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Since did us all a terrific service by touting the initial benefits of software as a service (SaaS) through its “No Software” branding, many SaaS companies have sprouted up, grown and had healthy exits. The explanation for this growth is simple — SaaS has become the best choice for replacing on-premises software, largely because the tangible benefits are staggering. SaaS requires no hardware costs, rolls out easily, boasts lower licensing costs, and often has significantly faster user adoption.

SaaS secretly offers significantly bigger benefits for customers than those afforded by simply replacing on-premises applications. This benefit lies in the massive, untapped potential of heavily mining the business data collected by SaaS applications to drive huge business process improvement by means of predictive process and behavior analytics. Unfortunately, most SaaS companies have not yet realized this potential land swell.

Many SaaS companies are multitenant, and as such, they have the ability to aggregate and analyze data from all of their customers. Done well, these companies could offer priceless industry benchmarks to help their community of customers perform better in their industry. Some great examples of this method in practice include Zendesk (customer service), which has an entire “Best Practices” section of their website that includes benchmarks derived from their data, and Coupa (indirect procurement), which updates its “Industry Benchmark of Key Performance Indicators” yearly. My company, Noosh, a SaaS for project-based sourcing and procurement currently targeted at marketing and print services, also recently released its benchmark report illustrating how collaboration drives productivity, business opportunity and lowers costs.

A competition of continuous improvement

As the customers of these SaaS companies use benchmarks to compare themselves with their peers and improve performance, the benchmarks themselves become more stringent, fostering year-over-year continuous improvement — on behalf of both the brands producing the benchmarks, and the customers that apply them. Next year’s benchmark includes the performance improvement gains from the previous year. Not only does the SaaS application help its customers perform critical business functions, it helps year-over-year by providing a virtual community against which companies compare themselves and strive for improvement.


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For example, at Noosh, we’ve analyzed the time from the point at which a buyer asks for a price to the point which the first price is received across our entire customer base. More than 31 percent of the time, the buyer receives the first estimate in less than an hour. That’s a strong benchmark and key metric for anyone in our industry. This is simply not discoverable with on-premises software deployments.

For this to work, of course, SaaS companies must first have and then meaningfully interpret a lot of business-process data. Web companies like Google, Facebook, and LinkedIn are all data companies. These companies collect consumer data by delivering software services and leveraging that data to sell targeted advertising. If the “big data benefit” of business-to-business (B2B) SaaS companies does come to fruition, the same sentence can be re-written for the future of B2B SaaS, simply by replacing “consumer” and “targeted advertising” with “business process” and “offer real-time predictive business-process insight.”

Clearly stated: These visionary B2B SaaS companies collect business-process data through delivery of a software service and leverages the data to offer real-time predictive business-process insight.

The beauty of interoperability

Beyond analyzing historical data and publishing benchmarks, future waves of SaaS companies will leverage data their systems have collected to build predictive business-process improvements directly into their applications.

A good example comes from Saba, which provides SaaS solutions for talent management. Saba has implemented machine-learning algorithms based on data from its customers as “The Intelligent Mentor,” or TIM. TIM makes personalized and relevant recommendations about content, classes, and mentors that can help employees be more productive on the job and develop in their careers. Other potential examples could come from sourcing apps to predict the impact of user actions in realizing cost reductions. Consider the business implications of an application that can predict the value of receiving another price quote regarding savings potential (in its simplest case) or which can predict whether the next quote would be low — or high — based on the previous behavior of the actual suppliers yet to respond or seasonality variations.

Add to this the increasing interoperability of SaaS solutions, and new applications will be mashed up in ways we can’t conceive of, which will result in a new set of applications. This “second cloud front,” as suggested by recent Future of Cloud research, will be an order of magnitude bigger than the first. Imagine the possibility of collecting data from multiple business-process applications to predict outcomes and optimize entire, cooperative business processes.

One could argue that supply-chain software companies have been leveraging big data and algorithms for inventory replenishment for years. While true, these hard-coded algorithms and data are isolated instances — islands generated by one company, for the benefit of that sole company. B2B SaaS is different; it affords companies the ability to leverage and apply big data benefits, such as dynamic predictive insights, to business processes by using data collected from the industry at large. These wide, industry-shifting benefits are only possible with the data stored by SaaS platforms in the cloud.

Ofer Ben-Shachar is the founder and chief executive of Noosh, a leading software-as-a-service platform for integrated project and procurement management.

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