Yet Another Benefit of SaaS

5 SaaS-ready revenue models you should know about

Saas Revenue ModelsIn previous articles we have explored and promoted the benefits of applications software companies pursuing an all-SaaS model.  One of the benefits we touched on was the number and variety of revenue models available to SaaS companies, many of which for architectural and infrastructure reasons are not available to non-SaaS equivalents.  In this article, we explore those revenue models and their benefits in more detail. 

In each case, the revenue model takes advantage of some fundamental pillar of SaaS product architecture.  SaaS products primarily execute on systems within a vendor’s data center or a cloud service provider’s data center (Amazon, Rackspace, etc.), and the end user’s desktop or mobile app is regularly connected to the back-end service. As a result, a SaaS vendor is able to capitalize by employing revenue models that exploit this connectivity thruway for better and often more relevant and timely user experiences.

Furthermore, a longstanding frustration of application software vendors and their customers has been the difficulty in deploying revenue models that are directly related to value provided. Traditional software licensing is based on either servers, CPUs, named users, or desktops. Traditional SaaS licensing tends to be priced on a per-user monthly or annual subscription.  In each case, customers often complain that the licensing metric does not tie directly to value and is at best a poor proxy for value delivered.

Imagine the customer that has to buy 100 “seats” of some financial package because all 100 users use the software occasionally, but in fact only 25 users use the software regularly.  In such cases the 25 users might get their “money’s worth”, but the other 75 are overpaying for the amount of value they are getting from the software.   Some of the SaaS revenue models we will explore overcome this dilemma by using proxies that are much more closely related to value than their legacy counterparts.

Freemium: an Easy Start

Recent years have seen a tremendous increase in the number of software companies offering a ‘freemium’ model – where a base version of the product is available for free and a more comprehensive version is sold as an upgrade to the free product.  It is not a surprise that this trend has coincided with the growth and popularity of SaaS.  Because the SaaS vendor has connectivity to the user (a pipe for monitoring how/if/when the product is being used) and a way to dynamically remind the customer to upgrade at the right times (for example, when they are using a feature that isn’t fully enabled in the free version), the freemium model has been a particularly potent tool for SaaS vendors.  Some examples of well-known freemium models include Gmail, LinkedIn, Box, Dropbox, and Shutterfly.

Ad-Supported: a Real Estate Play

The ad-supported revenue model is another model made possible by the advent of SaaS solutions.  In an ad-supported model, users get free or low-cost access to a software application in exchange for the vendor’s ability to advertise within the screen real estate of the application. Gmail was one of the first large-scale applications to make widespread use of this model.  Vendors can command high dollar advertising rates by using the knowledge they have of the content and of the user demographics to deliver highly targeted or personalized ads. In Google’s case, they sell ads with their own ad network, but other software vendors use 3rd party ad networks to deliver ads to their users through their applications.

In the past few years, this model has been expanded to mobile.  While iPhone apps are priced notoriously low on a per-device basis, Apple has enabled its iAd network to facilitate ad delivery within an app, thus opening up a new revenue stream for mobile app ISVs (independent software vendors).  Apple shares revenue with the ISVs who make the apps, typically offering the ISV 70% of the ad revenue. Google offers a similar service to developers with equivalent or better revenue share. Between mobile and web, there are now hundreds of well-known applications using the ad-supported model. Some examples include WeatherBug (mobile), Gmail (mobile and web), YouTube (mobile and web), Facebook (mobile and web), LinkedIn (mobile and web), and Skype.

Value Based: Back to Basics

Ultimately, the most efficient form of pricing (particularly in competitive situations) is a revenue and pricing model that is based on a metric that most closely resembles the value a customer gets from a product.  Several SaaS vendors in a few sectors have been able to price in such a way.  In this model, the more users use the software and the more valuable the features they use, the more they pay. In particular, when there is significant COGS (cost of goods sold) associated with a SaaS service, it can be important to price along a dimension that is proportional to those COGs.  For example, when a SaaS product uses extreme bandwidth, storage, or CPU resources to deliver parts of its service, it is reasonable for a customer’s costs to scale as that underlying cost scales.

Examples in recent years include email marketing (which has significant back-end bandwidth and processing costs), online backup, integration software, and business intelligence (also requiring significant bandwidth and processing).   Email marketing platforms tend to offer pricing models tied to list size or email frequency, online backup applications by quantity of data archived, BI tools by the quantity of data analyzed, and integration platforms by the number of rows processed.  Example companies include Constant Contact and Exact Target in email marketing, Mozy (EMC) in online backup, Tableau in BI, and Informatica and Scribe Software in integration.  In the near future, we should expect to see traditional players who now offer SaaS options to start experimenting with some of these alternative revenue and pricing models that are not purely per-user based.

Add-on Transactional Services: Increase Wallet Share

Because SaaS vendors provide products which are in the data-flow of a customer’s work stream, they have a unique ability not only to add on new services but also to make offers to customers at the best times, namely when the customer is using the product in a way that lends itself to additional features, modules, or add-on products.  Often the add-on products, also called “upsells”, are transactional in nature and sometimes are services that are resold from another vendor.  When this is the case we call these capabilities “Add-on Transactional Services”.  Examples might include an HR software vendor offering payroll or background checking services on a transactional basis (pay per use), a CRM (customer relationship management) provider offering email or phone number verification, a marketing automation ISV offering digital ad placement services, or a financial management software firm offering online bill pay.

In each case, the vendor prices the core product using some per user model or one of the previously mentioned revenue models but also offers the add-on service on a transactional basis, sometimes per month or sometimes per incident.  For example, online bill pay could be priced per payment, email verification might be priced per 100 verifications, and ad placement from a marketing automation platform could be priced on a CPM (cost per impression) or CPC (cost per click) basis. Examples include QuickBooks online offering bill pay, Nanigans offering Facebook ad placement, and Oracle/Taleo offering background checks and job board placement from its application tracking suite.

Data Monetization: For the Data Age

Finally we have the most controversial revenue model, data monetization.  This comes primarily in two forms.  In the first, a SaaS ISV aggregates data collected from its users and resells that data in either raw or research report form. In the second form, personalization and/or behavioral data is monetized by selling it to ad networks that use the data to deliver premium targeted digital ads.

Examples in the aggregation category include firms such as Alexa (web analytics), Dyn (internet performance), and the granddaddy of them all ADP (payroll and labor data).  Examples of companies that collect personalization data then use it to target ads (or sell the data to companies who use it to target ads) include Rocket Fuel, Yahoo, LinkedIn, and of course Google.


In addition to the many obvious reasons for pursuing a SaaS model over a perpetual or on-premises model, the variety of available revenue models provides an even more compelling rationale for an ISV. By incorporating the right mix of revenue models in an offering, an ISV is more likely to extract economics proportional to the value it delivers and ultimately provide the best opportunity to meet its growth aspirations and distinguish itself amongst its competition.

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