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Modeling Software as a Service and Accounting for Profitability

Sean Salleh 16 Oct 2013 Modeling methods

Modeling Software as a Service and Accounting for Profitability

What should you expect in terms of profitability when modeling software as a service? Conventional business theory says an enterprise cannot be unprofitable over too long a time. If a venture is consistently unprofitable, there’s something wrong. The Internet boom turned that model on its head, for a while at least. But even now, some SaaS companies appear to be operating with a web business model that has ongoing loss built-in, while still attracting investors. Is there a problem with the SaaS model? With the market? Or with the definition of profitability?

How SaaS compares to the previous model of ASP (Application Service Provider) Image source: mwadvisors.com

Look at Our Impressive Losses

Jason Cohen writes about the upside-down SaaS business model in venturebeat.com. He decries the mindset that sees modeling software as a service as a matter of accepting that company growth will always be unprofitable. The thinking is that each new customer means initial expenses for the company, which are only gradually amortized as the monthly service fees are paid by that customer. Profitability per customer is therefore always delayed. If you continually increase the number of new customers, you multiply the losses and eat up the profit generated by the smaller number of existing customers. In order to show a profit, simply stop growing – a difficult proposition in the light of market expectations.

To Improve, First Understand

If this strange situation is to be reversed, the underlying mechanisms for a software as a service offering need to be understood. Modeling software as a service with Analytica allows the different factors and relationships to be defined, and outcomes modeled. On a per-customer basis this might involve customer acquisition costs/payback period, average customer retention time, costs to serve the customer and other enterprise costs. This can then be built up to the case with many customers arriving at different times with varying degrees of certainty. By using the integrated probability distribution functions in Analytica, you can then see how revenue and profit play out over different periods. You can also see which factors have the most influence on profit (or loss) levels.

Changing the SaaS Profit Model

There are a number of ways in which SaaS companies might tend towards ongoing profit. These include using upsells to existing customers (sell more but without the acquisition costs); reducing customer acquisition costs (reducing expensive sales force effort, using viral growth); and improving gross profit margin (reducing customer service expenses, hopefully while maintaining quality). Some potential improvements can be accounted for by changing values or relationships within the SaaS model. Others require adding further dimensions to the model. Either way, Analytica makes modeling software as a service flexible, while immediately showing how changes and new factors affect overall profitability.

Up, Down, In, Out – SaaS Provision According to Customer Behavior

With many customers arriving, departing, or scaling their SaaS usage up or down, interplays are complex. How a particular offering is built to meet such demands depends on service design philosophy. Some recommend consideration of future SaaS revenue streams when architecting the offering; others suggest an agile approach, in which developments are made as and when customers ask for them, but not before. But that choice is also a whole new modeling topic in itself.

If you’d like to know how Analytica, the modeling software from Lumina, gives you visibility throughout your business models in any industry, then try a thirty day free evaluation of Analytica to see what it can do for you.

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Sean Salleh

Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.

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