing over the years, and by 2019 we found ourselves looking at a fleet of somewhere around 50 machines in constant need of management, software updates, security patches, and so on. And with new projects in our pipeline, we expected our compute power needs to double by the end of 2020. Why Move and Why Now? As great of a choice DigitalOcean was, our organic growth was pushing the boundaries of our setup over the years. We faced challenges with multiple areas, some fixable and preventable, some not. Various Failures Ad-hoc unannounced maintenance windows that suddenly broke production. Hardware failures on several occasions, affecting our primary – replica database setup (e.g., droplets entering a ”live migration state to other hardware machines” without notice, meaning 1-2 hours of downtime for that droplet.) Unexplained networking issues with latency between our machines that DigitalOcean’s support team never cleared out (this was critical for our Postgres read-replicas lag, Redis instances, and HA in general). AMS2 Region Deprecation Our DigitalOcean region (AMS2) was announced as “soon to be retired”, meaning limited support. We could not secure additional resources on-demand, and executing simple tasks usually meant long planning and wasted resources. Simple things such as upgrading a Postgres version and provisioning a new machine to perform a task were becoming impossible to do. Limited Hardware Choices Being in the subscription analytics space means data-intensive operations, large volumes, and the ability to often scale accordingly. Modern machines with more extensive hardware resources were only available in other regions. Network performance degradation was a frequent occurrence, and we soon realized that migrating to a different region was our best bet. Lack of Modern Cloud Features and Managed Services The volume of operational work to maintain our infrastructure to keep up with the growth rate (and deal with tech debt simultaneously) increased. We had to take a hard look at our setup and understand whether moving into a different DigitalOcean region or a new cloud provider was the best choice. Should We Stay or Should We Go? We started looking into the benefits of staying with DigitalOcean and simply moving to a new region – a more leisurely, quicker, cheaper, less painful option. But at the same time, we treated this move as an opportunity to modernize parts of our stack in service of expected user growth and an increased rate of progress. By the end of our assessment, we realized that specific must-have requirements would be hard to achieve by staying and simply switching regions. The most important ones were: Flexibility in auto-scaling compute resources. Managed databases. Provisioning of resources based on temporary usage. Low(er) latency. Service interoperability. Container-based infrastructure with Kubernetes orchestration. This list of requirements along with the challenges listed in the previous section tipped the scale in favor of switching providers. Why AWS? Choosing a new cloud provider to power ChartMogul infrastructure was a long journey. We researched the market and discovered many tradeoffs and advantages a new provider could bring to the table. Our options were Amazon Web Services (AWS), Google Cloud (GCP), and Azure. Ultimately, we decided to go with AWS. We list some of the main reasons below. Team Expertise We were already using some AWS services in production (e.g., S3 for storing incremental Postgres backups). More importantly, a few of our engineers had prior professional experience using various AWS services extensively in production systems. Scalability We can ramp AWS instances up or down at the push of a button. We can instantly provision resources like RDS databases and compute resources temporarily. We can iterate through experiments and proof of concepts quickly. The flexibility and scalability of Kubernetes node pools backed up by EC2 auto-scaling are hard to beat. Data Security and Compliance Data security has always been top of mind. Over the years, AWS security capabilities have grown substantially. The number of new services AWS developed around data security covers most of our needs in the container/Kubernetes space. They play nicely with well-established services such as private VPC isolation, fine-grain control of policies, and IAM roles. Compliance-wise, we plan to become SOC II certified asap, and we found AWS to be an advantage that can help fast-track that journey. compliance programs Managed Services Postgres is at the heart of what we do at ChartMogul, and we’ve typically spent a lot of time actively managing our database fleet of machines to support our growth. High availability and reliability of databases were becoming growing concerns, so we decided to evaluate multiple offers from major cloud providers with managed PostgreSQL. AWS RDS was the clear winner. Managed Kubernetes was another major factor to consider, and this was head to head with Google Cloud (GCP). Google’s managed Kubernetes (GKE) felt better than what AWS had at the time, but comparing RDS to CloudSQL wasn’t close feature-wise. Nowadays it seems that AWS is catching up with EKS however; We benefit from great RDS features such as snapshots flexibility, backup durability (with SLA), read replicas for Postgres, painless upgrades, dedicated IOPS, Cloudwatch metrics, Performance Insights, and the list goes on. The Insane Number of AWS Services At the time of writing, AWS offers over 200 services. Most of them give you the ability to get instant access to managed services from so many areas like compute, databases, data analytics, data warehousing, serverless, and storage. Our engineering teams can now leverage top-notch integrations to solve core problems quickly and prioritize buy vs. build where it makes sense. Disaster Recovery AWS cloud is an essential part of our Disaster Recovery plan. That’s because instances are easy to spin up, we can promote RDS read-replicas to primary at the click of a button, snapshots are a breeze, we can host in multiple regions, and we have a top-notch integration with our IaC tool of choice. AWS Credits We secured $100k worth of credits through the AWS Startup program. We were able to plan, test, and complete our migration without considerable expenses. Migration to AWS Our migration from DigitalOcean to AWS was a ten-month-long journey. The entire effort was backed up by volunteers from all of our engineering teams and driven by a DevOps engineer, a backend engineer, and our head of engineering. Some things involved trial & error. We tried multiple ways of: Moving data from Postgres to RDS with near-zero downtime. Moving our app and services from VM-based architecture to containerized ones in Kubernetes. Fundamentally changing the way we deploy. A perfect plan was in place, and everything looked good to go on paper, but we learned the hard way that things will not always go to plan. At times, our near-zero downtime migration goal was at serious risk, and back to the drawing board we went. Perseverance, drive, and fantastic team effort helped us overcome the challenges we faced. Careful planning did wonders too; Given our capacity, we established early on that breaking down the actual migration into three stages (or days) would work best. Week Prior D-Day Start Postgres replication from DigitalOcean to RDS instances. Review our AWS future production infrastructure. Configuration of secrets (AWS Parameter Store). Ensure CI/CD pipelines are ready to deploy to our new Kubernetes clusters. The Day Before D-Day Prepare our AWS temp webhook recorder infrastructure (losing events during our migration was not an option). Move some data in advance (e.g., DigitalOcean Spaces to S3). Update all Parameter Store secrets to production values. Prepare DNS changes. Set all Kubernetes deployments to zero pods to prevent services from accessing production data during migration. D-day: Flicking the switch Redirect all webhooks to AWS temporary recorder. Stop all the services on DigitalOcean. Wait for Postgres replication to catch up on the latest updates. Compare DigitalOcean and RDS Postgres data (to ensure integrity and replication catch up). Drop the subscription from RDS to Postgres running in DigitalOcean. Create RDS read replicas. Update our Parameter Store secrets with new RDS endpoints and secrets. Deploy to Kubernetes and restart PgBouncer to load new configurations. Switch DNS records for to AWS. app.chartmogul.com At this point, we were running our production workload on the shiny new infrastructure! We finished the whole thing in 10 hours (we initially estimated 8 hours – not too bad). Challenges With AWS The biggest struggle was with the DMS service (AWS managed service to move databases into RDS). It was not as easy to use as advertised. In our case with Postgres, it was not helpful. Eventually, we developed a custom way of moving data into AWS. We also came to the hard realization that moving databases with zero downtime to AWS with webhook support is complicated. We developed a custom approach to support this setup. More on these custom approaches in future articles. Future Articles in the Series Look out for future articles documenting our migration journey from DigitalOcean to AWS. We will touch on topics such as: Why we chose Kubernetes to power ChartMogul. How we migrated PostgreSQL to RDS. How we migrated our Rails app to Kubernetes. How we set up an IPSEC tunnel to AWS VPC.

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ChartMogul lost a large number of legacy customers when migrating them to a new pricing model, however, the highly engaged `good fit` customers enabled ChartMogul’s continued growth. Early Pricing Experiments ChartMogul’s pricing model was customer-based at the company’s inception. The price you paid for using CharMogul was dependent on the number of customers your business served. Even in the early years of ChartMogul, we encountered several challenges with customer-based pricing: We frequently couldn’t close deals without discounting.


We encountered a lot of friction with the entry-level pricing.


Paying customers that were growing into higher pricing tiers would reach out regularly to ask for discounts. Ultimately, we ended up with many customers having discounts and all sorts of individual agreements and a large number of plans. There was a fundamental lack of a price to value alignment. ChartMogul’s pricing page in 2017 The Decision to Move Away From Customer-Based Pricing In the summer of 2018, we conducted extensive research into alternative pricing models. The research concluded with a clear recommendation: revenue-based pricing would result in a better price to value alignment. The decision to migrate from customer-based pricing to revenue-based pricing was just a first step. We decided that after the release of revenue-based pricing to new customers, we would also migrate all legacy customers to the new pricing. To understand the impact of the new pricing on our current recurring revenue, we mapped the whole customer base to MRR bands. We did this by creating ranges in $10K MRR increments such as $10-20K MRR, $20-30K MRR, and so forth. This showed us the customer count in each band and what the total amount paid would be. ChartMogul’s pricing page in 2022** Before Migrating Our First Customer While preparing for the pricing migration, we determined that the account management team would take the lead on the project since we anticipated that re-selling and additional onboarding would be necessary to migrate some of these accounts successfully. We divided our customer base into groups, segmented by price and plan. Later, we prepared customizable messaging templates that reflected our company values like integrity and empathy. An Iterative, Empathetic Pricing Migration Process The migration process started out with the oldest legacy plans and the lowest paying accounts in them. We worked through migrations in batches and set a quarterly goal for the account management team. The batching helped us to tweak our messaging as we went along. We found that showing strong empathy in our communication and allowing the customer to be heard really helped to get the buy-in. In many cases, we were asking for a lot of extra money so we aimed to always identify opportunities to deliver more value and expand the usage of ChartMogul. Some discussions took several weeks of back and forth and occasionally involved the product and engineering teams. What surprised us most was that many customers accepted the new pricing with little to no resistance. Challenges Encountered A number of customers had strong emotional responses. Getting them to agree to the new pricing was a journey that required patience from both sides which at times resulted in several weeks of back and forth. When a conversation wasn’t going anywhere, myself or our CEO would get involved to help work towards a win-win solution. Some founders and executives responded better when talking to someone they perceived as an equal. In some cases, the price increases were extreme where a customer would move from paying something like $300 to paying $2500. We decided to be flexible when working with these types of high-value accounts. We developed a system we called gradual pricing alignment. Instead of asking a customer to pay the full amount right away, we would roll out the increase in increments over a more extended period of time. Letting Go of Bad-Fit Customers We determined during the pricing migration that some customers were simply not the right fit. Looking closer at these accounts, we found that we had been trying to keep some customers happy for years at great expense to our business. These accounts were leftovers from the early time of ChartMogul, when our positioning wasn’t that clear yet, which attracted all types of customers. Some had become bad fit customers over time. The gap between what ChartMogul could do for them and what they wanted to use our product for had grown significantly since signing up. When a customer had such a limited usage profile and no interest in expanding it, there was often no pathway to get the buy-in to our new pricing. 44.7% Account Retention is Not the End of the World To understand the result of the migration, let’s dive into one customer segment and look at ChartMogu’s Pro Plan customers that migrated to the new Scale or Volume Plans. In that customer segment, we lost 178 accounts. From 322 subscribers in September 2018, we went to 144 in February 2020. Account retention was 44.7%. Chart: Migrating from Pro Plan to Scale or Volume Plans – Subscribers** At first glance, this was a huge loss for the business. However, looking at MRR in that same segment tells a different story. We went from $57,640 to $85,838 and attained almost 148.9% MRR retention. Chart: Migrating from Pro Plan to Scale or Volume Plans – MRR** Moreover, these accounts expanded further since we completed the pricing migration in Q1/2020. Our Takeaways From the Pricing Migration All in all, the project took 1.5 years to complete. We consider the pricing migration a big success for several reasons. The remaining customers were primarily highly engaged, good-fit users that took full advantage of the platform. This made it easier for our success team to manage those customers. We saw our support volume relative to our customer number decrease. The pricing migration also helped our positioning. We learned that B2B SaaS was our sweet spot which impacted our product roadmap and marketing strategy. We gained a better understanding of our pricing power. Value-based pricing reduced friction for our sales team to convert leads to paying subscribers and our account managers to retain customers. My favorite part of the pricing migration was all the engaging conversations we had with our customers. These helped us build better, value-based relationships with them. Our team learned a ton about where our customers are realizing the most value. We gained a better understanding of our customers’ future needs which will empower our company to prepare for the future to grow alongside their changing needs. ChartMogul lost a large number of legacy customers when migrating them to a new pricing model, however, the highly engaged `good fit` customers enabled ChartMogul’s continued growth. ChartMogul ChartMogul Early Pricing Experiments Early Pricing Experiments ChartMogul’s pricing model was customer-based at the company’s inception. The price you paid for using CharMogul was dependent on the number of customers your business served. Even in the early years of ChartMogul, we encountered several challenges with customer-based pricing: We frequently couldn’t close deals without discounting. We encountered a lot of friction with the entry-level pricing. Paying customers that were growing into higher pricing tiers would reach out regularly to ask for discounts. We frequently couldn’t close deals without discounting. We frequently couldn’t close deals without discounting. We encountered a lot of friction with the entry-level pricing. We encountered a lot of friction with the entry-level pricing. Paying customers that were growing into higher pricing tiers would reach out regularly to ask for discounts. Paying customers that were growing into higher pricing tiers would reach out regularly to ask for discounts. Ultimately, we ended up with many customers having discounts and all sorts of individual agreements and a large number of plans. There was a fundamental lack of a price to value alignment. ChartMogul’s pricing page in 2017 The Decision to Move Away From Customer-Based Pricing The Decision to Move Away From Customer-Based Pricing In the summer of 2018, we conducted extensive research into alternative pricing models. The research concluded with a clear recommendation: revenue-based pricing would result in a better price to value alignment. The decision to migrate from customer-based pricing to revenue-based pricing was just a first step. revenue-based pricing We decided that after the release of revenue-based pricing to new customers, we would also migrate all legacy customers to the new pricing. To understand the impact of the new pricing on our current recurring revenue, we mapped the whole customer base to MRR bands. We did this by creating ranges in $10K MRR increments such as $10-20K MRR, $20-30K MRR, and so forth. This showed us the customer count in each band and what the total amount paid would be. ChartMogul’s pricing page in 2022** Before Migrating Our First Customer Before Migrating Our First Customer While preparing for the pricing migration, we determined that the account management team would take the lead on the project since we anticipated that re-selling and additional onboarding would be necessary to migrate some of these accounts successfully. We divided our customer base into groups, segmented by price and plan. Later, we prepared customizable messaging templates that reflected our company values like integrity and empathy. An Iterative, Empathetic Pricing Migration Process An Iterative, Empathetic Pricing Migration Process The migration process started out with the oldest legacy plans and the lowest paying accounts in them. We worked through migrations in batches and set a quarterly goal for the account management team. The batching helped us to tweak our messaging as we went along. We found that showing strong empathy in our communication and allowing the customer to be heard really helped to get the buy-in. In many cases, we were asking for a lot of extra money so we aimed to always identify opportunities to deliver more value and expand the usage of ChartMogul. Some discussions took several weeks of back and forth and occasionally involved the product and engineering teams. What surprised us most was that many customers accepted the new pricing with little to no resistance. Challenges Encountered Challenges Encountered A number of customers had strong emotional responses. Getting them to agree to the new pricing was a journey that required patience from both sides which at times resulted in several weeks of back and forth. When a conversation wasn’t going anywhere, myself or our CEO would get involved to help work towards a win-win solution. Some founders and executives responded better when talking to someone they perceived as an equal. In some cases, the price increases were extreme where a customer would move from paying something like $300 to paying $2500. We decided to be flexible when working with these types of high-value accounts. We developed a system we called gradual pricing alignment. Instead of asking a customer to pay the full amount right away, we would roll out the increase in increments over a more extended period of time. Letting Go of Bad-Fit Customers Letting Go of Bad-Fit Customers We determined during the pricing migration that some customers were simply not the right fit. Looking closer at these accounts, we found that we had been trying to keep some customers happy for years at great expense to our business. These accounts were leftovers from the early time of ChartMogul, when our positioning wasn’t that clear yet, which attracted all types of customers. Some had become bad fit customers over time. The gap between what ChartMogul could do for them and what they wanted to use our product for had grown significantly since signing up. When a customer had such a limited usage profile and no interest in expanding it, there was often no pathway to get the buy-in to our new pricing. 44.7% Account Retention is Not the End of the World 44.7% Account Retention is Not the End of the World To understand the result of the migration, let’s dive into one customer segment and look at ChartMogu’s Pro Plan customers that migrated to the new Scale or Volume Plans. In that customer segment, we lost 178 accounts. From 322 subscribers in September 2018, we went to 144 in February 2020. Account retention was 44.7%. Chart: Migrating from Pro Plan to Scale or Volume Plans – Subscribers ** Chart: Migrating from Pro Plan to Scale or Volume Plans – Subscribers At first glance, this was a huge loss for the business. However, looking at MRR in that same segment tells a different story. We went from $57,640 to $85,838 and attained almost 148.9% MRR retention. Chart: Migrating from Pro Plan to Scale or Volume Plans – MRR ** Chart: Migrating from Pro Plan to Scale or Volume Plans – MRR Moreover, these accounts expanded further since we completed the pricing migration in Q1/2020. Our Takeaways From the Pricing Migration Our Takeaways From the Pricing Migration All in all, the project took 1.5 years to complete. We consider the pricing migration a big success for several reasons. The remaining customers were primarily highly engaged, good-fit users that took full advantage of the platform. This made it easier for our success team to manage those customers. We saw our support volume relative to our customer number decrease. The pricing migration also helped our positioning. We learned that B2B SaaS was our sweet spot which impacted our product roadmap and marketing strategy. We gained a better understanding of our pricing power. Value-based pricing reduced friction for our sales team to convert leads to paying subscribers and our account managers to retain customers. My favorite part of the pricing migration was all the engaging conversations we had with our customers. These helped us build better, value-based relationships with them. Our team learned a ton about where our customers are realizing the most value. We gained a better understanding of our customers’ future needs which will empower our company to prepare for the future to grow alongside their changing needs.

Alternative Pricing: Letting Go of Bad-Fit Customers is For the Best

Customer churn rate, also known as logo churn rate is a key indicator of the health of your SaaS business. At a high level, the gives you the rate at which your customers are canceling their subscriptions. customer churn rate A high customer churn rate can be deadly for your business. In this post, we analyze, anonymized and aggregated data from to find out what is a good target customer churn rate for a SaaS subscription business. ChartMogul We also discuss why it is important to track customer churn rate and the underlying reasons behind a high customer churn rate. Monthly Customer Churn Rate by ARPA & MRR Bands The chart below shows the median Customer churn by a companies average revenue per account (ARPA) per month. As a reminder, ARPA is the average revenue per account i.e. average MRR across all your customers. It is also known as ARPU or APRC. You can find your ARPA within your . ChartMogul Dashboard As you’ll notice, the customer churn rate for the median company varies by the ARPA band you are in. For companies with ARPA per month in the range of $0-10, the median monthly customer churn rate is 6.8%. It decreases to 1.9% as your ARPA per month increases to >$500. In addition, to the median, the chart below also shows the 25th and 75th percentiles of the customer churn rate. The trend remains the same. For companies with lower ARPA, the customer churn rate is higher while for companies with higher ARPA the customer churn rate is lower. The best companies should target a customer churn rate of <2% per month. This goes down to <1% as your ARPA increases. Below is the data for monthly customer churn rate split by monthly recurring revenue (MRR) band. As you’ll notice, the monthly Customer churn rate of a median company is higher in the initial stages of the business (6.8% for <$10k MRR, 4.6% for those with MRR b/w $10-50k). As more companies find product-market fit and hone into their customer category the churn reduces. Down to about 3.5% per month. Although it then stabilizes at that level and doesn’t reduce even when a company grows. The chart below also shows you the 25th and 75th percentiles of the customer churn rate by MRR band. The best companies should target a monthly churn rate <2%. Calculation Methodology. Monthly Customer churn rate is calculated as the ratio of the number of customers who churned in the month (excluding any customers who both joined & churned in the same month) / Total number of customers at the start of the month. We calculate the aggregates over a 3 month period (Nov ‘21, Dec ‘21 & Jan ‘22). We use the for all companies for easier comparability. standard B2B churn formula Why Should You Care About Customer Churn Rate? Customer Churn Rate is helpful because it gives you: Quick feedback — compared to other cohort-based metrics such as Net or Gross dollar/customer retention, churn gives you immediate feedback. You can run tests on your platform and then see feedback in a few days or months. Easy to measure — the formula for customer churn rate is straightforward and easy to measure. Also, it is relatively hard to game this metric. If your customer churn rate is high that means that i) You’ll not be able to compound your user base and hence your revenue, and ii) The money you just spent to acquire your customers i.e. the customer acquisition cost (CAC) goes down the drain sooner. Churn is deadly. A monthly churn rate of 5% corresponds to an annual churn rate of 46%. That means you’ll lose close to half your existing customers in the next year if you don’t contain your churn. Here is a helpful table that helps you look at yearly churn corresponding to a particular monthly churn number. The formula we use to get the annual churn rate from monthly churn rate is the following: Annual Churn Rate = (1- (1-Monthly Churn Rate)12) Why Do You Lose Customers, Anyway? Customers leave you primarily because they don’t see value in your product. The underlying reason as to why they don’t see the value could be threefold: They haven’t been onboarded well: They can’t get to the ‘Aha’ moment where they get to see the magic of your product. Your product is too complex to understand or they are not sure of what job your product accomplishes. Support docs are not easy to understand. Customers are searching for an answer and they just can’t get it. When trying out a new product what you don’t have is time/patience. Price doesn’t correlate with value — even if some of your customers see the value in your product, they can get more value for the same price elsewhere. Hence they churn. Wrong customer segment — Maybe they weren’t the right customer segment who should be using your product in the first place. Your marketing strategy needs to change in this case. And probably you’ll need to too. fire some of your customers Unrelated to your product value, there is also some Involuntary churn that happens. This is because of card expiry, declined payments, etc. Customers also leave because they get acquired, and their new home no longer uses your product. Consider digging deeper to get the real reason behind the churn. A way to do this is to talk to your customers. Find out why they churned? Was it because of your or was it because your product was missing a key feature. And then test your hypotheses (eg. features, pricing, plan). Repeat. pricing

Why Does a 'Good Customer Churn Rate' Matter?

Tring Tring. You just received a notification from ChartMogul. A new customer signed up. Oh yes! Your monthly recurring revenue (MRR) just shot up. Tring Tring. Another notification. A customer just canceled. Oh no! Your MRR went down. Tring Tring. A customer just upgraded their plan from basic to premium. Yes! MRR goes up again. You get the point. Your monthly recurring revenue (MRR) moves every day. It’s because of all the underlying dynamics of customers joining, upgrading, canceling, or downgrading. Understanding and being on top of all these MRR movements can sometimes get confusing. To simplify this a bit, you can think that your MRR movements only have two components: MRR Added — i.e. the revenue that your business added in a particular period MRR Lost — i.e. the revenue that your business lost Below is a screenshot of the MRR movements chart in ChartMogul. The blue bars show the MRR added while the pink ones show the MRR lost. If your goal is to grow your business, you need to add more MRR than you lose. So the blue bars need to add up to more than the pink ones. Understanding MRR Movements Let’s dive deeper into how your SaaS business adds or loses MRR. And how those changes are categorized in SaaS as MRR movements. You can add MRR to your business by: Attracting new customers — in SaaS jargon, this is called a New Business MRR When existing customers pay more — this is called Expansion MRR Re-attracting ex-customers — Reactivation MRR And, you can lose MRR when your: Customers cancel — Churn MRR Existing customers start paying less — Contraction MRR So that’s how you lose or add MRR to your business. Now that you have a basic understanding of MRR movements, let’s look at these metrics for an average startup. Benchmarking MRR Movements by MRR Bands MRR Added Benchmarks As we discussed above, MRR added is made up of three components: New Business, Expansion & Reactivation We analyzed, aggregated and anonymized data from ChartMogul to find out how MRR movements split up across these three sub-components. The chart below shows new business MRR as a percentage of MRR added. As you would expect, new business makes up the largest component of MRR added for most businesses. Specifically, for startups with MRR <$10k, new business MRR makes up for 77.7% of their MRR added. As businesses grow, the proportion of revenue generated from new business decreases. It goes down to almost half (50.7%) for startups with MRR >$1m. This is because as SaaS businesses grow, they focus their energy on increasing revenue from their existing customers. Hence, the proportion of expansion revenue as a percentage of total MRR added increases. As you’ll notice in the chart below, startups with >$1m in MRR, generate 36.1% of MRR added from expansion activities. This is the classic “land and expand” strategy that many SaaS businesses employ. The third component of MRR added is reactivation. This component remains roughly stable throughout business growth, contributing to 9-13% of MRR added. MRR Lost Benchmarks Now, let’s move on to MRR lost. As a reminder, MRR lost can be grouped into two components: Churn and Contraction. The chart below shows Churn MRR as a percentage of total MRR lost. Churn makes up for the bulk of the MRR lost — roughly 70-80%. Here too, as the business grows, contraction MRR as a percentage of MRR lost increases in proportion. Although, it does eventually stabilize around that 30% range. Benchmarking MRR Movements by ARPA Bands In the section above, we looked at MRR movements by the MRR band. In this section, we look at MRR movements by bands of average revenue per account (ARPA) per month. As a reminder, ARPA is the average revenue per account, i.e. average MRR across all your customers. It is also known as ARPU or APRC. You can find your ARPA in . ChartMogul Why look at MRR movements by ARPA band? Well, at a given ARPA value. The way you sell, the way you service, and the way you retain all depend on your ARPA. everything is sort of the same MRR Added Benchmarks by ARPA Bands The chart below shows new business MRR as a percentage of MRR added across bands of ARPA per month. As you’ll notice, here too, new business MRR makes up the largest share of MRR added. Although, at higher ARPA, this proportion does get smaller. This is because, at a higher ARPA, expansion starts to contribute more to the MRR added. For example, companies with ARPA per month of >$500 or annual contracts over $6k get 43.7% of their revenue through expansion. For companies with an ARPA of less than $10, it’s only 7.9%. The reactivation proportion of MRR also depends on your ARPA. At a higher ARPA, your reactivation MRR decreases. One reason for this could be that at a higher ARPA, . And because of that informed decision, fewer come back. fewer customers take the knee-jerk reaction of churning MRR Lost Benchmarks by ARPA Bands As for MRR lost, Churn is the largest contributor. Although, for startups with ARPA >$500, churn contributes to only a little more than half of the revenue lost. The rest is contraction MRR. If your business is in a higher ARPA band, retaining existing customers and making sure they are using your product becomes of key importance. Calculation Methodology. We used anonymized and aggregated data from ChartMogul’s platform to carry out this analysis. We categorized companies into MRR & ARPA bands and then calculated aggregates over 3 months (Dec ‘21, Jan ‘22 & Feb ‘22). Three Questions You Should Ask Yourself Now that you understand the basics of MRR movements, there are three questions that you should ask yourself: Do you understand the dynamics of MRR movements for your particular business? For example, do you know how much of your MRR added comes from new business vs expansion or reactivation? Do you have an expansion loop built-in within your product? As businesses grow, one of the key drivers for their growth is expansion revenue. Does your pricing strategy take this into account? Do you have a strategy in place to contain contraction? Contraction can be as high as 45% of all MRR lost at higher ARPA’s. Having a strategy in place to contain that could help you grow faster. As is wisely said, MRR Saved = MRR Gained :)

How to Take a Hold of Your MRR Movements

Over the last six months, we built and refined a dedicated outbound sales strategy. The result? A swath of data and learning, but, ultimately, the decision to pull the plug. This is the story of our most comprehensive attempt at outbound which was quick to remind us that 1) every business is different and 2) sometimes dedicated planning and grit still result in failure. ChartMogul is a product-led business. We spend most of our waking hours thinking about how to build a reliable, beautiful product that helps subscription businesses grow faster using their customer and billing data. This mentality extends, of course, to our marketing strategy, which is why folks still print out and read . our cheat sheets our weekly roundup It also extends to our customer acquisition and sales strategy. More than 90% of our leads come through our 14-day trial. Then, it’s our job to help those businesses get set up, evaluate our product, and ultimately, make a purchase decision. We’ve grown sustainably for six years in this way. Enter: the allure of an outbound sales motion. In a recent completed in Q2 of 2016, less than 20 percent of mid-size B2B companies relied on solely inbound or outbound strategies; 84 percent of respondents used some mix of both. Act-On and Demand Metric study We know who our prospective buyers are, what is important to them, and how to engage them — after all, we’ve been working with SaaS founders and operators consistently for some time. And importantly, our retention rate for these customers is high. What would it mean for our business if we could crack outbound sales? If we could reliably generate new opportunities and onboard right-fit customers? Could introducing a new sales motion accelerate our ability to hit our next few revenue targets? Our outbound sales strategy We set a plan to find out. To properly test outbound, we knew that we needed the following: Hungry salespeople trained on and devoted to outbound efforts Appropriate process and tooling Target accounts and reliable contact information Personalized, on-brand messaging that illustrates how and why we’re the right subscription analytics partner. We hired two SDRs (Leo and Verena), trained them on our value proposition, outreach style, and strategy. We consumed the latest and greatest prospecting content from sales leaders like and . We even took Winning by Design’s 4-week course on prospecting. Becc Holland Josh Braun We decided to focus our efforts exclusively on our best-fit customers: B2B SaaS. We curated a list of these businesses and researched their pricing and billing strategies. Notion housed a dedicated outbound sales playbook, complete with best practices, templates, and creative ideas. Thomas, our senior sales lead in EMEA, that automatically sourced contact details for qualified prospects at those businesses. built an impressive process and toolset Before we started, we mapped out the success criteria for measuring outbound — we required consistency, strong sales qualification, and larger opportunities (±2x ASP because many ChartMogul customers pay $100-$250/month). Our SDR team strived to contact 40 new accounts/week with highly personalized 1-1 messaging. What worked with our strategy So, how did it go? The energy invested in personalization proved fruitful. Prospects were receptive. Our email outreach yielded meetings, and we were able to create qualified opportunities. We coupled email outreach with social, and Twitter proved a hearty avenue for conversations with SaaS founders. Many SDR teams strive to book meetings, and while that was a top priority for us, we also encouraged prospects to trial our product. This is because many of our buyers prefer to evaluate the capabilities of our product without sales assistance — and ‘being aligned’ with prospects is a core value of our sales culture. We know that trial users with imported data convert to paying customers at a high rate, so getting prospects to try the product is half the job. We were hoping that an outbound strategy can help us add even more fuel to our . product-led motion Through effort and determination, we managed to get new users on the product, reactivate old trials, book discovery calls, create opportunities, and win new customers. Importantly we did this in a way that was true to ourselves without burning our brand. What didn’t work so well… Unfortunately, we weren’t able to win enough customers to consider the program a success. In just under six months, we were able to: Set 39 total meetings set from an average of 1,650 emails sent/month (~10K emails sent total) Create 13 deals (which is less than 1 deals created/month/SDR) Convert 7 new customers (which is less than 1 sales/month/SDR) at a win rate of 54% and ASP = 1.5x You can see that the funnel breaks down from outreach to meetings booked. To counteract this, we increased the volume of outreach after several weeks. Still, we were unable to get enough consistent meetings to ensure the program’s success. I have a few hypotheses why the program didn’t work as we were hoping it would… though it’s most likely a combination of all of the following: B2B SaaS founders are the recipients of lots of outbound, so it’s perhaps a less effective strategy than it was five years ago Our messaging and timing was weak The order/sequencing of our outreach was lacking Prospects on our target list are satisfied with their current state Our segmentation was too broad What’s next? Continuing the program could mean winning a new large customer here and there, but the allure of outbound promises consistency. And we must employ sustainable strategies that give us energy and keep us focused. This is, of course, a bit frustrating, but our dedicated attempt was comprehensive enough to learn that outbound sales is not a viable GTM strategy given current inputs. And maybe that’s true for you too. We hear a lot about the importance of focus for product teams, but rarely for commercial teams. Sales and marketing teams are all too often pursuing a quick win or growth hack instead of sustainable growth via select channels. It’s essential to keep an eye on your limited resources and use them in the best way possible. For now, we’ve decided to retire these efforts and turn our focus on opportunities with potential for viral results. Stay tuned!

Outbound Sales Strategy and Why We Decided Against It

Pricing localization is a strategy where you present the price of your SaaS differently depending on where the customer lives. You may also hear it referred to as multi-currency pricing or localized pricing. If I asked you how much a house cost, could you give me an answer? The answer is likely no because there’s no such thing as a universal price, even for an identical house. You’ve heard it before—location, location, location. A similar property has a different price tag in Toronto, Paris, São Paulo, Cairo, Seoul, and Bangalore. The same could also be true for your SaaS business. In our , we detail the importance of SaaS pricing and how to get it right for your audience. We also shared that revisiting your monetization strategy is an essential part of growing your business. While some companies might want to stick with the rates that work right now, there may come the point where you can’t rely on growing your market share at the same rate as your company’s early days. B2B SaaS Pricing Masterclass One way your pricing can change as your company evolves is by using multi-currency pricing. Here’s what you need to know about SaaS pricing localization. What is pricing localization? Pricing localization is a strategy where you present the price of your SaaS differently depending on where the customer lives. You may also hear it referred to as multi-currency pricing or localized pricing. Even the simplest pricing localization method requires new workflows and considerations, so it’s understandable if you’re apprehensive about jumping in feet first. Here are some of the benefits of SaaS pricing localization that can make the transition worthwhile: Potential customers may feel more comfortable when they browse your pricing page and see their local currency Optimizing pricing for different countries could help you grow your market share in a region Experimenting with localized pricing lets you find the ideal strategy for each country and increase revenue 2 types of pricing localization Not all pricing localization strategies are created equally. There are two to choose from, and they each come with their benefits and challenges. Change your display currency The first and most straightforward type of pricing localization is where you only change the currency shown. This localization strategy is also called “cosmetic” localization. When you use this strategy, customers avoid any sticker shock at checkout or after payment since they see the price they’ll pay upfront. Having local pricing also makes it easier for potential customers to imagine the cost in their budget without mental math for the exchange rate. For example, shows pricing in local currency for select countries. The America pricing page has plans starting at $99 while French browsers see prices from €99. In this scenario, the pricing strategy is the same, and the only variable is the currency that customers see and use. Hotjar The benefit of cosmetic pricing localization is how simple it is to implement. There’s no need to adjust your pricing strategy or tiers—simply update the currency based on the person’s location. This pricing localization strategy works best when the exchange rate and cost of living between the base currency and display currency are similar. Change your product cost The second type of SaaS pricing localization is more strategic than a simple cosmetic change in currency. Instead, you can customize your pricing based on your customer’s cost of living and purchasing power and have “true” localization. A country or region with a higher cost of living or exchange rate closer to your home currency has a similar or identical price to home. A country with a lower cost of living or buying power pays a proportionally smaller price. Netflix uses a true pricing localization strategy to the fullest extent, with special pricing for . Monthly pricing varies by location and is either displayed in local currency or USD. If you ever wondered what Netflix costs in Japan, Afghanistan, Swaziland, you can . many of the 190 countries it serves browse pricing here Wondering how SaaS companies come up with these local prices? While we don’t have the inside scoop on Netflix’s exact pricing strategy, you can use the . Yes, that’s the real name. The Economist compares the price of a Big Mac across the world using the same base currency, like USD. You can to compare pricing using your base currency. Big Mac Index use this tool Here’s an example. Imagine your entry-level plan is $10 a month USD, and you want to localize to Russia. If you use the proportion for the current Big Mac Index ($5.67 in the US vs. $2.20 in Russia), you would divide your base price by 2.5 (the Big Mac ratio) for a Russia-localized price of $4 a month USD. While customizing your SaaS pricing based on the cost of living is more work than changing the display currency, it could be the way to go for some regions. If you want to expand to markets with very different economics than your own (whether they have more or less purchasing power), true pricing localization makes you a more accessible option. Pricing localization may even give you a competitive advantage if you’re the only option doing it. Though it might be hard to quantify, it’s also possible that customizing a sales page to a person’s location could feel more personal. How to decide when to use localized pricing for your SaaS If you’ve read this far, chances are you’re curious about trying SaaS pricing localization on for size. Here are a few questions to ask yourself to see if it’s time to experiment with multi-currency pricing. If pricing plays a critical role in your positioning Before you start changing your pricing, you need to think about your customers in each region. For subscription apps like Headspace, a global audience is part of their growth ambition. , former Product Marketing Lead at Headspace and how it aligns with their goals of bringing mental health to the world. Cristina Poindexter talked about price localization strategies Her recommendation is to begin your pricing localization discussions with positioning. Talking about whether you want to be a premium product or mass-market solution upfront makes every other pricing localization decision easier. If you want to hear more about Headspace’s experience with pricing localization, . this discussion in partnership with Google Play is worth watching If you’re at a stage where you want to appeal to as many SMBs as possible, then a competitively low price could be ideal. In that case, true pricing localization that makes your cost attainable in your target countries could be the way to go. On the other hand, there can be such a thing as a “too low” price for enterprise customers if it makes them feel that you can’t handle their scale. If a higher price is essential for positioning, a less complicated cosmetic localization strategy offers easier customization. It’s also worth noting that it’s easier to lower existing prices than raise them, so experimenting with higher prices is worth exploring. If you have (or want) a global audience Until you have a solid pricing strategy at home, you likely don’t need to sweat the small details of global pricing localization. Earlier stage companies that focus on capturing more of their existing potential TAM (total addressable market) in one country or region can skip pricing localization for now. If you want to grow in a specific country or notice more and more customers from a region, then you can consider localized pricing. When in May of 2021, , founder of , commented on how the change would benefit global creators. Stephen shared that, “the hope [with localized pricing] would be that this ultimately makes it possible for MORE creators to make revenue on Twitch and more fans supporting their fav creators.” Twitch announced localized pricing Stephen Ellis Pipeline If conversion rates are much lower for a country or region Maybe you’re here because you’ve realized that as hard as you’re trying to break into a new market, you just aren’t gaining the traction you’d like to see. It’s possible that pricing could be to blame, particularly if it’s a country with a vastly different cost of living. Pricing alone isn’t necessarily the culprit, but something to experiment with. Trying price localization could also help if you want to grow in a region where you’re nearing the ceiling of the total addressable market. Rather than adding new customers at the same rate as you used to, you could try customizing pricing to reach your revenue goals. ChartMogul can help you understand your customers and where they are hailing from and answer questions like: Which regions or countries have the highest Average Revenue Per Account? Where is churn most prominent? What to expect when you take on multi-currency pricing Your wheels are turning now, and you think you want to pitch the idea of pricing localization to your team. The only problem? It’s going to be a team effort. Implementing a new and variable pricing strategy comes with many moving parts, and everyone has a role. Your marketing team needs to manage positioning Questions about the impact of pricing on your positioning will likely go to the marketing team. They’ll need to research the market you want to enter (or optimize) and understand how pricing affects decisions. Questions for your marketing team to explore: How are competitors pricing their tiers in the country? What do buyers in this country look for in a SaaS? Does our messaging need to change alongside the pricing update? Your product team needs to customize displays by location If your website is currently the same wherever you access it, some updates to the pricing page are necessary. The simplest route would be creating a guide where people can browse pricing by country . To make it seamless, though, you’ll probably want your pricing page to update based on a person’s location. , as Netflix has Questions for your product team to explore: How many versions of the pricing page will we need? How will we update pricing based on location? Does pricing localization also impact other steps in the checkout and billing process? Your finance team needs to manage multi-currency invoices Unsurprisingly, your finance team will probably have the most adjustments to handle when switching to localized pricing. While you’ll still manage your accounting in your home currency, things like invoices and typical payments will vary between customers. Questions for your finance team to explore: How will we manage multi-currency invoices, contracts, revenues, and payments? What tools will help us manage conversions between currencies? Are there any tax or accounting implications to managing multiple currencies and countries? Your sales team needs to monitor fluctuating MRR In ChartMogul, you can set your primary currency, whether it is EUR, USD, or the currency where your business is headquartered. When ChartMogul comes across an invoice that is billed in a different currency, it will automatically convert the MRR amount to your account’s primary currency. Your ‘Primary currency’ is the currency in which your revenue is reported in your ChartMogul account. We found that this is the most common and useful way in order to track subscription metics. If your global customers pay different amounts, our projections and reporting will vary. Even if you go the simpler route of “cosmetic” localization, you still need to manage exchange rates. This means that metrics like your MRR will fluctuate, even if there aren’t actual changes in accounts. Questions for your sales team to explore: How will we adjust reporting and analysis to account for exchange rate fluctuations? Do we need separate goals and benchmarks for each region? How do we expect pricing to impact close rates? ChartMogul helps you manage global subscriptions You can’t manage what you don’t measure, but luckily for your teams, ChartMogul makes it easy to monitor global subscriptions. Our subscription analytics platform helps you measure, understand, and grow your recurring revenue. There are also features to help you manage exchange rates, so you have an accurate view of MRR and change your base currency at any time.

SaaS Pricing Localization: How to Do It the Right Way

To my fellow SaaS bootstrappers still relying on a creaky home-grown metrics system — or worse, not having access to any metrics at all — I recommend you start using to get accurate insights into the health of your product. ChartMogul I’m a ChartMogul customer, and I’m a bootstrapper. I’m the founder of Feature Upvote, a bootstrapped and profitable SaaS. As a cash-strapped bootstrapper, if I spend money on a service, it has to be . I need to choose wisely where I spend my company’s limited funds. Because, as 100% owner of my company, the money I spend is money coming out of my own pocket. essential So why do I spend some of that precious, limited money on ChartMogul? Before ChartMogul: Home-grown Metrics In the early days of my business, I wrote my own system for calculating metrics. I say ‘metrics’, but it was really just the singular ‘metric’ of MRR. I only cared about MRR because I hadn’t yet learned about other important SaaS metrics such as Churn and Lifetime Value. MRR was what many bootstrappers were sharing on Twitter and Indie Hackers. Calculating MRR seemed easy, something I could do with one SQL query and a basic web page. And it was easy — when I had just one price point and a few customers. But as my business got traction, pricing got more complex. There were discount plans, coupons, and refunds. My SQL query to calculate MRR was becoming complex and indecipherable. I also had no way to view history and trends. Knowing MRR alone is not useful if I can’t compare it to MRR from a month, six months, or a year ago. Then I added multiple currencies. That completely broke my home-grown metrics. The data it showed was simply wrong. But I didn’t want to spend time maintaining it. I was in the unpleasant situation of having to make business decisions with little data, and what data I had was wrong. As my fellow bootstrappers will know, you always have too many things to do. It is hard to find the time to maintain an internal system used only by the founder. It is the type of work that always finds itself pushed off the to-do list. Discovering ChartMogul’s free plan I read a blog post from another bootstrapper that featured an attractive chart of their MRR growth over time. This chart was generated by ChartMogul and it was awesome! I wanted one for myself! So I took a look at ChartMogul. I instantly realized this was so much better than what my home-grown solution ever could be. Even better, because my MRR was below $10K, I could use ChartMogul’s free plan. More metrics, attractive charts, and free? How could I use ChartMogul, at least until Feature Upvote grew out of the free tier. not Best of all, unlike my homebuilt system, the data was actually accurate. What I initially learned from ChartMogul ChartMogul has excellent that explain what SaaS metrics are and how I can use them. From them I learned that SaaS metrics are more than just MRR. free resources Even just the way MRR is reported in ChartMogul was eye-opening. It breaks down my MRR growth into “New Business” MRR and “Expansion” MRR. This concept of expansion growth was new to me. I soon learnt that it is important for success in SaaS. Expansion revenue is revenue growth that comes from existing customers when they upgrade to a more expensive plan. If your product doesn’t yet have a pricing model that allows your happy customers to give you more money/month, I highly recommend adjusting your pricing model. I discovered that by accident, I had baked into my pricing model. expansion revenue Another SaaS metric that ChartMogul gave me was , a metric I now watch as closely as MRR. This shows how quickly I’m losing those hard-won customers. In the early days, this churn was way too high for my company to ever be sustainable. I got churn under control, partly by improving my product, support and docs, but mostly by increasing prices. Gross Churn Rate $10K MRR: Time to start paying Happy day! I got a notification from ChartMogul telling me I had hit $10K in MRR. But also a sad day, because now I would no longer be able to use ChartMogul for free. My first impulse was to cancel my ChartMogul account. I was still cash-strapped, with too many competing demands on my limited revenue. Paying money to see some pretty charts each month didn’t seem to be a sensible business decision. And yet, I realized it was much more than pretty charts. I had come to rely on the information that ChartMogul gave me. It had become essential in my decision-making. To save money, I resurrect my home-built SaaS metrics system, and devote some time to make it accurate and show more metrics. But when I estimated how long that would take, it seemed like a silly thing to do, when I could instead pay ChartMogul to keep getting the information they were already providing. could In the faced by SaaS founders, buy was the right decision in this case. age-old conundrum of buy-vs-build Decisions I make using ChartMogul I want to share how spending money on ChartMogul has helped me, financially and psychologically. When to pause Google Ads Last year I started running my first Google Ads campaign. After two months of learning and tweaking, Google Ads started to give me new paying customers. But were they paying enough to justify the ad spend? What I needed to know was my “Customer Lifetime Value” or LTV in ChartMogul. I discovered that it wasn’t high enough based on my cost per acquisition from Google Ads. So I paused the ad campaign until we could improve our LTV. Knowing that I should pause the Google Ads campaign saved me way more than I was paying ChartMogul. From that decision alone, my ChartMogul subscription has been worth it. Once our LTV improved, I restarted my Google Ads campaign, confident that doing so was now worthwhile. How did I increase LTV? By increasing prices, which I’ll tell you about further down. Focus on the long term More than I like, my self-esteem as a bootstrapped founder goes up and down with MRR growth. Month-over-month growth greater than 10%? I’m the champion of champions, I tell myself, a true business visionary! But growth only 1% or 2% this month? What am I doing pretending to be something I’m not? Clearly, I have no business acumen at all and should start checking the job ads. A way I’ve found to deal with the yoyo-ing of my business self-belief is to look at longer-term trends in ChartMogul. One way is to compare metrics year-on-year. ChartMogul does this comparison with many metrics by default. This long term view keeps me relatively calm. Another way is to smooth out the curves by grouping data by quarter. I set the date range to all-time and group data by quarter instead of by month, then view the MRR chart. This quarterly view shows me a smooth and steady increase in MRR. Instead of being blinded by monthly swings, I can see that our long-term progress is good. Effect of a large price rise I have the developer’s mentality, shared by many technical founders, whereby we vastly undervalue our work. For a long time I wasn’t charging enough for Feature Upvote. Slowly and steadily I’ve become willing to experiment with pricing, but every price rise still causes me anxiety. My first price experiments were small and cautious price increases — followed by weeks of closely monitoring the result in ChartMogul. Are people still signing up for paid accounts? How does behavior differ at different pricing levels? Who is churning? I monitor the long-term results of pricing changes via ChartMogul’s “Average Revenue Per Account” or ARPA chart. This is perhaps my favorite view of my business in ChartMogul. Watching the consistent ARPA growth helps steady my nerves as our prices go up. After the initial price increases went well, I started to feel bold, so in late 2020 I abruptly doubled our prices for new customers. (Existing customers remained on the old pricing.) Using ChartMogul’s segments feature, I can compare customers by pricing: one segment for customers on old pricing plans, and one segment for customers on the new much higher plan. Comparing customer segments by price has been a revelation. Customers at the new price point have much lower churn. They have higher LTV, way more than double. SaaS veterans already know this, but I had to learn this myself. Only by seeing the result of a large price increase in my own business did I really understand what happens — increasing prices overwhelmingly leads to better customers, who pay more and churn less. I can conclude that raising prices — despite causing a drop in customer signups — was a very good decision. A vital tool for decision making My product was launched four years ago this month, and I’ve been using ChartMogul for three of those four years. It remains a vital part of my decision making process. I discarded my old home-grown metric system long ago, and it is never coming back. Although I was hesitant at first about paying for ChartMogul, it has helped me earn much more than I pay for it.

Tracking Metrics as a Bootstrapper

Building in public and sharing your progress as you go is a great way to attract customers. One way to do this is to publicly share your monthly recurring revenue (MRR) progress on your Twitter profile. There are a number of SaaS founders who already do this, such as Jon Yongfook, Damon Chen, and Levels, to name a few. Like them, you can also add MRR progress bars to your Twitter profile (in as little as one minute). Here, we’ve compiled 4 ways to add them: 1. MRR Meter Generator tool (quickest to set up) — created by Jim Raptis. It’s straightforward: just enter your current MRR and Goal MRR. Then, copy the template from the site and paste it into the Location section of your Twitter profile. This method is by far the quickest and easiest to set up. The only drawback is that every time your MRR changes, you’ll need to go to the tool and update your profile manually. 2. TweetSync.io (best for automatic updates) — created by Julien Nahum. takes away the burden of updating your profile manually every time your MRR changes. It pulls data from ChartMogul Metrics API and automatically updates your Twitter profile. It hardly takes a minute to set up. Head over to TweetSync.io, connect your Twitter account, add your read-only ChartMogul API key, and voila! TweetSync.io 3. Integromat (best if you want to display the exact MRR) If you want more flexibility, you can create an Integromat scenario to get the work done. The workflow is simple, although it’ll take you around 15 mins to set up. At a high level, you use Integromat to pull data from ChartMogul’s Metrics API and then push that data to your Twitter account. The benefit of using this method is that you can show the exact MRR in addition to showing your progress. The drawbacks are that it requires more work to set up and the Twitter integration costs a fee. Here is a template that you can import in Integromat to get started. You’ll need to request API access tokens from your ChartMogul account and connect your Twitter account to Integromat. You’ll also need to set your Target MRR in Step 2 of the above Integromat scenario. The current workflow is set up to highlight the MRR and your journey to target (along with the bars). Feel free to edit the output as it suits your needs. Don’t forget to switch on scheduling within Integromat, so that the progress bars are updated automatically at a periodic interval. Many thanks to Jacky Tan & Mohd Danish whose work inspired the template. 4. Automatic updates via API (requires code) If you prefer code over no-code (ahem!), Joe Previte has created a script. It uses the Stripe API to pull MRR data and then pushes it to Twitter. You’ll need to replace the Stripe API with ChartMougl’s Metrics API for this to work. Let us know how you get on and if you need any help with the setup. Happy MRR sharing! PS: If you prefer to show different metrics such as ARR or subscriber count, you can use similar workflows to share those. You can also update your profile headline on LinkedIn (using similar workflows) in addition to Twitter.

How to Add MRR Progress Bars to Your Twitter Profile

Many SaaS startups (including ChartMogul) offer their service at a discount to customers that are part of an incubator or accelerator which has become known as a startup program. Why should you consider creating a startup program for your business? Why build a startup program? 1. Revenue expansion from your successful customers Large companies such as Airbnb and Dropbox moved through incubators when they were still small businesses. A startup program is your chance to catch the next wave of large businesses early and to grow with them. 2. The startup program can become a lead source Once you have on-boarded enough members with a portfolio relevant to your product, you can attain a constant lead flow through your startup program. 3. Increased brand awareness and promotional partnerships Incubators, Accelerators and VC firms can offer many opportunities to promote your brand through internal and external listings, events, knowledge sharing and recommendations by members and partners. Initiating a startup program is easier than you think and will cost little to setup and maintain. This guide outlines the steps how you can build your own. Step 1: Value proposition The first thing you want to do is to think about the value proposition as well as the conditions. Answering these questions might help you define both: Why should a customer join this program? What makes this program exclusive? How long will the program be? How much of a discount is being offered? What profile does the customer need to have to be eligible to join? Be brief and specific to make it easy for the customer to understand the conditions. You want the program to be perceived as exclusive and beneficial by laying out actionable outcomes which the customer will get from using your product. Step 2: Landing page The next step is to build a dedicated landing page to promote your startup program. An effective landing page should include the following elements Value proposition Conditions List of a few key customers from the verticals you service Quote from customer or thought leader that demonstrates value List of supported incubators Sign-up form This landing page will enable you to market the startup program to potential customers as well as potential incubators. Important: Make sure you include a field that prompts a potential customer to supply a contact at their current incubator if it is not currently listed. This helps you to capture incubator leads from potential customers that are already interested in using your product. This is a great incentive for an incubator to agree to join your program. Make sure you include a way for people to recommend their accelerator, if it’s not listed. Route the form submissions through to your CRM so that you can easily manage and measure the conversations. At Chartmogul, we forward the data captured by the form submission to our Zendesk account. By adding a tag in the process, we can automatically categorize and route the ticket to the relevant party. Step 3: Incubator outreach The last step to get started is to research incubator programs that are suitable for your business in order to build a list for your outreach. You want to target incubators that have a sizable portfolio of the verticals your product aims to serve. I would recommend not only focusing on incubators and accelerators but to also target larger VC firms. I keep two lists for this in Google Sheets. One for incubators that I am already talking to or am planning to contact. The second consists of incubators that I have successfully on-boarded. Any CRM will also do to manage these lists and relationships. Promotion (spreading the word!) Now that the ground work has been completed, it’s time to tell the world about your awesome new startup program! To speed up the outreach, I would recommend preparing a number of macros for the common scenarios that you will encounter Incubator, accelerator, VC outreach first contact Follow-up with no response Scheduling call with incubator Onboarding incubator Onboarding customer You can adjust these macros as you learn the kind of messaging that’s most effective. Tip: One of the best ways I found to reach out to incubators is via introductions through your personal network or the network of your colleagues. Telling your team about your new startup program might surface a wealth of existing contacts you can utilize. Here are a few other key learnings that will help you spread the word: Ask your paying and potential customers for introductions to incubator programs they are part of. Turn those working in a customer facing role in your company into active promoters of your startup program. Ask the contact within an incubator that you have on-boarded already for recommendations to other incubators that could benefit. The best time to do so is right away when they have agreed to join your program. Most incubator programs have a person that is in charge of offers which is the best point of contact. Promote your startup program to potential customers and new incubators during SaaS meetups as well as conferences. You can often find a contact form or email address on an incubators website but be prepared to not hear back from many that you try to reach out to via this channel. The pitch “You will usually only get one opportunity to make your pitch. If you don’t garner enough interest during this conversation, chances are that your follow-up emails won’t be returned.” You might find that a good percentage that you contact or talk to in person will want to get on a phone call to learn more about your offer. Have a strong distilled value proposition ready that explains how their portfolio can benefit and be ready to give a product demo on the spot. While you have them on the call, you want to ask them for the things you need to complete the next step. You might want to ask for the incubators logo so that you can add the top ones you on-board to your startup program website. You could also ask them to tweet about the new partnership and offer to do the same. You will usually only get one opportunity to make your pitch. If you don’t garner enough interest during this conversation, chances are that your follow-up emails won’t be returned. Step 4: Incubator on-boarding Once your conversation was successful and you are ready to on-board an incubator, you can send your welcome packet to your contact by email. Sometimes there will be custom requirements your contact might need to share your program with their members. Many incubator programs have an internal offering web page that only they can access. Some might give you access to it so you can post up the data about your program yourselves. Others fire out a quick email to all their portfolio companies or message a select number that they think can benefit most from your offer. A good starting point would be to include the following in your welcome packet template: Description of your service with screenshots Company logo Offer description Redemption method I would recommend having program specific coupons so that you can easily track where a lead came from later on. After I send out the welcome package, I add the name of the new member to the list of supported incubators on our startup program’s website. In the final step of on-boarding, I share the new incubator member and a link to their website with our team. I fire out a tweet and LinkedIn post announcing the new partnership. I also add the contact(s) at the incubator as a new LinkedIn connection. Maintenance To maintain your startup program keep your members updated of changes, visit your incubators and accelerators when possible and carefully manage the retention cycle when your customers transition off the startup program losing their discount. Touch base with your incubator contacts whenever you have plans to visit their town to see if you can present to their portfolio. Many incubators love hosting events for knowledge sharing which is a great way to build your brand through education without directly trying to sell anything. To manage retention, pick a time at least a month ahead of the expiry of the startup program to see if there are any sticking points that would discourage your customer to renew at the non-discounted rate. We manage this process in our Sales CRM . Close.io Goals and KPIs In order to define what you want to accomplish with your startup program, I would recommend setting some clear goals and to establish KPIs that you can track from the beginning in order to see how they evolve over time. For example, your goal could be to on-board a certain number of incubators in x amount of time while on-boarding x number of customers onto the startup program. In terms of KPIs, you could track the following in your subscription analytics platform: Lead volume Customer number Retention rates at end of startup program Retention rates by incubator Average expansion per account In order to track these different metrics in ChartMogul, I added an attribute with the startup program they are on to the customer. I obtained the list of the customers who have used a particular coupon from the billing system. Takeaways Setting up a startup program is a long term investment in your business. It gives you an opportunity to attract early stage companies during a cash-sensitive stage. You will earn some positive brand awareness for supporting entrepreneurship which can easily translate into great word of mouth marketing. If done right, your startup program can blossom into a predictable revenue stream for years to come.

Build a Startup Program for Your SaaS Business: A How to Guide

As 2021 approaches, discussions about forecasting MRR are becoming commonplace in companies of all sizes. In this article, Travis from SaaSync shares his tested method for predicting revenue growth. The spreadsheet used to make the charts included in this blog post can be found here. Ugh! It’s budget season. How many meteorologists does it take to accurately predict the weather? Nobody knows, it’s never been done. I’ve built a lot of revenue and growth models in my life. It’s not something that I particularly enjoy, but it’s something that I think I do a pretty reasonable job of. It’s sort of like being the weather forecaster — perfection unattainable. One thing I do enjoy is going back to look at the models that I’ve built in the past. It’s fun to have hindsight on assumptions that you made in your model. It’s also sometimes humbling to look at models that were outrageously aggressive. I just reviewed a forecasting model that I built 6 months ago where actual MRR is going to beat the modeled prediction for MRR a month early. In this case, a 2-month period with substantially lower than normal churn caused the model to miss the mark. The funny thing is that even with hindsight, we can’t really put our finger on the reason for those 2 months having abnormally low churn. So what is the best way to model MRR growth for SaaS businesses? I once built a model, and the response I got to it was very lackluster It’s not complex enough.  On another occasion I was told $15M ARR is the number I want, work the model back from that.
To a large degree MRR is a vanity metric for CEOs and a yardstick for venture capitalists. If we are being honest, to a large degree is a vanity metric for CEOs and a yardstick for venture capitalists. Because of that truth, MRR forecasts and goals many times get set based on emotion, a nice round number, or what you should do because that’s what other companies of your size have done. There’s nothing wrong with that, but this model should be a good sanity check. MRR So let’s dive in. There are probably 1001 ways to model out a forecast for MRR. What I’m going to suggest is a very quick and dirty way that seems to work best for me, while pointing out some of the pitfalls that I’ve encountered. To start with, I’m going to assume that your business or product has at a minimum a few years of history. Should that not be the case, you will need to try a different approach. I’m also going to assume that you use a product like that allows you to have an in-depth understanding of your historical MRR changes by month. ChartMogul Simplicity The model that I like to use is very simplistic. It’s based on month-over-month growth rates of MRR. The reason I pick a model that is so simplistic is that I fundamentally believe that for every assumption you make in a model, the greater the error introduced into the model, many times in a compounding way. For every assumption you make in a model, the greater the error introduced into the model. The guy that told me It’s not complex enough, what he really meant was he wanted something analogous to a zero-based budget model that predicted MRR growth based on growth channels, organic and paid — starting from the ground up. That model started at the top of the funnel estimating conversion rates on websites and multiple app stores for organic traffic. Likewise, for paid acquisition, it estimated conversion rates on advertising through various channels. Then once a customer converted, it accounted for retention / expansion / contraction of users across the various platforms we had. Had the model worked it would have been beautiful, but it didn’t. Our funnel was very complex: we were aggressively ramping up our advertising and we didn’t have a good enough understanding of the paid acquisition funnel metrics. I think a model like this might work for more mature companies that have a lot of data, measure and track that data, and have the time and patience to build out the model. But most SaaS businesses don’t have this luxury. There are . You could build your model at that layer (new business, expansion, contraction, churn, and reactivation) but frankly that’s five things that could go wrong. Each one of them is a complex thing to model in and of itself. Or you could even go a layer lower, modeling the things that make up each of the five components — but again — that is destined for error without a lot of effort and reliable historical data. 5 components that make up MRR I start my forecasting models by first building out the history of the month-to-month growth rate of MRR. Step 1 is to export my historical MRR data from ChartMogul into a spreadsheet. History Knowing the history of a business will help you understand the present and predict the future.  This should help you understand a few possible trends. Does the company see seasonality in its growth? Has the company noticed a slight decline in growth rate as it grows? Are there any other anomalies that stand out? I like to not only look at the history by year, by overall average, but I also like to do a weighted average. What happened last year is probably more indicative of future growth than what happened 5 years ago. Usually, a picture is worth 1000 words. It does indeed look like the growth rate is reducing over time. As the company ages, the  growth has slowed — it’s a lot harder to grow big numbers than small numbers. April 2020 stands out like a sore thumb. I’m sure you can guess what happened there. I wouldn’t expect future Aprils to take on this same behavior, however, your business may have some sort of impact in general in 2021 and beyond that may need to be accounted for. While it’s not super obvious, it looks like there might possibly be some seasonality in growth? If we dig into this data a bit further by normalizing the data on an annual basis, a few things stand out even further. What we see is that there indeed seems to be some seasonality.  December through March have historically been the better growth months while July through September have been slower months. That said, the seasonality isn’t extreme such as some companies that do all of their business in the months of November and December. I should point out that I like to use shades of grey to represent years, using a very light shade as you go further back. The reason for this is that when I look at trends I want the more recent years to stand out more — I’m less interested in trends from 2016. Future The next thing I do is look to the future, carefully. What are the things that the company is doing that might impact your growth rates? Are there major product improvements or changes in go-to-market strategies? This is probably where a lot of models go sideways. They become overly optimistic about what the future holds. As I mentioned before, the company growth rate tends to slow down as a company ages up until it’s about 12 years old where it then stabilizes. So as you build your model and you begin to estimate future monthly growth rates keep in mind that gravity — or in this case the size of your company — is pulling against you. At this point, you could dig deeper if you desire. Is there seasonality in the components that make up your MRR, such as in your New MRR or your Churn MRR? But that’s for another day. Scenarios: 30-60-90 Model I don’t know who introduced this methodology to me, possibly , but it’s something that we used regularly in my last company and it was a really good way of framing forecasts and budgets. Here is the way that it works. Brad Feld The 90% Case represents the base case. This model should be met or exceeded 9 out of 10 times. So if we use this model for the next decade, it should only miss one time. As we saw in 2020, sometimes things happen that just can’t be accounted for in a simplistic model. The 60% Case represents a more aggressive case. In this model, you are only 60% confident that you will hit the target, or 6 of 10 times you will meet the mark. The 30% Case represents the most aggressive case. In this model, you expect to only hit the target 3 of 10 times. While this topic is just looking at forecasting MRR, this same concept can be applied to financial budgets and forecasts. Typically the 30% model while much more aggressive in revenue also has accompanying additional expenses — as growth usually doesn’t come for free. We now start working through each scenario, only altering the monthly growth rates. Here is where the cognitive dissonance starts to kick in. On one hand, we believe that we have all of these great things that we are going to do to increase our growth. On the other hand, we know that as we get larger our growth rates will tend to slow. Every business is different, and the approach you take is probably unique to your situation. For the sake of simplicity and given that my data set is for a fake company, I’m going to do the following: For the 90% case, I’m going to assume that we see growth rates that are 100% of last year’s growth rates. For the 60% case, I’ll assume that we do 10% better than we did last year. And for the 30% case, I’ll assume that we beat last year’s growth rates by 25%. I’m also going to assume that the huge decline in growth in April of 2020 was an anomaly so I’ll factor that out. I’m also going to smooth out a couple of months where I believe the peaks and valleys aren’t warranted. Finally, I’m going to assume that in this particular case, I don’t feel like Covid-19 has any appreciable impact on my 2021 growth. I picked 2020 as the baseline, you could have easily used a weighted average or really a variety of other methods. I think much of it comes down to how realistic you believe 2020 was representative of how things will go moving forward. Again, I like to visualize the data and the forecasts including previous years’ data as I find that it helps to drive home a model based more on reality. Nonetheless, you can change this to be your own, be more aggressive, less aggressive, it’s up to you. You’ll notice that up until this point there has been no discussion on the actual MRR of the company in this data set. Ignoring that to start with, and focusing solely on the monthly growth rates removes the emotion and bias from the forecast. Really, at this point, it comes down to 12 assumptions that you have made, all of which are backed up by historical data that is very relevant in where your growth will likely be in the following years. Finally, we now add the MRR to the equation to see where these growth rates project the company to be over the course of 2021. These 3 scenarios would put this fictitious company at an annualized growth rate of 34% in the most conservative case and 44% in the most aggressive scenario. If we look at a company that is roughly an $8M ARR company in the Growth Rate by ARR graphic above, we’ll see that these projections would put this company at just shy of the 50th percentile in growth rates for companies of its size. So why do multiple scenarios? I think it’s a good exercise to go through — granted there is usually a cost that comes with stretching growth. Examples might include extra advertising spend, hiring another account representative, etc. I also highly encourage you to talk to your team about ideas as to how to hit some of the stretch scenarios. It tends to help get buy-in which is always a good thing when an annual MRR goal comes down from a high.

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