David Cummings on Startups

Category: Operations

  • The Sales Ops Role – Operational Rigor for the Sales Team

    One of the sales roles I hear talked about more lately is sales operations, better known as sales ops. As sales organizations worked to become more data driven and process oriented, it became clear that there often wasn’t the time, or skill set, to bring more operational rigor to the department. Enter the sales ops role.

    Here are a few thoughts on the sales ops role:

    • Think of the sales ops person (or team) like an industrial engineer that’s constantly evaluating the data and process
    • For the data, the common systems are the CRM, the sales engagement platform, and an automated analytics platform
    • Analysis is also done in Google Sheets or Microsoft Excel to find trends and areas of improvement
    • Ensuring consistent usage of the systems, like recording activities and changes to opportunities, is critical otherwise it won’t be possible to analyze the real data (e.g. follow the sales stages)
    • Monitoring data quality and integrity is an ongoing element to make sure standards are met and processes followed
    • Training and documentation is another important part of the role

    Sales ops operationalizes the sales department by incorporating more process and data analysis resulting in more predictability and success. Look for the sales ops role to grow and become more commonplace.

    What else? What are some more thoughts on the sales ops role?

  • Customer Cohort Analysis for SaaS

    One of the terms I had never heard before getting into the SaaS business is cohort analysis. As you might expect, a cohort is a set of customers grouped together by common characteristics. The most common types of customer cohorts are by time (e.g. customers that signed up in a given month) and size (e.g. customers based on how much they spend). Cohort analysis is primary used to understand patterns and trends of customer groups over time with the most important metrics being renewal rates and account expansion.

    Here are a few thoughts on customer cohort analysis in SaaS:

    • Keep cohorts simple while there’s limited data and add complexity as the customer base grows
    • Remember that not all customers are equal and the cohorts should reflect a reasonable level of segmentation (e.g. customers by month by size divided into small, medium, and large)
    • Consider cohorts on longer tail metrics to see if any insights emerge (e.g. # of logins, module usage, NPS, etc.)
    • Look for the “smile” where the revenue expansion of a cohort is expanding (turning up like a smile) vs shrinking (turning down like a frown)

    Cohort analysis takes a fair amount of time to initially put together but it’s well worth it — every SaaS company should track their customer cohorts.

    What else? What are some more thoughts on customer cohort analysis for SaaS?

  • Faster SaaS Growth Equals Greater Losses

    Continuing with yesterday’s post on Gross Margin as Part of Lifetime Customer Value, David Skok has a important post up titled SaaS Metrics 2.0. In the article, he touches on a critical topic that isn’t well understood: faster SaaS growth equals greater losses. Here’s how he visualizes it:saas_growth

    The idea is that when you sign a new customer, there’s a payback period, which is why gross margin is an important consideration. New SaaS customers are money losers for an extended period of time — often one year — but then are very profitable after that. Intuitively, this makes sense as payments are spread out over time. So, if you lose $X for a new customer until they’re profitable, it only follows that if you sign five times the number of customers, you’re going to lose $5x until they’re profitable (more customer onboarding help, more servers, more infrastructure, etc.).

    Entrepreneurs would do well to understand that faster SaaS growth equals greater losses, and that it should be planned for accordingly.

    What else? What are some more thoughts on faster SaaS growth equaling greater losses?

  • Gross Margin as Part of Lifetime Customer Value

    Continuing with yesterday’s post on SaaS CAC to LTV Metric, there’s another important element that needs to be addressed: gross margin. Gross margin is the percent of revenue left over after taking out the costs required to serve the customer (SaaS cost of goods sold). So, a company having gross margins of 70%+ (as SaaS companies should have), will have more money, as a percent of revenue, to dedicate to acquiring new customers.

    In the context of the lifetime value (LTV) of a customer, a company with 90% gross margins has a much more valuable customer than a company with 70% gross margin (or a lower gross margin, as is often the case).

    When talking about SaaS CAC to LTV, it’s actually better stated as CAC to the LTV gross margin. The idea for the ratio is how efficiently customers are acquired. Well, companies with very different gross margins shouldn’t be comparing their CAC to LTV ratios. Rather, CAC to LTV gross margin ratio would be a better comparison.

    The next time you’re talking about the lifetime value of a customer, talk about the gross margin of the lifetime value of a customer.

    What else? What are some more thoughts on incorporating gross margin into the lifetime value of a customer?

  • SaaS CAC to LTV Metric

    Continuing with The Magic Number for SaaS, there’s another phrase that’s bandied around quite a bit: CAC to LTV. Here’s a quick definition of CAC and LTV:

    • CAC – Cost of customer acquisition (how much it costs to get a customer, on average)
    • LTV – Lifetime value of the customer (how much the customer pays, on average, over the period of time they’re a customer)

    When people talk about CAC to LTV, they mean the ratio of the cost to acquire a customer relative to how much a customer pays over time. Generally, the question is whether or not the company can profitably acquire customers. For several years, often when the startup is sub-scale or investing in growth ahead of profitability, the cost to acquire a customer exceeds the value of the customer. CAC to LTV is an important measure of the efficiency of the business model, especially as it pertains to the repeatable customer acquisition model stage in a startup.

    CAC to LTV is one of the most important metrics for SaaS entrepreneurs and should be well understood.

    What else? What are some more thoughts on the SaaS CAC to LTV metric?

  • The Magic Number for SaaS

    Way back in 2008 Lars Leckie published a seminal piece on SaaS metrics titled Magic Number for SaaS Companies. From the piece, here are the stages of evolution of the company:

    1. Product: build a rock solid product. Prove you can sell it as founders before moving past this step.
    2. Sell: Sell like crazy, build out a team, hire some QBSRs (Quota Bearing Sales Reps)
    3. Retention: focus on churn and retention issues, hire more QBSRs
    4. Marketing: spend on marketing, hire more QBSRs

    Then, on to the magic number. The magic number is a ratio of the scaling of recurring revenue to the sales and marketing spend. Here’s the formula:

    (Quarterly Revenue – Previous Quarter Revenue)*4 / (Previous Quarter Total Sales and Marketing Expense)

    So, take the growth in revenue between the quarters, annualize it by multiplying by four, then divide by the total of all sales and marketing expenses. If this number is greater than 1, things are going well and more should be spent on sales and marketing. If this number is less than 1, the cost of customer acquisition relative to the value of the customer is too high and the focus should be on making sales and marketing for effective.

    Scaling a SaaS startup is expensive. Use the SaaS Magic Number to understand how efficiently the business is growing based on relative growth to customer acquisition costs.

    What else? What are some more thoughts on the SaaS Magic Number?

  • Scenario Planning

    One of the fun things to do with spreadsheets is build out different “what if” scenarios around sales, marketing, operations, fundraising, exit opportunities, and more. So many different inputs, data points, metrics, and formulas to test. Only, to do this efficiently and effectively, it’s imperative to have accurate data and tools.

    Here are a few thoughts on scenario planning:

    • Ask an advisor, mentor, board member, or fellow entrepreneur for an existing spreadsheet to use as an example
    • Resist the temptation to just plug in a number (e.g. 2% of calls will result in a demo), and instead use real data from the current team in the current time frame (e.g. use a system like SalesLoft to capture the outbound call, email, and demos scheduled data)
    • Build multiple scenarios for each “what if”, including the high/medium/low outcomes or optimistic/average/conservative outcomes
    • Run the results by an experienced CFO or entrepreneur and solicit feedback
    • Shares the results with key members of the team and use it to inform decision making

    Scenario planning is a common strategy entrepreneurs use to grow their business. Build out different “what if” scenarios and make better decisions.

    What else? What are some more thoughts on scenario planning?

  • More Accurate Sales Forecasting

    One of the areas that becomes critical as a startup hits the scalable business model phase is sales forecasting. Early on, it’s easy to build a bottom-up sales forecast using inputs like number of quota-bearing sales reps, size of quota, and estimated quota attainment. Only, as the business gets bigger, and has more current and historical sales data, forecasting needs to become more scientific.

    Here are a few metrics to incorporate for more accurate sales forecasting:

    • Higher in the Y-funnel metrics like number of SDR demos/appointments required for a sales accepted lead (SAL) and number of SALs to win a deal
    • Historical win rate by sales rep and deal type (size, account type, etc.)
    • Average sales cycle by sales rep and deal type (to be able to flag deals that are at risk due to falling outside the norms)
    • Projected bookings based on statistical models of historical data
    • Best case/worst case scenario planning

    Sales forecasting becomes more critical as the business grows and is a key part of high performing companies. Consider reporting and analytics systems to make sales forecasting more accurate.

    What else? What are some more thoughts on improving the accuracy of sales forecasting?

  • Managing with Data

    One of the topics I’m passionate about is managing with data. Now, data is often scarce in the early startup days, especially when there’s the elusive search for product/market fit. As the company moves from product/market fit to the search for a repeatable customer acquisition process and beyond to a scalable business, data becomes more plentiful.

    Here are a few thoughts on managing with data:

    Data shouldn’t be the only focus in the business but it should have an important role in the company. Let routine set you free and include data with it.

    What else? What are some more thoughts on managing with data?

  • Annual Recurring Revenue Greater than Cash Burned

    One of the metrics I like when thinking about SaaS company efficiency is annual recurring revenue (ARR) being greater than or equal to cash burned all time. Successful SaaS startups suffer from the J-curve where things start out with steep losses while revenue begins to ramp up and eventually revenue grows much faster than losses (hopefully!).

    Here are a few thoughts on ARR being greater that cash burned:

    When considering a SaaS startup’s capital efficiency, look and see if the annual recurring revenue is greater than cash burned. If so, and there’s a good growth rate, it’s likely a sign of a potential successful outcome.

    What else? What are some more thoughts on ARR being greater than cash burned for SaaS startups?