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?

Hindsight, Insight, and Foresight

Lately I’ve been thinking more about about going beyond merely looking at backward-looking data and metrics (e.g. The Definitive List of Weekly Operational Metrics for SaaS Startups) and learning how historical data can be used to inform what will happen (predictive analytics). Derek Kane has a Slideshare titled Building and Sustaining Predictive Analytics Capabilities. On slide 26 he defines hindsight, insight, and foresight.

  • Hindsight – What is happening?
  • Insight – Why is it happening?
  • Foresight – What will happen? What should happen?

As startups mature and improve their operational excellence, insight and foresight become logical additions to the weekly business review. Entrepreneurs would do well so start asking the questions “Why is it happening?” and “What will happen?”

What else? What are some more thoughts on hindsight, insight, and foresight?

Build a Sales Playbook

One of the first things a new sales leader (or entrepreneur running sales) should do is build a sales playbook. A sales playbook, put simply, is a central resource for tracking everything from the basics, like the elevator pitch, to the more advanced items, like differentiating from specific competitors. With more knowledge and training, sales reps speak more confidently and intelligently, helping win more deals.

To start, make a Google Doc sales playbook and include these items:

  • Corporate information
  • Sales pitch
  • Elevator pitch
  • Market space
  • Recent trends
  • Target customer
  • Types of buyers
  • Features and benefits
  • List of references
  • Sales process
  • CRM process
  • Competitors and differentiators
  • Objection handling
  • Glossary

Revisit the playbook on a weekly basis and ensure that the team contributes to it. With sales, the more you know, the more you sell.

What else? What are some more thoughts on building a sales playbook?

Analyzing Data Over Time

Every entrepreneur I know loves to analyze data and metrics about their business over time. How’s our revenue growing? How many daily active users are we averaging? Only, the data is often in summary form in a spreadsheet making it hard to analyze. Eventually, an analytics and reporting system is necessary to better analyze data over time and present it in an actionable manner.

Here are some common questions to ask when analyzing data over time:

  • How are we doing this month/quarter compared to this time last month/quarter (ideally with a chart showing both lines)?
  • What’s our trailing 30-day average as measured on a daily/weekly basis?
  • What’s the rate of change on a weekly/monthly/quarterly basis (are we accelerating or decelerating)?
  • What’s our expected results for the rest of this month/quarter based on the previous data and corresponding results (predictive analytics)?

Analyzing data over time is a critical part of every weekly leadership team. Use analytics and data platforms to automate the collection of data and generation of reports that show both the metrics and more detailed analysis.

What else? What are some other common questions to ask when analyzing data over time?